Abstract

Chronic pain is a multifaceted condition characterized by persistent sensory, cognitive, and emotional distress that extends beyond normal healing time. Affecting nearly one in five individuals globally, chronic pain represents one of the leading causes of disability and reduced quality of life. Opioid analgesics, historically the cornerstone of moderate-to-severe pain treatment, are increasingly recognized as problematic in long-term use due to tolerance, dependence, hyperalgesia, and addiction risk. Consequently, there is an urgent need to identify alternative approaches that are both effective and sustainable. Recent scientific interest has turned to classic serotonergic psychedelics such as psilocybin, lysergic acid diethylamide (LSD), and N,N-dimethyltryptamine (DMT), which may exert analgesic effects through modulation of serotonin receptors, enhancement of neuroplasticity, and reorganization of large-scale brain networks involved in pain perception and emotional distress. This review synthesizes current knowledge of chronic pain neurobiology, evaluates the mechanisms and limitations of opioids, and examines emerging evidence for psychedelics in pain management. Particular focus is placed on receptor-level pharmacology, network-level brain circuit changes, and clinical evidence across pain disorders. While preliminary data suggest that psychedelics may offer durable relief and address both sensory and affective dimensions of pain, challenges remain regarding safety, variability of response, and regulatory hurdles. Psychedelics represent a promising, though experimental, paradigm shift in chronic pain management that warrants rigorous investigation.

Introduction

Chronic pain is not merely a prolonged form of acute pain but rather a distinct pathological state involving maladaptive changes in peripheral and central nervous system function [1,2]. Unlike acute pain, which serves an adaptive role by signaling tissue injury, chronic pain often persists independent of nociceptive stimuli and is increasingly understood as a disorder of central processing and neural plasticity [3]. The personal, societal, and economic impact of chronic pain is profound, with high prevalence, reduced productivity, psychiatric comorbidities such as depression and anxiety, and substantial health care utilization [4,5].

Traditional treatments for chronic pain include nonsteroidal anti-inflammatory drugs (NSAIDs), anticonvulsants, antidepressants, and opioids [6]. Of these, opioids remain the most potent pharmacological tools available, particularly for acute and cancer-related pain [7]. However, in the context of chronic non-cancer pain, long-term opioid therapy has proven controversial due to limited functional benefit and high risks of tolerance, dependence, and addiction [8,9]. The ongoing opioid crisis underscores the dangers of widespread opioid prescribing and highlights the necessity of alternative strategies [10].

Parallel to the opioid story, psychedelics have re-emerged in biomedical research after decades of prohibition. Early investigations in the mid-20th century explored their potential in psychiatry and pain management before regulatory restrictions halted progress [11]. In recent years, renewed interest has focused on the ability of psychedelics to modulate brain networks, enhance neuroplasticity, and produce long-lasting therapeutic effects across psychiatric conditions [12-14]. These properties raise the possibility that psychedelics could also play a role in addressing chronic pain syndromes.

This review aims to provide a comprehensive synthesis of chronic pain neurobiology, the pharmacological basis and limitations of opioids, and the mechanistic and clinical rationale for psychedelic-assisted approaches to pain.

Chronic Pain and Its Neurobiology

Chronic pain is typically defined as pain that persists for longer than three months and extends beyond the expected period of tissue healing [15,16]. In contrast to acute pain—which is transient, protective, and usually linked to a discrete injury or illness—chronic pain represents a maladaptive condition characterized by persistent activation of pain pathways within both the peripheral and central nervous systems [3,4]. Rather than serving as a biological warning signal, chronic pain often becomes a disease state in itself, marked by complex neurobiological, psychological, and social dimensions [5].

The clinical manifestations of chronic pain are heterogeneous and can be categorized according to their underlying mechanisms and anatomical origins. Neuropathic pain arises from injury or dysfunction of the somatosensory nervous system and includes conditions such as peripheral neuropathy, sciatica, trigeminal neuralgia, and phantom limb pain, in which pain is perceived in an amputated limb [17]. Musculoskeletal pain involves bones, joints, and muscles, and is common in disorders such as osteoarthritis, rheumatoid arthritis, fibromyalgia, and myofascial pain syndrome [18]. Inflammatory pain develops in the context of immune dysregulation or persistent inflammation, as seen in autoimmune diseases such as systemic lupus erythematosus or localized conditions including tendinitis and bursitis [19]. Visceral pain, originating from internal organs, is often diffuse, difficult to localize, and exemplified by conditions such as irritable bowel syndrome, endometriosis, and chronic pelvic pain [20]. Finally, centralized pain is increasingly recognized as a distinct category, characterized by hypersensitivity of central nervous system processing mechanisms and evident in conditions such as fibromyalgia and chronic migraine. In these cases, pain persists without any ongoing peripheral injury and is frequently amplified by dysregulated central neural networks [4].

A key feature of chronic pain is its impact on brain circuitry. Neural processing of persistent pain involves the so-called “pain matrix, ” a distributed network of cortical and subcortical regions that underlies the sensory, affective, and cognitive dimensions of pain [21]. Within this matrix, the primary and secondary somatosensory cortices encode the intensity and spatial localization of painful stimuli, while the anterior cingulate cortex integrates the emotional salience of pain. The insula plays a central role in interoception and the integration of sensory information with autonomic and affective states, and the prefrontal cortex contributes to higher-order modulation, decision-making, and appraisal of pain experiences [22]. Neuroimaging studies have demonstrated that chronic pain is associated with structural and functional remodeling of these regions, including altered gray matter density, aberrant connectivity, and maladaptive patterns of activation [23].

These changes are underpinned by neuroplasticity, the brain’s capacity to reorganize and form new synaptic pathways in response to persistent stimuli. While neuroplasticity normally supports adaptation and learning, in the context of chronic pain it becomes maladaptive, leading to hyperexcitability of nociceptive pathways, enhanced pain perception, and decreased efficacy of endogenous pain inhibition [3]. Such changes can also contribute to psychological comorbidities, including depression, anxiety, and cognitive impairment, further compounding the burden of chronic pain. Thus, rather than being a passive symptom, chronic pain represents a dynamic driver of neurobiological remodeling that perpetuates its own persistence and complexity [5].

Opioid Treatment for Pain Management

Opioids have long been employed as a primary pharmacological intervention for a range of chronic pain conditions, yet their clinical efficacy and appropriateness depend critically on the underlying etiology and severity of pain [24]. Mild to moderate pain, such as early-stage musculoskeletal disorders or postoperative discomfort, is often managed with lower-potency opioids, including codeine or tramadol. While these agents are comparatively less potent than full agonists, they nonetheless carry notable risks of dependence and addiction. For moderate to severe pain, particularly neuropathic conditions such as diabetic neuropathy or sciatica, stronger opioids—such as morphine, oxycodone, or hydrocodone—are typically prescribed. In instances of severe, refractory pain, including advanced cancer or complex regional pain syndrome, high-potency opioids such as hydromorphone or fentanyl may be indicated. Fentanyl, in particular, exhibits up to 100-fold greater potency than morphine and is generally reserved for patients with significant opioid tolerance [24]. Notably, opioids demonstrate limited efficacy in certain centralized pain syndromes, including fibromyalgia, where non-opioid interventions are often preferred.

The analgesic effects of opioids are primarily mediated through the activation of mu-opioid receptors (μORs), a class of G-protein-coupled receptors distributed throughout both the central and peripheral nervous systems [25]. Within the brain, MORs are densely expressed in key regions associated with pain processing, modulation, and affective response, including the periaqueductal gray (PAG), thalamus, anterior cingulate cortex (ACC), amygdala, and nucleus accumbens (NAc). MOR activation inhibits adenylate cyclase activity, reduces cyclic AMP production, decreases calcium influx, and increases potassium efflux across neuronal membranes, leading to hyperpolarization and attenuated excitability of nociceptive neurons. In the PAG and rostral ventromedial medulla (RVM), opioids engage descending inhibitory pathways, while in the thalamus and somatosensory cortices, they modulate sensory-discriminative aspects of pain. Simultaneously, opioid effects in the amygdala and prefrontal cortex influence the emotional and cognitive dimensions of pain perception. Importantly, the NAc and ventral tegmental area (VTA) mediate opioid-induced reward and reinforcement, contributing to the high addictive potential of these drugs [25].

Chronic opioid exposure frequently results in tolerance, necessitating escalating doses to maintain analgesia, and can precipitate physical dependence and addiction. The activation of reward circuits further reinforces compulsive drug-seeking behaviors. Adverse effects such as respiratory depression, constipation, sedation, and endocrine dysregulation complicate long-term therapy and can significantly impair quality of life. Moreover, opioids often fail to adequately address the affective and psychological components of chronic pain, which are central to its persistence and functional impact. Collectively, these limitations underscore the pressing need for alternative pain management strategies that target both the sensory and affective dimensions of pain while minimizing the risks associated with chronic opioid therapy.

Psychedelics: An Emerging Alternative

Classic serotonergic psychedelics, including psilocybin, lysergic acid diethylamide (LSD), and N,N-dimethyltryptamine (DMT), have garnered increasing attention for their potential role in the management of chronic pain [26]. Both preclinical and clinical investigations suggest that these compounds may exert analgesic effects through mechanisms involving agonism of the serotonin 5-HT2A receptor and modulation of functional connectivity across key brain regions implicated in pain processing, including the thalamus, anterior cingulate cortex (ACC), and prefrontal cortex (PFC) [27]. Importantly, psychedelics appear to disrupt rigid neural patterns and entrenched circuit dynamics, a property that may be particularly relevant for chronic pain conditions in which affective, cognitive, and perceptual factors contribute substantially to symptom persistence [27].

Pain is inherently a subjective experience, shaped not only by peripheral nociceptive input but also by higher-order emotional, cognitive, and contextual influences. This complexity is exemplified by disorders such as fibromyalgia, which often lack identifiable tissue pathology and are considered central sensitization syndromes. In these conditions, patients frequently perceive pain as an emergent property of altered central processing rather than a direct reflection of peripheral injury [28]. Therapeutic interventions such as hypnosis and mindfulness-based practices have demonstrated that pain perception can be modulated by top-down processes, highlighting the brain’s capacity to suppress or reframe nociceptive experiences. Psychedelics may act through analogous mechanisms, promoting neuroplasticity and facilitating the decoupling of maladaptive neural circuits from conscious pain perception, thereby enabling patients to reinterpret or disengage from their pain experience [28].

Emerging evidence suggests that the analgesic effects of psychedelics can be durable. Clinical studies have reported that single or limited psychedelic-assisted sessions may produce sustained reductions in pain intensity lasting several weeks or months, in contrast to opioids, which require repeated administration and are associated with tolerance, dependence, and opioid-induced hyperalgesia [29]. These enduring effects are thought to arise from profound modulation of brain network dynamics, particularly within the default mode network (DMN), and enhanced integration of sensory, limbic, and prefrontal circuits, which collectively alter both the perception and the affective salience of pain [29].

Despite their promise, psychedelic therapies are not without limitations. Individual responses are highly variable, and while many patients report significant improvements in pain and quality of life, adverse psychological reactions—such as anxiety, paranoia, or perceptual disturbances—can occur [30]. Psychedelics are contraindicated in individuals with a history of psychosis or certain mood disorders, necessitating careful screening and administration within controlled therapeutic settings [30]. Furthermore, legal and regulatory barriers pose substantial challenges, as most classic psychedelics are classified as Schedule I substances under federal law, restricting clinical access and research opportunities [30].

In summary, serotonergic psychedelics represent a compelling, mechanistically distinct alternative to conventional pharmacologic therapies for chronic pain, particularly in conditions that are refractory to standard interventions. Their capacity to modulate both neurobiological circuits and cognitive-affective frameworks of pain positions them as a promising, though complex, frontier in pain medicine [26, 27,30]. Ongoing research is required to elucidate their precise mechanisms of action, optimize dosing protocols, and establish robust safety and efficacy profiles to support their responsible integration into clinical practice.

Serotonin Receptors and Psychedelic Action

The analgesic and therapeutic potential of classic psychedelics in chronic pain management is closely linked to their modulation of serotonin receptors, with the 5-HT2A receptor playing a central role, as seen in figure 2 [31]. These receptors are densely expressed in cortical regions critical for pain processing, including the prefrontal cortex (PFC), anterior cingulate cortex (ACC), and insula, which collectively govern sensory perception, cognitive evaluation, and affective integration of pain. 5-HT2A receptors are also present in subcortical structures such as the thalamus and amygdala, which mediate the relay of nociceptive signals and the emotional appraisal of painful stimuli [31].

Activation of 5-HT2A receptors by serotonergic psychedelics, including psilocybin and LSD, enhances excitatory glutamatergic signaling within cortical pyramidal neurons [32]. This increase in cortical excitability facilitates greater inter-regional connectivity and disrupts rigid neural network dynamics, mechanisms thought to underpin both the altered states of consciousness characteristic of the psychedelic experience and the observed analgesic effects [32]. Functional neuroimaging in humans has demonstrated that psychedelic-induced 5-HT2A activation reduces coherence within the default mode network (DMN) while simultaneously enhancing connectivity between sensory, limbic, and associative cortical regions. These network-level changes correlate with reductions in perceived pain intensity and improvements in cognitive and emotional coping with pain [32].

Preclinical studies further support the mechanistic role of 5-HT2A signaling in pain modulation. Animal models reveal that 5-HT2A agonists reduce nocifensive behaviors, whereas pharmacological blockade of these receptors abolishes the analgesic effects, underscoring a causal link between serotonergic modulation and pain attenuation [33]. Additional serotonin receptor subtypes, including 5-HT1A and 5-HT2C, may also contribute to anxiolytic effects and affective regulation, complementing the analgesic properties of 5-HT2A activation [33]. As seen in figure 3, these findings suggest that modulation of serotonin receptors—particularly 5-HT2A agonism—constitutes a key mechanistic bridge connecting psychedelic neuropharmacology to durable reductions in chronic pain [31-33].

Brain Circuits Linking Pain and Psychedelic Experience

Pain perception is mediated by a distributed neural network that integrates both sensory-discriminative and affective-motivational dimensions. The thalamus serves as a critical relay, transmitting nociceptive signals to the primary and secondary somatosensory cortices, which encode the location, intensity, and quality of painful stimuli. Concurrently, the anterior cingulate cortex (ACC) and insula process the emotional and motivational aspects of pain, linking the sensory experience to affective and autonomic responses. These regions form a tightly interconnected system: thalamic projections reach both the somatosensory cortices and the ACC, while the ACC and insula communicate with the amygdala and prefrontal cortex (PFC) to regulate the emotional salience and cognitive appraisal of nociceptive input. The medial PFC, in particular, plays a pivotal role in self-referential processing, evaluating pain in the context of one’s personal identity, and often amplifying distress when pain is perceived as a threat to the self [27].

Classic serotonergic psychedelics, including psilocybin, LSD, and DMT, modulate these pain-related circuits by altering functional connectivity within and between the default mode network (DMN), limbic structures, and sensory pathways. As seen in table 1, their primary mechanism involves agonism at the 5-HT2A receptor, with additional contributions from 5-HT2C and 5-HT1A receptors [27]. Activation of 5-HT2A receptors enhances excitatory glutamatergic signaling in layer V cortical pyramidal neurons, promoting cross-talk between sensory and associative cortical regions while concurrently reducing top-down inhibitory control exerted by the DMN [27]. This disruption of rigid network dynamics leads to a disintegration of entrenched self-referential processing in the medial PFC and posterior cingulate cortex, effectively reshaping the emotional interpretation of pain. By weakening hyperconnected pain-related circuits—such as thalamus-ACC-PFC loops—and strengthening communication between limbic and sensory regions, psychedelics may attenuate the negative affective weight of nociceptive input and enhance cognitive flexibility in pain reappraisal [27].

Functional neuroimaging studies further demonstrate that psychedelics reduce DMN coherence and alter connectivity within the limbic system, including the amygdala and hippocampus, regions critically involved in the emotional amplification of pain [34]. Altered ACC and PFC activity under psychedelic influence has been associated with decreased affective suffering and improved adaptive coping strategies, while reductions in amygdala reactivity may diminish the emotional salience of pain. One emerging hypothesis posits that psychedelics “reset” maladaptive neural circuitry by promoting neuroplasticity and disrupting entrenched pain pathways, thereby enabling the reorganization of networks implicated in pain chronification [35]. This network “reset” may reduce hyperconnectivity within pain circuits and restore more adaptive patterns of brain activity, offering a novel neurobiologically informed avenue for chronic pain therapy [27,34].

Clinical Evidence and Trials

Recent studies provide preliminary evidence that classic psychedelics may modulate both the sensory-discriminative and affective-motivational dimensions of pain [36]. Early human research, including small pilot trials, has demonstrated reductions in headache burden and migraine frequency following psilocybin administration [33]. Complementary studies in healthy volunteers using crossover designs have reported increased tolerance to noxious stimuli, such as cold-pressor tasks, alongside reductions in the perceived unpleasantness of pain [36]. These effects are hypothesized to arise from 5-HT2A receptor-mediated reorganization of neural networks and top-down recalibration of salience and affective appraisal mechanisms [36].

Preclinical rodent models support these findings, showing that serotonergic psychedelics reduce inflammatory hyperalgesia and neuropathic allodynia while promoting cortical plasticity within thalamo-cortico-limbic circuits implicated in the chronification of pain [36]. Despite these encouraging results, current evidence is limited by several methodological constraints, including small sample sizes, open-label designs susceptible to expectancy effects, heterogeneous pain phenotypes, variable dosing regimens, limited active-placebo controls, short follow-up periods, and the frequent exclusion of individuals on long-term opioid therapy [36]. These factors constrain both generalizability and mechanistic inference [36].

Future research directions aim to overcome these limitations through rigorous, translationally oriented studies. Promising strategies include adequately powered randomized controlled trials with active placebo comparators, integration of quantitative sensory testing and neuroimaging (including resting-state and pain-evoked fMRI/EEG) to assess circuit-level changes, and the incorporation of inflammatory and neurotrophic biomarkers as potential mediators [33]. Comparative studies evaluating macrodosing versus low-dose protocols, the development of non-hallucinogenic 5-HT2A-biased analogs, and protocols that combine psychedelic administration with behavioral pain-rehabilitation or affect regulation training may further leverage experience-dependent neuroplasticity to optimize therapeutic outcomes [36].

Ethical, Regulatory, and Translational Challenges

The reintroduction of psychedelics into medicine raises ethical and regulatory challenges. Despite growing evidence, psychedelics remain Schedule I substances under international law, classified as having high abuse potential and no accepted medical use [37]. This status imposes significant barriers to research, including restricted funding and complex regulatory approvals [37].

Ethical concerns include the intensity and unpredictability of psychedelic experiences, the necessity of careful screening and psychotherapeutic support, and the potential for adverse psychological reactions [36]. Unlike opioids, psychedelics are not drugs of daily administration but are instead delivered in structured, supervised sessions. This model requires new infrastructure, trained facilitators, and integration with psychological care [35].

There are also questions of accessibility, cultural sensitivity, and equitable distribution. Without careful regulation, there is risk that psychedelic therapies could become commodified and inaccessible to those most in need [37].

Discussion

The emerging evidence comparing classic psychedelics to traditional opioid analgesics highlights fundamentally distinct mechanisms of action and clinical profiles. Opioids exert analgesia primarily through activation of G-protein-coupled mu-opioid receptors, leading to neuronal hyperpolarization and reduced nociceptive transmission [38]. While opioids are highly effective for acute pain, their long-term use is limited by tolerance, physical dependence, risk of misuse, and numerous adverse effects [39]. By contrast, classic serotonergic psychedelics act primarily as 5-HT2A receptor agonists and modulate pain indirectly by altering cortical network dynamics, affective processing, and cognitive appraisal rather than directly blocking nociceptive input [40]. This mechanistic distinction suggests that psychedelics may be particularly well-suited to chronic pain states with pronounced affective-cognitive components, such as neuropathic pain, headache disorders, and centralized pain syndromes [40].

Clinical evidence for psychedelic-assisted pain therapy is promising but remains preliminary. Systematic reviews, small controlled trials, and case series indicate that psychedelic interventions can reduce pain intensity, improve mood, and enhance functional outcomes [41]. However, sample sizes are small, study designs are heterogeneous, and large-scale randomized trials are largely lacking [41]. By comparison, opioids have robust evidence for short-term analgesia but demonstrate limited efficacy for many chronic pain conditions in the long term and carry well-documented risks [42].

Mechanistically, psychedelics appear to “reset” maladaptive neural circuits involved in pain chronification, promoting neuroplasticity and network reorganization that may reduce hyperconnectivity in pain-related pathways while enhancing adaptive processing of sensory and affective input [44]. This contrasts with opioids, which primarily suppress nociceptive signaling without addressing the cognitive and emotional dimensions of chronic pain [42]. Such network-level effects may allow psychedelics to provide sustained analgesia and improvements in quality of life following a limited number of treatment sessions, unlike opioids, which require ongoing administration and are associated with tolerance and dependence [41,42].

It is important to note that this review does not include non-classical psychoactive compounds such as ketamine, Salvia divinorum, or MDMA, each of which has distinct pharmacological and clinical profiles. Ketamine acts primarily as an NMDA receptor antagonist with rapid antidepressant and analgesic effects and interacts with the endogenous opioid system [40], whereas Salvinorin A, the active component of Salvia divinorum, is a potent kappa-opioid receptor agonist with dysphoric subjective effects [42]. MDMA is an entactogen whose primary actions involve robust monoaminergic release [41]. Furthermore, MDMA, Salvia divinorum, and ketamine are highly addictive drugs. Since these drugs do not overcome the risk of addiction, they are not a viable substitute for opioid in treating pain. These differences underscore that the current focus on classic serotonergic psychedelics provides a coherent mechanistic framework distinct from other psychoactive therapies [42].

Conclusion

In conclusion, classic psychedelics offer a mechanistically unique, non-opioid approach to chronic pain management, targeting both neurobiological and psychological dimensions. While preliminary findings suggest potential for durable analgesia, affective modulation, and network-level neuroplasticity, robust, adequately powered clinical trials and mechanistic studies are required. Careful attention to safety, therapeutic context, and regulatory constraints will be essential for translating these promising compounds into responsible clinical interventions that may complement or, in selected cases, provide alternatives to conventional opioid therapy.

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About the author

Emerson G. Brown

Emerson Brown is a student researcher with strong interests in psychology, chemistry, and neurobiology. She has conducted independent research under the mentorship of Dr. Adam Behensky, focusing on the intersection of neuroscience and medicine. Beyond her academic studies, she is an active competitor in Science Olympiad, where she explores scientific inquiry in a team-based setting. Emerson is passionate about medicine and aspires to contribute to advancements in patient care and biomedical research.

The post Exploring Psychedelics as Alternatives to Opioids for Chronic Pain Management: A Neurobiological Perspective appeared first on Exploratio Journal.

Introduction

Understanding how the brain functions during sport and exercise is critical for optimizing athletic performance, preventing injury, and supporting recovery. This is especially relevant in collision and high-impact sports, where concussions and other forms of traumatic brain injury (TBI) are relatively common, yet often difficult to detect in real time. As McCrory et al. (2017) emphasize, sport-related concussion represents “a traumatic brain injury induced by biomechanical forces, ” but timely and accurate detection during play remains a major challenge.

Current real-time assessments typically rely on computerized cognitive tools like ImPACT, which offer objective reaction time and memory measures; however, these tools may yield inconsistent results in subacute or chronic phases and should not be used in isolation for return-to-play decisions (Powell et al., 2021). Portable neuroimaging technologies, such as wearables and head impact sensors, are increasingly being explored to detect subtle functional and structural brain changes immediately after impacts, offering more sensitive and direct indicators of injury status (Zhan et al., 2020).

Traditional neuroimaging methods such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have significantly advanced our understanding of brain function. However, applying these techniques in sporting contexts poses practical challenges. fMRI offers excellent spatial resolution but requires participants to remain entirely still and can only be used in laboratory settings, making it unsuitable for use during or immediately after physical activity. While EEG offers greater mobility and excellent temporal resolution, it provides poor spatial accuracy and remains highly susceptible to motion artifacts—a factor well-documented in mobile brain imaging research (Gramann et al., 2014).

Functional near-infrared spectroscopy (fNIRS) is an emerging neuroimaging technique that addresses many of the limitations of traditional methods. fNIRS works by emitting near-infrared light onto the scalp and skull to measure changes in oxygenated (HbO) and deoxygenated hemoglobin (HbR) in the cortical surface. These hemodynamic changes reflect underlying neural activity, enabling researchers to infer brain function with reasonable spatial and temporal resolution in more naturalistic settings compared to fMRI and EEG (Ferrari & Quaresima, 2012).

This paper evaluates whether portable fNIRS holds significant promise as a neuroimaging tool for examining brain function in real-world sporting settings. First, it addresses the limitations of conventional methods such as fMRI and EEG in athletic contexts. Next, the mechanisms of fNIRS are explained, emphasizing features that make it particularly well-suited for use during or immediately after physical activity—namely, its portability, non-ionizing nature, and resilience in mobile, dynamic environments (Rahimpour Jounghani et al., 2025; Scholkmann et al., 2014). Following this, the review explores emerging sports neuroscience applications of fNIRS, including its use in assessing motor control, monitoring cognitive workload and fatigue, and informing concussion management. The paper concludes by discussing key challenges and feasibility concerns surrounding its implementation, as well as future directions for integrating fNIRS into sports research and practice. Taken together, this analysis demonstrates how fNIRS can contribute valuable insights into athletic performance, injury prevention, and recovery strategies.

Overview of Neuroimaging Methods

Neuroimaging techniques such as fMRI, EEG, and PET have each contributed valuable insights into brain function. However, they face substantial limitations when applied to athletic and physically active contexts.

fMRI measures blood-oxygen-level–dependent (BOLD) signals and offers excellent spatial resolution (Logothetis, 2008), but it requires complete stillness in a controlled laboratory environment. This makes it unsuitable for capturing brain activity during or immediately following physical exertion. EEG, by contrast, is portable and capable of recording rapid neural responses with high temporal precision. Yet, it suffers from low spatial resolution and is highly sensitive to motion artifacts, limiting its utility in dynamic, real-world environments (Gramann et al., 2014). PET is useful for investigating long-term brain function through metabolic and neurotransmitter activity (Villringer & Dirnagl, 1995), but its reliance on radioactive tracers renders it invasive and impractical for use with athletes (Huang, 2000).

Taken together, these limitations highlight the need for a neuroimaging technique that is portable, non-invasive, and robust against movement—something that can deliver reliable insights into brain function directly in field or sporting environments.

Functional Near-Infrared Spectroscopy (fNIRS)

Because of its motion tolerance and portability, fNIRS has special promise for the study of sport. Oxygenated as well as deoxygenated hemoglobin may be detected to enable fNIRS to provide information regarding brain activity. These changes reflect neural activity caused by neurovascular coupling, in much the same manner as the blood-oxygen-level-dependent (BOLD) signal in fMRI (Ferrari & Quaresima, 2012). For example, Yu et al. (2023) found that athletes who were allowed to remain in play showed more vigorous hyperactivation while performing a visual attention task than athletes who were taken out immediately, the suggestion being that remaining on the field following injury will alter patterns of brain activity visible through fNIRS — patterns otherwise missed in traditional sideline testing.

fNIRS is particularly well suited to study athletes under natural sport environments and has many advantages. First, with its wearable and mobile configuration, fNIRS can be utilized to gather data when an athlete is training, sideline testing, or for other field-based studies (Pinti et al., 2015). Secondly, because of its accommodation to moderate levels of physical activity, it is far more practical to use than fMRI or EEG in dynamic settings. Third, as it provides acceptable spatial and temporal resolution, it permits both transient instability of brain activity and trends over extended periods spanning many sessions to be tracked. For example, a systematic review of 29 exercise studies demonstrated that regular exercise increases oxygenated hemoglobin in both the prefrontal and motor cortices. This increase is related to improved working memory and inhibitory control. Besides, the method is motion-robust, making it suitable to be used in real sporting settings (Shen et al., 2024). By avoiding tracer injection or exposure to high magnetic fields, this non-invasive method poses little safety concerns for athletes compared to imaging techniques such as PET or fMRI involving radiation or high-field magnet exposure. Lastly, scientists can observe neurovascular coupling directly, relate physical effort, mental demand, and brain function by measuring oxygenated and deoxygenated hemoglobin — a method that can be applied to optimize performance as well as avert damage (Gramann et al., 2014).

Potential Applications in Sports

Monitoring cognitive load during training and competition – By measuring prefrontal cortex activation, fNIRS can help assess how mental demands fluctuate during complex plays, multitasking, or high-pressure decision-making (Herff et al., 2014).

Studying focus, attention, and decision-making – Because fNIRS is able to capture changes in cortical activity, it can help find neural markers of optimal focus, allowing coaches to tailor drills and game strategies accordingly in practice to maximize focus. This includes revealing how athletes with different skill levels allocate attention and process information during sport tasks. For example, juggling and table tennis athletes showed much larger increases in oxygenated hemoglobin compared to novices within motor-related cortical regions (e.g., M1, premotor cortex, inferior parietal cortex). This indicates that an important reason for efficient integration of motor control and decision-making processes is a higher skill level (Perrey, 2008).

Evaluating fatigue – By tracking brain activity during states of physical and mental fatigue, fNIRS can detect alterations in brain function that correlate with slower reactions, impaired decisions, and heightened injury risk. This is significant because Van Cutsem et al. (2017) showed that subsequent disturbances in balance, motor skills, and decision-making processes could potentially increase the vulnerability to injury – meaning that alterations in the brain that can be measured by fNIRS can directly cause detectable decreases in function that make the individual more likely to be involved in an accident.

Supporting safer return-to-play decisions after concussion – Taylor (2021) showed that fNIRS is a valid tool when assessing concussion in athletes undergoing return-to-play protocols, showing that it can provide objective physiological confirmation of recovery — an advantage over relying solely on symptom reports, which can be incomplete or inaccurate.

Integration with other wearable technologies – Linking fNIRS data with other information from heart rate monitors, GPS trackers, and motion sensors enhances the ability to assess athlete performance and workload in depth (Pinti et al., 2020).

Challenges and Limitations

Despite its promise, fNIRS is not without limitations. Firstly, it is less suitable for investigating deep brain structures involved in sports performance because its measurement depth is restricted to only the outer cortical layers (Ferrari & Quaresima, 2012). Additionally, other factors can interfere with signal quality, such as hair density and color, as can ambient light in certain environments (Scholkmann et al., 2014). Because data analysis requires specialized expertise, unqualified analysis can lead to improper handling and eventually improper interpretations (Boas, Elwell, Ferrari, & Taga, 2014). There are also ethical considerations: brain data is inherently sensitive, and its collection raises privacy concerns, especially when linked to athletic performance and health outcomes (Ienca & Andorno, 2017). Before implementing fNIRS, informed consent and data governance protocols are essential in sports contexts and really, any context.

Future Directions

Technological developments could greatly expand the utility of fNIRS in sports. Developments that are current are making headsets lighter, more ergonomic, with greater sensor sensitivity and stronger motion artifact resistance, a very critical constraint for fNIRS (Yücel et al., 2021). For example, Spectroscopy Online (2024) reports that “a wireless, wearable brain-monitoring device using functional near-infrared spectroscopy (fNIRS) to detect cognitive fatigue in real time” already exists, which shows that such developments are not hypothetical but functional in real-world settings. Employing machine learning to analyze signals could streamline and enrich interpretation, lowering the expertise level required (Zhao et al., 2023). Athletes can also potentially be monitored over a season to identify trends in cognitive load, recovery, and injury risk, which can be referred to as large-scale monitoring. In combination with behavioral performance measures, biomechanical measures, and physiological monitoring, researchers would be able to provide individualized training plans and improve return-to-play decision safety (Perrey et al., 2024).

Conclusion

fNIRS holds strong promise for advancing the field of sports neuroscience. Its portability and ability to measure cortical activity in real-world settings provide a unique advantage over traditional imaging tools. By enabling researchers and clinicians to assess cognitive load, fatigue, focus, and recovery in naturalistic environments, fNIRS has the potential to directly improve both athlete performance and safety. At the same time, the successful integration of fNIRS into sports research and practice will require continued progress in device technology, data analysis methods, and ethical safeguards to protect athletes’ privacy. While challenges remain, the growing body of evidence demonstrates that fNIRS can bridge the gap between laboratory neuroscience and applied sports performance, making it a powerful tool for the next generation of athlete monitoring and individualized training.

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About the author

Emil Volvovsky

Emil is a New York City high-school senior. For the last several years, he’s been a member of the Inside the Brain Club, which has influenced his desire to learn about the brain and its function. Being a competitive athlete, Emil has been looking for ways to integrate these two passions. This summer, he took on a research project, looking at the best ways to evaluate one of the most common injuries in sports – concussions and TBI.

The post Portable fNIRS in Sports: Measuring Brain Function in Real-World Settings appeared first on Exploratio Journal.

Abstract 

Chronic stress affects many pathways of the brain, leading to innumerable complications in human functioning. Various studies have found links between stress and the prefrontal-striatal circuit as well as the prefrontal amygdala pathway. The disruption of these mechanisms has been proven to prompt abnormal decision-making, anxiety, and depression. Changes to dendritic morphology, hypothalamus, hippocampus structure also occur during stress, leading to psychological disorders including schizophrenia and memory loss. Additionally, diseases such as obesity and cancer are aggravated and can even be brought on by chronic stress. Finally, prolonged stress can disrupt sleep patterns, eating habits, and optimistic views on life through the dysregulation of cortisol secretion. This article traces the vastly contrasting and widespread effects of chronic stress on health through evidence compiled from various experiments. The following discussion therefore points to stress as a significant subject for future research and treatment development. 

Keywords: chronic stress, anxiety, brain pathway, cortisol, disease, depression

Chronic Stress and Its Toll on Human Health 

Stress, specifically chronic stress, affects a large amount of the human population numerous national surveys conducted by the American Psychological Association (APA) indicate that over 50 percent of US citizens are chronically stressed (American Psychological Association, 2023). Simultaneously, a significant proportion of the American population struggle with a variety of health issues – ranging from mental health disorders such as depression of schizophrenia to potentially fatal diseases such as cancer. Indeed, chronic stress is an important factor in the development of many medical conditions (Russel & Lightman, 2019). Combined with copious studies establishing links between stress and various health concerns, this review makes the conclusion that chronic stress does, in fact, cause and aggravate both mental, physical, and cognitive diseases (Russel & Lightman, 2019).  

Psychological stress should first be separated into two categories of acute and chronic stress. Acute stress, being an essential part of homeostasis – the body’s self-regulating process by which organisms maintain stability – is beneficial to the human mind and body. Characterized by a rhythmic, pulsatile secretion of cortisol – a hormone released into the bloodstream – acute stress is often a part of the body’s natural fight or flight system. Cortisol levels rise quickly when exposed to acute stress, allowing the body to use its energy to handle the stressor, and return to baseline soon after. On the other hand, chronic stress often results in this system going awry. Non-pulsatile, irregular patterns of cortisol are seen in prolonged stress, which will be the main form of stress discussed in this article. When exposed to chronic stress, cortisol levels tend to increase for extended periods of time, therefore causing implications in other parts of the body. (Russel & Lightman, 2019). 

In each section of this review, multiple systems of the brain and body will be analyzed through evidence obtained through surveys, models, and scientific experiments. Chronic stress, through its disruption of various brain mechanisms, will be proved to result in a wide spectrum of severe and sometimes fatal physiological and cognitive impairments. This discussion urges the development of effective and safe treatment to combat chronic stress and relieve the millions struggling from it. 

Disrupted Brain Pathways and Structure

Prefrontal-Striatal Circuit 

Chronic, prolonged stress has been proven to disrupt the brain’s prefrontal-striatal circuit. This neural pathway is essential to the decision-making process; therefore, dysfunction of this circuit leads to abnormal decision-making. In one study establishing this connection, experimenters presented a choice of pure chocolate milk paired with a strong light as opposed to the choice of diluted chocolate milk paired with a dim light to groups of control and chronically stressed rats (Friedman, et al., 2017). This task provided the rats with the challenge of choosing high reward and high cost, or low benefit and low cost. It was found that the control group chose the low-reward, low-cost option more frequently, representing normal decision-making and therefore normal cost-benefit integration. Stressed rats, however, were more inclined to take the pure chocolate milk, regardless of the costs (Friedman, et al., 2017). 

Further optogenetic manipulation, a technique that uses light to modify the activity of neurons in behaving animals, revealed that chronic stress in rats causes changes in neural circuits including the one being discussed here, the prefrontal-striatal circuit. These changes include an altered spike in neurons of the medial prefrontal cortex, increased activity in striatal projection neurons, and a decrease in activity of fast-firing interneurons. The alterations listed above have serious implications on the decision-making process: They affect how neurons encode and process information related to cost-benefit integration, and they also influence the balance of excitation and inhibition toward excitation in the neural circuitry, misbalancing neuron currents (Friedman, et al., 2017). Abnormal cost-benefit integration is often a symptom of many mental and mood disorders, such as anxiety and depression. In fact, the increased activity in striosomes caused by chronic stress can even lead to changes in dopaminergic activity, leading to a reduction in overall striatal dopamine release (Friedman, et al., 2017). This finding demonstrates the effect of the disruptions of this circuit not only on abnormal decision-making involving irregular cost-benefit integration but also on major depressive disorder, showing how chronic stress’ changes to brain function impact the mind and day-to-day functioning. 

Prefrontal-Amygdala Pathway 

An additional brain circuit, among many others, altered by chronic stress, includes the prefrontal cortex to the amygdala pathway, which processes emotional stimuli and regulates emotional behaviors. Specifically, it has been found that communication between the dorsal medial prefrontal cortex and projection neurons located in the basolateral amygdala – which play a role in processes involved with emotions, reward, fear, and anxiety – becomes disrupted. Miscommunication between these two areas of the brain leads to a drastic shift in the E-I balance towards excitation, impairing cognitive function (Liu, et al., 2020). Researchers tested the effects of this on rats using two behavior assessments to measure anxiety levels. They found that stressed mice exhibited behavior indicating increased anxiety (Liu, et al., 2020). Using light stimulation, researchers also found that stress slowed the decay of NMDAR-meditated currents, a form of glutamate channels. This, in turn, proves that stress increases the glutamate release at these channels. Overall, the effects of the disruption of this brain pathway include an increase in excitation and an increase in glutamate release, both of which result in heightened anxiety (Liu, et al., 2020).

Additional research shows that medial prefrontal-cortex neurons projected to the basolateral amygdala are more resilient to stress than ones projected to different brain regions. BLA-projecting neurons can maintain their centric structure and dendric spine density despite chronic stress (Liu, et al., 2020). This conveys how variable the effects of chronic stress are.

The detrimental effects of chronic stress on the prefrontal to amygdala pathway are key to understanding the impact stress has on the mind and body as they have been proven to lead to anxiety and mood disorders such as depression. (Liu, et al., 2020)

Dendritic Morphology 

The impacts of chronic stress can be more granular than one might expect, for example, in its effect on dendrites, a part of the neuron which receives synaptic input from axons. To explore the exact changes in dendritic morphology caused by stress, researchers conducted an experiment consisting of two groups. Male rats were exposed to either three hours of daily restraint stress or left untouched. On the last day of this experiment, the rats were exterminated and their brains stained using a Golgi-Cox procedure, which allowed researchers to do a detailed examination of the changed morphology of the neurons (Cook & Wellman, 2004). Thorough analysis of the brains of the stressed and unstressed rats revealed a significant difference in the structure of dendrites between the two groups; the number as well as the length of apical dendrite branches – dendrites that emerge from the tip of the cell – were reduced by about 18 and 32%, leaving basilar dendrites – dendrites that emerge from the base of the cell – unaffected. Additionally, there was a 58% decrease of dendritic material lateral to the soma, the body of the neuron (Cook & Wellman, 2004).

The consequences of stress on dendritic morphology are now proven, but what impact does this have on day-to-day life and functioning? The retraction of dendrites as seen in the experiment above is known to reduce glutamatergic transmission, leading to medial prefrontal cortex dysfunction and altered connectivity with the amygdala. The dysfunction of this pathway has several impacts on human mental, physical, and cognitive health, as discussed in the previous section (Liu, et al., 2020). Equally important are the roles these stress-induced alterations– especially in the prefrontal cortex – play in the development of psychological disorders such as major depressive disorder and schizophrenia (Cook & Wellman, 2004). Even so, more research is needed to solidify the contribution of these dendritic morphological variations on these disorders.  

Hippocampus and Hypothalamus 

The hypothalamus and hippocampus both play necessary roles in human living and function. The hypothalamus controls homeostasis, involved in primary functions such as body temperature, heart rate, and hunger. On the other hand, the hippocampus is central to memory and learning. Chronic stress can disrupt many of these processes. One study found the following after inducing three weeks of unpredictable stress in a group of rats. To start, stress was found to inhibit input to neurons involved in the HPA axis, discussed previously (Joëls, et al., 2004). These neurons are found in the hippocampus, and disrupt homeostasis, leading to disease. In addition, chronic stress amplifies glutamate transmission in the dentate gyrus, part of the hippocampus specifically pertained to the formation of new memories (Joëls, et al., 2004). The researchers also found a significant decrease of cell growth in the dentate gyrus, impairing the acquisition, storage, and retrieval of new information in the brain. Synaptic plasticity – the capability of the brain to adapt to its constantly changing environment – diminishes as well because of prolonged stress. Along with this, neurogenesis – neuronal formation – and apoptosis – the death of harmful cells, such as cancerous cells – are reduced in these regions. Final outcomes of unpredictable stress included changes in the GABA receptors and calcium channel subunit composition. These mechanisms are involved in a variety of functions: muscular contraction, hormone excretion, and gene expression. Changes in these brain areas therefore lead to an inhibition of the functions mentioned above (Joëls, et al., 2004). 

These reverberations in the hippocampus and hypothalamus all contribute to the decline of memory, and inability of the brain to encode, store, and retrieve information. In addition, a serotonin decrease was found in an area of the hippocampus called CA1, and when paired with increased glutamate transmission mentioned previously, can contribute to depression and anxiety, exhibiting the importance of these effects on daily life (Joëls, et al., 2004).

Stress-Related Diseases 

Obesity 

According to the CDC, almost 42 percent of Americans were obese between 2017 and 2020, which compared to the almost 50 percent of chronically stressed adults, is not at all surprising. The relation between chronic stress and obesity is concrete, through a decrease in fitness levels, an increase in appetite, and a change in hormone levels and signals (Centers for Disease Control and Prevention, 2024).

In a survey of over 12,000 participants, chronic stress proved to be related to a decrease in physical activity, leading to obvious weight gain (Ng & Jeffery, 2003). Additional survey studies produce patterns concerning an increase in appetite when stressed, specifically for foods higher in sugar, fat, and calories (Torres & Nowson, 2007). An increase in appetite can also originate from an increase in dopamine – a neurotransmitter associated with movement, motivation and most importantly, pleasure reward – induced by chronic stress (Dallman, 2010).

The biochemistry of the development of obesity is arguably most important when discussing chronic stress’ role. Primarily two hormones are used to regulate appetite: leptin and ghrelin. These hormones, being opposite of each other, work to prevent the body from unnecessarily large food intake in different ways. Ghrelin – a hormone signaling caloric need – communicates the body’s hunger to brain (Russel & Lightman, 2019). Leptin, on the other hand, – a hormone signaling fullness – informs the brain when the body is no longer in need of excess calories (Russel & Lightman, 2019). Chronic stress weakens the signals of these hormones, resulting in inaccurate messages of caloric need to the brain (Russel & Lightman, 2019). This prompts the body’s unawareness of satiation, leading to an increased food intake through no fault of one’s own.  

Obesity is one of the leading health concerns in the world – a serious risk factor in an abundance of causes of death, including heart disease, stroke, diabetes, and cancer (Ritchie & Roser, 2017). As already indicated, chronic stress can increase the likelihood of obesity, therefore, providing a source of treatment for stress could result in a beneficial treatment for obesity. 

Cancer and the Immune System 

The immune system is crucial in protecting the body from various diseases, including preventing the growth and spread of tumor cells. This system can be greatly disrupted by chronic stress causing cancer cells to form and spread rapidly through the body. In addition, numerous studies and research have linked a promotion in breast cancer, pancreatic cancer, and gastric cancer due to prolonged stress. 

The body’s immune system – an integral part of defending the body against infection and protecting its own cells – can be harmed by stress in a variety of ways. For one, they increase levels of inflammatory cytokines – molecules that regulate the immune system – which can lead to disease and weaken the system, diminishing its ability to effectively fight against cancer cells (Russel & Lightman, 2019). 

Regarding cancer itself, stress induces a specific microRNA called miR-337-3p to effectuate EMT in breast cancer – a cellular process activated during cancer progression facilitating tumor development. A study conducted by Du et al. also demonstrated the effects of chronic stress on breast tumors of female mice. A significant increase in metastasis – the spread of cancer cells from an original tumor – was found in the stressed group rather than the control group (Du, et al., 2020).

The metastasis of pancreatic cancer has also been found to be affected by stress. Researchers discovered that chronic exposure of pancreatic cancer cells to epinephrine – a hormone significantly increased by stress – dramatically escalated cell production and migration (Al-Wadai, Al-Wadei, Ullah, & Schuller, 2012). Additionally, in stress-exposed mice, tumor tissue in the pancreatic region was enlarged, compared to unstressed mice. 

Furthermore, chronic stress is correlated with the promotion of gastric cancer, also known as stomach cancer. A study conducted by Zhang and other researchers found a significant enlargement in the gastric tumors of chronically stressed mice (Zhang, et al., 2019). An increase in tumor weight and volume was also observed and like the previous study, the exposure of cells to epinephrine led to increased cancer metastasis. 

According to the American Cancer Society, cancer is the second-leading cause of death worldwide. This in mind, along with the immense contribution of prolonged stress to the progression of various forms of tumors, demonstrates the need for chronic stress treatment, to reduce the number of cancer deaths (American Cancer Society, 2020).

Other Harmful Effects of Chronic Stress 

Memory 

As discussed earlier, memory can be disrupted when damage to the hippocampus occurs. This damage is often a result of prolonged, chronic stress. A series of experiments focusing on hippocampal calbindin – a protein which binds to calcium to regulate calcium levels in cells – has found specific links between stress and memory loss, and the effects of stress-induced memory on the brain. 

Researchers first measured the effect of stress on the calbindin levels in hippocampal excitatory and inhibitory neurons – neurons that work together to create neural circuits – of rats. They found that calbindin levels peaked during about the ninth day of the experiment and found the effect of stress exposure to be considerably higher in the stressed group rather than the control group (Li, et al., 2017). Further use of markers to identify the location of specific proteins revealed that interneurons containing calbindin were significantly decreased in stressed mice, stipulating that stress diminishes levels of hippocampal excitatory and inhibitory neurons (Li, et al., 2017). To determine the role of this decrease in memory, the researchers conducted several hippocampal-dependent location tasks, which measure spatial memory specifically, and found that only stressed mice were not able to differentiate objects in the inaccurate location. It was also established that the stressed group performed significantly worse than the controls, which indicates spatial memory deficits (Li, et al., 2017). 

Memory, extremely important to the daily lives of both humans and animals, naturally declines as one ages. However, earlier memory loss creates prominent disturbances and unnecessary challenges, as does chronic stress. The several experiments above highlighting the link between memory loss and chronic stress are the first steps in the research needed to decrease these complications. 

Sleep 

The detrimental effects chronic stress has on sleep are no surprise, however most are unaware of its extent and basis. One study aims to help people understand this through conducting an experiment measuring the effect stress brought on by school has on teenager’s sleep efficiency – the percentage of time spent asleep while in bed. 

Using the amount of sleep attained during holidays as a baseline, researchers found that school attendance drastically reduced time spent in bed by about 90 minutes and total sleep time by about an hour. A significant change in percent of sleep efficiency was not observed (Astill, Verhoeven, Vijzelaar, & Van Someran, 2013). Moreover, the researchers compared these levels of stress brought on by simply school attendance to stress levels during important exams – a time of increased pressure and anxiety. To no surprise, examination further decreased time spent in bed by about 17 minutes and reduced sleep efficiency by about 1.5% – seemingly insignificant values, however, when accumulated for years can be injurious to one’s health (Astill, Verhoeven, Vijzelaar, & Van Someran, 2013).

The basis of these effects on sleep is just as, if not more, important to consider in the discussion of chronic stress. As discussed previously, chronic stress elevates cortisol levels during its daily cycle – its circadian rhythm. Regarding sleep, cortisol levels are specifically increased during the lowest points of this daily rhythm, called the nadir. Over time, constant exposure of these elevated levels can lead to sleep deprivation, causing a variety of health problems (Russel & Lightman, 2019). Sleep deprivation additionally interferes with daily life, leading to problems at school, work, and even simple tasks such as driving. 

Sleep itself is obviously extremely necessary and beneficial to human functioning, but combined with its additional needs, it should not be disregarded. Because of the harmful effects chronic stress has on human’s daily sleep patterns, the importance of finding solutions and treatment becomes increasingly vital.   

Positive Outlook 

As evidenced thus far, chronic stress has unmistakable effects on one’s health and well-being, fostering implications in mood and everyday life. One such complication includes the consequence of chronic stress on human’s outlook of the world.  

An effective experiment administered hydrocortisone – cortisol in a medication form – to otherwise healthy men in two methods, pulsatile or non-pulsatile, and later assigned them to several facial recognition tasks. As reviewed in earlier discussion, a pulsatile pattern of cortisol secretion is seen in acute stress, and is beneficial to the body, while continuous or non-pulsatile secretion is seen in chronic stress. Subjects given hydrocortisone in a pulsatile pattern were more likely to accurately recognize positive faces than negative, while those administered the medication in a non-pulsatile pattern recognized positive faces more accurately, but to a much weaker extent (Kalafatakis, et al., 2018). Pulsatile secretion also led to a bias towards happy rather than fearful facial expression while non-pulsatile secretion resulted in a bias towards neither (Kalafatakis, et al., 2018).

In summary, a pulsatile pattern of cortisol secretion, as seen in acute stress, contributes to a more optimistic view on emotional faces, which can ultimately give rise to a more positive outlook on life. In contrast, a non-pulsatile secretion of cortisol, as seen in chronic stress, has a considerably diminished effect on a person’s optimistic outlook, leading to mental health disorders such as depression. 

Future Directions of Chronic Stress Research and Treatment

As demonstrated through this review of the literature, the effects of chronic stress are more extensive than is assumed by most. Chronic stress has been proven to play a role in the disruption of two major brain pathways – the prefrontal striatal circuit and the prefrontal to amygdala pathway – as well as in the onset and development of serious diseases such obesity and cancer. Additionally, stress disrupts daily functioning, through its implications in memory, sleep, and positive outlook. 

Chronic stress has the potential to disrupt brain function affects fundamentally all areas of the human body. Because of this, and all other evidence discussed pointing to this conclusion, it is increasingly vital that greater research is conducted to facilitate the development of more effective and safe treatment. Finding effective treatment for this condition is more difficult than most others as not many medications exist which mimic the specific bursts of rising and falling cortisol levels seen in adaptive, acute stress without producing a large amount of harmful side effects. However, according to Yale Medicine, stress management techniques such as exercise, adequate sleep intake, and a healthy diet can be used to prevent the acute stress response from becoming maladaptive (Yale Medicine , 2007). Chronic stress’ dangerous nature as well as its commonality makes it a significant subject for further research and effort to produce treatment. This is critical not only to provide comfort for the millions struggling mentally but also physically, due to stress’ widespread effects on the body

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About the author

Diya Desai

Diya is currently a junior at Sunny Hills High School and is interested in the biological sciences. At school, Diya is a member of the Health Sciences Club, a tutor at Algebra Center, and a part of the dance program. She is also a member and president of the volunteer organization, Lions Heart. In her free time, she enjoys playing the piano, volunteering at the animal shelter, and spending time with her family and friends.

The post Chronic Stress and Its Toll on Human Health appeared first on Exploratio Journal.

Abstract

One of the most significant public health crises in the United States is the opioid epidemic. The opioid epidemic is categorized by misuse of opioids such as heroin, prescription opioids, and synthetic opioids such as fentanyl. The rise in the use of opioids has led to increased overdoses due to the increase in accessibility to this class of drugs. The increase in the opioid epidemic is not only due to how accessible opioids are but also how addictive they are due to how they increase feelings of euphoria in the brain. 

Introduction (The Origins and Escalation of the Opioid Epidemic): 

United States Overdose Deaths:

Box 1: Waves of the Opioid Epidemic 

1. Prescription Opioids: Classes of drugs physicians or healthcare workers prescribe to patients to relieve pain in the body. 
2. Overdose deaths: When an individual takes a drug more than the quantity needed, which can often lead to death or other harmful complications. 
3. Heroin: An opioid drug that consists of morphine
4. Synthetic opioids: Opioids that are made in laboratory settings and act similarly or the same in the brain as natural opioids to reduce pain (increasing pain tolerance). 

My Interest

 One day while I was shadowing in the ER with a doctor, we went into one of the ER rooms where I saw a homeless man who was not feeling well. He could barely speak. He did not answer any of the doctor’s or nurses’ questions. However, the only words he used in his voice were to utter “I just want some opioids.” I was shocked that he did not care about his health and all he cared about were opioids. At the time I did not understand what opioids were but then when I learned more about them from the ER doctor and how addictive they can be, I realized that opioid addiction is an epidemic in the United States. Substances such as opioids are often used by individuals as a coping mechanism to manage pain and relieve symptoms of serious illnesses but many also get addicted in the process. 

Background 

Because substances such as opioids alter the mental and physical state of a person, these substances have a higher potential for misuse. Misuse can be when individuals use the substance more than the recommended prescription dose to produce the desired mind-altering effect. Even though the desirable effect may be achieved by using the substance in accurate doses, misuse can cause negative consequences both physically and mentally. Substance abuse is commonly categorized as a mental health disorder. Individuals who have health complications fail to meet dosing guidelines as prescribed and take higher doses which leads to dependence and addiction. One substance that has been commonly abused is prescription opioids (Opioids, 2024). 

Opiates are a class of painkillers that come either directly from the opium poppy or manufactured in a laboratory and are one of the oldest pain-relief drugs. Common opiates are codeine, heroin, morphine, and fentanyl with the most common opiates being prescription opioids and heroin (Bolshokova et al., 2019). Opiates are effective analgesics, a medication that treats pain and inflammation (Wang et al, Wang).  

Common opiates are heroin, morphine, and fentanyl with the most common opiates being prescription opioids and heroin (Bolshokova et al., 2019). There have been three waves when it comes to the rise in opioid overdose deaths during the opioid epidemic: a rise in prescription opioid overdoses, heroin overdoses, and synthetic opioid overdoses. Heroin and prescribed opioid overdoses are slowing down due to abuse-deterrent formulations, overdose deaths have also recently lowered from 2023 to 2024 due to the betterment of the pandemic, and synthetic opioid overdoses are increasing at a fast rate. The opioid epidemic is still very much prevalent and needs to be mitigated even more (Lewis, 2024). 

This paper will present why opioids are highly addictive by discussing the molecular aspect of opioids and how opioids interact with the brain, the intersection between social determinants of health and the opioid epidemic, who is most impacted, and public health initiatives and solutions that need to be taken to mitigate the opioid epidemic. 

How Opioids Hijack the Brain and Cause Negative Side Effects:

Disinhibition of GABA:

Figure 3 – This figure illustrates the effects of morphine (a type of opioid drug) on the brain. Morphine interacts with the brain to create a rewarding feeling for the individual by impacting the chemical GABA (which is responsible for the release of dopamine). Morphine inhibits the reduction of the release of dopamine (A neurotransmitter or hormone in the brain that creates feelings of pleasure in individuals) by GABA and instead allows increased release of dopamine. By releasing the increased amounts of dopamine in the brain, opioids such as morphine become highly addictive as a result (Listos, 2019). 

How Vulnerability in Opioid Use Disorders Occur: 

Activation of Opioid Receptor by Opioid:

The Molecular Impacts of Opioids and Their Side Effects: 

1. Nerve Cells: Types of cells in the body that send messages from the body to the brain or the other way around.
2. Opioid Receptor Activation: Opioids bind to the opioid receptor, causing activation to occur where the pain relief begins to occur. 

Molecular Aspect of Opioids 

Opioids function by activating the opioid receptors on the nerve cells. Opioids act as ligand molecules (molecules that attach or bind to other molecules) and attach to the opioid receptors (Proteins that act as communication between the internal cellular membrane and the external environment by interacting with and binding to either drugs or neutron emitters. These receptors are found on the surface of the cell membrane) in the body such as in the spinal cord, brain, etc. When opioids bind to the opioid receptors in the body, they inhibit pain messages through the spinal cord to the brain (How Opioid Drugs Activate Receptors, 2018). 

While blocking pain, these opioids can also increase euphoric feelings and emotions by activating the reward pathway in the brain. Opioids have a high potential for misuse and addiction due to the euphoric response and addictive properties as well as causing respiratory depression, which all contribute to the ongoing opioid crisis (Bolshokova et al., 2019). Opioid receptors are often involved in various physiological functions and side effects such as pain modulation, euphoria, sedation, cardioprotection, neuroprotection, and respiratory depression (Shenoy et. al, 2023). 

Side Effects 

Some of the less common types of side effects from opioid addictions are delayed gastric emptying, muscle rigidity, immunologic and hormonal dysfunction, cardiac arrhythmia, pruritus, xerostomia, and hyperplasia (Koptnik et. al, 2023). Opioid addicts may not fully comprehend the extent of their impairment from the opioids, causing them to attempt to operate machinery that could cause them to be dangerous to themselves and others such as driving a car while under the influence of opioids. While chronic opioid users may not have impairments such as those taking it for the first time because of their tolerance to the drug, long-term opioid use can lead to other neurocognitive deficits (Opioid Use Disorder, 2021). 

Uncovering the Faces Who Are Impacted Most by the Opioid Epidemic: 

Box 3: The Intersection between Social Determinants and the Opioid Epidemic 

Determinants concerning Opioid Use:

Risk Factors Leading to Opioid Use:

The Impacts of Social Determinants on the Opioid Epidemic: 

1. Neuropsychiatric: Medical field combining psychiatry and neurobiology to see how behavior is impacted. 
2. Allelic variations: Number of various alleles that form at specific parts on a chromosome. 
3. Chromosome: Located in the nucleus of a cell, structures that are composed of DNA. 
4. SUD (Substance Use Disorder): a disorder that leads to negative health harms due to the inability to control substance addiction.
5. Social Position: An individual’s social class in life. 
6. Family System Challenges occurring in the family unit related to emotional, life transitions, etc. 

Who is impacted by the Opioid Epidemic

There has been a recent rise in opioid addictions especially in urban settings. This is mainly due to the easy accessibility of fentanyl-contaminated heroin on the streets which is highly addictive and in some cases deadly. Fentanyl is a type of synthetic opioid that has been known to be mixed with illicit drugs to make it easier for users to have a high. Often because of the lethal doses of fentanyl mixed with other drugs, it has caused a large increase in overdoses (What if Fentanyl, 2021). 

This epidemic has especially impacted the African American population in the U.S.  during and after the COVID-19 pandemic. This can be due to the biased prescribing of opioids. There has been a huge increase in the prescription of opioids to certain populations in society based on race and ethnicity. Whites are being prescribed opioids for emergencies while more Blacks are prescribed for surgical intervention or cancer treatments. This has been associated with increased social stigma and mental health disorders combined with greater genetic risk which has led to an increase in the illicit use of opioids and painkillers leading to an increase in the epidemic and an increase in overdose deaths in certain races and ethnicity populations (Gondré-Lewis et. al, 2022). 

Social Determinants Intersection with the Opioid Epidemic 

Social determinants of health play a key role in specific populations such as minorities and poorer populations in becoming more addicted to and exposed to opioids than their white counterparts. Types of social determinants are systemic, structural, neighborhood, and interpersonal. Especially in populations and communities with a lack of access to proper healthcare or healthcare at all, opioid misuse and overdose have been exacerbated. However, this can also go the other way where individuals with greater access to prescription opioids with more access to healthcare systems have also a great chance of opioid misuse and overdose. Individuals who are full-time employees, have private insurance, are of older ages, and have higher education levels have had the most success in completing treatment against opioid use. Surprisingly, patients from the criminal justice system who were sent for treatment also had high rates of treatment completion. However, the success of treatment completion and increased rates of treatment completion decreased with each previous additional attempt at treatment (Gondré-Lewis et al., 2022). 

When researching completion and rehabilitation treatment programs that were funded publicly in the United States, it was found that opioid-use patients completed treatment in states with greater availability of treatment facilities that accept state insurance and adjusting prices based on the ability and means of the patient to be able to pay for treatment. Individuals in states with a large amount of population living in rural regions mostly had lower completion rates for treatment while residential treatment (Patient care in a non-hospital setting focusing on treating mental health and substance abuse issues in patients) patients had greater success (about four times greater success) in treatment completion rates. It was also found that patients who have longer wait times for treatment were less likely to follow up on their treatment and complete it compared to those who had to barely wait or even had to wait zero days (Kreek, 2019).  

The Vital Role of Legislation and Public Health in Creating a Healthier Future: 

Solutions in the Healthcare Setting 

To be able to limit the opioid epidemic, healthcare providers need to play a major role in mitigating this epidemic. Healthcare providers can limit opioid prescriptions, encourage patients to participate in exercise therapy, alternate treatment options (such as NSAIDs, serotonin-norepinephrine re-uptake inhibitors, tricyclic antidepressants, etc.), guide patient education in risks of addiction, monitor drug monitoring programs, and work with teams such as therapists and pharmacists to understand a patient’s medication history to optimize medication safety (Koptinik et. al, 2023).

One of the largest causes of the opioid epidemic is individuals getting opioids from patients who are their family or friends in an attempt to self-medicate for pain. The Mainstreaming Addiction Treatment (MAT) Act provision expands evidence-based therapies by expanding the availability of evidence to mitigate the opioid epidemic. This act allows healthcare workers to have a controlled standard when it comes to prescribing buprenorphine for opioid use disorder (OUD) to integrate substance use disorder treatment in various healthcare settings (Dydyk et. al, 2024). Universal prevention, which emphasizes drug usage fear and increasing education on the negative impacts of drug usage, is very effective in decreasing drug usage rates. Regulatory prevention has also been found to decrease drug availability via laws or taxes and has been lowering levels of harm for younger populations when it comes to accessible substances but can be hard to do when it comes to prescription-based drugs only obtained from medical professionals. Instead, addicts receive prescription opioids from family or friends who have been prescribed opioids for pain management purposes (Edemekong, et. al). 

The CDC recommends only prescribing opioids when deemed necessary and usage be carefully monitored by a healthcare professional and that non-opioid therapy should be instead used for pain management unless extreme cases such as when a patient is battling cancer or undergoing end-of-life care with the main goal of preventing abusing prescription opioids. Some examples of replacements for opioids include tricyclic antidepressants, topical agents, muscle relaxants, and nonsteroidal anti-inflammatory drugs. When it comes to treatment plans, healthcare workers should provide treatment goals and expectations to patients whenever prescribing opioids to patients is necessary with 1-4 weeks follow-up being best to evaluate progress and monitoring for every three months to determine if opioid use is required. The CDC states that three days of pills or less are needed and seven days is only for rare cases when it comes to pain relief. Before prescribing patients opioids, it is recommended that doctors assess a patient’s substance abuse and depression history (as they may act as risk factors for substance abuse) (Guideline Recommendations and Guiding Principles, 2022). 

Legislation Efforts 

In terms of legislation, a prescription drug monitoring program (PDMP) has been implemented by the United States. This program allows healthcare workers to verify if prescribed opioids were given to patients from other medical sources in a state-based database system, which has been found to significantly reduce “doctor shopping” (opioid addicts going to multiple medical sources and healthcare workers to be prescribed opioids). The issue with the PDMP is that these programs are not utilized as much by medical professionals and take a long time to retrieve information on opioid prescriptions. Oregon established a statewide coalition named the Oregon Coalition for Responsible Use of Medications (OrCRM) to reduce overdoses and misuse of medications such as opioids through community engagement with a community effort to mitigate opioid-related overdoses. This has been done by partnering with members of the community in businesses such as law enforcement, healthcare, churches, and public health offices. This strategy has been proven to be effective (Holton et. al, 2018). 

Conclusion

The opioid epidemic is a type of crisis that is impacted by various factors such as health disparities, risk factors, etc. Opioids are used for pain management but are highly addictive drugs because opioids impact the release of dopamine by inhibiting GABA, causing increased dopamine levels. To better the opioid epidemic and mitigate its impacts, there needs to be an effort between everyone in the community by working with healthcare workers and institutions, education institutions, and government institutions. Public policy is important when it comes to mitigating the opioid epidemic by implementing more policies regarding opioid monitoring programs to prevent the overprescribing of opioids. More policies on increasing access to opioid addiction treatment centers and increasing education on opioid addiction should also be done to combat the opioid epidemic. Especially focusing on the at-risk populations such as those of poorer communities or minority communities, would be a large step toward mitigating the opioid epidemic and its impact on society. Ultimately, the opioid epidemic needs to be combated and this can only be done by focusing on effective public health initiatives and taking multifaceted approaches by working with not only healthcare institutions but also public policy and educational institutions. 

Acknowledgments: 

The author would like to acknowledge Dr. Pan for his support and guidance during the research and publishing of this paper. 

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About the author

Ankith Sureddi

Ankith is a current high school senior who wants to pursue a career in the medical field. At his high school, Ankith is in the Communications Arts Program on the Journalism and Debate Track. He is passionate about writing, science, and public health, leading him to find innovative ways to combine these passions by not only writing and researching medical and public health concepts that interest him but also by finding ways to communicate and disseminate that information to his peers.

The post Navigating the Pain: Understanding and Preventing Current and Future Opioid Epidemics appeared first on Exploratio Journal.

Abstract

Heart disease, also known as cardiovascular disease, encompasses a range of conditions affecting the heart or blood vessels. It is a significant health concern that cuts across race, gender and nationality. Various types of heart disease exist, including coronary artery disease, heart failure, arrhythmias, and valvular heart disease, each with its own set of symptoms and risk factors such as diet and genetics. Similarly, neurodegenerative diseases like Alzheimer’s, Parkinson’s, and Huntington’s disease pose challenges to cognitive and motor function, with Alzheimer’s being the most common form of dementia. These diseases affect individuals of different ethnic backgrounds and genders, with African Americans having a higher prevalence of Alzheimer’s.

The connection between heart disease and neurodegenerative diseases is substantial, with inflammation, oxidative stress, vascular dysfunction, hypertension, and dyslipidemia playing significant roles in both disease groups. Lifestyle factors such as diet and physical activity contribute to the development of these diseases. The heart-brain axis underscores the interplay between cardiovascular and brain health, with cardiovascular diseases influencing brain function and vice versa. Sleep apnea, often associated with heart failure, can exacerbate cognitive impairments due to its impact on oxygen flow to the brain.

Understanding the complex relationship between heart disease and neurodegenerative diseases is crucial for developing effective prevention and treatment strategies. Addressing common risk factors and promoting a healthy lifestyle may mitigate the burden of these diseases on individuals and healthcare systems. Further research into the mechanisms underlying their connection is warranted to advance clinical management and improve patient outcomes.

Keywords: cardiovascular, coronary, neurodegenerative, heart-brain, Alzheimer, Parkinsons

Introduction

Background Information

Heart disease, also known as cardiovascular disease, refers to a class of diseases that involve the heart or blood vessels. It is a broad term that encompasses various conditions, each affecting different parts of the cardiovascular system. In regard to ethnicity, the majority of people who die due to heart disease are African American. Asian people take up 18.6%, black people take up 22.6%, white people take up 8%, and hispanic people take up 11.6%. (1) In regard to sex, there are more male cases of heart disease compared to women. According to the american heart association, 52.9% were men and 47.1% were women in 2016 and this trend continues strongly and has been seen even in the many years after that. (2)

Types of heart disease

There are many different types of heart diseases. A few examples include coronary artery disease, heart failure, arrhythmias, and valvular heart disease. Coronary artery disease is the most common type and occurs when the blood vessels supplying the heart muscle become narrowed or blocked. (5) It can lead to chest pain, also known as angina, or a heart attack. It is generally due to the buildup of plaque which blocks the body’s blood supply and increases the strain on the heart.

Plaque buildup causes the arteries to narrow over time which is a process known as atherosclerosis. The most common cause of coronary artery disease is heart failure due to the weakening of the heart over time. Heart Failure occurs when the heart can’t pump blood effectively, leading to insufficient blood flow to meet the body’s needs. This can happen if the heart can’t fill up with enough blood or when the heart is too weak to pump blood properly. As the heart loses the ability to pump blood, blood backs up into other parts of the body; for example, it can back up into the lungs and cause shortness of breath.

Arrhythmias are irregular heartbeats. They can either be too fast, a condition known as tachycardia, too slow (bradycardia) or irregular. Arrhythmias can affect the heart’s ability to pump blood effectively when the electrical signals that tell the heart to beat don’t work properly. Valvular Heart Disease involves damage to or defects in the heart valves, impairing the flow of blood through the heart. The heart can’t pump blood effectively and has to work harder to pump, while the blood can be leaking back into the chamber or a narrow opening. This can also lead to heart failure since the heart can’t pump a sufficient amount of blood for the whole body. (6) There are many causes for heart disease, one leading factor being diet. Sodium intake plays a major role. The average American consumes about 3,400 mg of sodium each day, which is well over the recommended 2,300 mg. Eating too much sodium can lead to high blood pressure. Having a high blood pressure damages the lining of the arteries making them more susceptible to the buildup of plaque and narrowing of the arteries. (7) Congenital heart disease is a heart problem a baby has at birth. In most cases, congenital heart disease has no known reason, but it can be due to family history and chromosomal abnormalities (both genetic factors), or due to maternal factors (including medications or substances the mother comes in contact with while pregnant). (8)

Neurodegenerative diseases

Neurodegenerative diseases are a group of disorders characterized by the progressive degeneration of the structure and function of the nervous system. These conditions primarily affect neurons, the building blocks of the nervous system, leading to a decline in cognitive function and, in some cases, motor function. These diseases are generally characterized by neuron loss. A few examples of these kinds of diseases are Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and multiple sclerosis. (9) Going more in depth, Alzheimer’s Disease affects memory, thinking, and behavior. It is the most common cause of dementia. The brain itself shrinks causing brain cell connections and the cells themselves degenerate and die, causing the loss of memory. This disease mainly involves the parts of the brain that controls thought, memory, and language. (10) Parkinson’s Disease involves the degeneration of dopamine-producing neurons, leading to tremors, stiffness, and difficulty with balance and coordination. Nerve cell damage in the brain can cause the dopamine levels to drop. It affects the nervous system and parts of the body controlled by nerves. (11) Huntington’s Disease causes the progressive breakdown of nerve cells in the brain which can lead to the development of many different neurodegenerative diseases like Parkinson’s. A genetic mutation in the HTT gene causes Huntington’s disease. The HTT gene makes a protein known as huntingtin. This protein helps the neurons function. If the body is unable to make this protein, it can cause the development of this disease. (12) Amyotrophic Lateral Sclerosis affects motor neurons, leading to muscle weakness and atrophy. It is the progressive degeneration of nerve cells in the spinal cord and brain. As the motor neurons, which are the neurons that control the voluntary muscles decline, it stops being able to send signals to the muscles. There are 2 types of motor neurons: upper motor neurons and lower motor neurons. The upper controls the brain and spinal cord and its role is to control the lower motor neurons. The lower consists of cells in the brian stem which is the lower part of the brain. They first receive instructions from the upper neurons then send messages that tell the muscles in the body to move. Multiple Sclerosis is due to nerve damage disrupting the communication between the brain and the body. The immune system attacks the protective sheet, myelin, that protects and covers nerve fibers which cause communication problems between the brain and the rest of the body. The disease can cause permanent damage and deterioration to the nerve fibers.

For Alzheimer’s disease, African Americans still have the highest percentage of cases, which is 13.8%. Hispanic people have 12.2%, white people have 10.3%, and American Indian and Alaska Native individuals have 9.1% of the cases. (3) In this case, women are more likely to develop a neurodegenerative disease compared to men (3:1 ratio). (4)

Cerebral ischemia and ischemic strokes

A failing heart cannot pump a sufficient amount of blood to the body when it’s at rest or during physical activity. This can lead to a risk of the brain not getting enough blood, a condition known as cerebral ischemia. Atherosclerosis can lead to both cerebral ischemia and an ischemic stroke, the most common type of stroke. (13) An ischemic stroke occurs when the blood supply to part of the brain is blocked or reduced. Plaque buildup can reduce the blood flow to one part of the brain but it can also lead to blood clots which can completely block the flow. When a plaque formation becomes inflamed, it can rupture, causing the formation of a blood clot. This can prevent the brain cells from getting nutrients and oxygen it needs, causing them to die very quickly. (14) Once neurons start dying at a fast rate, people can start developing many different neurodegenerative diseases such as Alzheimers and Parkinsons. During the development of Alzheimer’s disease the brain starts shrinking and since neurons are dying, many connections in the brain are also lost. In the case of Parkinson’s disease, dopamine producing neurons start to degenerate. These neurons are only in specific parts of the brain, such as the prefrontal cortex, so blockages in the arteries near these parts of the brain can lead to the development of Parkinson’s.

Body

Mutual Influence of heart disease and neurodegenerative diseases:

Links between heart disease and neurodegenerative diseases

It is undeniable that there are some links that can affect both heart disease and neurodegenerative disease. Some autopsy studies show that as many as 80% of individuals with Alzheimer’s disease also have cardiovascular disease. This is very important because it shows a major correlation between the two seemingly distinct diseases. They can be linked together through a variety of ways that will further be looked into. (15) One main link is inflammation. In heart disease, inflammation contributes to the development and progression of atherosclerosis by promoting the accumulation of inflammatory cells in arterial walls, leading to plaque formation and increasing the risk of cardiovascular diseases.

In neurodegenerative diseases, inflammation contributes to neuronal damage; activated immune cells release pro-inflammatory molecules, starting the degeneration of neurons and contributing to the progression of conditions like Alzheimer’s, Parkinson’s, and Amyotrophic Lateral Sclerosis (ALS). Another link is oxidative stress. In heart disease, oxidative stress plays a role in the progression of atherosclerosis and cardiovascular events by causing damage to lipids, proteins, and DNA, contributing to inflammation. In neurodegenerative diseases, oxidative stress is implicated in the pathogenesis (development/ progress) as it leads to the accumulation of cellular damage in neurons, exacerbating inflammation, and contributing to the degeneration of nerve cells in conditions like Alzheimer’s, Parkinson’s, and ALS.

Vascular dysfunction also has a link between both diseases. In heart disease, vascular dysfunction impairs blood vessel function, leading to reduced blood flow, compromised oxygen delivery, and an increased risk of conditions like atherosclerosis. In neurodegenerative diseases, vascular dysfunction can contribute to reduced cerebral blood flow, impacting nutrient delivery to the brain and potentially showing cognitive decline in conditions like Alzheimer’s.

In heart disease, hypertension strains the heart and arteries, promoting vascular dysfunction and increasing the risk of coronary artery disease, heart failure, and stroke. In Neurodegenerative Disease, hypertension is a risk factor for small vessel disease and may contribute to cognitive impairment and an increased risk of neurodegenerative conditions. (16)

In Heart Diseases, dyslipidemia (elevated cholesterol levels), contributes to atherosclerosis, leading to vascular dysfunction and an increased risk of heart attacks and strokes. In Neurodegenerative Diseases, dyslipidemia may contribute to the development of vascular-related neurodegenerative disorders by affecting blood flow to the brain and promoting inflammation.

And of course, lifestyle is a key factor to living a good and healthy life. An unhealthy lifestyle, with factors such as poor diet and physical inactivity, contribute to vascular dysfunction, fostering conditions conducive to the development of heart disease and neurodegenerative diseases. (17)

Heart brain axis

It is believed that cardiovascular diseases can affect brain function and many brain diseases are associated with heart dysfunction which is the heart-brain axis. The brain is the one in charge of regulating the function of the heart and impaired brain function can lead to the development of cardiovascular diseases. Similarly, cardiovascular diseases can reduce the amount of blood flow sent to the brain which can lead to the development of various brain diseases. (18) Firstly, how do brain diseases contribute to the development of cardiovascular diseases? Cardiac rhythm changes, also known as arrhythmia, can reflect abnormal brain function and be biomarkers for different brain diseases. The autonomic nervous system (ANS) is in control of the body’s involuntary muscles such as the heart and lungs. The brain is in control of the ANS. When the body has a seizure, the brain’s electrical activity is disturbed and those changes can lead to a disruption in the ANS, also leading to arrhythmias.

Next, how do cardiovascular diseases contribute to brain diseases? Diseases such as heart failure and atrial fibrillation are risk factors to many different brain diseases such as dementia or Alzheimer’s disease. Cardiac rhythms and electroencephalograms are also found to be highly synchronized, which is another link between the brain and heart. Electroencephalography is the measurement of electric activity in different parts of the brain. This means the electrical rhythms of the heart and brain are very synchronized further confirming the idea of the heart-brain axis.

Sleep apnea

Heart failure can influence sleep apnea. One way in which this works is through the accumulation of fluids. Heart failure often leads to fluid buildup in the lungs and other tissues, which can block the airways. This makes it more challenging for the air to flow easily during breathing. The heightened resistance contributes to obstructive sleep apnea. Sleep apnea has a lot of effects. The first one being memory loss. Due to the lack of sleep, people can start to develop short term or long term memory loss. Another effect is brain damage due to the lack of oxygen going to the brain. By starving the brain of oxygen, the development of cerebral hypoxia can occur. Since brain cells are very sensitive to lack of oxygen, they can start dying rapidly especially as more and more oxygen is getting cut off.

Preventative measures for developing heart or neurodegenerative diseases

There isn’t a specific way to cure heart or neurodegenerative diseases so the important thing in these cases is prevention, with lifestyle and environmental factors being key. It’s important to focus on getting enough rest at night and staying regular with working out. Eating a healthy diet and cutting back on foods that have high sodium, cause high blood pressure and cholesterol is extremely important. The best thing you can do for your body and health is eat healthy, exercise regularly, and get enough sleep. Learning new things everyday can create new connections in the brain which delay the onset of diseases like Alzheimer’s or makes it less damaging for the brain. It’s simple to do this, start by learning a new language or picking up a new hobby. These small changes in your life can create a significant difference in the outcome of your health. That being said, since there’s no cure for Alzheimer’s, protecting your heart’s health can be key to delaying the onset of or reducing the effects of these neurodegenerative diseases.

Summary

Our research explores the relationship between heart disease and neurodegenerative disorders, emphasizing the impact of cardiovascular health on brain function. Our research has highlighted the significance of a healthy lifestyle in mitigating the risk of diseases like Alzheimer’s and Parkinson’s. We believe our findings can contribute to the field of cardiovascular and neurodegenerative diseases, offering fresh insights into prevention and treatment strategies. We thus conclude that understanding the interplay between heart and brain conditions is crucial for patient care and hope that our research could prompt further study and innovation in this area.

References

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About the author

Veena Mahalingam

Veena is currently an 11th grader at the American International School in Chennai. She is passionate about medicine and her favorite subject is biology — she expands her knowledge on this by interning at cancer research labs. In her free time, Veena enjoys swimming, reading, and playing the guitar.

The post Interplay Between Heart Disease & Neurodegenerative Diseases: Implications for Clinical Management appeared first on Exploratio Journal.

Abstract

In the realm of neuroscience, brain mapping emerges as a pivotal method for comprehending the human brain’s structure and function. Utilizing Functional Magnetic Resonance Imaging (fMRI), researchers gain insights into real-time brain activity, offering unparalleled understanding of cognitive processes. The non-invasive nature and superior spatial resolution of fMRI distinguish it from other imaging modalities like PET scans and EEGs. However, interpreting fMRI data demands sophisticated analysis techniques, a challenge addressed by Statistical Parametric Mapping (SPM). SPM serves as a robust software tool for preprocessing, statistical analysis, and visualization of neuroimaging data, including fMRI studies. In this paper, we focus on group-level analysis of fMRI data from a cohort subjected to the Eriksen Flanker task, utilizing SPM12 for analysis. The paper provides an in-depth tutorial on SPM12, covering preprocessing steps and group-level analysis procedures. By integrating fMRI with SPM analysis, researchers gain unprecedented insights into neural activity patterns underlying perception, cognition, and behavior. This synthesis of cutting-edge technology and analytical tools offers a gateway to understanding the complexities of the human mind, with implications for advancing neuroscience research and clinical applications.

Keywords

Voxel – A three-dimensional pixel, representing a volume element in a digital image or dataset

Timing Files – A data file that contains precise information, often in the form of timestamps or time intervals, used to synchronize experimental events with neuroimaging data during analysis

Run – Refers to a single session of data acquisition during neuroimaging experiment, typically capturing brain activity over a specific duration or set of experimental conditions.

T-distribution – A probability distribution that arises from estimating the mean of a normally distributed population when the sample size is small or the population standard deviation is unknown.

SPM12 – A sophisticated software package used for the analysis of neuroimaging data, offering a comprehensive suite of tools for preprocessing, statistical analysis, and visualization.

Introduction

In the realm of neuroscience, one of the most captivating frontiers is the exploration of the human brain. From deciphering its intricate architecture to understanding its complex functionalities, researchers have been long engaged in unraveling the mysteries of this organ. At the heart of this pursuit lies brain mapping, a multidisciplinary method that harmonizes diverse imaging techniques to visualize and understand both the structure and function of the brain. (Li et al., 2014)

Functional Magnetic Resonance Imaging or fMRI enables researchers to observe brain activity in real-life, offering unprecedented insights into the neural processes underlying perception, cognition, emotion, and behaviour.The biggest advantage of using fMRI rather than a PET Scan or an EEG, is that it is essentially non-invasive and returns imaging of good spatial resolution.(Pan et al., 2011) By detecting changes in blood flow and oxygenation levels associated with neuronal activity, fMRI provides a non-invasive window into the brain’s functional architecture. However, making any sense of extracting any useful information from this fMRI data requires advanced analysis techniques adept at distinguishing genuine neural signals from background noise, while pinpointing activity patterns linked to particular cognitive tasks or experimental parameters.

Statistical Parametric Mapping (SPM) stands as a robust software tool extensively employed in the analysis of neuroimaging data, encompassing fMRI studies. SPM offers a comprehensive suite of tools for preprocessing, statistical analysis for groups of subjects, and visualization of brain images. Its adaptable framework enables the modeling of intricate experimental setups, evaluation of observed effects significance, and creation of compelling visual representations to convey findings with precision.(Barone et al., 2018)

In this paper, we dive head-first into the vast realm of brain mapping, focusing specifically on the utilization of fMRI and SPM analysis. We are conducting group level analysis on a group of 20 subjects that were subjected to the Eriksen Flanker task.(Servant & Logan, 2019) We also provide an in-depth introduction to SPM12, guiding readers through the steps involved in analyzing the fMRI data that we have. Figure 1 gives us an accurate illustration of the Flanker paradigm.

By merging the advanced capabilities of fMRI technology with the analytical prowess of SPM, researchers gain unprecedented access to the inner workings of the human mind. This powerful combination enables us to explore the intricacies of neural activity, shedding light on the mechanisms behind perception, cognition, and behavior.

Metholodogy

fMRI Machine

Figure 3 depicts an fMRI machine that is used to assess how the brain is working. Hospitals extensively use these machines to help determine potential risk of surgeries or other invasive procedures. The scans from these machines help diagnose strokes, brain tumors, brain injuries, Alzheimer’s Disease, Epilepsy and so much more.

From a research standpoint, Researchers use these scans to help them understand which part of the brain is associated with which action which can be pivotal in studies that help them in the process of brain-mapping. An fMRI imaging scan takes advantage of activated neurons requiring more oxygen from red blood cells. This increase in activity leads to a change in blood flow. fMRI detects these changes. By indirectly measuring the alterations in blood flow and electrical activity, fMRI assesses brain activity.

The several benefits of using functional MRI scan are firstly, the nature of it being noninvasive, which means that it does not require surgery to obtain. Secondly, the versatility of fMRI scans allows researchers to assess both structure and function of the brain.

Dataset

The dataset comprises data collected from 20 healthy adults while they performed a slow event-related Eriksen Flanker Task. During the interval of each trial, The participants used one of the two buttons that were present on the response pad to indicate the direction of the central arrow in a sequence of 5 arrows. In the congruent trials, the flanking arrows point in the same direction as the central arrow, for example, (< < < < <), while in more demanding incongruent trials the flanking arrows pointed in the opposite direction, for example , ( < < > < <).

The subjects performed two 5-minutes blocks, each containing 12 congruent and 12 incongruent trials which were presented in a pseudorandom order.

Resulting data obtained is 146 contiguous echo planar imaging (EPI) whole – brain functional volumes during both the congruent as well as the incongruent task blocks. A high resolution T1-weighted anatomical image was also acquired using a magnetization prepared gradient echo sequence.

The data collected has 146 continuous images of the entire brain using a technique called Echo Planar Imaging(EPI), which is a MRI technique used to rapidly acquire images. This method allows us to capture brain activity while the subjects complete tasks where they match or mismatch stimuli. Additionally, detailed pictures of the brain’s structure were taken which had excellent contrast between different types of brain tissue.(Iturria-Medina et al., 2008)

Figure 4: (A) displays the anatomical image of subject-08, providing a detailed representation of the brain’s structure. (B) displays the functional image of subject-08. Capturing dynamic changes in brain activity during a specific task or resting state.

Preprocessing

After downloading the data, we have to clean the brain imaging data so that it can be later used for group analysis. An fMRI volume contains not only the signal that we are interested in changes in oxygenated blood – but also fluctuations that we are not interested in, such as head motion, random drifts, breathing, and heartbeats. We call these other fluctuations noise, since we want to separate them from the signal that we are interested in. This noise will be removed by the preprocessing methods that we shall employ (Ganzetti et al., 2018). Figure 5 shows comprehensive process of acquiring functional MRI images. This process was followed for our 20 subjects.

Realignment

The brain imagining data is a time-series one. Time-series data can be thought of as a deck of cards, where each volume is a different card. The first step that comes in preprocessing of this data is Realignment. Basically what realignment does is it will put all the cards in the same orientation and all the sides line up.

Slice-Timing Correction

Unlike other photographic data, in which the entire picture is taken in a single moment, an fMRI volume is acquired in slices. Each of these slices takes time to acquire – from tens to hundreds of milliseconds. There are 2 commonly used methods for creating volumes, which are:

1) sequential slice acquisition

2) interleaved slice acquisition

Coregistration

Although most people’s brains are similar – everyone has a hypothalamus and the cingulate gyrus for example – there are also differences in brain size and shape. Due to this difference, group analysis is not possible unless each voxel for each subject corresponds to the same part of the brain. This essentially means that for example, if we are studying a voxel in the visual cortex, for the group analysis we would want to make sure that the visual cortex for all the subjects are aligned.

This will be employed by Registering and Normalizing the data. This could be thought of as folding clothes to fit them inside of a suitcase, similarly each brain image needs to be reconstructed to have the same size, shape and dimensions. We warp our data to a template. A template serves as a standardized framework with predefined dimensions and coordinates, universally adopted by researchers for reporting their findings. Before we move onto seeing how Registering and Normalizing works, we must define what Affine Transformation is to better understand the end-to-end process of Coregistration.

Affine Transformations

Affine Transformations are what help warp our images to the template that we use. It is very similar to the rigid-body transformation which we have described above in Motion Correction, however it adds to it by adding two more dimensions, namely – Zooms and Shears. Translations and rotations are actions that can be done with everyday items such as pencils, whereas zoom and shear are more unusual – zooms either shrink or enlarge the image, while shears take the diagonally opposite corners of the image and stretch them away from each other.

Registration

Our goal is to warp the functional images to the template so that we can finally perform a group-level analysis across all our subjects. While it might appear logical to directly warp the functional images onto the template, this approach often proves ineffective in practice. This is because the low-resolution of the images makes it less probable for them to align accurately with the anatomical details of the template. Consequently, the anatomical image presents a superior option for this purpose.

While performing this step doesn’t get us any closer to the final goal, in turn warping the anatomical image can assist with bringing the functional images into standardized space. Since our anatomical image is already normalized to a template and recorded what transformations are done, we can apply the same transformations to the functional images.

When aligning functional and anatomical images, it’s crucial to ensure they are roughly in the same location. The outlines of the images must be aligned accordingly if they aren’t. The initial step sets the foundation for accurate registration.

A key advantage lies in the distinct contrast weightings of anatomical and functional images. Dark areas in the anatomical image, like cerebrospinal fluid, appear bright in the functional image, and vice versa. Leveraging the mutual information, the registration algorithm strategically manipulates the images to explore various overlays. It seeks to match bright voxels in one image with dark voxels in the other, and vice versa, iteratively testing until it finds the optimal alignment. The iterative process is driven by a cost function, aiming to maximize alignment quality.

Segmentation

The brain consists primarily of two tissue types: Grey Matter, which contains dense concentrations of unmyelinated neurons, and White Matter, which contains dense concentrations of myelinated neurons. Surrounding the brain is Cerebrospinal Fluid(CSF), with significant amounts found within internal spaces known as ventricles.

It is essential to map each voxel to their corresponding tissue type, for normalizing the anatomical image, while aligning it to the standardized template. SPM utilizes six tissue priors, each representing an estimation of tissue distribution in standardized space. (Ashburner & Friston, 2005)

Normalization

Normalization marks the final step for preprocessing of the data. It is essential to finally bring our data to form on which we can perform group-level analysis. After the anatomical image has been segmented, we can use those segmentations to normalize our images.

While Normalizing using the SPM GUI, we have used the default values for voxel resolution which is 2x2x2. The reason this value is taken is because it creates higher-resolution images. One down side of this is the larger space required to store these images.(3x3x3 can be used but will avail smaller files with lower resolution)

Smoothing

fMRI data contains a lot of noise, and this noise usually outweighs the signal itself. To combat this noise, we use smoothing to reduce this noise. Smoothing entails replacing each voxel with a weighted average of that voxel’s neighbors. Even though we are making the image blurrier, the noise reduction proves to make a significant change in the later stage when we are performing group analysis.

We have now performed all the necessary steps required for preprocessing and can move onto the first level analysis

Statistical Analysis

Creating the Ideal Time-Series

Before we fit a model to our fMRI data, we know that our data is a time-series one. So we need the pertaining fitted time-series so that we can use the estimated beta weights in our group-level analysis.

Within our dataset, under each subject’s directory there is a “func” directory, there is a file named events.tsv. This file contains information that is required to create our timing files which are the name of the condition(i.e. Incongruent or congruent), the instance the condition took place(in seconds) relative to the start of the scan and lastly the duration of each trial. Once this information was extracted and formatted in a way that the SPM software can utilize it, we further created a timing file for each condition and then split that file according to which run the condition was in. This resulted in 4 timing files that were essentially:

1. Timings for the Incongruent trials that occurred during the first run (which we will call incongruent_run1.txt);
2. Timings for the Incongruent trials that occurred during the second run (incongruent_run2.txt);
3. Timings for the Congruent trials that occurred during the first run (congruent_run1.txt);
4. Timings for the Congruent trials that occurred during the second run (congruent_run2.txt).

All the timing files adhere to a consistent format, comprising of two columns namely, Onset time, in seconds, relative to the start of the scan and Duration of the trial, in seconds,

Figure 7 shows us the original.tsv file and how it is structured and then we use a script to allow us to transform this data into the required form that we need to help us fit a model in the SPM software. Figure 7 (B) has two columns like we discussed before, the leftmost one being the Onset time, in seconds and the rightmost one being the duration of the trial, in seconds.

Running The First-level Analysis

Specifying the Model

Having created the timing files in the above subsection, we can now finally use them in conjunction with the imaging data to create Statistical Parametric Maps. These maps essentially indicate the strength of the correlation between the ideal time-series and the time-series data that we got during the course of the experiment. The beta weights obtained for the multiple regressor in the model in turn are converted into a t-distribution.

To keep things organized, we have created a new directory named 1stLevel in each subject directory. Now, navigating to “Specify 1st-Level” on the SPM GUI, we start by entering the data.

For the directory, we pick the 1stLevel for the current subject, i.e. Sub-08. Next comes filling in the Timing patterns, for this we are using a value of 2 seconds for the Interscan Interval. Now we want to have 2 sessions because in our dataset, we have two runs of each subject. So we fill in the data with the 2 sessions. Followed by the setting of data, we also have to set the two conditions, which are Incongruent and Congruent in this case. Lastly, we need the onset time for each occurrence of the incongruent as well as congruent condition. Since in this experiment the duration of each trial lasted for 2 seconds, the value of the duration field is set to 2 seconds. Once this was done, we ran the following parameters.

The resulting GLM (General Linear Model) is generated and looks like the figure shown below.

Estimating the Model

Now that we have the General Linear Model or GLM, the next step is to estimate the beta weights for each condition. We navigate to the Estimate option on the SPM GUI, and select the SPM.mat file that was created in the 1stLevel directory in Sub-08’s directory and run it. Before being able to result, we need to create a contrast first. Essentially what we are doing is that, if we have a beta weight for the incongruent condition and a beta weight for the congruent condition, we can take the difference of them to calculate a contrast estimate at each voxel in the brain. After doing this task for every single voxel we will basically create a contrast map.

To create these contrasts, we navigate to the Results button of the SPM GUI, selecting the SPM.mat file that was generated after estimating the model. We will be greeted by a new window, with an empty design matrix on the right side of the panel. The next step is to define the new contrast with a name, in this case Inc-Con and a contrast vector of [0.5 -0.5 0.5 -0.5] and then we create this contrast.

In contrast, the reason we are using values of 0.5 and -0.5, instead of 1 and -1, it is because of accounting for the number of runs in the study, which is 2. To make the results comparable to other subjects or even other studies that may even try different amounts of runs. Hence, the contrast weights of 1 and -1 are divided by the number of runs that we have taken, i.e. 1 / 2 = 0.5 and -1 / 2 = -0.5.

Now let us use this contrast, once double-clicked, we had to choose a few more options, which were:

1. Apply masking: We set this to “none”, as we wanted to examine all the voxels in the brain.
2. p value: Again set this to none and set the uncorrected p-value to 0.01. This essentially tests each voxel at a p-threshold of 0.01.
3. Extent threshold (voxels): We have set this value to 10, which means the result will only show clusters of 10 or more contiguous voxels. We are setting it at 10, to avoid the specks of voxels that may show up due to them being in noisy regions.

After filling this information, we are finally displayed with the first-level analysis as seen in Figure 9. The result is displayed on a glass brain. The dark spots in the standardized space are the clusters of voxels that passed our statistical thresholds. A plus point is that we can see all the locations as well as the statistical significance of each cluster. Set Level informs us on the probability of seeing the current number of voxels. The Cluster-level column shows the significance for each cluster using different correction methods. The Peak-Level column informs us the t- and z-statistics of the peak voxel within each cluster, with the main clusters marked in bold and any sub-clusters listed below the main cluster marked in light font. On the right most side, we have the MNI coordinates of the peaks of each cluster and sub-cluster listed.

Figure 9 shows us a comprehensive view about the different regions of interest within the statistics table such as set level, cluster-level, peak-level and the MNI coordinates. From the glass brain, we can see that one of the groups of voxels that are statistically significant is in the area containing the Dorsal Medial Prefrontal Cortex. (Maruyama et al., 2018)

This entire process was repeated for the remaining 19 subjects. Now we can move on to generalizing the results in the group analysis.

Group Analysis

Specifying the Model

Now that we have done our first level analysis on all the subjects. We can now move on to see how the results fare in the entire population and for us to draw our inferences from the Eriksen Flanker Task. To test this, we shall run a 2nd Level analysis, which essentially calculates the standard error and the mean for a contrast estimate, and then test whether the average estimate is statistically significant or not. This method conducts group-level analysis using a summary statistic approach that disregards variability in parameter estimates. Instead, it employs a t-test on the mean parameter estimates obtained from each subject.

Like we did for the first level analysis, where we created a directory for the result, we do the same here but created one directory in the root directory and named it 2ndLevel_Inc-Con. We then proceeded to the SPM GUI and clicked on Specify 2nd-Level. We were required to just fill in the location for the output to be placed and that would be the new directory we created and the scans that we needed to conduct the test on, which were the 1st Level analysis outputs of each subject. After everything was filled, we ran the test.

Estimating the Model

After specification, we needed to estimate our model. The procedure is exactly the same for when we performed it for the 1st Level estimate, however we use the SPM.mat file created. Once this was done, we were ready to check the results. We had to create a new contrast for the 2nd Level Analysis. There is only one regressor, hence we only have to define one weight in the weight vector,namely 1 (Han & Park, 2018). We named this contrast Inc-Con. Once we completed creating the contrast, we were prompted to fill in the following:

1. Apply masking -> None
2. p value adjustment to control -> None
3. Threshold (T or p value) -> 0.01
4. Extent Threshold (voxels) -> 20

The process of how 2nd Level Analysis is shown in Figure 10, how each 1st Level Analysis result is taken into account to generalize the showings. Again in the statistics table that is presented after the 2nd Level Analysis shows that the Dorsal Medial Prefrontal Cortex has a significant cluster shown by the red circle over the statistics table. (Barone et al., 2018)

For better visualization of the same result, however let us take a look at using MRIcroGL. It offers advantages in terms of real-time visualizations, three-dimensional rendering, and customizable display options. Additionally, MRIcroGL provides a user-friendly interface and intuitive navigation tools, making it particularly useful for researchers who prioritize interactive exploration and detailed inspection of brain structures and functional activations.

Conclusions

To summarize, brain mapping using various softwares such as SPM, can help us understand and detect how different parts of the brain are activated based on the activity performed. Like we see in the Eriksen Flanker Task, a psychological experiment that assesses selective attention and inhibitory function, subjects had to really focus on whether to press ‘<’ or ‘>’, in response to the Incongruent or Congruent scenario. When we performed our group level analysis, the dorsal medial prefrontal cortex, which is responsible for decision-making. It is observed to be more active in response to processing incongruent stimuli than congruent stimuli. This shows that the incongruent task requires more activity from certain parts of the brain that it would in the congruent task.

Just like the Eriksen Flanker Task, many more such tests can be run on the brain and recording the various active parts of the brain. Then using brain mapping, we can detect various Neurological conditions such as depression and anxiety. Brain mapping can also be used to detect Gliomas, the most common primary malignant brain tumor in adults which account for 80% of total cases. Which led to brain mapping also being used to measure the effectiveness of treatment using biomarkers, which are physical cues that are personalized to each patient’s brain.

On the other side of diseases, brain mapping has also been useful in understanding the brain. It can help us understand the brain’s connectivity and operations, and how humans learn. It can also help us develop visual insights into certain conditions, such as addiction or brain disorders. (Tavares et al., 2020)

As technology continues to evolve and interdisciplinary collaboration expands, the potential for brain mapping to unravel the mysteries of the mind and enhance human well-being is promising. With further research and refinement, brain mapping holds the key to unlocking the full potential of the most enigmatic organ in the human body.

References

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Han, H., & Park, J. (2018). Using SPM 12’s Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users. Frontiers in Neuroinformatics, 12. https://doi.org/10.3389/fninf.2018.00001

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About the author

Akarsh Gupta

Akarsh is in his final year of studying at the National Institute of Technology, Karnataka Surathkal, India, where he is pursuing his major in Electrical and Electronics Engineering and his minor in Artifical Intelligence. Akarsh has a keen interest in conducting research in applied machine learning as well as Natural Language Processing. Akarsh is also an avid basketball player.

The post Exploring Brain Mapping and Neuroimaging: A Comprehensive Journey into Brain Charting appeared first on Exploratio Journal.

Abstract

Research fields have continually developed methods to prevent and improve cognitive decline in the ever-growing aging population. In this paper, we explain the subtleties that may go unnoticed in cognitive decline in the aging population. Especially because of these subtleties, we emphasize the importance of early detection and intervention in cognitive decline, as well as seeking for assistance to maintain independence. Through recent research, we introduce the Computer-based Cognitive Interventions approaches (Augmented Reality and Virtual Reality) to improve cognitive decline. Furthermore, we evaluate the effectiveness of Computer-based Cognitive Interventions in supporting everyday living in the aging population.

Computer-based Cognitive Interventions are most effective when applied at the early stages of cognitive decline. They provide great improvements in neurocognitive domains and can last long after intervention. These Computer-based Cognitive Interventions also provide support in everyday living activities for increased safety and autonomy. When combined with other approaches such as physical training, there are significant potential effects in cognitive decline.

Keywords:
Cognition: all mental processes to acquire, store, retrieve, and process information
Cognitive impairment: difficulty with memory, learning, concentrating, or making decisions ranging from mild to severe
Neurocognitive domain: conscious mental activities that can be classified into language, learning and memory, social cognition, complex attention, executive function, and perceptual-motor function
Computer-based cognitive interventions (CCIs): approaches to address cognitive impairments and their impacts on neurocognitive domains using technology
Cognitive training (CT): guided practices with structured tasks in a range of difficulties according to individual to train cognitive processes
Cognitive stimulation (CS): wide range of activities to stimulate multiple cognitive domains (e.g. reality orientation)
Cognitive rehabilitation (CR): patients work together with healthcare professionals and family to perform everyday tasks according to individualized goals
Immersive/Non-Immersive Virtual Reality (VR): virtual environment that immerses user directly and shuts out physical world (e.g. head-mounted display) / indirectly (eg. computer)
Augmented Reality (AR): virtual content overlaid on physical world and enhance physical world environment

Introduction

Forgetting to turn the faucet off may seem like a trivial error, but if this forgetfulness repeats itself and goes untreated, the dismissal of the lapse in memory may significantly impair the ability to live independently. One woman dismissed these seemingly insignificant problems with her memory, but began to have trouble doing habitual tasks, such as driving or paying the bills (Ellison, 2024). Eventually, the condition can progress to a more severe decline in mental ability, just as in Dr. M., who showed great variability in emotion–lucid then agitated and distraught. He was admitted to the same hospital he was a director at and required constant attendance in cases where he wandered off. Dr. M. talked to the other patients like he was still a doctor there and could even be seen at the clinic writing prescriptions (Sacks, 2019).

These two patients both experienced decline in cognitive abilities–to varying degrees– beyond normal aging. While the fields of medicine and health have had tremendous successes over the last century, increasing life expectancy to about 80 years of age, preservation of cognitive health has remained a fundamental challenge. Pharmacological treatments have had a limited positive effect on cognition (Zuschnegg et al., 2023). Developing approaches to improve the quality of life in the elderly has therefore become a focus in research, especially with growing interest in Computer-based Cognitive Interventions (CCIs).

Employing the power of computers to preserve or improve cognition, these non-pharmacological treatments provide an alternative to pharmacological treatments. Among CCIs’ many benefits, they are easily personalized to each individual and have minimal maintenance costs compared to medications, which require continual usage and monitoring (Smart et al., 2017; Zuschnegg et al., 2023). As decline in cognitive abilities interfere with activities of daily living, including showering, eating, or brushing teeth, CCIs support aging population’s ability to live independently for as long as possible.

This paper will present how computer-based cognitive interventions can assist aging populations in twofold: explaining the varying tools of CCIs and expanding on their potential application areas.

Computer-based Cognitive Interventions (CCIs)

Augmented Reality (AR)

AR can be implemented on emerging technologies such as AR glasses or handheld devices like a mobile phone. AR applications are easily adaptable to technologies often found in homes, and thus provide a promising prospect for application in home settings for the aging population. With AR’s ability to overlay information in a real-world context, AR allows for contextualization in environments and scalability to communities as a software-based solution (Blattgerste et al., 2019). The technology also endows aid in everyday situations. Ranging from navigation to identifying objects around the user, AR lends a multitude of usages.

Among AR technologies, AR glasses represent an emerging field in technology. Some of their features include object recognition and eye-tracking, which enables features such as selecting objects and monitoring visual attention to a location AR glasses. They are easily portable, providing more autonomy, and expect to decrease in price as the field continues to develop. In terms of design, studies have shown that aging population have not found the glasses to be physically constraining. For ease of understanding, symbolic or pictorial representations are most helpful on the AR glasses (Blattgerste et al., 2019). AR glasses can be equipped with varying input modalities, allowing for a great scope of ways to help the cognitively impaired.

One of the input modalities AR glasses can pair with is a Brain-Machine Interface (BMI). BMI is an assistive technology that translates the brain’s signals into device commands. The device then uses this information to take into account a user’s current mental state. Developing to become affordable, BMI can be widely used in elderly care.

While with relatively limited input options, AR handheld devices provide the most adaptability and flexibility. These handheld devices be applied on household devices. Through AR applications on handheld devices, handheld AR devices can help patients and their caregivers. Caregivers can remotely supervise the elderly through the application. The devices are also easy to understand, as more elderly learn to navigate using technology (Blattgerste et al., 2019).

Virtual Reality (VR)

The immersive nature of VR–whether directly or indirectly–promises a future in intervention for the aging population. Immersive VR immerses the user to create the illusion of being inside a virtual world, usually through a Head Mounted Display (HMD). Many VR devices use gesture-based interactions, which can be especially useful since the user can not see their hands or body. This method of interaction allows easier usability and visualization in a virtual world (Zuschnegg et al., 2023).

Non-immersive VR allows for awareness of the user’s environment, an example being video games. Studies with non-immersive VR as CCIs are rare, but have the potential to be useful in CR and CS. Non-immersive VR’s approach permits straightforward integration into everyday living to support the aging population’s independence in living at home.

How can CCIs be applied?

The CCIs described above have the potential to apply to specific neurocognitive domains according to each individual (see Figure 2). In addition, CCIs can apply to multiple cognitive impairments ranging in the stage of cognitive decline (see Figure 3). To help the aging population, CCIs support treatment and assistance in daily living activities. By combining with other intervention methods, the effect on treating and preventing cognitive decline is bolstered.

Box 1

Neurocognitive domains

Those with cognitive impairments show a decline in one or more of the neurocognitive domains. Each neurocognitive domain includes subdomains, referring to specific abilities in cognitive processes. Identification of the neurocognitive domain and its subdomains affected in one’s cognitive impairments not only help to diagnose the cognitive impairment, but also assist in targeting specific cognitive abilities to support cognitive interventions. Cognitive interventions are directed to specific cognitive domains to personalize according to each individual. Each of the six cognitive domains shown in Figure 2 are explained below (Harvey, 2019):

1. Complex attention: abilities to choose what to pay attention to and to focus on multiple things simultaneously
   • Selective attention: ability to attend to relevant information and ignore irrelevant information
2. Executive function: high-level cognitive abilities to coordinate other cognitive abilities and perform tasks such as problem-solving and decision-making
   • Working memory: ability to temporarily store information, such as remembering digits of a phone number
3. Perceptual-motor function: abilities to interact with the environment through our senses and body movements
4. Language: abilities allowing for communication
5. Learning and memory: abilities supporting the recording of information and retrieving it
   • Free/cued recall: ability to retrieve information from memory without/with cues or prompts
   • Recognition memory: ability to recognize a familiar item or person
   • Semantic and autobiographical long-term memory: ability to store verbal information and personal history in long-term
   • Implicit learning: learning without conscious awareness
   •  Social cognition: process information between people to explain and predict the behavior of others and self
6. Social cognition: process information between people to explain and predict the behavior of others and self
   • Theory of mind: ability to understand others’ mental state
   • Insight: ability to reinterpret a situation to produce a new, nonobvious interpretation (e.g. understanding a joke or metaphor)

Box 2

Cognitive Impairments

Below are the three main cognitive impairments studied in the implementation of CCIs in order of progression in cognitive decline.

1. Subjective Cognitive Decline (SCD): Those with SCD perceive themselves to have cognitive decline although they have normal clinical cognitive abilities. SCD is one of the earliest noticeable symptoms for dementia, and early identification enables the alleviation of the future impact of cognitive impairments and to slow progression in cognitive decline. Having SCD leads to a greater risk to develop MCI, Alzheimer’s disease, or another neurocognitive disorder (Smart et al., 2017).
2. 2)  Mild-Cognitive Impairments (MCI): MCI is the intermediate stage between cognitive changes from normal aging to dementia. The condition may include problems with memory, language, or judgment, but does not interfere with daily living activities, fundamental to living independently. MCI may increase the risk of dementia caused by Alzheimer’s disease or other brain disorders (Mild Cognitive Impairment (MCI), 2024).
3. 3)  Dementia: Dementia, also known as major neurocognitive disorder, refers to a group of symptoms causing a significant decline in memory, thinking, and social abilities–interfering with daily life. Several diseases can cause dementia, and the most common cause for dementia in older adults is Alzheimer’s disease. Dementia can cause tragic effects in a person, leading to not only cognitive decline, but a loss of self (Dementia, 2024).

Treatment

“Time is brain,” said David Weisman, director of clinical research at Abington Neurological Associates in Abington, P.a., and current doctor of Dershem. First and foremost, early diagnosis is key to the effectiveness of interventions. Early diagnosis can delay progression and also lead to discovering a disorder or disease causing cognitive decline.

CCI extends another treatment option to patients, where pharmacological treatment may not have helped. There is mixed evidence for the positive impact of pharmacological treatments treating cognitive impairments, and taking these medications may also produce dangerous side effects (Ellison, 2024). In fact for those with SCD, CCI is one of their only options to treat their condition, as it is difficult to pinpoint a target using pharmacological treatments.

Since CCI works best when the cognitive impairment is diagnosed early, people with SCD and MCI can reap the most benefits from CCI. Those with SCD have relatively preserved cognitive function at present, which allows for a greater likelihood of benefiting from CCI before significant cognitive difficulties. At this early stage in cognitive decline, a web-based CT on a PC reported significant benefits on memory.

Adding to the benefits of CCI, CCI produced a significant effect for people with MCI immediately after the intervention, improving attention and executive functioning (see Figure 3 and 1 and 2 of Box 2). Ranging from non-immersive VR to AR, CCI has shown numerous effective applications to those with MCI. Computerized CT programs on a PC showed benefit to cognition in those with MCI, and VR-based cognitive interventions have improved attention and processing speed. One study has shown significant improvements in patients with MCI even 6 months after the intervention in memory and working memory (Zuschnegg et al., 2023).

Support in Daily Life

In addition to preserving or improving cognitive abilities, CCIs support those with cognitive impairments in everyday situations. One common challenge faced by those with cognitive impairments is wayfinding. CCI has developed to support the cognitive domains executive function and learning and memory. AR glasses provide a solution for navigation by displaying instructions on the lenses. While there may be concerns as to how cognitive impairments may influence the ability to follow the displayed directions, those with MCI have been able to understand and follow along.

Another difficulty as the aging population face cognitive decline comes remembering objects in a room and the location of their items. A handheld AR application annotates the environment to help identify objects, which can replace caregivers labeling objects or drawers with Post-it Notes (see Figure 4). One handheld AR device not only helps to identify objects and other people, but also tracks current location and medication intake to allow their caregivers to ensure their safety.

CCI can be employed to assist the elderly with cognitive impairments in everyday tasks and situations. Through CR, AR glasses are capable of aiding in tasks such as cooking, including elderly with dementia. AR glasses paired with a BMI can also bring improvement in the interactive abilities of the aging population. In addition, AR has seen positive impacts in complex tasks. Driving employs multiple cognitive domains, and cognitive impairments can significantly affect driving capabilities. With an AR windshield screen displaying navigation cues and warnings, those with MCI made fewer navigation errors and had improved selective attention (see 1a from Box 1; Blattgerste et al., 2019). CCI thus provides benefits not just in treating cognitive decline, but also supporting the aging population with cognitive impairments in various situations.

Combinations of Interventions

While CCI has positive impacts for cognitive impairments on its own, it is possible to boost the effects of CCI by combining physical training with CT. A analysis of 18 physical training studies with the aging population has shown that physical exercise has a “robust” effect on cognition in the elderly, therefore combining the added effects of each type of physical and cognitive training can prove more beneficial to cognitive functions in the elderly (Zokaei et al., 2017).

With a more severe neurocognitive disorder, interventions have a less to moderate effect. There can be limited capacity for improvements from training. One study examined the efficacy of aerobic training and exergaming, combining exercise with video games or a virtual environment. Exergaming employs a non-immersive virtual environment and CS, requiring mental flexibility and cognitive processes employing multiple cognitive domains such as problem-solving.

The study used CT in a relatively short 12-week training program adjusting the level of difficulty to each elderly’s progression in cognitive decline in dementia. After the program, it was concluded that the non-immersive VR improved psychomotor speed, which can be significant since it contributes to predicting cognitive decline. Yet, the program had limited effect on cognitive domains, as for those with dementia, it is more difficult to improve cognition in the latter stages of cognitive decline (Karssemeijer et al., 2019).

As CCI and its applications continue to be researched, there should be more studies that focus on the long-term effectiveness of the interventions. This emerging field of CCI would grow as applications for the aging population in home settings are developed.

Conclusion

In conclusion, CCI offers resources to enhance cognition and address cognitive impairments in novel ways. Their abilities to immerse users and utilize the practices of CT, CR, and CS provide ways to support a healthy quality of life for the aging population. CCI has proven to be especially effective in early intervention for those with MCI and SCD. For those with dementia, the effectiveness of CCI decreases, but a combination of CCI with physical training makes the most out of their individual advantages to improve cognitive abilities.

CCI and their implementations for the aging population continue to develop. Ultimately, CCI has the potential to transform the approaches to assisting the aging population through personalizing the intervention to specific cognitive domains and supporting the autonomy of the aging population.

References

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About the author

Sophie Cheung

Sophie is a junior at Sacred Heart Preparatory. She is the founder and director of Flowering Connections, a nonprofit connecting students with the elderly at senior centers in her local community. She organizes activities and performances to engage youth with the elderly. At school, Sophie participates as a Computer-Aided Designer in her robotics team, where Sophie and her team have qualified for the First Tech Challenge regionals competition.

With her passion for technology and helping the elderly, she grew a desire to further research into this interdisciplinary field. In her spare time, she enjoys running on her school cross country and track team, as well as singing.

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A paper titled “Neurons detect cognitive boundaries to structure episodic memories in humans” (Zheng et al., 2022) examined the question of how different cognitive boundaries influence neural activity and memory processes. This influential study was one of the key steps to help scientists understand how different types of memory are processed and retained. Cognitive boundaries are moments that break up an experience into meaningful parts and are known to play a role in structuring memory. Little is known about how this process is realized in the human brain. The paper under discussion attempts to fill in this gap by identifying neurons that code for cognitive boundaries and show their relationship to subsequent memory. The aim of the current paper is to summarize and evaluate Zheng et al.’s study and discuss how this new knowledge can be used in the field. One thing to note about each participant was that the participants all had drug-resistant epilepsy on whom an invasive procedure was performed for medical reasons to implant depth electrodes in the brain to identify the source of the epilepsy. These depth electrodes were placed in the amygdala, hippocampus, and parahippocampal gyrus which are known to have a role in episodic memory, and could also be leveraged to sense the firing of neurons in those specific regions of the brain This allowed the researchers to collect a specific neuron’s firing pattern or rate (Zheng et al., 2022).

The study was comprised of 3 different tasks: an encoding task, a scene recognition task, and a time discrimination task to mimic the way memories are conceived and recounted in natural life. Participants were introduced to movie clips in the ‘encoding task’ with different boundaries. In the encoding stage, boundaries were placed in the movie clips and a no-boundary section (NB) was also presented. High boundaries (HBs) cut to an entirely different movie while short boundaries (SBs) were movies that cut to a different part in the movie. Each participant saw 90 different clips. After the encoding stage, they were assessed on scene recognition and time discrimination, followed by a confidence rating from 1 – 6. In the scene recognition task participants were shown a random image of either a film they saw or a new unseen one (foils). Participants answered the questions about the film as “old” or “new”. New films and foils were equated on luminosity, colours, similar objects, etc. The time discrimination task was another type of memory performance test, but instead of recognition, the aspect of memory tested was temporal order memory. Participants were shown 2 frames and had to decide which one was seen first in the original video (Zheng et al., 2022).

The signals measured from the depth sensors were evaluated for the changes in neural activity through principal component analysis (PCA) to examine dynamics associated with the different types of boundaries. Analyzing the neural activity with PCA can reveal how neurons interact with each other in relation to episodic memory and boundaries, how different boundaries affect the memory of individuals, and the role firing rate has in episodic memory depending on the cognitive boundaries (Zheng et al., 2022).

The results provide new insights into how memory is encoded and recalled. The study found that events followed by a cognitive boundary were recognized better in the recognition questions, but HBs decreased performance in time recognition (order of events). The recognition accuracy decreased when the boundaries were farther away from the tested frames, so the order of test frames that appeared immediately after a boundary in the original video was better discriminated. Impaired time discrimination in the HB was likely due to less memory strength since more strength is required for time discrimination. Thus, frames immediately after a boundary were better recognized and the strength of activity in boundary-reactive neurons was predictive of memory strength. In scene recognition, confidence and the time taken to answer stayed the same across boundaries. The recognition accuracy decreased when the boundaries were farther away from the frame. On the other hand, impaired time discrimination was likely due to less memory strength since more strength is required for time discrimination. It became evident that different types of responses to the stimuli were beneficial for different types of memory. In scene recognition, large neural shifts were beneficial for memory but not in time discrimination. The contrast in reactions and results depending on the tasks actively demonstrates how unique memory functions are processed, retained, and recalled differently (Zheng et al., 2022).

Over the course of the study, an unexpected discovery was made: there were 2 types of cells which had different patterns in activity and reactions. The experiment classified these different types of cells as ‘boundary’ and ‘event’ cells to study their properties in the enhancement or manufacturing of memory. It was found that the cells’ reactivity was predictive of how well the clips were remembered, Boundary cells were cells that responded to HBs and SBs while event cells were only responsive to SBs. The parahippocampal gyrus (26.47%) had the most boundary cells, amygdala (7.10%) and hippocampus (3.50%). Their responsiveness was all graphed into response time using the principal component analysis and electrodes which showed implications that event cells are reliant on boundary cells to function as there is a time lag for them to possibly communicate. When applied to time distinction and recognition, the two types of cells showed different results that alluded to different functions for different purposes. Event cells were more reactive after HBs compared to SBs as the brain started paying more attention after the narrative of the film was disrupted. SB neural shifts were only visible when boundary cells were included. HB neural shifts were only visible when event cells were included. In short, theta phase locking (neuron firing rhythms) did not play a role in recognition memory success but did increase the temporal order memory when studying the event cells. The firing rate in boundary cells predicted scene recognition and increased performance. Boundary and event cells were used to predict different things, demonstrating the functions they play in the different types of memory they benefit (Zheng et al., 2022).

The findings from this experiment have progressed the understanding of how memory is structured pertaining to the different types of memory and the different cells responsible. The understanding can be applied to other implications in the field. One application to learning about how memory works can help maximize a human’s ability to remember in school settings or professional settings to better retain information. Knowing the cognitive boundaries is beneficial for scene recognition, for example, it can be a guiding point for how lessons or meetings are structured to optimize neuro functions. Another application can be in the medical field where memory disorders such as dementia and Alzheimer’s have a better understanding to be studied as researchers continue to develop solutions based on the work in this study concerning boundary and event cells. Due to their work with drug-resistant epilepsy patients, the implanted electrodes can push forward research in identifying and diagnosing where the seizures stem from in the brain. Finally, optimizing how memory is encoded in a human’s brain can be applied to how it is used in AI technology. Gabriel Kreiman, an ophthalmology professor at Harvard University commented on the findings of the study and how in the field of IA technology, it can be shifted to “build better algorithms that will incorporate episodic-like memories in artificial intelligence” (Kreiman, 2023). This can not only be a very plausible application of the research, but it can also be a sign that there are many more uses for these findings in the future as AI technology continues to develop (Zheng et al., 2022).

The experiment has been supported by other researchers who have found similar results after the study was conducted. South Korean researchers, Yujin Rah, Jiyun Kim, and Sangah Lee discovered parallel ideas to how boundaries affect memory. They specifically studied how it impacts children’s memories and spatial mapping. They found that the participant’s memory was indeed impacted by the type of boundary (2D line, 3D wall, and aligned objects) and children’s memory was negatively affected by boundaries (Rah, Lee, and Kim, 2022). Another study used a more similar approach to the original experiment as they targeted the recognition task more in-depth. Alexis Campbell made use of fMRI instead of Zheng et al.’s original method of depth electrodes which can detect where the brain is most active instead of targeting specific neurons. This allowed Campbell to see a more general and wider picture of how episodic memory is encoded for recognition (Campbell, 2023).

These discoveries derived from Zheng’s experiment have opened unexplored opportunities for different research areas with new progress to be made. The discovery of event cells and boundary cells in the way they behave and the different functions they offer is one of the most remarkable things about this work as it is an example of how there are still realms of the brain to be discovered and how research can better suit the complexities of neuroactivity and episodic memory research. Zheng’s experiment has contributed to how time discrimination and scene recognition are both very distinct areas of episodic memory that operate uniquely. With their analysis using depth electrodes, Zheng and her team were able to hone in on a couple of neurons to graph and study the patterns in their firing rates which were predictive of the participant’s memory performance task results in accuracy. With all of these applications to this knowledge, it is undoubted that new information can be built off of the understanding of episodic memory and cognitive boundaries as there already has been a growing number of experiments that have been conducted to deepen this understanding (Zheng et al., 2022).

References

Campbell, Alexis. “Behavioral and Neural Effects of Event Boundaries on Implicit Associations in Episodic Memory.” Digital Collections, 2023, https://digitalcollections.wesleyan.edu/_flysystem/fedora/2023-07/1667.pdf.

Rah, Y. J., Kim, J., & Lee, S. A. (2022). Effects of spatial boundaries on episodic memory development. Child Development, 93, 1574–1583. https://doi.org/10.1111/cdev.13776

Zheng, J., & Kreiman, G. (2022, May 31). Neurons detect cognitive boundaries to structure episodic memories in humans | The Center for Brains, Minds & Machines. MIT CBMM. Retrieved 2023, from https://cbmm.mit.edu/video/neurons-detect-cognitive-boundaries-structure-episodic-mem ories-humans-video

Zheng, J., Schjetnan, A.G.P., Yebra, M. et al.. Neurons detect cognitive boundaries to structure episodic memories in humans. Nat Neurosci 25, 358-368 (2022). Retrieved 2023, from https://www.nature.com/articles/s41593-022-01020-w#citeas

About the author

Riley Yen

Riley is an 11th grader in Manhattan, New York. She is passionate about neuroscience, psychology, and molecular biology. She is particularly interested in topics such as human behavioural patterns, social decision-making, and forensic science which she wishes to further explore in her studies in the future. Her curiosity in this field is due to her fascination with the relationship between how society operates and how it is intertwined with neurological and biological processes in humans.

In her free time, Riley also enjoys writing and composing music, baking for her friends and family, and reading books about social sciences.

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