Neuroscience Approach Research Articles

Defining social reward: A systematic review of human and animal studies.

Social rewards are strong drivers of behavior and fundamental to well-being, yet there is a lack of consensus regarding what actually defines a reward as "social." Because a systematic overview of existing social reward operationalizations is currently absent, a review of the literature seems necessary to advance toward a unified framework and to better guide research and theory. To bridge this gap, we preregistered and conducted the first comprehensive systematic review of human and animal experimental studies that used the term "social reward" and charted existing operationalizations, revealing the implicit and explicit definitions used in the field. Stimulus characteristics and measures of social reward were extracted from a total of 384 studies encompassing 42,118 participants and subjects. We provide detailed summaries of these elements, stratified by species (human/animal) and study type (behavioral, brain imaging, pharmacological, and physiological). Two main aspects were found to account for most of the difference in operationalizations: the sensory richness of a stimulus (intimacy) and engagement in social interaction (i.e., the synchronous observation and action between at least two individuals, viz., immediacy). Drawing insights from second-person neuroscience approaches and theoretical models in the field of human-computer interaction, we propose that human and animal research can greatly benefit from considering these properties, as they have important theoretical and practical consequences for human and translational research, with far-reaching implications for neighboring research fields such as those pertaining to social media and the development of artificial intelligence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Agoutis: A Future Model for Ecologically Relevant Neuroscience and Physiology In Natura

ABSTRACTThe overarching goal of neurobiology is to understand how complex behaviors are generated by the nervous system. The behavior of each species, and the brain that controls it, is shaped by the historical and current state of the environment that they inhabit. This fact is juxtaposed with the reductionist approach of neuroscience that isolates animals from their natural environment. Understanding how brains evolved to orchestrate the myriads of natural behaviors an animal performs in response to its environment requires an integrative approach to neuroscience that considers ecology, ethology, and evolution. Current technological developments are leading us to an inflection point at which studying brain functions in the wild is now possible. Ecological studies on how the environment affects behavior of animals (i.e., hibernation, foraging, food hoarding, and nest building) have framed a plurality of questions to be answered mechanistically, and yet, only few studies have addressed the relationship between the environment and the brain's anatomy and physiology. Neuroscience needs new animal models that allow us to tackle such questions in the wild. Here, we propose a new animal model for wild neuroscience, the agouti (Dasyprocta spp.), a large wild rodent playing a critical seasonal role in the maintenance of the central and south American rainforest ecosystems. We focus on how a rodent model, like the agouti, will allow for the investigation of large‐scale brain dynamics during seasonal behaviors of ecological importance: scatter‐hoarding and retrieval. We describe agouti evolution, ecology, and physiology as well as neuro‐anatomical and neurophysiological studies, which have set the foundation for future neuroscience in natura. We suggest agoutis have the potential to be a groundbreaking model for wild neuroscience.

Evaluating the Dynamics of Learning Approaches: A Systematic Review Investigating the Nexus Between Teaching Methods and Academic Performance in Medical and Dental Education

AbstractIn recent decades, a noticeable trend has emerged in medical and dental schools to adjust their curricula to promote learning strategies and habits geared towards long-term knowledge retention. This systematic review therefore sought to examine whether different teaching methods influence students’ preferred learning approaches (deep, strategic, and surface approach) and indirectly affect their academic performance. Furthermore, it provides a comprehensive overview of the inconsistencies evident in previous literature regarding the relationship between learning approaches and academic performance of medical and dental students. A thorough search across eight databases yielded 49 relevant studies published between the years 2000 and 2023. The majority of studies revealed that whilst deep and strategic learners tend to excel at medical or dental school, surface learners appear to struggle under intense workload and pressure. Hence, a common consensus emerged amongst the existing literature that there is a relative benefit in attempting to guide students towards deep and strategic learning habits and to minimise surface-related learning strategies. However, results also conveyed that merely modifying the teaching methods used in medical and dental schools has limited merit. As such, the results emphasised the lack of a ‘one-size-fits-all’ teaching method. Common factors influencing learning approaches were subsequently identified, and based on these findings, an outlook was put forth to integrate a cognitive neuroscience approach into higher education. These directions for future investigations aim to help students reach their full potential by understanding the processes underlying efficient learning and by being mindful of respective encouraging and discouraging factors.

Structural inequality and temporal brain dynamics across diverse samples.

Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries=10; healthy individuals=1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed. Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions. These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

Towards the definition of a standard in TMS-EEG data preprocessing

Combining Non-Invasive Brain Stimulation (NIBS) techniques with the recording of brain electrophysiological activity is an increasingly widespread approach in neuroscience. Particularly successful has been the simultaneous combination of Transcranial Magnetic Stimulation (TMS) and Electroencephalography (EEG). Unfortunately, the strong magnetic pulse required to effectively interact with brain activity inevitably induces artifacts in the concurrent EEG acquisition. Therefore, a careful but aggressive pre-processing is required to efficiently remove artifacts. Unfortunately, as already reported in the literature, different preprocessing approaches can introduce variability in the results. Here we aim at characterizing the three main TMS-EEG preprocessing pipelines currently available, namely ARTIST (Wu et al., 2018), TESA (Rogasch et al., 2017) and SOUND/SSP-SIR (Mutanen et al., 2018, 2016), providing an insight to researchers who need to choose between different approaches. Differently from previous works, we tested the pipelines using a synthetic TMS-EEG signal with a known ground-truth (the artifacts-free to-be-reconstructed signal). In this way, it was possible to assess the reliability of each pipeline precisely and quantitatively, providing a more robust reference for future research. In summary, we found that all pipelines performed well, but with differences in terms of the spatio-temporal precision of the ground-truth reconstruction. Crucially, the three pipelines impacted differently on the inter-trial variability, with ARTIST introducing inter-trial variability not already intrinsic to the ground-truth signal.

A dual-pathway architecture enables chronic stress to disrupt agency and promote habit formation.

Chronic stress can change how we learn and, thus, how we make decisions. Here we investigated the neuronal circuit mechanisms that enable this. Using a multifaceted systems neuroscience approach in male and female mice, we reveal a dual pathway, amygdala-striatal neuronal circuit architecture by which a recent history of chronic stress disrupts the action-outcome learning underlying adaptive agency and promotes the formation of inflexible habits. We found that the basolateral amygdala projection to the dorsomedial striatum is activated by rewarding events to support the action-outcome learning needed for flexible, goal-directed decision making. Chronic stress attenuates this to disrupt action-outcome learning and, therefore, agency. Conversely, the central amygdala projection to the dorsomedial striatum mediates habit formation. Following stress this pathway is progressively recruited to learning to promote the premature formation of inflexible habits. Thus, stress exerts opposing effects on two amygdala-striatal pathways to disrupt agency and promote habit. These data provide neuronal circuit insights into how chronic stress shapes learning and decision making, and help understand how stress can lead to the disrupted decision making and pathological habits that characterize substance use disorders and mental health conditions.

Freudian psychic apparatus and Luria's functional units: exploring intersections.

Introduction. In this paper, we aim to point out that Freudian concepts such as the id, ego, and superego may find equivalents in neuroanatomy/neurobiology. To this end, we explore the possible intersections between Sigmund Freud's psychoanalytic theoretical model and neurologist Alexander Luria's model of functional units. Although Freud distanced himself from the strictly localizationist neurological perspective of brain functions, his notions of the mind as a dynamic phenomenon persist and continue to inspire discussions in the field of neuroscience. Method. We establish a parallel between Freud's structural and dynamic model and Luria's model of functional units, highlighting the complex relationship between the id, ego, superego, and the neurological framework. Results. We emphasize that, although precise locations for these structures may be elusive, a systemic neuroscientific approach to psychic activity aligns with Freud's vision. Additionally, we explore the genetic and neurobiological dimensions of Freud's proposal and discuss how these elements could lead to a deeper understanding of neuropsychic structures. We argue that the distinction between the id and its differentiated structures remains nebulous, and that the id may have roots in brain regions governing basic survival, with origins in genetic inheritance. Conclusion. This paper does not provide definitive answers but aims to contribute to the complex relationship between Freudian psychoanalysis and neurobiology. We emphasize the imperative need for future investigations, particularly employing a microscopic/reductionist approach to neuropsychic differentiation.

Clinical and biobehavioral phenotypic assessments and data harmonization for the RE-JOIN research consortium: Recommendations for common data element selection

BackgroundThe Restoring Joint Health and Function to Reduce Pain (RE-JOIN) Consortium is part of the Helping to End Addiction Long-term® (HEAL) Initiative. HEAL is an ambitious, NIH-wide initiative to speed scientific solutions to stem the national opioid public health crisis. The RE-JOIN consortium’s over-arching goal is to define how chronic joint pain-mediating neurons innervate different articular and peri-articular tissues, with a focus on the knee and temporomandibular joints (TMJ) across species employing the latest neuroscience approaches. The aim of this manuscript is to elucidate the human data gathered by the RE-JOIN consortium, as well as to expound upon its underlying rationale and the methodologies and protocols for harmonization and standardization that have been instituted by the RE-JOIN Consortium. MethodsThe consortium-wide human models working subgroup established the RE-JOIN minimal harmonized data elements that will be collected across all human studies and set the stage to develop parallel pre-clinical data collection standards. Data harmonization considerations included requirements from the HEAL program and recommendations from the consortium’s researchers and experts on informatics, knowledge management, and data curation. ResultsMultidisciplinary experts − including preclinical and clinical researchers, with both clinician-scientists- developed the RE-JOIN’s Minimal Human Data Standard with required domains and outcome measures to be collected across projects and institutions. The RE-JOIN minimal data standard will include HEAL Common Data Elements (CDEs) (e.g., standardized demographics, general pain, psychosocial and functional measures), and RE-JOIN common data elements (R-CDE) (i.e., both general and joint-specific standardized and clinically important self-reported pain and function measures, as well as pressure pain thresholds part of quantitative sensory testing). In addition, discretionary, site-specific measures will be collected by individual institutions (e.g., expanded quantitative sensory testing and gait biomechanical assessments), specific to the knee or TMJ. Research teams will submit datasets of standardized metadata to the RE-JOIN Data Coordinating Center (DCG) via a secure cloud-based central data repository and computing infrastructure for researchers to share and conduct analyses on data collected by or acquired for RE-JOIN. RE-JOIN datasets will have protected health information (PHI) removed and be publicly available on the SPARC portal and accessible through the HEAL Data Ecosystem. ConclusionData Harmonization efforts provide the multidisciplinary consortium with an opportunity to effectively collaborate across decentralized research teams, and data standardization sets the framework for efficient future analyses of RE-JOIN data collected by the consortium. The harmonized phenotypic information obtained will significantly enhance our understanding of the neurobiology of the pain-pathology relationships in humans, providing valuable insights for comparison with pre-clinical models.

From E-Commerce to the Metaverse: A Neuroscientific Analysis of Digital Consumer Behavior.

The growing interest in consumer behavior in the digital environment is leading scholars and companies to focus on consumer behavior and choices on digital platforms, such as the metaverse. On this immersive digital shopping platform, consumer neuroscience provides an optimal opportunity to explore consumers' emotions and cognitions. In this study, neuroscience techniques (EEG, SC, BVP) were used to compare emotional and cognitive aspects of shopping between metaverse and traditional e-commerce platforms. Participants were asked to purchase the same product once on a metaverse platform (Second Life, SL) and once via an e-commerce website (EC). After each task, questionnaires were administered to measure perceived enjoyment, informativeness, ease of use, cognitive effort, and flow. Statistical analyses were conducted to examine differences between SL and EC at the neurophysiological and self-report levels, as well as between different stages of the purchase process. The results show that SL elicits greater cognitive engagement than EC, but it is also more mentally demanding, with a higher workload and more memorization, and fails to elicit a strong positive emotional response, leading to a poorer shopping experience. These findings provide insights not only for digital-related consumer research but also for companies to improve their metaverse shopping experience. Before investing in the platform or creating a digital retail space, companies should thoroughly analyze it, focusing on how to enhance users' cognition and emotions, ultimately promoting a better consumer experience. Despite its limitations, this pilot study sheds light on the emotional and cognitive aspects of metaverse shopping and suggests potential for further research with a consumer neuroscience approach in the metaverse field.

Amniotic Membrane-Derived Multichannel Hydrogels for Neural Tissue Repair.

In the pursuit of advancing neural tissue regeneration, biomaterial scaffolds have emerged as promising candidates, offering potential solutions for nerve disruptions. Among these scaffolds, multichannel hydrogels, characterized by meticulously designed micrometer-scale channels, stand out as instrumental tools for guiding axonal growth and facilitating cellular interactions. This study explores the innovative application of human amniotic membranes modified with methacryloyl domains (AMMA) in neural stem cell (NSC) culture. AMMA hydrogels, possessing a tailored softness resembling the physiological environment, are prepared in the format of multichannel scaffolds to simulate native-like microarchitecture of nerve tracts. Preliminary experiments on AMMA hydrogel films showcase their potential for neural applications, demonstrating robust adhesion, proliferation, and differentiation of NSCs without the need for additional coatings. Transitioning into the 3D realm, the multichannel architecture fosters intricate neuronal networks guiding neurite extension longitudinally. Furthermore, the presence of synaptic vesicles within the cellular arrays suggests the establishment of functional synaptic connections, underscoring the physiological relevance of the developed neuronal networks. This work contributes to the ongoing efforts to find ethical, clinically translatable, and functionally relevant approaches for regenerative neuroscience.

Integrating data science and neuroscience in developmental psychopathology: Formative examples and future directions.

This commentary discusses opportunities for advancing the field of developmental psychopathology through the integration of data science and neuroscience approaches. We first review elements of our research program investigating how early life adversity shapes neurodevelopment and may convey risk for psychopathology. We then illustrate three ways that data science techniques (e.g., machine learning) can support developmental psychopathology research, such as by distinguishing between common and diverse developmental outcomes after stress exposure. Finally, we discuss logistical and conceptual refinements that may aid the field moving forward. Throughout the piece, we underscore the profound impact of Dr Dante Cicchetti, reflecting on how his work influenced our own, and gave rise to the field of developmental psychopathology.

Temporal variation in children's reactions to tourism advertisement

This study adopts a neuroscientific approach to explore children's real-time emotional experiences in responses to tourism advertisements with different valences. Sixty children were exposed to three high-positive valence and three low-positive valence tourism advertisements while their brain waves were collected via an electroencephalogram (EEG). Dynamic emotional trajectories of frontal alpha asymmetry, a neural indicator representing a person's positive emotions, were extracted from EEG data using time-frequency analysis. Children's emotions on five key moments (i.e., peak, end, beginning, trough, and middle) were analyzed. Results show that the high-positive valence videos resulted in higher asymmetry scores in middle moments rather in other moments. Moreover, gender and age could partially moderate the impact of video valence on asymmetry scores in middle moments.

From the Reflex Machine to the Changeable Brain

AbstractThe brain is exceedingly complex. Neuroscientists have used the tried and tested simplification methods of physics, such as mathematical idealization, in order to deal with neural complexity. This paper queries the appropriateness of certain physics-derived strategies within this biological science by presenting a historical case study (the reflex theory) in which scientists fell into the trap of over-simplifying the brain and nervous system. It then considers whether more recent computational and dynamical systems approaches in neuroscience are also at risk of abstracting away from critical features of neural functionality related to the inherent changeability of those systems.

Automating literature screening and curation with applications to computational neuroscience.

ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.

The Impact of Translational Neuroscience Approaches on the Treatment of Substance Use Disorders

The Impact of Translational Neuroscience Approaches on the Treatment of Substance Use Disorders

Dimensions of early life adversity and their associations with functional brain organisation

Abstract Early life adversity is associated with differences in brain function and an elevated risk for poor mental health. Using data from children aged 10 (N = 5,798) from the Adolescent Brain Cognitive Development (ABCD) cohort, we investigated how adversity relates to functional brain organisation using a network neuroscience approach. We derived four data-driven categories of adversity by fitting a mixed graphical model: household/community instability, physical/sexual abuse, parental neglect, and financial difficulties. Analyses revealed that multiple forms of adversity were associated with reduced clustering and increased assortativity across the entire brain and that these local measures of organisation captured greater adversity-related variance than mesoscale measures like modularity. The most pronounced effects were in the somatosensory and subcortical networks. Financial difficulties showed the strongest and most widespread associations with brain organisation, with evidence of a mediating effect of assortativity on the association between financial difficulties and internalising symptoms. Adding race as a covariate attenuated most brain-adversity relationships, suggesting that experiences of adversity are strongly related to race/ethnicity in the ABCD sample. These results demonstrate that different forms of adversity are associated with both shared and unique variations in functional brain organisation, highlighting its potential significance for explaining individual differences in mental health outcomes following early life adversity.

Social Perception in the Infant Brain and Its Link to Social Behavior.

The current longitudinal study (n = 98) utilized a developmental cognitive neuroscience approach to examine whether and how variability in social perception is linked to social behavior in early human development. Cortical responses to processing dynamic faces were investigated using functional near-infrared spectroscopy at 7 months. Individual differences in sociability were measured using the Early Childhood Behavior Questionnaire at 18 months. Confirming previous work with infants and adults, functional near-infrared spectroscopy results show that viewing changing faces recruited superior temporal cortices in 7-month-old infants, adding to the view that this brain system is specialized in social perception from early in ontogeny. Our longitudinal results show that greater engagement of the right superior temporal cortex at 7 months predicts higher levels of sociability at 18 months. This suggests that early variability in social perception is linked to later differences in overtly displayed social behavior, providing novel longitudinal evidence for a social brain-behavior association.

Bridging the Gap Between Biological Plausibility and Practicality in Neuron Modeling: Challenges and Perspectives

Neuron modeling has been a fundamental approach in computational neuroscience for understanding the dynamics and function of the central nervous system (CNS). Over the past decades, a plethora of mathematical models, ranging from the seminal Hodgkin-Huxley model to more complex multicompartmental and stochastic models, have been developed to capture the intricate behavior of neurons and their interactions. However, despite the significant advances in modeling, there remains a substantial disconnect between the complexity of the models and their practical applicability in day-to-day medical and laboratory settings. This article explores the challenges and limitations of current neuron modeling approaches, particularly focusing on the trade-off between biological realism and computational tractability. I discuss the variability of neuronal properties within and across different regions of the brain, which poses a significant challenge for developing models that can accurately represent the full complexity of the CNS. I also highlight the computational bottlenecks associated with simulating highly detailed, which often require extensive computational resources and specialized software. Considering these challenges, we emphasize the need for a balance between biological plausibility and practicality in neuron modeling. We discuss the role of simplified models, such as the leaky integrate-and-fire (LIF) and Izhikevich models, which sacrifice some biological detail in favor of computational efficiency and ease of use. These models have proven valuable for large-scale simulations of neural networks and have provided important insights into the collective behavior of neuronal populations. Furthermore, I explore the potential of data-driven approaches, such as machine learning and artificial neural networks, in bridging the gap between biological realism and practical applicability. These approaches can learn the dynamics of neuronal systems directly from experimental data, bypassing the need for explicit mathematical models. I present a simple example using linear regression to predict neuronal firing rates based on input features, showcasing the potential of machine learning techniques in capturing the complex relationships between neuronal variables. I also highlight the need for more extensive validation of models against experimental data and the development of standardized benchmarks for assessing model performance. In conclusion, while the quest for biologically plausible neuron models has driven significant advances in our understanding of the CNS, the disconnect between model complexity and practical applicability remains a major challenge. By embracing simplified models, data-driven approaches, and interdisciplinary collaborations, we can work towards the development of more efficient and practical neuron models that can bridge the gap between biological realism and computational feasibility, ultimately facilitating the translation of theoretical insights into clinical and experimental applications.

Uncovering the determinants of brain functioning, behavior and their interplay in the light of context.

Notwithstanding the huge progress in molecular and cellular neuroscience, our ability to understand the brain and develop effective treatments promoting mental health is still limited. This can be partially ascribed to the reductionist, deterministic and mechanistic approaches in neuroscience that struggle with the complexity of the central nervous system. Here, I introduce the Context theory of constrained systems proposing a novel role of contextual factors and genetic, molecular and neural substrates in determining brain functioning and behavior. This theory entails key conceptual implications. First, context is the main driver of behavior and mental states. Second, substrates, from genes to brain areas, have no direct causal link to complex behavioral responses as they can be combined in multiple ways to produce the same response and different responses can impinge on the same substrates. Third, context and biological substrates play distinct roles in determining behavior: context drives behavior, substrates constrain the behavioral repertoire that can be implemented. Fourth, since behavior is the interface between the central nervous system and the environment, it is a privileged level of control and orchestration of brain functioning. Such implications are illustrated through the Kitchen metaphor of the brain. This theoretical framework calls for the revision of key concepts in neuroscience and psychiatry, including causality, specificity and individuality. Moreover, at the clinical level, it proposes treatments inducing behavioral changes through contextual interventions as having the highest impact to reorganize the complexity of the human mind and to achieve a long-lasting improvement in mental health.

Impact of Early Life Stress on Neurobiological Vulnerability to Obsessive-Compulsive Disorder: Early Life Stress & Neurobiological OCD Risk

The impact of early life stress on neurobiological vulnerability to obsessive-compulsive disorder (OCD) is a major focus of psychiatric research. Individuals with OCD frequently report that psychosocial stress exacerbates their symptoms, with many attributing the onset of symptoms to stressful life events. However, the exact pathophysiological relationship between stress and OCD is not well understood. Stress has been shown in preclinical studies to have significant effects on cortico-striatal and limbic circuitry, including neuronal atrophy in the frontal cortices, dorsomedial striatum, and hippocampus, as well as neuronal hypertrophy in the dorsolateral striatum and amygdala. These neurobiological effects may contribute to an imbalance between goal-directed and habitual behaviour, which is associated with OCD symptoms. Furthermore, genetic and environmental factors, including early life stress, play an essential role in the advancement of OCD. Understanding gene-environment interactions and pathogenic mechanisms is critical to advancing precision medicine and improving treatment outcomes for OCD and related disorders. This review emphasises the need for additional research into how early life stress interacts with genetic factors to cause the behavioural, cellular, and molecular changes seen in OCD. Integrating global mental health and translational neuroscience approaches shows potential for enhancing our understanding and treatment of OCD and related disorders.