Neuroscience Framework Research Articles

NEURAL PATHWAYS LINKING CHILDHOOD TRAUMA AND ADOLESCENT SUBSTANCE USE: THE ROLE OF LEARNING AND DECISION-MAKING

Childhood trauma significantly increases the risk for adolescent substance use disorders (SUDs). Dysfunction in neurocircuitry associated with learning and decision making is thought to underlie both trauma- and substance use (SU)-related psychopathologies, and represents a potential pathway by which childhood trauma is associated with later SUD. We will examine neural correlates of learning and decision making across 3 youth samples in the context of trauma exposure and traumatic stress, SU, and their combination. Clinical researchers will present fMRI and EEG findings from tasks examining aspects of learning and decision making, including reinforcement learning, delay discounting, and uncertainty processing. Studies include youth from clinical and community samples. Talk 1: Among children who have been exposed to interpersonal violence, PTSD symptom severity predicted: 1) event-related potentials for unpredictable unpleasant pictures; and 2) brain response to startle probes presented during unpredictable unpleasant pictures. Talk 2: Youth at high SUD risk showed stronger engagement of regions responsible for time encoding, reward valuation, and choice classification during the delayed choice. Talk 3: More severe neglect was associated with reduced responsivity in reward neurocircuitry during reinforcement learning, and alcohol use disorder (AUD) symptom severity compounded these effects. Talk 4: Trauma and AUD symptom severity are associated with reduced reward prediction error representation during reinforcement learning. Across these talks, childhood trauma and SU were associated with overlapping dysfunction in reinforcement learning neurocircuitries, as well as processing unpredictable or high-uncertainty information. Longitudinal work is needed to understand how relationships between childhood trauma and adolescent SUD unfold across time. However, learning and decision-making neurocircuitry dysfunction may represent a brain biomarker for trauma- and SU-related problems. Further, findings suggest that genetic risk and externalizing psychopathology represent important moderators for consideration in future work. The discussant will synthesize findings in both a developmental psychopathology and cognitive neuroscience framework.

Realizing Active Inference in Variational Message Passing: The Outcome-Blind Certainty Seeker.

Active inference is a state-of-the-art framework in neuroscience that offers a unified theory of brain function. It is also proposed as a framework for planning in AI. Unfortunately, the complex mathematics required to create new models can impede application of active inference in neuroscience and AI research. This letter addresses this problem by providing a complete mathematical treatment of the active inference framework in discrete time and state spaces and the derivation of the update equations for any new model. We leverage the theoretical connection between active inference and variational message passing as described by John Winn and Christopher M. Bishop in 2005. Since variational message passing is a well-defined methodology for deriving Bayesian belief update equations, this letter opens the door to advanced generative models for active inference. We show that using a fully factorized variational distribution simplifies the expected free energy, which furnishes priors over policies so that agents seek unambiguous states. Finally, we consider future extensions that support deep tree searches for sequential policy optimization based on structure learning and belief propagation.

The Emerging Theoretical Framework of Two-Person Neuroscience

Whether the submandibular gland (SMG) can be preserved during neck dissection in the surgical treatment of oral squamous cell carcinoma (OSCC) is controversial. This study investigated the SMG involvement rate and provides a basis for preserving the SMG during neck dissection in appropriate cases of OSCC.A comprehensive systematic review was conducted on the PubMed and MEDLINE, Embase, and Cochrane Library databases for studies on SMG involvement in OSCC published before December 2017 with a data analysis technique. Predictor variables were numbers of patients and resected SMGs, primary site, and tumor, node, and metastasis stage. Outcome variables were the number of involved SMGs and mode of involvement. Other variables, namely first author, publication year, mean age, and condition of neck lymph nodes at level Ib, also were extracted. A random-effects model was used to analyze the rate of SMG involvement in OSCC.Twelve studies involving 2,126 patients with OSCC who underwent neck dissection were included in the study. Fifty-two SMGs were involved, and the pooled involvement rate was 2% (I2 = 73%; 95% confidence interval [CI], 1-3). Forty-eight SMGs were involved through direct spread from the primary site or extracapsular spread of positive lymph nodes, and the pooled involvement rate was 1.9% (I2 = 72%; 95% CI, 0.9-3.1). Except for direct spread, 4 SMGs were involved through the intraglandular lymph node or carcinoma growing along Wharton ducts, and the pooled involvement rate was only 0.1% (I2 = 0%; 95% CI, 0-0.2).The rate of SMG involvement in OSCC is very low, and the most common mode of involvement is by direct spread. The SMG might be preserved during neck dissection in OSCC when it is unlikely to be involved through direct spread.

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

BackgroundMany scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of these tools to formulate new theoretical hypotheses. New methodWe propose a simple and versatile method to help characterize the information used by a classifier to perform its task. Specifically, noisy versions of training samples or, when the training set is unavailable, custom-generated noisy samples, are fed to the classifier. Multiplicative noise, so-called “bubbles”, or additive noise are applied to the input representation. Reverse correlation techniques are then adapted to extract either the discriminative information, defined as the parts of the input dataset that have the most weight in the classification decision, and represented information, which correspond to the input features most representative of each category. ResultsThe method is illustrated for the classification of written numbers by a convolutional deep neural network; for the classification of speech versus music by a support vector machine; and for the classification of sleep stages from neurophysiological recordings by a random forest classifier. In all cases, the features extracted are readily interpretable. Comparison with existing methodsQuantitative comparisons show that the present method can match state-of-the art interpretation methods for convolutional neural networks. Moreover, our method uses an intuitive and well-established framework in neuroscience, reverse correlation. It is also generic: it can be applied to any kind of classifier and any kind of input data. ConclusionsWe suggest that the method could provide an intuitive and versatile interface between neuroscientists and machine-learning tools.

The bilingual structural connectome: Dual-language experiential factors modulate distinct cerebral networks

Bilingualism is a natural laboratory for studying whether the brain’s structural connectome is influenced by different aspects of language experience. However, evidence on how distinct components of bilingual experience may contribute to structural brain adaptations is mixed. The lack of consistency, however, may depend, at least in part, on methodological choices in data acquisition and processing. Herein, we adopted the Network Neuroscience framework to investigate how individual differences in second language (L2) exposure, proficiency, and age of acquisition (AoA) – measured as continuous between-subject variables – relate to whole-brain structural organization. We observed that L2 exposure modulated the connectivity of two networks of regions subserving language comprehension and production. L2 proficiency was associated with enhanced connectivity within a rostro-caudal network, which supports language selection and word learning. Moreover, L2 AoA and exposure affected inter-hemispheric communication between control-related regions. These findings expand mechanistic knowledge about particular environmental factors associated with specific variation in brain structure.

Outlier blindness: A neurobiological foundation for neglect of financial risk

How do people record information about the outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are hampered in their perception of outcomes that they expect to seldom encounter. We provide experimental evidence for such “outlier blindness” and discuss how it provides a microfoundation for neglected tail risk by investors in financial markets.

Understanding Global Brain Network Alterations in Glioma Patients.

Introduction: Glioma patients show increased global brain network clustering related to poorer cognition and epilepsy. However, it is unclear whether this increase is spatially widespread, localized in the (peri)tumor region only, or decreases with distance from the tumor. Materials and Methods: Weighted global and local brain network clustering was determined in 71 glioma patients and 53 controls by using magnetoencephalography. Tumor clustering was determined by averaging local clustering of regions overlapping with the tumor, and vice versa for non-tumor regions. Euclidean distance was determined from the tumor centroid to the centroids of other regions. Results: Patients showed higher global clustering compared with controls. Clustering of tumor and non-tumor regions did not differ, and local clustering was not associated with distance from the tumor. Post hoc analyses revealed that in the patient group, tumors were located more often in regions with higher clustering in controls, but it seemed that tumors of patients with high global clustering were located more often in regions with lower clustering in controls. Conclusions: Glioma patients show non-local network disturbances. Tumors of patients with high global clustering may have a preferred localization, namely regions with lower clustering in controls, suggesting that tumor localization relates to the extent of network disruption. Impact statement This work uses the innovative framework of network neuroscience to investigate functional connectivity patterns associated with brain tumors. Glioma (primary brain tumor) patients experience cognitive deficits and epileptic seizures, which have been related to brain network alterations. This study shows that glioma patients have a spatially widespread increase in global network clustering, which cannot be attributed to local effects of the tumor. Moreover, tumors occur more often in brain regions with higher network clustering in controls. This study emphasizes the global character of network alterations in glioma patients and suggests that preferred tumor locations are characterized by particular network profiles.

Affective Neuroscience: The Suitability of a Web App to Monitor Affective States at Work.

This work describes in detail the use of a new tool, a web-app, based on the conceptual framework of affective neuroscience, in particular on Panksepp’s 7 basic emotional systems. Affective neuroscience has been used effectively in many areas, but there have been very few applications in the workplace, due to the lack of a smart implementation tool. The novelty of this work does not lie in the new information, but in a new “clinical” approach. There is a theoretical framework that allows data to be interpreted rather than simply described. Furthermore, the knowledge of working realities through the web app is specific and longitudinal. Finally, emotions are detected in hic et nunc, so the role of reflexive-cognitive mediation and recall bias are minor. This “more situated” knowledge can then guide specific leadership strategies. This paper presents the results of the tool’s application in a company in Northern Italy. The findings of our project, which recorded basic affective states and the functioning of several working teams, are detailed herein. The project’s 488 web-app records are summarized in this report, alongside our examination of related mood tags. Through this project, our analysis has enabled to determine affective neuroscience profiles of the teams analyzed, allowing the researchers to identify areas of possible interventions. The data appear very encouraging.

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment.

The ability to regulate emotions is key to goal attainment and well-being. Although much has been discovered about neurodevelopment and the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework to parse the neural underpinnings of emotion regulation skill acquisition, while accounting for age, in a sample of children and adolescents (N = 70, 34 female, aged 8-17 years). Focusing on three key network metrics-network differentiation, modularity, and community number differences between active regulation and a passive emotional baseline-we found that the control network, the default mode network, and limbic network were each related to emotion regulation ability while controlling for age. Greater network differentiation in the control and limbic networks was related to better emotion regulation ability. With regards to network community structure (modularity and community number), more communities and more crosstalk between modules (i.e., less modularity) in the control network were associated with better regulatory ability. By contrast, less crosstalk (i.e., greater modularity) between modules in the default mode network was associated with better regulatory ability. Together, these findings highlight whole-brain connectome features that support the acquisition of emotion regulation in youth.

The first prior: From co-embodiment to co-homeostasis in early life

The idea that our perceptions in the here and now are influenced by prior events and experiences has recently received substantial support and attention from the proponents of the Predictive Processing (PP) and Active Inference framework in philosophy and computational neuroscience. In this paper we look at how perceptual experiences get off the ground from the outset, in utero. One basic yet overlooked aspect of current PP approaches is that human organisms first develop within another human body. Crucially, while not all humans will have the experience of being pregnant or carrying a baby, the experience of being carried and growing within another person’s body is universal. Specifically, we focus on the development of minimal selfhood in utero as a process co-embodiment and co-homeostasis, and highlight their close relationship. We conclude with some implications on several critical questions fuelling current debates on the nature of conscious experiences, minimal self and social cognition.

A scientometric analysis and review of spatial cognition studies within the framework of neuroscience and architecture

Although there is a large potential overlap between neuroscience and architecture, the studies bridging these fields are rare and newly emerging. Moreover, a gap is observed between the constructs, terminologies and research methods of architecture and neuroscience. In order to alleviate these problems, this study investigates main research themes and developments in spatial cognition research over the past two decades on the basis of the author keywords of published articles. Vosviewer© bibliometric analysis software was utilized to conduct keyword co-occurrence analysis, keyword clustering, and timeline analysis of keywords and current research on spatial cognition is discussed accordingly. The paper also reviews existing research at the intersection of neuroscience and architecture and comments on how this nexus could evolve in the future. It is concluded that embodied spatial cognition and affordances of architectural design provide a robust framework to guide further studies in this track.

Automatic wavelet-based assessment of behavioral sleep using multichannel electrocorticography in rats.

During the last decade, the reportedprevalence of sleep-disordered breathing in adults has been rapidly increasing. Therefore, automatic methods of sleep assessment are of particular interest. In a framework of translational neuroscience, this study introduces a reliable automatic detection system of behavioral sleep in laboratory rats based on the signal recorded at the cortical surface without requiring electromyography. Experimental data were obtained in 16 adult male WAG/Rij rats at the age of 9 months. Electrocorticographic signals (ECoG) were recorded in freely moving rats during the entire day (22.5 ± 2.2 h). Automatic wavelet-based assessment of behavioral sleep (BS) was proposed. The performance of this wavelet-based method was validated in a group of rats with genetic predisposition to absence epilepsy (n=16) based on visual analysis of their behavior in simultaneously recorded video. The accuracy of automatic sleep detection was 98% over a 24-h period. An automatic BS assessment method can be adjusted for detecting short arousals during sleep (microarousals) with various duration. These findings suggest that automatic wavelet-based assessment of behavioral sleep can be used for assessment of sleep quality. Current analysis indicates atemporal relationship between microarousals, sleep, and epileptic discharges in genetically prone subjects.

The Treachery of Images: How Realism Influences Brain and Behavior.

Although the cognitive sciences aim to ultimately understand behavior and brain function in the real world, for historical and practical reasons, the field has relied heavily on artificial stimuli, typically pictures. We review a growing body of evidence that both behavior and brain function differ between image proxies and real, tangible objects. We also propose a new framework for immersive neuroscience to combine two approaches: (i) the traditional build-up approach of gradually combining simplified stimuli, tasks, and processes; and (ii) a newer tear-down approach that begins with reality and compelling simulations such as virtual reality to determine which elements critically affect behavior and brain processing.

Cerebellar-Subcortical-Cortical Systems as Modulators of Cognitive Functions.

Over the past few decades, research has established that the cerebellum is involved in executive functions; however, its specific role remains unclear. There are numerous theories of cerebellar function and numerous cognitive processes falling under the umbrella of executive function, making investigations of the cerebellum's role in executive functioning challenging. In this review, we explored the role of the cerebellum in executive functioning through clinical and cognitive neuroscience frameworks. We reviewed the neuroanatomical systems and theoretical models of cerebellar functions and the multifaceted nature of executive functions. Using attention deficit hyperactivity disorder and cerebellar tumor as clinical developmental models of cerebellar dysfunction, and the functional magnetic resonance imaging literature, we reviewed evidence for cerebellar involvement in specific components of executive function in childhood, adolescence, and adulthood. There is evidence for posterior cerebellar contributions to working memory, planning, inhibition, and flexibility, but the heterogeneous literature that largely was not designed to study the cerebellum makes it difficult to determine specific functions of the cerebellum or cerebellar regions. In addition, while it is clear that cerebellar insult in childhood affects executive function performance later in life, more work is needed to elucidate the mechanisms by which executive dysfunction occurs and its developmental course. The limitations of the current literature are discussed and potential directions for future research are provided.

The neuroscientific framework for understanding nostalgia experience: Comments on Hori and Takahashi’s article

The neuroscientific framework for understanding nostalgia experience: Comments on Hori and Takahashi’s article

An Inherent Bond: External Visual Aid Has a Minor Effect on the Rate of Co-Speech Gestures

Abstract. Traditionally, the purpose of representational co-speech gestures is to repeat or represent the semantic content of accompanying speech and so to facilitate speech comprehension. To test this belief, each of 22 participants was asked to deliver an informative speech once with the support of visual aid in the form of data-show (DS) projector slides and then to deliver the same speech without using any visual aid (NDS) in a different session; the purpose was to see if using visual aid had any significant effect on gesture rate during speech production. The theoretical framework of the study is based on findings in the Information Packaging Hypothesis, the Gesture as Simulated Action framework and relevant findings in cognitive psychology and neuroscience. The results showed that all participants used gestures during both sessions; the average number of co-speech gestures was 7.2 during the NDS and 6 during the DS sessions. This shows that using visual aid that supports the semantic content of speech did not lead to a significant reduction in the number of co-speech gestures in the DS sessions; it also indicates that the role of co-speech gestures is not merely to repeat the semantic content of accompanying speech. These results confirm previous findings in cognitive psychology that speech and accompanying gesture are cognitively and instinctively connected as one unit and that co-speech gestures possibly have an essential role in facilitating speech conceptualization and production. Speech and co-speech gestures are neurologically interconnected and they are impulsively produced whenever a speaker intends to communicate a message. These findings also add further evidence to relevant research which emphasizes that co-speech gestures are not produced merely as visual aid that aims to supplement speech.

The Role of Exercise in Management of Mental Health Disorders: An Integrative Review.

A large and growing body of evidence suggests that physical activity (PA) may hold therapeutic promise in the management of mental health disorders. Most evidence linking PA to mental health outcomes has focused on the effects of aerobic exercise training on depression, although a growing body of work supports the efficacy of both aerobic and resistance exercise paradigms in the treatment of anxiety and post-traumatic stress disorder. Despite abundant evidence linking PA and mental health, use of exercise training as a mental health treatment remains limited due to three important sources of uncertainty: (a) large individual differences in response to exercise treatment within multiple mental health domains; (b) the critical importance of sustained PA engagement, not always achieved, for therapeutic benefit; and (c) disagreement regarding the relative importance of putative therapeutic mechanisms. Our review of treatment data on exercise interventions and mental health outcomes focuses primarily on depression and anxiety within a health neuroscience framework. Within this conceptual framework, neurobiological and behavioral mechanisms may have additiveor synergistic influences on key cognitive and behavioral processes that influence mental health outcomes. We therefore highlight sources of treatment heterogeneity by integrating the critical influences of (a) neurobiological mechanisms enhancing neuroplasticity and (b) behavioral learning of self-regulatory skills. Understanding the interrelationships between dynamic neurobiological and behavioral mechanisms may help inform personalized mental health treatments and clarify why, and for whom, exercise improves mental health outcomes. The review concludes with recommendations for future studies leveraging individual differences to refine treatment approaches to optimize mental health benefits.

Outlier Blindness: A Neurobiological Foundation for Neglect of Financial Risk

How do people record information about the outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are hampered in their perception of outcomes that they expect to seldom encounter. We provide experimental evidence for such ‘outlier blindness’ and discuss how it provides a microfoundation for neglected tail risk by investors in financial markets.

Brain imaging technologies as source for Extrospection: self-formation through critical self-identification

Brain imaging technologies are increasingly used to find networks and brain regions that are specific to the functional realization of particular aspects of the self. In this paper, we aim to show how neuroscientific research and techniques could be used in the context of self-formation without treating them as representations of an inner realm. To do so, we show first how a Cartesian framework underlies the interpretation and usage of brain imaging technologies as functional evidence. To illustrate how material-technological inventions and developments can have a significant and lasting impact on views of the self, we show how this framework was influenced by another technology: the camera obscura. Subsequently, we show that brain imaging technologies challenge the idea that privileged access to the self can be obtained merely through introspection, indicating a strong discontinuity between the Cartesian and the current neuroscientific framework. Building on these insights, we reframe the self in terms of self-formation. This view neither regards the brain as an independent realizer of aspects of the self, nor assumes that self-knowledge can be obtained through introspection. From this perspective, self-formation is realized through critical self-identification: instead of offering representational knowledge of an ‘inner self,’ the potential use of brain imaging technologies within this framework lies in their capacity to offer what we call ‘extrospective knowledge’ that pragmatically can contribute to self-formation. Brain imaging technologies contribute to this process because they foreground our neurophysiology, which helps to critically integrate biological aspects into self-formation.

Understanding Conceptual Change and Science Learning through Educational Neuroscience

ABSTRACTAlthough the field of educational neuroscience has grown in recent years, little research has been conducted on conceptual change and science learning through an educational neuroscience framework. Educational neuroscience is frequently used to study processes of language and mathematics cognition, but is not extensively applied to conceptual change and science learning. This review integrates insights from extant conceptual change educational neuroscience studies to inform the fields of educational psychology and science education. These new insights shed light on the persistence of misconceptions and the roles of error detection, inhibition, executive function, and memory in conceptual change. Future directions for the study of conceptual change and educational neuroscience are discussed.