A distributed fMRI-based signature for the subjective experience of fear

AbstractThe specific neural systems underlying the subjective feeling of fear are debated in affective neuroscience. Here, we combine functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n = 67), validation (n = 20) and generalization (n = 31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated ‘fear centers’.

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Beyond fear centers – a distributed fMRI-based neuromarker for the subjective experience of fear

10.1101/2020.11.23.394973 ◽

2020 ◽

Author(s):

Feng Zhou ◽

Weihua Zhao ◽

Ziyu Qi ◽

Yayuan Geng ◽

Shuxia Yao ◽

...

Keyword(s):

Machine Learning ◽

Negative Affect ◽

Basal Forebrain ◽

Subjective Experience ◽

Neural Representation ◽

Neural Systems ◽

Subjective Feeling ◽

Affective Neuroscience ◽

Novel Treatments ◽

Neural Signature

AbstractThe specific neural systems underlying the subjective feeling of fear remain vigorously debated in affective neuroscience. Here, we combined functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n=67), validation (n=20) and generalization (n=31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated ‘fear centers’. This signature provides a neuromarker for monitoring fear-related neuropathology and evaluating novel treatments targeting pathological fear.

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Intranasal vasopressin modulates resting state brain activity across multiple neural systems: evidence from a brain imaging machine learning study

Neuropharmacology ◽

10.1016/j.neuropharm.2021.108561 ◽

2021 ◽

pp. 108561

Author(s):

Xinling Chen ◽

Yongbo Xu ◽

Bingjie Li ◽

Xiaoyan Wu ◽

Ting Li ◽

...

Keyword(s):

Machine Learning ◽

Brain Imaging ◽

Resting State ◽

Brain Activity ◽

Neural Systems ◽

Learning Study

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Affective Neuroscience: A Crucial Role in Psychiatry

European Psychiatry ◽

10.1016/s0924-9338(09)71130-7 ◽

2009 ◽

Vol 24 (S1) ◽

pp. 1-1

Author(s):

L.C. Castro

Keyword(s):

Biological Sciences ◽

Stress Response ◽

Emotional Disorders ◽

Neural Control ◽

Subjective Experience ◽

Psychiatric Morbidity ◽

Human Behaviour ◽

Psychiatric Research ◽

Affective Neuroscience ◽

Brain Functions

Background:Neuroscience has been a growing revolutionary field of scientific knowledge. The increasing recognition of the importance of emotional processes and subjective experience in several aspects of human behaviour parallel the growing amount of research in the field of affective neuroscience. Affective neuroscience studies the brain mechanisms subjacent to emotional behaviour.Aim:To discuss the relevance of affective neuroscience research in social and biological sciences, namely within psychiatric and psychological researches.Methods:Review of the literature. MEDLINE and PubMed databases searches for peer-reviewed studies, published between 1994 and 2008, using combinations of the Medline Subject Heading terms affective neuroscience, emotions, affective sciences and psychiatry, psychology, biological sciences, social sciences.Results:Several studies addresses brain functions and how emotions relate to genetics, learning, primary motivations, stress response and human behaviour. Some actual areas of research within affective neuroscience include: emotional learning, affective behaviour, emotional empathy, psychosomatic medicine, functional and structural biomarkers, emotional disorders and stress response, among others.Discussion:In Psychiatry, affective neurosciences find application in understanding the neurobiology of mood disorders, the neural control of interpersonal and social behaviour and the emotional systems that underlie psychopathology. Affective neuroscience reflects the integration of knowledge across disciplines allowing a broader understanding of human functioning. The field of affective neuroscience is an exciting field of future psychiatric research and it provides an investigational framework for studying psychiatric morbidity.

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Mice are not automatons; subjective experience in premotor circuits guides behavior

10.1101/2021.06.23.449617 ◽

2021 ◽

Author(s):

Drew C. Schreiner ◽

Christian Cazares ◽

Rafael Renteria ◽

Christina M Gremel

Keyword(s):

Decision Making ◽

Motor Cortex ◽

Adaptive Behavior ◽

Subjective Experience ◽

Contextual Factors ◽

Neural Mechanisms ◽

Neural Representation ◽

Recent Experience ◽

Checking Behavior ◽

Corticostriatal Circuits

Subjective experience is a powerful driver of decision-making and continuously accrues. However, most neurobiological studies constrain analyses to task-related variables and ignore how continuously and individually experienced internal, temporal, and contextual factors influence adaptive behavior during decision-making and the associated neural mechanisms. We show mice rely on learned information about recent and longer-term subjective experience of variables above and beyond prior actions and reward, including checking behavior and the passage of time, to guide self-initiated, self-paced, and self-generated actions. These experiential variables were represented in secondary motor cortex (M2) activity and its projections into dorsal medial striatum (DMS). M2 integrated this information to bias strategy-level decision-making, and DMS projections used specific aspects of this recent experience to plan upcoming actions. This suggests diverse aspects of experience drive decision-making and its neural representation, and shows premotor corticostriatal circuits are crucial for using selective aspects of experiential information to guide adaptive behavior.

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The Neural Codes Underlying Internally Generated Representations in Visual Working Memory

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01702 ◽

2021 ◽

pp. 1-16

Author(s):

Qing Yu ◽

Bradley R. Postle

Keyword(s):

Working Memory ◽

Visual Cortex ◽

Visual Working Memory ◽

Subjective Experience ◽

Neural Representation ◽

Delayed Recall ◽

Early Visual Cortex ◽

Memory Representations ◽

Internal Sources ◽

Scaling Analyses

Abstract Humans can construct rich subjective experience even when no information is available in the external world. Here, we investigated the neural representation of purely internally generated stimulus-like information during visual working memory. Participants performed delayed recall of oriented gratings embedded in noise with varying contrast during fMRI scanning. Their trialwise behavioral responses provided an estimate of their mental representation of the to-be-reported orientation. We used multivariate inverted encoding models to reconstruct the neural representations of orientation in reference to the response. We found that response orientation could be successfully reconstructed from activity in early visual cortex, even on 0% contrast trials when no orientation information was actually presented, suggesting the existence of a purely internally generated neural code in early visual cortex. In addition, cross-generalization and multidimensional scaling analyses demonstrated that information derived from internal sources was represented differently from typical working memory representations, which receive influences from both external and internal sources. Similar results were also observed in intraparietal sulcus, with slightly different cross-generalization patterns. These results suggest a potential mechanism for how externally driven and internally generated information is maintained in working memory.

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Neural representation of abstract task structure during generalization

10.1101/2020.07.21.213009 ◽

2020 ◽

Author(s):

Avinash R. Vaidya ◽

Henry M. Jones ◽

Johanny Castillo ◽

David Badre

Keyword(s):

Prior Knowledge ◽

Functional Mri ◽

Neural Representation ◽

Human Cognition ◽

Neural Systems ◽

Task Structure ◽

Task Knowledge ◽

Additional Training ◽

Task Representations

AbstractAbstract task representations enable generalization, including inferring new behaviors based on prior knowledge without additional training. However, evidence for a neural representation that meets this benchmark is surprisingly limited. Here, using functional MRI (fMRI), we observed that abstract task structure was represented within frontoparietal networks during generalization. These results reveal the neural systems supporting a vital feature of human cognition: the abstraction of task knowledge to infer novel behaviors.

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Correction: Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity

PLoS ONE ◽

10.1371/journal.pone.0213756 ◽

2019 ◽

Vol 14 (3) ◽

pp. e0213756 ◽

Cited By ~ 1

Author(s):

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Negative Affect ◽

Positive And Negative Affect ◽

Affect Intensity

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Paying attention to reading: The neurobiology of reading and dyslexia

Development and Psychopathology ◽

10.1017/s0954579408000631 ◽

2008 ◽

Vol 20 (4) ◽

pp. 1329-1349 ◽

Cited By ~ 251

Author(s):

Sally E. Shaywitz ◽

Bennett A. Shaywitz

Keyword(s):

Great Part ◽

Functional Brain Imaging ◽

Neural Systems ◽

Adjunct Treatment ◽

Hyperactivity Disorder ◽

Effective Interventions ◽

Neural Signature ◽

First Time ◽

Attentional Mechanisms

AbstractExtraordinary progress in functional brain imaging, primarily advances in functional magnetic resonance imaging, now allows scientists to understand the neural systems serving reading and how these systems differ in dyslexic readers. Scientists now speak of the neural signature of dyslexia, a singular achievement that for the first time has made what was previously a hidden disability, now visible. Paralleling this achievement in understanding the neurobiology of dyslexia, progress in the identification and treatment of dyslexia now offers the hope of identifying children at risk for dyslexia at a very young age and providing evidence-based, effective interventions. Despite these advances, for many dyslexic readers, becoming a skilled, automatic reader remains elusive, in great part because though children with dyslexia can be taught to decode words, teaching children to read fluently and automatically represents the next frontier in research on dyslexia. We suggest that to break through this “fluency” barrier, investigators will need to reexamine the more than 20-year-old central dogma in reading research: the generation of the phonological code from print is modular, that is, automatic and not attention demanding, and not requiring any other cognitive process. Recent findings now present a competing view: other cognitive processes are involved in reading, particularly attentional mechanisms, and that disruption of these attentional mechanisms play a causal role in reading difficulties. Recognition of the role of attentional mechanisms in reading now offer potentially new strategies for interventions in dyslexia. In particular, the use of pharmacotherapeutic agents affecting attentional mechanisms not only may provide a window into the neurochemical mechanisms underlying dyslexia but also may offer a potential adjunct treatment for teaching dyslexic readers to read fluently and automatically. Preliminary studies suggest that agents traditionally used to treat disorders of attention, particularly attention-deficit/hyperactivity disorder, may prove to be an effective adjunct to improving reading in dyslexic students.

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Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior

Brain Sciences ◽

10.3390/brainsci9030067 ◽

2019 ◽

Vol 9 (3) ◽

pp. 67 ◽

Cited By ~ 2

Author(s):

Monique Ernst ◽

Joshua Gowin ◽

Claudie Gaillard ◽

Ryan Philips ◽

Christian Grillon

Keyword(s):

Machine Learning ◽

Large Data ◽

Neural Systems ◽

Complex Processes ◽

Systems Model ◽

Motivated Behavior ◽

Behavioral Transitions ◽

The Neural Networks ◽

Ultimate Objective ◽

Brain Behavior

Uncovering brain-behavior mechanisms is the ultimate goal of neuroscience. A formidable amount of discoveries has been made in the past 50 years, but the very essence of brain-behavior mechanisms still escapes us. The recent exploitation of machine learning (ML) tools in neuroscience opens new avenues for illuminating these mechanisms. A key advantage of ML is to enable the treatment of large data, combing highly complex processes. This essay provides a glimpse of how ML tools could test a heuristic neural systems model of motivated behavior, the triadic neural systems model, which was designed to understand behavioral transitions in adolescence. This essay previews analytic strategies, using fictitious examples, to demonstrate the potential power of ML to decrypt the neural networks of motivated behavior, generically and across development. Of note, our intent is not to provide a tutorial for these analyses nor a pipeline. The ultimate objective is to relate, as simply as possible, how complex neuroscience constructs can benefit from ML methods for validation and further discovery. By extension, the present work provides a guide that can serve to query the mechanisms underlying the contributions of prefrontal circuits to emotion regulation. The target audience concerns mainly clinical neuroscientists. As a caveat, this broad approach leaves gaps, for which references to comprehensive publications are provided.

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