scholarly journals Fear-induced brain activations distinguish anxious and trauma-exposed brains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhenfu Wen ◽  
Marie-France Marin ◽  
Jennifer Urbano Blackford ◽  
Zhe Sage Chen ◽  
Mohammed R. Milad
Keyword(s):  
Neural Network ◽  
Fear Conditioning ◽  
Psychiatric Diagnosis ◽  
Data Analytics ◽  
Conditioned Fear ◽  
Brain Regions ◽  
Brain Network ◽  
Neuroimaging Data ◽  
Task Irrelevant ◽  
Translational Models

AbstractTranslational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

scholarly journals The dynamic connectome of speech control

2021 ◽  
Vol 376 (1836) ◽  
Author(s):  
Davide Valeriani ◽  
Kristina Simonyan
Keyword(s):  
Neural Network ◽  
Effective Connectivity ◽  
Vocal Learning ◽  
Brain Regions ◽  
Brain Network ◽  
Motor Output ◽  
Network Activity ◽  
Imaging Data ◽  
Whole Brain ◽  
Cost Efficient

Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during the production of meaningful English sentences and meaningless syllables. We identified that the two dynamic connectomes have distinct architectures that are dependent on the complexity of task production. The speech was regulated by a dynamic neural network, the most influential nodes of which were centred around superior and inferior parietal areas and influenced the whole-brain network activity via long-ranging coupling with primary sensorimotor, prefrontal, temporal and insular regions. By contrast, syllable production was controlled by a more compressed, cost-efficient network structure, involving sensorimotor cortico-subcortical integration via superior parietal and cerebellar network hubs. These data demonstrate the mechanisms by which the neural network reorganizes the connectivity of its influential regions, from supporting the fundamental aspects of simple syllabic vocal motor output to multimodal information processing of speech motor output. This article is part of the theme issue ‘Vocal learning in animals and humans’.

scholarly journals SUN-235 Deficient Fear Extinction in PRKAR1A-Defective Mice

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Margaret Keil ◽  
Enrica Paradiso ◽  
Rita S Keil ◽  
Maddalena Ugolini ◽  
Evan Harris ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fear Conditioning ◽  
Conditioned Fear ◽  
Critical Role ◽  
Fear Memory ◽  
Brain Regions ◽  
Fear Extinction ◽  
Carney Complex ◽  
Bdnf Gene ◽  
Extinction Learning

Abstract Background: The role of the cAMP/PKA signaling in molecular pathways involved in fear memory is well established: PKA is required for fear memory formation and is a constraint for fear extinction. Previously we reported that a Prkar1a heterozygote (HZ) mouse that was developed in our lab to investigate Carney complex (CNC), the disease caused by PRKAR1A mutations, showed brain region-specific increased PKA activity that was associated with anxiety-like behavioral phenotype and threat bias (Keil, 2010, 2013). We hypothesized that Prkar1a+/- (HZ) mice would have deficits in fear extinction behavior. Brain derived neurotrophic factor (BDNF) has a critical role in formation of fear memory and its transcription is regulated by PKA/CREB. A mouse model with down regulation of PKA provides an opportunity for the first time to investigate the effect of altered PKA signaling on fear conditioning and extinction. Method: Fear conditioning, fear extinction learning, and fear extinction recall were tested in adult male HZ and wild-type (WT) mice as follows: fear conditioning training followed 24hr later by extinction training (new context), then 24hr later by extinction recall training. Percentage of time freezing was used to assess conditioned fear response. We measured BDNF gene expression in brain regions after completion of extinction recall training. Results: As expected, fear conditioning (learning) behavior was similar in HZ and WT mice. However, HZ mice showed a significant deficit in the early phase of fear extinction learning compared to WT. There was no difference in extinction recall between genotypes. Alterations in BDNF gene expression in the prefrontal cortex and amygdala was associated with deficit in fear extinction. Conclusion: Mice with a downregulation of Prkar1a gene demonstrate intact fear conditioning but impaired fear extinction learning, consistent with prior studies that report that PKA inhibition is necessary to facilitate extinction learning. Prkar1a+/- mice provide a valuable model to investigate impaired fear extinction to identify mechanisms for therapeutic targets for anxiety and trauma-related disorders.

scholarly journals GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Bi ◽  
Zhixun Liu ◽  
Yao He ◽  
Xiangguo Zhao ◽  
Yongjiao Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Euclidean Space ◽  
Brain Regions ◽  
Brain Network ◽  
Graph Representation ◽  
Learning Performance ◽  
Graph Classification ◽  
Learning Methods ◽  
Brain Images ◽  
Graph Learning

Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer’s disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space. However, it is still challenging to learn complex network structures and the connectivity of brain regions in non-Euclidean space. To address this problem, in this paper, we exploit the study of brain network classification from the perspective of graph learning. We propose an aggregator based on extreme learning machine (ELM) that boosts the aggregation ability and efficiency of graph convolution without iterative tuning. Then, we design a graph neural network named GNEA (Graph Neural Network with ELM Aggregator) for the graph classification task. Extensive experiments are conducted using a real-world AD detection dataset to evaluate and compare the graph learning performances of GNEA and state-of-the-art graph learning methods. The results indicate that GNEA achieves excellent learning performance with the best graph representation ability in brain network classification applications.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

scholarly journals Mechanisms Underlying the Cognitive and Behavioural Effects of Maternal Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 240
Author(s):  
Kyoko Hasebe ◽  
Michael D. Kendig ◽  
Margaret J. Morris
Keyword(s):  
Animal Models ◽  
Adverse Effects ◽  
Gut Microbiome ◽  
Maternal Obesity ◽  
Maternal Diet ◽  
Affective State ◽  
Brain Regions ◽  
Brain Network ◽  
Physical And Mental Health ◽  
Obesogenic Diet

The widespread consumption of ‘western’-style diets along with sedentary lifestyles has led to a global epidemic of obesity. Epidemiological, clinical and preclinical evidence suggests that maternal obesity, overnutrition and unhealthy dietary patterns programs have lasting adverse effects on the physical and mental health of offspring. We review currently available preclinical and clinical evidence and summarise possible underlying neurobiological mechanisms by which maternal overnutrition may perturb offspring cognitive function, affective state and psychosocial behaviour, with a focus on (1) neuroinflammation; (2) disrupted neuronal circuities and connectivity; and (3) dysregulated brain hormones. We briefly summarise research implicating the gut microbiota in maternal obesity-induced changes to offspring behaviour. In animal models, maternal obesogenic diet consumption disrupts CNS homeostasis in offspring, which is critical for healthy neurodevelopment, by altering hypothalamic and hippocampal development and recruitment of glial cells, which subsequently dysregulates dopaminergic and serotonergic systems. The adverse effects of maternal obesogenic diets are also conferred through changes to hormones including leptin, insulin and oxytocin which interact with these brain regions and neuronal circuits. Furthermore, accumulating evidence suggests that the gut microbiome may directly and indirectly contribute to these maternal diet effects in both human and animal studies. As the specific pathways shaping abnormal behaviour in offspring in the context of maternal obesogenic diet exposure remain unknown, further investigations are needed to address this knowledge gap. Use of animal models permits investigation of changes in neuroinflammation, neurotransmitter activity and hormones across global brain network and sex differences, which could be directly and indirectly modulated by the gut microbiome.

scholarly journals Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiao Li ◽  
Jakob Seidlitz ◽  
John Suckling ◽  
Feiyang Fan ◽  
Gong-Jun Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Structural Changes ◽  
Brain Regions ◽  
Cell Type ◽  
Major Depressive ◽  
Structural Differences ◽  
Neuroimaging Data ◽  
Cell Type Specific

AbstractMajor depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

scholarly journals Fear conditioning and stimulus generalization in association with age in children and adolescents

2021 ◽  
Author(s):  
Julia Reinhard ◽  
Anna Slyschak ◽  
Miriam A. Schiele ◽  
Marta Andreatta ◽  
Katharina Kneer ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Fear Conditioning ◽  
Repeated Measures ◽  
Conditioned Fear ◽  
Fear Learning ◽  
Stimulus Generalization ◽  
Healthy Children ◽  
Older Individuals ◽  
Age Related ◽  
Current Task

AbstractThe aim of the study was to investigate age-related differences in fear learning and generalization in healthy children and adolescents (n = 133), aged 8–17 years, using an aversive discriminative fear conditioning and generalization paradigm adapted from Lau et al. (2008). In the current task, participants underwent 24 trials of discriminative conditioning of two female faces with neutral facial expressions, with (CS+) or without (CS−) a 95-dB loud female scream, presented simultaneously with a fearful facial expression (US). The discriminative conditioning was followed by 72 generalization trials (12 CS+, 12 GS1, 12 GS2, 12 GS3, 12 GS4, and 12 CS−): four generalization stimuli depicting gradual morphs from CS+ to CS− in 20%-steps were created for the generalization phases. We hypothesized that generalization in children and adolescents is negatively correlated with age. The subjective ratings of valence, arousal, and US expectancy (the probability of an aversive noise following each stimulus), as well as skin conductance responses (SCRs) were measured. Repeated-measures ANOVAs on ratings and SCR amplitudes were calculated with the within-subject factors stimulus type (CS+, CS−, GS1-4) and phase (Pre-Acquisition, Acquisition 1, Acquisition 2, Generalization 1, Generalization 2). To analyze the modulatory role of age, we additionally calculated ANCOVAs considering age as covariate. Results indicated that (1) subjective and physiological responses were generally lower with increasing age irrespective to the stimulus quality, and (2) stimulus discrimination improved with increasing age paralleled by reduced overgeneralization in older individuals. Longitudinal follow-up studies are required to analyze fear generalization with regard to brain maturational aspects and clarify whether overgeneralization of conditioned fear promotes the development of anxiety disorders or vice versa.

scholarly journals Npas4a expression in the teleost forebrain is associated with stress coping style differences in fear learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthew R. Baker ◽  
Ryan Y. Wong
Keyword(s):  
Neural Plasticity ◽  
Coping Style ◽  
Molecular Mechanisms ◽  
Conditioned Fear ◽  
Coping Styles ◽  
Brain Regions ◽  
Contextual Fear Conditioning ◽  
Fear Learning ◽  
Stress Coping ◽  
Contextual Fear

AbstractLearning to anticipate potentially dangerous contexts is an adaptive behavioral response to coping with stressors. An animal’s stress coping style (e.g. proactive–reactive axis) is known to influence how it encodes salient events. However, the neural and molecular mechanisms underlying these stress coping style differences in learning are unknown. Further, while a number of neuroplasticity-related genes have been associated with alternative stress coping styles, it is unclear if these genes may bias the development of conditioned behavioral responses to stressful stimuli, and if so, which brain regions are involved. Here, we trained adult zebrafish to associate a naturally aversive olfactory cue with a given context. Next, we investigated if expression of two neural plasticity and neurotransmission-related genes (npas4a and gabbr1a) were associated with the contextual fear conditioning differences between proactive and reactive stress coping styles. Reactive zebrafish developed a stronger conditioned fear response and showed significantly higher npas4a expression in the medial and lateral zones of the dorsal telencephalon (Dm, Dl), and the supracommissural nucleus of the ventral telencephalon (Vs). Our findings suggest that the expression of activity-dependent genes like npas4a may be differentially expressed across several interconnected forebrain regions in response to fearful stimuli and promote biases in fear learning among different stress coping styles.

Core-brain-network based muti-layer convolutional neural network for emotion recognition

2021 ◽  
pp. 1-1
Author(s):  
Zhongke Gao ◽  
Rumei Li ◽  
Chao Ma ◽  
Linge Rui ◽  
Xinlin Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Brain Network
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