Neural substrate

Résumé

A neural substrate is a term used in neuroscience to indicate the part of the central nervous system (i.e., brain and spinal cord) that underlies a specific behavior, cognitive process, or psychological state. Neural is an adjective relating to "a nerve or the nervous system", while a substrate is an "underlying substance or layer". Some examples are the neural substrates of language acquisition, memory, prediction and reward, pleasure, facial recognition, envisioning the future, intentional empathy, religious experience, spontaneous musical performance, and anxiety. See also

Neural correlate

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https://en.wikipedia.org/wiki/Neural_substrate

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Students understand basic concepts and methods of machine learning. They can describe them in mathematical terms and can apply them to data using a high-level programming language (julia/python/R).

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Rat model: neural substrates of the battered woman syndrome

Xavier Fontana

This study is part of a long term study on a male rat model of peripubertal stress in the Laboratory of Behavioural Genetics that shows increase aggressive behaviours against their partners. We are using here these rats in order to create a female rat model victim of Intimate Partner Violence during pregnancy and to study the generalisation of fear to all male, that is a symptom of this type of aggression, through behavioural analysis, serotonergic cell activation of the Dorsal Raphe Nucleus (DRN) and activation of other regions known to interact with the DRN such as Periaqueductal Gray (PAG) and Amygdala. This study also reveal an in-utero transmission of aggressive behaviours to the offspring of peripubertally stressed male. Our findings suggest that our model show a generalisation of fear that depends on the type of aggression experienced. This generalisation is mediated by an increase of activation of serotonergic cells of the DRN as well as a general disruption of pattern of activation at the basal level. Interestingly, our results do not show a clear direct link between activation of DRN, PAG and Amygdala during generalisation of fear

2010

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Perceiving others in pain generally leads to empathic concern, consisting of both emotional and cognitive processes. Empathy deficits have been considered as an element contributing to social difficulties in individuals with autism spectrum disorders (ASD). Here, we used functional magnetic resonance imaging and short video clips of facial expressions of people experiencing pain to examine the neural substrates underlying the spontaneous empathic response to pain in autism. Thirty-eight adolescents and adults of normal intelligence diagnosed with ASD and 35 matched controls participated in the study. In contrast to general assumptions, we found no significant differences in brain activation between ASD individuals and controls during the perception of pain experienced by others. Both groups showed similar levels of activation in areas associated with pain sharing, evidencing the presence of emotional empathy and emotional contagion in participants with autism as well as in controls. Differences between groups could be observed at a more liberal statistical threshold, and revealed increased activations in areas involved in cognitive reappraisal in ASD participants compared with controls. Scores of emotional empathy were positively correlated with brain activation in areas involved in embodiment of pain in ASD group only. Our findings show that simulation mechanisms involved in emotional empathy are preserved in high-functioning individuals with autism, and suggest that increased reappraisal may have a role in their apparent lack of caring behavior.

Nature Publishing Group2014

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Fast Recognition of Anticipation Related Potentials

Ricardo Andres Chavarriaga Lozano, Gangadhar Garipelli

Anticipation increases the efficiency of daily tasks by partial advance activation of neural substrates involved in it. Here we develop a method for the recognition of electroencephalogram (EEG) correlates of this activation as early as possible on single trials which is essential for Brain-Computer Interaction (BCI). We explore various features from the EEG recorded in a Contingent Negative Variation (CNV) paradigm. We also develop a novel technique called Time Aggregation of Classification (TAC) for fast and reliable decisions that combines the posterior probabilities of several classifiers trained with features computed from temporal blocks of EEG until a certainty threshold is reached. Experiments with 9 naive subjects performing the CNV experiment with GO and NOGO conditions with an inter-stimulus interval of 4 s show that the performance of the TAC method is above 70% for four subjects, around 60% for two other subjects, and random for the remaining subjects. On average over all subjects, more than 50% of the correct decisions are made at 2 s, without needing to wait until 4 s.

Institute of Electrical and Electronics Engineers2009

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