64 research outputs found
Brain Circuitries Involved in Semantic Interference by Demands of Emotional and Non-Emotional Distractors
BACKGROUND: Previous studies have indicated that the processes leading to the resolution of emotional and non-emotional interference conflicts are unrelated, involving separate networks. It is also known that conflict resolution itself suggests a considerable overlap of the networks. Our study is an attempt to examine how these findings may be related. METHODOLOGY/PRINCIPAL FINDINGS: We used functional magnetic resonance imaging (fMRI) to study neural responses of 24 healthy subjects to emotional and non-emotional conflict paradigms involving the presentation of congruent and incongruent word-face pairs based on semantic incompatibility between targets and distractors. In the emotional task, the behavioral interference conflict was greater (compared to the non-emotional task) and was paralleled by involvement of the extrastriate visual and posterodorsal medial frontal cortices. In both tasks, we also observed a common network including the dorsal anterior cingulate, the supplemental motor area, the anterior insula and the inferior prefrontal cortex, indicating that these brain structures are markers of experienced conflict. However, the emotional task involved conflict-triggered networks to a considerably higher degree. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that responses to emotional and non-emotional distractors involve the same systems, which are capable of flexible adjustments based on conflict demands. The function of systems related to conflict resolution is likely to be adjusted on the basis of an evaluation process that primarily involves the extrastriate visual cortex, with target playing a significant role
Modelling human choices: MADeM and decision‑making
Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)
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Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation
The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI’s ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH); Computational Psychiatry Program at NIMH; Theoretical and Computational Neuroscience Program at NIMH; 'Machine Intelligence in Healthcare: Perspectives on Trustworthiness, Explainability, Usability and Transparency' workshop at NIH/NCATS; SUBNETS program at DARPA; GARD programs at DARPAOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
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