Authors:
Elvis Lira da Silva
1
;
Gabriela Castellano
1
;
João Ricardo Sato
2
;
Ellison Fernando Cardoso
3
and
Edson Amaro Jr.
3
Affiliations:
1
University of Campinas - UNICAMP; CInAPCe Program (Cooperação Interinstitucional de Apoio a Pesquisas sobre o Cérebro, Brazil
;
2
University of São Paulo; Universidade Federal do ABC; CInAPCe Program (Cooperação Interinstitucional de Apoio a Pesquisas sobre o Cérebro), Brazil
;
3
University of São Paulo; CInAPCe Program (Cooperação Interinstitucional de Apoio a Pesquisas sobre o Cérebro), Brazil
Keyword(s):
Dynamic Causal modelling, fMRI, Connectivity, Parkinson’s disease, Face perception.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Facial perception is a fundamental task in our daily life and plays a critical role in social interactions. Evidence from neuropsychological, neurophysiologic, and functional imaging studies indicated that face perception is mediated by a specialized system in the human brain. We investigated the neural connectivity induced by face presentation with different emotional valences in Parkinson's disease (PD) patients and a control group of healthy, drug-free volunteers, using event-related fMRI in a parametric design. In this study, we focused on applying Dynamic Causal Modelling (DCM), an approach that allows the assessment of effective connectivity within cortical networks (Friston et al. 2003), to the study of effective connectivity between maximally activated brain regions in response to passive viewing of facial stimuli. A connectivity model was built based on the literature and in our fMRI analyses, which included the fusiform gyrus, anterior cingulate gyrus, dorsolateral prefront
al cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC). The results showed differences in connectivity between the PD group and the control group. We found that the effective couplings among DLPFC/DMPFC and FG, DLPFC/DMPFC and ACG, were higher in PD patients than healthy subjects, while the effective coupling among FG and ACG was lower in PD patients.
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