Reference : Depression alters "top-down" visual attention: a dynamic causal modeling comparison b...
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Human health sciences : Psychiatry
http://hdl.handle.net/2268/95121
Depression alters "top-down" visual attention: a dynamic causal modeling comparison between depressed and healthy subjects.
English
Desseilles, Martin mailto [Université de Liège - ULg > Département des sciences cliniques > Psychiatrie et psychologie médicale]
Schwartz, Sophie [University of Geneva > Department of Psychiatry > > >]
Dang Vu, Thien Thanh [Université de Liège - ULg > Département des sciences cliniques > Neurologie]
Sterpenich, Virginie [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Ansseau, Marc mailto [Université de Liège - ULg > Département des sciences cliniques > Psychiatrie et psychologie médicale]
Maquet, Pierre mailto [Université de Liège - ULg > > Centre de recherches du cyclotron]
Phillips, Christophe mailto [Université de Liège - ULg > > Centre de recherches du cyclotron]
2011
NeuroImage
Elsevier Science
54
2
1662-8
Yes (verified by ORBi)
International
1053-8119
1095-9572
Orlando
FL
[en] Attention/physiology ; Brain/physiopathology ; Depressive Disorder, Major/physiopathology ; Humans ; Magnetic Resonance Imaging ; Models, Neurological ; Photic Stimulation ; Visual Perception/physiology
[en] Using functional magnetic resonance imaging (fMRI), we recently demonstrated that nonmedicated patients with a first episode of unipolar major depression (MDD) compared to matched controls exhibited an abnormal neural filtering of irrelevant visual information (Desseilles et al., 2009). During scanning, subjects performed a visual attention task imposing two different levels of attentional load at fixation (low or high), while task-irrelevant colored stimuli were presented in the periphery. In the present study, we focused on the visuo-attentional system and used "Dynamic Causal Modeling" (DCM) on the same dataset to assess how attention influences a network of three dynamically-interconnected brain regions (visual areas V1 and V4, and intraparietal sulcus (P), differentially in MDD patients and healthy controls. Bayesian model selection (BMS) and model space partitioning (MSP) were used to determine the best model in each population. The best model for the controls revealed that the increase of parietal activity by high attention load was selectively associated with a negative modulation of P on V4, consistent with high attention reducing the processing of irrelevant colored peripheral stimuli. The best model accounting for the data from the MDD patients showed that both low and high attention levels exerted modulatory effects on P. The present results document abnormal effective connectivity across visuo-attentional networks in MDD, which likely contributes to deficient attentional filtering of information.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/95121
also: http://hdl.handle.net/2268/111275
10.1016/j.neuroimage.2010.08.061
Copyright (c) 2010 Elsevier Inc. All rights reserved.

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