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A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders

Authors :
Lorenzo Mancuso
Alex Fornito
Tommaso Costa
Linda Ficco
Donato Liloia
Jordi Manuello
Sergio Duca
Franco Cauda
Source :
NeuroImage, Vol 222, Iss , Pp 117220- (2020)
Publication Year :
2020
Publisher :
Elsevier, 2020.

Abstract

Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.

Details

Language :
English
ISSN :
10959572
Volume :
222
Issue :
117220-
Database :
Directory of Open Access Journals
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
edsdoj.5c0e8d32a6e64404bd78c678e4dbefef
Document Type :
article
Full Text :
https://doi.org/10.1016/j.neuroimage.2020.117220