Altered network architecture of cortical thickness in schizophrenia
Poster No:
480
Submission Type:
Abstract Submission
Authors:
Bin Wan1, Varun Warrier2, Richard Bethlehem3, Sara Larivière4, Clara Moreau5, Yuankai He6, Stefan Kaiser1, Paul Thompson7, Theo van Erp8, Jessica Turner9, Boris Bernhardt10, Sofie Valk11, Matthias Kirschner12
Institutions:
1University Hospitals of Genève, Genève, Switzerland, 2Department of Psychiatry, University of Cambridge, Cambridge, Cambridge, 3Department of Psychology, University of Cambridge, Cambridge, Cambridge, 4Université de Sherbrooke, Sherbrooke, QC, 5University of Montréal, Montréal, CA, 6University of Cambridge, Cambridge, Cambridge, 7University of Southern California, Los Angeles, CA, 8University of California, Irvine,, Irvine, CA, 9Wexner Medical Center, The Ohio State University, Columbus, OH, 10McGill University, Montreal, Quebec, 11Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, 12HUG-Hôpitaux Universitaires Genève, Genève, Switzerland
First Author:
Co-Author(s):
Introduction:
Schizophrenia (SCZ) is a severe psychiatric condition characterized by widespread cortical alterations. A hierarchically organized and small-world network architecture guides the distribution and progression of these regional alterations (1–4). Yet, how SCZ and related genetic risk factors disrupt cortical network architecture remains unclear. Here, we examine how brain network architecture is associated with both SCZ clinical phenotype and polygenic score (PGS).
Methods:
We included 36 data sites from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) SCZ Working Group (nSCZ = 3,686 and ncontrols = 5,285) and 59,588 neurotypical individuals from the UK Biobank. Cortical thickness data of 68 cortical regions were used to calculate intra-individual structural covariance networks. We employed nonlinear dimension reduction (5, 6) and graph analysis (7, 8) to compute gradients of cortical organization and small-world index including the first two principal components (G1 and G2), regional shortest path length, and regional clustering coefficient. After obtaining t-maps in the two samples, we combined the interaction between age and diagnosis map to determine the genetic-sensitive and aging-sensitive regions.
Results:
In the ENIGMA controls, structural covariation analyses identified two main gradients: superior-to-inferior (G1) and anterior-to-posterior (G2), explaining 35.0% and 27.2% of eigenvariance, respectively (Figure 1). The SCZ group exhibited widespread changes in both gradients (PFDR < 0.05), with the most significant alterations observed in the right superior temporal and left medial orbitofrontal cortices in G1 and the right inferior parietal and left middle temporal cortices in G2. Graph analysis revealed altered clustering coefficient (C) and shortest path length (L) mainly in the parietal, somatosensory, temporal, and anterior cingulate cortices. In the UK Biobank sample, we characterized the regions that were associated with higher SCZ PGS (Figure 2a). We then identified those regions that show significant case-control differences (Figure 2b) and are either sensitive to significant aging effects (ENIGMA aging) or genetic associations (UKB genetics). We observed three (G1), six (G2), three (L), and three (C) genetic-sensitive regions, and three (G1) and three (G2) aging-sensitive regions (Figure 2c).
Conclusions:
Our findings show robust alterations in structural gradient organization and network topology associated with established SCZ as well as SCZ-related PGS in otherwise neurotypical individuals. This supports the extension of altered structural network architecture from genetic vulnerability to disease manifestation in SCZ.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetics Other 2
Keywords:
Cortex
Psychiatric
Schizophrenia
1|2Indicates the priority used for review
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