Heterogeneity of gray matter volume in 1,792 individuals with schizophrenia
Poster No:
529
Submission Type:
Abstract Submission
Authors:
Yuchao Jiang1, Cheng Luo2, JianFeng Feng3
Institutions:
1Shenzhen University, Shenzhen, shenzhen, 2University of Electronic Science and Technology of China, Chengdu, Sichuan, 3Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
First Author:
Co-Author(s):
JianFeng Feng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Introduction:
Schizophrenia is characterized with greater variability beyond the mean differences in brain structures [1]. This variability is often assumed to be static, reflecting the presence of heterogeneous subgroups, but this assumption and alternative explanations remain untested. No two individuals with schizophrenia have the same anatomical change in the brain. Is this variability a fixed feature of schizophrenia or does it become more pronounced at later stages?
Methods:
We evaluate the brain structural heterogeneity using a discovery sample including cross-sectional magnetic resonance imaging (MRI) T1-weighted scans from a total of 1,799 individuals diagnosed with schizophrenia (762 females, mean age=29.9±11.9 years) and 1,532 healthy controls (702 females, mean age=31.3±11.8 years). Brain images were processed by using FreeSurfer. Regional GMV measure was adjusted by regressing out the factors of no interest, such as sex, age, square of age, total intracranial volume (TIV) and site. To explore whether individuals with schizophrenia demonstrate higher variability of regional GMV, we computed the variability by comparing the relative variability of patient to control measures, using the log variability ratio (lnVR) as an index [1]. We used a relative mean-scaled variability (lnCVR), which accounts for differences in mean GMV. Two sample t test was performed to compare the mean difference in covariates-adjusted regional GMV between patients and controls. Permutation test was used to compare the variability difference in regional lnCVR between patients and controls.
Results:
We found significant group differences of mean GMV in 73 brain regions (p<0.05, FDR corrected) (Figure 1a); the largest effect size is located at bilateral hippocampus. We calculated each regional lnCVR in the whole sample and two independent subsamples at early or late illness stages (first-episode drug-naïve subsample [n=478, age=23.1±7.6 years, 239 females] and chronic medicated subsample [n=398, age=37.9±12.1 years, 139 females]). Figure 1b-d show regional lnCVR values across all brain regions by mapping them to a brain template. Compared with healthy controls, significant greater lnCVR (p<0.05, FDR corrected) was found in 50 regions in the whole patient group, at a much greater frequency in the first-episode group (73 regions), compared to the chronic group (28 regions; Chi-square test, p=5.0×10-13). The greater lnCVR in first-episode than chronic group was also replicated in females (p=2.7×10-21) or males (p=9.5×10-8). The areas with largest variability were mainly located at the frontotemporal and thalamus for first-episode patients, or the hippocampus and caudate for chronic patients (Figure 1b-d). Significantly lower variability than controls was not found in any regions in the whole patient group or in the first-episode group, but in bilateral pericalcarine, left cuneus and left parahippocampal in the chronic subsample (p<0.05, FDR corrected). In addition, the average lnCVR across all regions was higher in the first-episode subsample than chronic subsample in a head-to-head comparison (t=10.8, p=1.7×10-7) (Figure 1e). We found that the lnCVR map was significantly spatially correlated with effect size map in case-control difference (r=0.367, p=6.7×10-4) (Figure 1f). Furthermore, it showed a stronger spatial correlation (r=0.489, p=7.4×10-6), when correlation test was limited on these regions with smaller volume (i.e., Cohen's d>0) in patients relative to controls (Figure 1g).
·Figure 1. Group difference of mean volume and variability in regional gray matter (GM) measures between patients with schizophrenia and healthy controls.
Conclusions:
Our work finds support for a space-time interaction along a shared pathophysiological continuum (network-based trans-neuronal diffusion), as a possible explanatory model for inter-subject variability. These findings contribute to the understanding that inter-individual variability in schizophrenia may arise from a common cohesive process that varies in its state (across time) and space (across brain regions).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
MRI
Psychiatric Disorders
Schizophrenia
STRUCTURAL MRI
1|2Indicates the priority used for review