Robust hierarchically organized whole‐brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography
Erin W. Dickie, Saba Shahab, Colin Hawco, Dayton Miranda, Gabrielle Herman, Miklos Argyelan, Jie Lisa Ji, Jerrold Jeyachandra, Alan Anticevic, Anil K. Malhotra, Aristotle N. Voineskos- Neurology (clinical)
- Neurology
- Radiology, Nuclear Medicine and imaging
- Radiological and Ultrasound Technology
- Anatomy
Abstract
Background
Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface‐based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico‐subcortical networks from multi‐cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles.
Methods
We utilized resting‐state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume‐based approaches, (2) a surface‐based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT).
Results
The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface‐based approach (Cohen's D volume vs. surface 0.27–1.00, all p < 10−6) and further increased after PINT (Cohen's D surface vs. PINT 0.18–0.96, all p < 10−4). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface‐based approach and PINT (Number of differing pairwise‐correlations: volume: 404, surface: 570, PINT: 628, FDR corrected).
Conclusion
Surface‐based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.