Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after Personalized Intrinsic Network Topography

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 using individualized connectivity profiles. Methods: We utilized resting state and anatomical MRI data from n=406 participants (n = 203 SSD, n = 203 healthy controls) from four cohorts. For each participant, functional timeseries were extracted from 80 cortical regions of interest, representing 6 intrinsic networks using 1) a volume-based approach 2) a surface-based group atlas approach, and 3) Personalized Intrinsic Network Topography (PINT), a personalized surface-based approach (Dickie et al., 2018). Timeseries were also extracted from previously defined intrinsic network subregions of the striatum (Choi et al 2011), thalamus (Ji et al 2019), and cerebellum (Buckner et al 2011). Results: Compared to a volume-based approach, 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 vs HC comparisons, controlling for age, sex, scanner and in-scanner motion, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 357, surface: 562, PINT: 630, FDR corrected). These patterns were found from four different cortical networks -- frontal-parietal, sensory-motor, visual, and default mode -- to subcortical regions. Conclusion: Our results indicate that 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 (Murray et al 2019). Our results also change our understanding of the specific network-network functional connectivity alterations in people with SSDs, and the extent of those alterations. Future work will examine these new patterns of dysconnectivity with behaviour using dimensional models.

Transdiagnostic Neurobiology of Social Cognition and Individual Variability as Measured by Fractional Amplitude of Low-Frequency Fluctuation in Schizophrenia and Autism Spectrum Disorders

“…The increased variability in fALFF in SSD and ASD is a key finding consistent with a growing body of literature noting greater heterogeneity of functional brain activation/connectivity patterns in psychiatric samples [24,36,[64][65][66][67][68][69]. Mean correlational distance quantifies how "typical", or close to the average, one's pattern of brain activity is.…”

Bayesian Optimization of Neurostimulation (BOONStim)

“…Clinical and experimental transcranial magnetic stimulation (TMS) to brain regions exhibits large inter-individual variation in efficacy [1]. Individuals have unique functional connectivity and brain topography patterns [2][3][4] and individualized functional magnetic resonance imaging (fMRI) approaches and surface-based brain parcellations offer stronger brain-behavior links as compared to standard group-based parcellations [2,5,6]. Further, treatment response to TMS may be influenced by individual differences in functional brain organization and personalized functional targeting may improve outcomes [7][8][9].…”

“…Clinical and experimental application of transcranial magnetic stimulation (TMS) to brain regions is affected by large inter-individual variation in efficacy [1]. Individuals have unique functional connectivity and brain topography patterns [2][3][4] and individualized functional magnetic resonance imaging (fMRI) approaches and surface-based brain parcellations offer stronger brain-behavior links as compared to standard meta-analytic targets [2,5,6], which is highly relevant for neurostimulation. Recent work has highlighted that findings can also vary widely based on methodological variation in fMRI analyses [3].…”