Baseline brain structural and functional predictors of clinical outcome in the early course of schizophrenia

Abstract

Although schizophrenia is considered a brain disorder, the role of brain organization for symptomatic improvement remains inadequately defined. We investigated the relationship between baseline brain morphology, resting-state network connectivity and clinical response after 24-weeks of antipsychotic treatment in patients with schizophrenia (n=95) using integrated multivariate analyses. There was no significant association between clinical response and measures of cortical thickness (r=0.37, p=0.98) and subcortical volume (r=0.56, p=0.15). By contrast, we identified a strong mode of covariation linking functional network connectivity to clinical response (r=0.70; p=0.04), and particularly to improvement in positive (weight=0.62) and anxious/depressive symptoms (weight=0.49). Higher internal cohesiveness of the default mode network was the single most important positive predictor. Key negative predictors involved the functional cohesiveness of central executive subnetworks anchored in the frontoparietal cortices and subcortical regions (including the thalamus and striatum) and the inter-network integration between the default mode and sensorimotor networks. The present findings establish links between clinical response and the functional organization of brain networks involved both in perception and in spontaneous and goal-directed cognition, thereby advancing our understanding of the pathophysiology of schizophrenia.

Evaluation of the spatial variability in the major resting‐state networks across human brain functional atlases

“…Functionally, RSNs can be divided into those involved in internally guided, higher order mental functions (default‐mode [DMN], central executive [CEN], and salience [SAL] networks) and those supporting externally driven, specialized sensory and motor processing (visual [VIS] and sensorimotor [SMN] networks) (Damoiseaux et al, ; Doucet et al, ; Smith et al, ). Examination of RSNs in healthy populations has been instrumental in identifying processes involved in brain development (Gu et al, ) and aging (Ferreira et al, ; Shaw, Schultz, Sperling, & Hedden, ; Siman‐Tov et al, ) while investigation of RSNs in clinical samples has yielded new insights in disease mechanisms (Dong, Wang, Chang, Luo, & Yao, ; Doucet, Moser, Luber, Leibu, & Frangou, ; Lee, Doucet, Leibu, & Frangou, ; Repovs, Csernansky, & Barch, ).…”

“…The spatiotemporal configuration of RSNs is based on their functional connectivity (FC) which represents the temporal correlation of the blood oxygen level-dependent signals between their constituent brain regions (Biswal, Yetkin, Haughton, & Hyde, 1995 (Damoiseaux et al, 2006;Doucet et al, 2011;). Examination of RSNs in healthy populations has been instrumental in identifying processes involved in brain development (Gu et al, 2015) and aging (Ferreira et al, 2016;Shaw, Schultz, Sperling, & Hedden, 2015;Siman-Tov et al, 2016) while investigation of RSNs in clinical samples has yielded new insights in disease mechanisms (Dong, Wang, Chang, Luo, & Yao, 2018;Doucet, Moser, Luber, Leibu, & Frangou, 2018;Lee, Doucet, Leibu, & Frangou, 2018;Repovs, Csernansky, & Barch, 2011).…”

Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis

“…We implemented sCCA [33] in MatlabR2018b using an in-house script in accordance with our previously published work [34,35,46] to test the association between the linguistic, clinical, and neuroimaging data (details in Supplementary Material, Section 1.4). We considered four datasets; a nonimaging dataset comprising the clinical variables (Supplementary Table S2), a nonimaging dataset comprising the linguistic variables ( Supplementary Table S3), a functional dataset comprising the functional network connectivity variables ( Supplementary Table S4), and a structural dataset comprising the morphometric variables (Supplementary Table S5).…”

Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study

“…Additionally, the dorsal attention network played an important role in reflecting symptoms changes in schizophrenia (Kraguljac, White, Hadley, Visscher et al, 2016). Most recently, links between connectome organization of resting-state networks and predicting short-term clinical outcomes of schizophrenia have been established (Doucet, Moser, Luber, Leibu, & Frangou, 2018). All of these studies focusing on the clinical implications of connectivity or large-scale networks in schizophrenia indicate that fMRI-based brain changes are a pivotal signature for predicting antipsychotic treatment response.…”