Consistent Functional Connectivity Alterations in Schizophrenia Spectrum Disorder: A Multisite Study

Abstract

Schizophrenia (SZ) is a severe mental illness with high heritability and complex etiology. Mounting evidence from neuroimaging has implicated disrupted brain network connectivity in the pathophysiology. However, previous findings are inconsistent, likely due to a combination of methodological and clinical variability and relatively small sample sizes. Few studies have used a data-driven approach for characterizing pathological interactions between regions in the whole brain and evaluated the generalizability across independent samples. To overcome this issue, we collected resting-state functional magnetic resonance imaging data from 3 independent samples (1 from Norway and 2 from Sweden) consisting of 182 persons with a SZ spectrum diagnosis and 348 healthy controls. We used a whole-brain data-driven definition of network nodes and regularized partial correlations to evaluate and compare putatively direct brain network node interactions between groups. The clinical utility of the functional connectivity features and the generalizability of effects across samples were evaluated by training and testing multivariate classifiers in the independent samples using machine learning. Univariate analyses revealed 14 network edges with consistent reductions in functional connectivity encompassing frontal, somatomotor, visual, auditory, and subcortical brain nodes in patients with SZ. We found a high overall accuracy in classifying patients and controls (up to 80%) using independent training and test samples, strongly supporting the generalizability of connectivity alterations across different scanners and heterogeneous samples. Overall, our findings demonstrate robust reductions in functional connectivity in SZ spectrum disorders, indicating disrupted information flow in sensory, subcortical, and frontal brain regions.

Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors

“…Such large patient cohorts frequently surpass the recruitment capacity of single clinical centres. A number of initiatives have pooled multisite data on normal or patient cohorts, such as attention deficit hyperactivity disorder (Brown et al, 2012) , autism spectrum disorder (Di Martino et al, 2017;Nielsen et al, 2013) , diabetes (Saggar et al, 2017) , depression (Drysdale et al, 2017) , schizophrenia (Cheng et al, 2015;Skåtun et al, 2017) , Alzheimer's disease (Alzheimer's Disease Neuroimaging Initiative ), population imaging genetics 1 (UK Biobank ) and normal brain development (Adolescent Brain Cognitive Development 2 Study or ABCD ). However, it is still unclear to what degree the use of multiple scanners 3 introduces additional variance in neuroimaging measures, especially for studies that will collect data for several years.…”

Recognition of early-onset schizophrenia using deep-learning method

“…Resting-state connectivity presented good potential classification capacity (79.3% for classification accuracy, 87.4% for sensitivity, 82.2% for specificity, p < 0.05 for permuted test). Many other studies used resting-state functional connectivity to differentiate schizophrenia patients from controls (Mikolas et al 2016;Cabral et al 2016;Arbabshirani et al 2013;Shen et al 2010;Du et al 2012;Skåtun et al 2016). Two strategies were used in these studies to overcome over-fitting problem.…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…[18][19][20]; and (ii) differentiating between mental health conditions with similar symptomatology, e.g. [21,22].…”

“…The majority of studies considered neuroimaging data (e.g., magnetic resonance imaging (MRI), electroencephalography (EEG), and positron emission tomography (PET)). For example, fMRI data has been used with ML to improve the diagnosis of schizophrenia [19]. Further, MRI data was used to diagnose patients with Alzheimer's disease and cognitive impairment, achieving reasonable accuracy [20].…”