Based on our a priori hypothesis, we analyzed a large sample resting-state functional magnetic resonance imaging (fMRI) dataset from first-episode drug-naïve SCZ patients (n = 112) and healthy controls (n = 82) using the SN as the seed region for an investigation of striato-thalamo-cortical FC. This was done in the standard band of slow frequency oscillations and then in its subfrequency bands (Slow4 and Slow5). Results: The analysis showed in SCZ: (1) reciprocal functional hypo-connectivity between SN and striatum, with differential patterns for Slow5 and Slow4; (2) functional hypo-connectivity between striatum and thalamus, as well as functional hyper-connectivity between thalamus and sensorimotor cortical areas, specifically in Slow4; (3) correlation of thalamo-sensorimotor functional hyper-connectivity with psychopathological symptoms. Conclusions: We demonstrate abnormal dopamine-related SN-based striato-thalamo-cortical FC in slow frequency oscillations in first-episode drug-naive SCZ. This suggests that altered dopaminergic function in the SN leads to abnormal neuronal synchronization (as indexed by FC) within subcortical-cortical circuitry, complementing the dopamine hypothesis in SCZ on the regional level of resting-state activity.

Categorizing ‘deficit schizophrenia’ (DS) as distinct from ‘non-deficit’ schizophrenia (NDS) may help reduce heterogeneity within schizophrenia. However, it is unknown if DS has a discrete white matter signature. Here we used MRI to compare white matter volume (voxel-based morphometry) and microstructural integrity (fractional anisotropy, FA) in first-episode treatment-naïve patients with DS and NDS and their unaffected relatives to control groups of similar age. We found that white matter disruption was prominent in DS compared to controls; the DS group had lower volumes in the cerebellum, bilateral extra-nuclear and bilateral frontoparietal regions, and lower FA in the body of corpus callosum, posterior superior longitudinal fasciculus and uncinate fasciculus. The DS group also had lower volume in bilateral extra-nuclear regions compared to NDS, and the volume of these clusters was negatively correlated with deficit symptom ratings. NDS patients however, had no significant volume alterations and limited disruption of microstructural integrity compared to controls. Finally, first-degree relatives of those with DS shared volume abnormalities in right extra-nuclear white matter. Thus, white matter pathology in schizophrenia is most evident in the deficit condition, and lower extra-nuclear white matter volumes in both DS patients and their relatives may represent a brain structural ‘endophenotype’ for DS.

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Recent neuroanatomical pattern recognition studies have shown some promises for developing an objective neuroimaging-based classification related to schizophrenia. This study explored the feasibility of reliably identifying schizophrenia using single and multimodal multivariate neuroimaging features. Multiple brain measures including regional gray matter (GM) volume, cortical thickness, gyrification, fractional anisotropy (FA), and mean diffusivity (MD) were extracted using fully automated procedures. We used Gradient Boosting Decision Tree to identify the most frequently selected features of each set of neuroanatomical metric and fused multimodal measures. The current classification model was trained and validated based on 98 patients with first-episode schizophrenia (FES) and 106 matched healthy controls (HCs). The classification model was trained and tested in an independent dataset of 54 patients with FES and 48 HCs using imaging data acquired on a different magnetic resonance imaging scanner. Using the most frequently selected features from fused structural and diffusion tensor imaging metrics, a classification accuracy of 75.05% was achieved, which was higher than accuracy derived from a single imaging metric. Most prominent discriminative features included cortical thickness of left transverse temporal gyrus and right parahippocampal gyrus, the FA of left corticospinal tract and right external capsule. In the independent cohort, average accuracy was 76.54%, derived from combined features selected from cortical thickness, gyrification, FA, and MD. These features characterized by GM abnormalities and white matter disruptions have discriminative power with respect to the underlying pathological changes in the brain of individuals having schizophrenia. Our results further highlight the potential advantage of multimodal data fusion for identifying schizophrenia.