The present study reviewed voxel-based morphometry (VBM) studies on high-risk individuals with schizophrenia, patients experiencing their first-episode schizophrenia (FES), and those with chronic schizophrenia. We predicted that gray matter abnormalities would show progressive changes, with most extensive abnormalities in the chronic group relative to FES and least in the high-risk group. Forty-one VBM studies were reviewed. Eight high-risk studies, 14 FES studies, and 19 chronic studies were analyzed using anatomical likelihood estimation meta-analysis. Less gray matter in the high-risk group relative to controls was observed in anterior cingulate regions, left amygdala, and right insula. Lower gray matter volumes in FES compared with controls were also found in the anterior cingulate and right insula but not the amygdala. Lower gray matter volumes in the chronic group were most extensive, incorporating similar regions to those found in FES and high-risk groups but extending to superior temporal gyri, thalamus, posterior cingulate, and parahippocampal gryus. Subtraction analysis revealed less frontotemporal, striatal, and cerebellar gray matter in FES than the high-risk group; the high-risk group had less gray matter in left subcallosal gyrus, left amygdala, and left inferior frontal gyrus compared with FES. Subtraction analysis confirmed lower gray matter volumes through ventral-dorsal anterior cingulate, right insula, left amygdala and thalamus in chronic schizophrenia relative to FES. Frontotemporal brain structural abnormalities are evident in nonpsychotic individuals at high risk of developing schizophrenia. The present meta-analysis indicates that these gray matter abnormalities become more extensive through first-episode and chronic illness. Thus, schizophrenia appears to be a progressive cortico-striato-thalamic loop disorder.

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

The human brain is inherently organized as separate networks, as has been widely revealed by resting-state functional magnetic resonance imaging (fMRI). Although the large-scale functional connectivity can be partially explained by the underlying white-matter structural connectivity, the question of whether the underlying functional connectivity is related to brain metabolic factors is still largely unanswered. The present study investigated the presence of metabolic covariant networks across subjects using a set of fluorodeoxyglucose (¹⁸F, FDG) positron-emission tomography (PET) images. Spatial-independent component analysis was performed on the subject series of FDG-PET images. A number of networks that were mainly homotopic regions could be identified, including visual, auditory, motor, cerebellar, and subcortical networks. However, the anterior-posterior networks such as the default-mode and left frontoparietal networks could not be observed. Region-of-interest-based correlation analysis confirmed that the intersubject metabolic covariances within the default-mode and left frontoparietal networks were reduced as compared with corresponding time-series correlations using resting-state fMRI from an independent sample. In contrast, homotopic intersubject metabolic covariances observed using PET were comparable to the corresponding fMRI resting-state time-series correlations. The current study provides preliminary illustration, suggesting that the human brain metabolism pertains to organized covariance patterns that might partially reflect functional connectivity as revealed by resting-state blood oxygen level dependent (BOLD). The discrepancy between the PET covariance and BOLD functional connectivity might reflect the differences of energy consumption coupling and ongoing neural synchronization within these brain networks.