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IntroductionOne leading hypothesis suggests that schizophrenia (SZ) is a neurodevelopmental disorder caused by genetic defects in association with environmental risk factors that affect synapse and myelin formation. Recent magnetic resonance imaging (MRI) studies of SZ brain showed both gray matter (GM) reduction and white matter (WM) fractional anisotropy reduction. In this study, we used T1‐weighted (T1w)/T2‐weighted (T2w) MRI ratio images, which increase myelin‐related signal contrast and reduce receiver‐coil bias.MethodsWe measured T1w/T2w ratio image signal intensity in 29 patients with SZ and 33 healthy controls (HCs), and then compared them against bias‐corrected T1w images.ResultsMean T1w/T2w ratio signal intensity values across all SZ GM and WM voxels were significantly lower than those for the HC values (analysis of covariance with age, gender, handedness, and premorbid intelligence quotient as nuisance covariates). SZ mean WM T1w/T2w ratio values were related to Global Assessment of Functioning (GAF) scores and were inversely related to the positive psychotic symptoms of the Positive and Negative Syndrome Scale. Voxel‐based analysis revealed significantly lower T1w/T2w ratio image signal intensity values in the right ventral putamen in SZ GM. T1w image intensities did not differ between the SZ and HC groups.ConclusionsT1‐weighted/T2‐weighted ratio imaging increased the detectability of SZ pathological changes. Reduced SZ brain signal intensity is likely due to diminished myelin content; therefore, mapping myelin‐related SZ brain changes using T1w/T2w ratio images may be useful for studies of SZ brain abnormalities.

Slow (<0.1 Hz) oscillatory activity in the human brain, as measured by functional magnetic imaging, has been used to identify neural networks and their dysfunction in specific brain diseases. Its intrinsic properties may also be useful to investigate brain functions. We investigated the two functional maps: variance and first order autocorrelation coefficient (r 1). These two maps had distinct spatial distributions and the values were significantly different among the subdivisions of the precuneus and posterior cingulate cortex that were identified in functional connectivity (FC) studies. The results reinforce the functional segregation of these subdivisions and indicate that the intrinsic properties of the slow brain activity have physiological relevance. Further, we propose a sample size (degree of freedom) correction when assessing the statistical significance of FC strength with r 1 values, which enables a better understanding of the network changes related to various brain diseases.

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