No abstract

Schizophrenia has been associated with abnormal task-related brain activation in sensory and motor regions as well as social cognition network. Recently, two studies investigated temporal correlation between resting-state functional magnetic resonance imaging (R-fMRI) low-frequency oscillations (LFOs) in schizophrenia but reported mixed results. This may be due to the different frequency bands used in these studies. Here we utilized R-fMRI to measure the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) in three different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.08 Hz; and typical band: 0.01-0.08 Hz) in 69 patients with schizophrenia and 62 healthy controls. We showed that there were significant differences in ALFF/fALFF between the two bands (slow-5 and slow-4) in regions including basal ganglia, midbrain, and ventromedial prefrontal cortex. Importantly, we also identified significant interaction between frequency bands and groups in inferior occipital gyrus, precuneus, and thalamus. The results suggest that the abnormalities of LFOs in schizophrenia is dependent on the frequency band and suggest that future studies should take the different frequency bands into account when measure intrinsic brain activity.

Since brain tissue is not readily accessible, a new focus in search of biomarkers for schizophrenia is blood-based expression profiling of non-protein coding genes such as microRNAs (miRNAs), which regulate gene expression by inhibiting the translation of messenger RNAs. This study aimed to identify potential miRNA signature for schizophrenia by comparing genome-wide miRNA expression profiles in patients with schizophrenia vs. healthy controls. A genome-wide miRNA expression profiling was performed using a Taqman array of 365 human miRNAs in the mononuclear leukocytes of a learning set of 30 cases and 30 controls. The discriminating performance of potential biomarkers was validated in an independent testing set of 60 cases and 30 controls. The expression levels of the miRNA signature were then evaluated for their correlation with the patients' clinical symptoms, neurocognitive performances, and neurophysiological functions. A seven-miRNA signature (hsa-miR-34a, miR-449a, miR-564, miR-432, miR-548d, miR-572 and miR-652) was derived from a supervised classification with internal cross-validation, with an area under the curve (AUC) of receiver operating characteristics of 93%. The putative signature was then validated in the testing set, with an AUC of 85%. Among these miRNAs, miR-34a was differentially expressed between cases and controls in both the learning (P = 0.005) and the testing set (P = 0.002). These miRNAs were differentially correlated with patients' negative symptoms, neurocognitive performance scores, and event-related potentials. The results indicated that the mononuclear leukocyte-based miRNA profiling is a feasible way to identify biomarkers for schizophrenia, and the seven-miRNA signature warrants further investigation.

These findings highlight several important factors (sex, medications, and smoking status) that strongly impact the study and interpretation of PPI deficits in patient populations. These results also support the concept that deficient PPI is associated with impaired functional status in schizophrenia.

Automated tract-based analysis of diffusion MRI is an important tool for investigating tract integrity of the cerebral white matter. Current template-based automatic analyses still lack a comprehensive list of tract atlas and an accurate registration method. In this study, tract-based automatic analysis (TBAA) was developed to meet the demands. Seventy-six major white matter tracts were reconstructed on a high-quality diffusion spectrum imaging (DSI) template, and an advanced two-step registration strategy was proposed by incorporating anatomical information of the gray matter from T1-weighted images in addition to microstructural information of the white matter from diffusion-weighted images. The automatic analysis was achieved by establishing a transformation between the DSI template and DSI dataset of the subject derived from the registration strategy. The tract coordinates in the template were transformed to native space in the individual's DSI dataset, and the microstructural properties of major tract bundles were sampled stepwise along the tract coordinates of the subject's DSI dataset. In a validation study of eight well-known tracts, our results showed that TBAA had high geometric agreement with manual tracts in both deep and superficial parts but significantly smaller measurement variability than manual method in functional difference. Additionally, the feasibility of the method was demonstrated by showing tracts with altered microstructural properties in patients with schizophrenia. Fifteen major tract bundles were found to have significant differences after controlling the family-wise error rate. In conclusion, the proposed TBAA method is potentially useful in brain-wise investigations of white matter tracts, particularly for a large cohort study.