Gamma oscillations in the mPFC: A potential predictive biomarker of depression and antidepressant effects.

Gamma oscillations have attracted much attention in the field of mood disorders, but their role in depression remains poorly understood. This study aimed to investigate whether gamma oscillations in the medial prefrontal cortex (mPFC) could serve as a predictive biomarker of depression. Chronic restraint stress (CRS) or lipopolysaccharide (LPS) were used to induce depression-like behaviors in mice; local field potentials (LFPs) in the mPFC were recorded by electrophysiological techniques; We found that both CRS and LPS induced significant depression-like behaviors in mice, including increasing immobility durations in the forced swimming test (FST) and tail suspension test (TST) and increasing the latency to feed in the novelty-suppressed feeding test (NSFT). Electrophysiological results suggested that CRS and LPS significantly reduced low and high gamma oscillations in the mPFC. Furthermore, a single injection of ketamine or scopolamine for 24 h significantly increased gamma oscillations and elicited rapid-acting antidepressant-like effects. In addition, fluoxetine treatment for 21 days significantly increased gamma oscillations and elicited antidepressant-like effects. Taken together, our findings suggest that gamma oscillations are strongly associated with depression, yielding new insights into investigating the predictive biomarkers of depression and the time course of antidepressant effects.

Quantitative separation of CEST effect by Rex-line-fit analysis of Z-spectra.

The process of chemical exchange saturation transfer (CEST) is quantified by evaluating a Z-spectra, where CEST signal quantification and Z-spectra fitting have been widely used to distinguish the contributions from multiple origins. Based on the exchange-dependent relaxation rate in the rotating frame (Rex), this paper introduces an additional pathway to quantitative separation of CEST effect. The proposed Rex-line-fit method is solved by a multi-pool model and presents the advantage of only being dependent of the specific parameters (solute concentration, solute-water exchange rate, solute transverse relaxation, and irradiation power). Herein we show that both solute-water exchange rate and solute concentration monotonously vary with Rex for Amide, Guanidino, NOE and MT, which has the potential to assist in solving quantitative separation of CEST effect. Furthermore, we achieve Rex imaging of Amide, Guanidino, NOE and MT, which may provide direct insight into the dependency of measurable CEST effects on underlying parameters such as the exchange rate and solute concentration, as well as the solute transverse relaxation.

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants (three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number.