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Zhang, R.-Y.

Publications and source records attributed to Zhang, R.-Y..

2 recordsLinked to original sources
bioRxiv2019

Hormone Levels Are Related to Altered Functional Connectivity in Prolactinomas

Background and ObjectiveProlactinomas may cause drastic hormone fluctuations throughout the body. It is not fully understood how endogenous hormone disorders such as prolactinomas reshape the patients brain. By employing the resting-state functional magnetic resonance imaging technique, we aimed to investigate the whole-brain functional connectivity (FC) and its relationship with hormone levels in patients with prolactinomas.\n\nMethodsUsing whole-brain and seed-based functional connectivity analyses, we compared FC metrics between 33 prolactinoma patients and 31 healthy controls matched with age, sex, and handedness. Then we performed partial correlation analysis to examine the relationship between FC metrics and hormone levels.\n\nResultsCompared to healthy controls, we found that prolactinoma patients showed significantly increased thalamocortical (visual cortex) and cerebellar-cerebral connectivity. In addition, endogenous hormone levels were positively correlated with the increased FC, and the hormone-FC relationships showed sex difference in prolactinoma patients.\n\nConclusionsOur findings are the first to reveal the altered FC patterns and sex-dependent hormone-FC relationships in prolactinoma patients, indicating the important role of hormone levels in the neural mechanism of brain reorganization and hyperactive intrinsic connections in prolactinomas.

neuroscience↗
bioRxiv2019

Understanding multivariate brain activity: evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

Previous studies have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We first replicate the classical finding that TCNCs impair population codes in a standard neuronal population. We then extend our analysis to fMRI data, and show that voxelwise TCNCs do not impair and can even improve MVPA performance when TCNCs are strong or the number of voxels is large. We also confirm these results using standard information-theoretic analyses in computational neuroscience. Further computational analyses demonstrate that the discrepancy between the effect of TCNCs in neuronal and voxel populations can be explained by tuning heterogeneity and pool sizes. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

neuroscience↗