Modulation of Beta Oscillations for Implicit Motor Timing in Primate Sensorimotor Cortex during Movement Preparation

Motor timing is an important part of sensorimotor control. Previous studies have shown that beta oscillations embody the process of temporal perception in explicit timing tasks. In contrast, studies focusing on beta oscillations in implicit timing tasks are lacking. In this study, we set up an implicit motor timing task and found a modulation pattern of beta oscillations with temporal perception during movement preparation. We trained two macaques in a repetitive visually-guided reach-to-grasp task with different holding intervals. Spikes and local field potentials were recorded from microelectrode arrays in the primary motor cortex, primary somatosensory cortex, and posterior parietal cortex. We analyzed the association between beta oscillations and temporal interval in fixedduration experiments(500 ms as the Short Group and1500 ms as the Long Group) and random-duration experiments(500 ms to 1500 ms). The results showed that the peak beta frequencies in both experiments ranged from15 Hz to 25 Hz. The beta power was higher during the hold period than the movement(reach and grasp) period.Further, in the fixed-duration experiments, the mean poweras well as the maximum rate of change of beta power in the first 300 ms were higher in the Short Group than in the Long Group when aligned with the Center Hit event. In contrast, in the random-duration experiments, the corresponding values showed no statistical differences among groups. The peak latency of beta power was shorter in the Short Group than in the Long Group in the fixed-duration experiments, while no consistent modulation pattern was found in the random-duration experiments. These results indicate that beta oscillations can modulate with temporal interval in their power mode. The synchronization period of beta power could reflect the cognitive set maintaining working memory of the temporal structure and attention.

Classification of schizophrenic patients and healthy controls using multiple spatially independent components of structural MRI data

Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia,which could potentially act as disease markers.Based on the findings of these meta-analyses,we developed a multivariate pattern analysis method to classify schizophrenic patients and healthy controls using structural magnetic resonance imaging(sMRI)data.Independent component analysis(ICA)was used to decompose gray matter density images into a set of spatially independent components.Spatial multiple regression of a region of interest(ROI)mask with each of the components was then performed to determine pathological patterns,in which the voxels were taken as features for classification.After dimensionality reduction using principal component analysis(PCA),a nonlinear support vector machine(SVM)classifier was trained to discriminate schizophrenic patients from healthy controls.The performance of the classifier was tested using a 10-fold cross-validation strategy.Experimental results showed that two distinct spatial patterns displayed discriminative power for schizophrenia,which mainly included the prefrontal cortex(PFC)and subcortical regions respectively.It was found that simultaneous usage of these two patterns improved the classification performance compared to using either of them alone.Moreover,the two pathological patterns constitute a prefronto-subcortical network,suggesting that schizophrenia involves abnormalities in networks of brain regions.