In the context of sensory processing, visual discrimination is a fundamental function that enables survival. Previous findings suggest that such discrimination function can be decoded from electroencephalographic brain responses, especially by using oscillation feature. However, how to evaluate the fast visual discrimination is still unclear. In this study, we hypothesize that brain’s oscillatory activity in a passive viewing condition can serve as a sensitive predictor of fast visual discrimination. A visual multi-feature paradigm which allowing investigation of several different change types was used to record both event-related potentials (ERPs) and behavioral responses. First, we investigated separating the behavioral hit rate as a function of reaction time (categorized from 200 ms to 1000 ms with step of 100 ms). In the subsequent step, we extract the slow theta component from ERP’s time frequency represents with time frequency principal component analysis (TF-PCA) and correlate its average power with behavioral performance. Our results showed that the significant detect window for different deviants’ level was from 400 to 600 ms, while the hit rates in such detect window showed a significant correlation with the averaged time frequency power in the slow theta band during 100-300 ms latency for the color and shape deviants. These findings suggest that the oscillation power, particularly in the slow theta range, of the brain responses is a predictor of fast visual discrimination.

Objective: Scalp electroencephalogram (EEG) provides a substantial amount of data about information processing in the human brain. In the context of conventional event-related potential (ERP analysis), it is typically assumed that individual trials share similar properties and stem from comparable neural sources, especially when employing group-level methods (including cluster analysis). However, those group analyses can miss important information about the relevant neural process due to a rough estimation of the brain activities of individual subjects while selecting a fixed time window for all the subjects. Method: We designed a multi-set consensus clustering method to examine cognitive processes at the single-trial level. The obtained clusters for the trials were processed via consensus clustering at the individual subject level. The proposed method effectively identified the time window of interest for each individual subject. Results: The proposed method was applied to real EEG data from the active visual oddball task experiment to qualify the P3 component. Our early findings disclosed that the estimated time windows for individual subjects can provide more precise ERP identification than considering a fixed time window for all subjects. Moreover, based on standardized measurement error and established bootstrap for single-trial EEG, our assessments revealed suitable stability for the calculated scores for the identified P3 component. Significance: The new method provides a more realistic and information-driven understanding of the single trials’ contribution towards identifying the ERP of interest in individual ERP potential data.

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realize the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonization methods are the two primary methods used to eliminate scanner/site-related effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonization methods to remove site effects completely when the signals of interest and scanner/site-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonization strategy that implements dual-projection (DP) theory based on ICA to remove the scanner/site-related effects more completely. This method can separate the signal effects correlated with site variables from the identified site-related effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a traveling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of DP-based ICA harmonization method. Results show that DP-based ICA harmonization has superior performance for removing site effects and enhancing the sensitivity to detect signal of interest as compared with GLM-based and conventional ICA harmonization methods.

Static features of the executive control network (ECN), dorsal attention network (DAN), default mode network (DMN), and salience network (SN) have displayed dysfunction in people with autism spectrum disorder (ASD). However, research on the dynamic brain function of these networks in ASD is rare. In this study, co-activation pattern (CAP) analysis was performed on the whole cortex to study dynamic dysfunction in ASD using a large multisite resting-state fMRI dataset (295 ASDs, 446 healthy controls). Eight transient network states (TNSs) were defined, the dwell time, persistence, and transitions of each TNS were calculated to evaluate dynamic brain function. Using hierarchical clustering, the eight TNSs were divided into three clusters: ‘DMN activating’, ‘SN activating’, and ‘ECN and DAN activating’. We found ‘ASD-biased’ DMN and SN TNSs, which showed larger dwell time and longer persistence in ASD group than healthy control (CON) group. More transition within ‘ASD-biased’ TNSs were found in ASD group. Dwell time of the ‘ASD-biased’ ‘SN activating’ TNS was significantly correlated with social deficits only in the ASD group. Our results imply the dynamic dysfunction of ASD does not come from the occurrence of DMN, ECN, or SN, but comes from the atypical co-activation patterns within them. Our results also indicate people with ASD have stronger negative connectivity between DMN and ECN in childhood. This connection dosen’t change significantly with age in ASD group, but is supposed to increase with age until adulthood as the growth trajectory in healthy inviduals, which implies the early overgrowth of ASD children.