Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model (HMM) analysis for resting-state functional MRI (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange (ABIDE). Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls (HCs). Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks [including visual network, auditory network, and sensory and motor network (SMN)]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD.

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This study specifically designed an eye-tracking supported scientific problem-solving assessment: hands-on manipulation task system to explore the differences in visual attention and cognitive processes between high and low science achievement groups. Thirteen students with high science achievement and fourteen students with low science achievement participated. Students needed to complete the hands-on manipulation assessment, consisting of three modules, including selecting experimental equipment, experimental design, and building the experimental model. Behavioral and eye movement data were collected during the process. The results showed that the high science achievement group allocated more visual attention to the hands-on manipulation task, acquired more information through visual fixation, and assigned more attention to the key area. In module three of the hands-on manipulation task, the high science achievement group transformed from a visual channel to a tactile channel, and they generated more hands-on behaviors depending on the experimental area. Furthermore, the results showed a high correlation between students’ eye movement behavior and the performance of scientific problem-solving assessments. Eye movement behavior could predict students’ performance in scientific problem-solving. The average fixation duration and the average fixation duration of the interest area were two significant determining parameters. The implications of the experimental results for front-line science education, curriculum designers, and science assessment were also discussed.