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Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain’s dynamic and adaptive activities. In this study, we adopted $k$-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter “dwell time” implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a “default mode” in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.

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This study focuses on the functional assessment of the upper extremity of stroke patients via analysis of angular kinematics features. Amplitudes and angular velocities of multi-joint movements more precisely describe functional state at different impairment levels. However, the arm movement as a whole could be analyzed by means of joint angle–angle diagrams, which illustrate the 2D trajectory of upper extremity during movement and show the visual ranges of upper extremity in different cycles of motion. The functional range of motion of each upper extremity segment in all patient groups for more accurate assessment of capability was calculated. Moreover, we calculated the area (S) between two curves in joint angle–angle diagrams as a novel index of the complete upper extremity movement range evaluation. Our findings correspond to clinical rates and upper extremity assessment based on joint angle–angle diagrams seems to be a promising method for accurate assessment and/or predicting the outcomes of rehabilitation programs.