Comparison of Functional Connectivity Estimated from Concatenated Task-State Data from Block-Design Paradigm with That of Continuous Task

Functional connectivity (FC) analysis with data collected as continuous tasks and activation analysis using data from block-design paradigms are two main methods to investigate the task-induced brain activation. If the concatenated data of task blocks extracted from the block-design paradigm could provide equivalent FC information to that derived from continuous task data, it would shorten the data collection time and simplify experimental procedures, and the already collected data of block-design paradigms could be reanalyzed from the perspective of FC. Despite being used in many studies, such a hypothesis of equivalence has not yet been tested from multiple perspectives. In this study, we collected fMRI blood-oxygen-level-dependent signals from 24 healthy subjects during a continuous task session as well as in block-design task sessions. We compared concatenated task blocks and continuous task data in terms of region of interest- (ROI-) based FC, seed-based FC, and brain network topology during a short motor task. According to our results, the concatenated data was not significantly different from the continuous data in multiple aspects, indicating the potential of using concatenated data to estimate task-state FC in short motor tasks. However, even under appropriate experimental conditions, the interpretation of FC results based on concatenated data should be cautious and take the influence due to inherent information loss during concatenation into account

Dynamic Adaptation of Brain Networks from Rest to Task and Application to Stroke Research

Examining how motor task modulates brain activity plays a critical role of understanding how cerebral motor system works and could further be applied in the research of motor disability due to neurological diseases. Recent advances in neuroimaging have resulted in numerous studies focusing on motor-induced modulation of brain activity and most widely used strategy of these studies was identifying activated brain regions during motor task through event-related/block-design experiment paradigms. Despite progress obtained, motor-related activation analysis mainly focused on modulation of brain activities for individual brain regions. However, human brain is known to be an integrated network, and the adaptation of brain in response to motor task could be reflected by the modulation of brain networks. Thus, investigating the spatiotemporal modulation of task-state brain networks during motor task would provide system-level information regarding the underlying adaptation of cerebral motor system in response to the motor task and could be further applied in the research of motor disability due to neurological diseases. Although the task-state functional connectivity (FC) and networks have been examined by previous studies, there are still several aspects needed to be explored: (1) Previous task-state FC studies were mainly based on functional magnetic resonance imaging (fMRI) and potential of applying functional near-infrared spectroscopy (fNIRS), which is a promising complementary modality to fMRI because of its low cost and relatively high temporal resolution (10 Hz in sampling rate compared with less than 1Hz in sampling rate for fMRI), in task-state FC studies should be explored. (2) In the fMRI studies, the task-state brain network was mainly investigated from the perspective of static FC, which focuses on the spatial pattern of the task-state brain networks. However, brain activities and cognitive processes are known to be dynamic and adaptive and the newly emerging dynamic FC analysis could further provide temporal patterns of the task-state brain networks. Thus, the spatiotemporal pattern of task-state brain network during motor task is still needed to be investigated by dynamic FC analysis based on fMRI; (3) Task-state brain network analysis has not been applied in the research of stroke, and the relationship between task-state brain network and stroke recovery has not been investigated; Therefore, in this thesis, we aim to investigate the task-state brain networks during motor task using both static and dynamic FC analysis based on fNIRS and fMRI to reveal the motor task-specific spatiotemporal changes of brain networks compared with resting conditions, and further applied the task-state brain network analysis in the research of stroke recovery In this thesis, fMRI and fNIRS were employed to record the brain hemodynamic signals, and static FC and dynamic FC were used to investigate spatiotemporal pattern of task-state brain network during motor task. In addition, the fMRI data of stroke patients were recorded at four time points post stroke, and the reorganization of task-state brain network as well as its relationship to stroke recovery were examined. Specific results are described as follow: (1) Through static FC analysis of fNIRS during rest and motor preparation, increased FC were identified during motor preparation, especially the FC connecting right dorsolateral prefrontal cortex (DLPFC) with contralateral primary somatosensory cortex (S1) and primary motor cortex (M1) as well as the FC connecting contralateral S1 with ipsilateral S1 and M1. Channels located in sensorimotor networks and right DLPFC were also found activated during motor preparation. Our findings demonstrated that the sensorimotor network was interacting with high-level cognitive brain network to maintain the motor preparation state. (2) Through dynamic FC analysis of fNIRS during rest and motor execution, increased variability of FC connecting contralateral premotor and supplementary motor cortex (PMSMC) and M1 was identified, and the nodal strength variability of these two brain regions were also increased during motor execution. Our findings demonstrated that contralateral M1 and PMSMC were interacting with each other actively and dynamically to facilitate the fist opening and closing. (3) Through dynamic FC analysis on fMRI data, two principal FC states during rest and one principal FC state during motor task were identified. The 1st principal FC state in rest was similar to that in task, which likely represented intrinsic network architecture and validated the broadly similar spatial patterns between rest and task. However, the presence of a 2nd principal FC state with increased FC between default-mode network (DMN) and motor network (MN) in rest with shorter "dwell time" could imply the transient functional relationship between DMN and MN to establish the "default mode" for motor system. In addition, the more frequent shifting between two principal FC states in rest indicated that the brain networks dynamically maintained the "default mode" for the motor system. In contrast, during task, the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity, validating the distinct temporal patterns between rest and task. Our findings suggested that the principal states could show a link between the rest and task states, and verified our hypothesis on overall spatial similarity but distinct temporal patterns of dynamic brain networks between rest and task states. (4) Task-state motor network was applied in the research of motor disability due to stroke and topological reorganization of task-state motor network was identified during sub-acute phase post stroke. In addition, for the first time, our study found the topological configuration of task-state motor network at the early recovery stage were capable of predicting the motor function restoration during sub-acute phase. In general, the findings demonstrated the reorganization and potential prognostic value of task-state brain network after stroke, which provided new insights into understanding the brain reorganization and stroke rehabilitation. In summary, this thesis used two neuroimaging modalities (fMRI and fNIRS) to investigate how brain networks, especially the motor network and high-level cognitive network, would reorganize spatiotemporally from resting-state to motor tasks through both static and dynamic FC analysis, and further applied the task-state brain network analysis in the research of motor disability due to stroke. Our findings revealed the underlying spatiotemporal adaptation of brain networks in response to motor task and demonstrated the potential clinical prognostic value of task-state motor network during stroke recovery. The novelties of this thesis are as follow: (1) dynamic FC was innovatively applied in revealing the motor task-specific spatiotemporal changes of brain networks compared with resting conditions; (2) task-state motor network was applied in the research of stroke recovery; (3) static and dynamic FC analysis were innovatively applied in fNIRS data.Ph.D., Biomedical Engineering -- Drexel University, 201