Characterization of task-free/task-performance brain states

  • Authors:

  • Xin Zhang;Lei Guo;Xiang Li;Dajiang Zhu;Kaiming Li;Zhenqiang Sun;Changfeng Jin;Xintao Hu;Junwei Han;Qun Zhao;Lingjiang Li;Tianming Liu

  • Affiliations:

  • School of Automation, Northwestern Polytechnical University, Xi'an, China, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA;School of Automation, Northwestern Polytechnical University, Xi'an, China;Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA;Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA;Biomedical Imaging Technology Center, Emory University, Atlanta, GA;The School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China;The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China;School of Automation, Northwestern Polytechnical University, Xi'an, China;School of Automation, Northwestern Polytechnical University, Xi'an, China;Department of Physics and Astronomy and Bioimaging Research Center, The University of Georgia, Athens, GA;The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China;Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA

  • Venue:
  • MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
  • Year:
  • 2012

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Abstract

Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during fMRI scans, it has been very challenging to tell whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states in task-free/task-performance. This paper presents a novel approach to characterizing the brain's functional status into task-free or task-performance states. The basic idea here is that the brain's functional state is represented by a whole-brain quasi-stable connectivity pattern (WQCP), and an effective sparse coding procedure was then applied to learn the atomic connectivity patterns (ACP) of both task-free and task-performance states based on training R-fMRI and T-fMRI data. Our experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. However, a certain portion of ACPs from R-fMRI and T-fMRI datasets are overlapping, suggesting that those subjects with overlapping ACPs were not in the expected task-free/task-performance states during R-fMRI/T-fMRI scans.