Title | Multivariate network-level approach to detect interactions between large-scale functional systems. |
Publication Type | Journal Article |
Year of Publication | 2010 |
Authors | Gao, Wei, Hongtu Zhu, Kelly Giovanello, and Weili Lin |
Journal | Med Image Comput Comput Assist Interv |
Volume | 13 |
Issue | Pt 2 |
Pagination | 298-305 |
Date Published | 2010 |
Keywords | Algorithms, Brain, Brain Mapping, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Multivariate Analysis, Nerve Net, Reproducibility of Results, Sensitivity and Specificity |
Abstract | The question of how large-scale systems interact with each other is intriguing given the increasingly established network structures of whole brain organization. Commonly used regional interaction approaches, however, cannot address this question. In this paper, we proposed a multivariate network-level framework to directly quantify the interaction pattern between large-scale functional systems. The proposed framework was tested on three different brain states, including resting, finger tapping and movie watching using functional connectivity MRI. The interaction patterns among five predefined networks including dorsal attention (DA), default (DF), frontal-parietal control (FPC), motor-sensory (MS) and visual (V) were delineated during each state. Results show dramatic and expected network-level correlation changes across different states underscoring the importance of network-level interactions for successful transition between different states. In addition, our analysis provides preliminary evidence of the potential regulating role of FPC on the two opposing systems-DA and DF on the network level. |
DOI | 10.1007/978-3-642-15745-5_37 |
Alternate Journal | Med Image Comput Comput Assist Interv |
Original Publication | Multivariate network-level approach to detect interactions between large-scale functional systems. |
PubMed ID | 20879328 |
PubMed Central ID | PMC2963578 |
Grant List | R21 AG033387-02 / AG / NIA NIH HHS / United States AG033387 / AG / NIA NIH HHS / United States R01 NS055754-05 / NS / NINDS NIH HHS / United States R21 AG033387-01A1 / AG / NIA NIH HHS / United States P01CA142538-01 / CA / NCI NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States R01 NS055754 / NS / NINDS NIH HHS / United States R01 NS055754-04 / NS / NINDS NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R01 NS055754-02 / NS / NINDS NIH HHS / United States R01 NS055754-01A1 / NS / NINDS NIH HHS / United States UL1 RR025747-01S1 / RR / NCRR NIH HHS / United States P01 CA142538-01 / CA / NCI NIH HHS / United States NS R01055754 / NS / NINDS NIH HHS / United States R01 MH086633-02 / MH / NIMH NIH HHS / United States P01 CA142538-02 / CA / NCI NIH HHS / United States UL1 RR025747-02 / RR / NCRR NIH HHS / United States R01 MH086633-01A1 / MH / NIMH NIH HHS / United States MH086633 / MH / NIMH NIH HHS / United States UL1 RR025747 / RR / NCRR NIH HHS / United States R01 NS055754-03 / NS / NINDS NIH HHS / United States UL1-RR025747-01 / RR / NCRR NIH HHS / United States R21 AG033387 / AG / NIA NIH HHS / United States |
Multivariate network-level approach to detect interactions between large-scale functional systems.
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