Different interaction modes for the default mode network revealed by resting state functional magnetic resonance imaging.
The default mode network (DMN), which, in the resting state, is in charge of both the brain's intrinsic mentation and its reflexive responses to external stimuli, is recognized as an essential network in the human brain. These two roles of mentation and reflexive response recruit the DMN nodes and other task networks differently. Existing research has revealed that the interactions inside the DMN (between nodes within the DMN) and outside the DMN (between nodes in the DMN and nodes in task networks) have different modes, in terms of both strength and timing. These findings raise interesting questions. For example, are the internal and external interactions of the DMN equally linear or nonlinear? This study examined these interaction patterns using datasets from the Human Connectome Project. A maximal information-based nonparametric exploration statistics strategy was utilized to characterize the full correlations, and the Pearson correlation was used to capture the linear component of the full correlations. We then contrasted the level of linearity/nonlinearity with respect to the internal and external interactions of the DMN. After a brain-wide exploration, we found that the interactions between the DMN and the sensorimotor-related networks (including the sensorimotor, sensory association, and integration areas) showed more nonlinearity, whereas those between the intra-DMN nodes were similarly less nonlinear. These findings may provide a clue for understanding the underlying neuronal principles of the internal and external roles of the DMN.
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Nianming
N
Zuo
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Ming
M
Song
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Lingzhong
L
Fan
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.|The Queensland Brain Institute, The University of Queensland, Brisbane, Qld, Australia.
Simon B
SB
Eickhoff
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.|Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany.|Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
Tianzi
T
Jiang
D000328:Adult; D001921:Brain / Q000502:physiology*; D063132:Connectome; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009434:Neural Pathways / Q000502:physiology; D009778:Occipital Lobe / Q000502:physiology; D010296:Parietal Lobe / Q000502:physiology; D017397:Prefrontal Cortex / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D013702:Temporal Lobe / Q000502:physiology; D055815:Young Adult
default mode network; functional magnetic resonance imaging; human; linear; nonlinear