The dynamic modular fingerprints of the human brain at rest
The human brain is a dynamic modular network that can be decomposed into a set of modules and its activity changes permanently over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at subsecond temporal scale. Here, we seek to decipher...
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| Abstract | The human brain is a dynamic modular network that can be decomposed into a set of modules and its activity changes permanently over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at subsecond temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationship to RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track dynamics of modular brain networks, in three independent datasets (N= 568) of healthy subjects at rest. We show the presence of striking spatiotemporal network pattern consistent over participants. We also show that some RSNs, such as default mode network and temporal network, are not necessary unified units but rather can be divided into multiple sub-networks over time. Using the resting state questionnaire, our results revealed also that brain network dynamics are strongly correlated to mental imagery at rest. These findings add new perspectives to brain dynamic analysis and highlight the importance of tracking fast reconfiguration of electrophysiological networks at rest. Competing Interest Statement The authors have declared no competing interest. |
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| AbstractList | The human brain is a dynamic modular network that can be decomposed into a set of modules and its activity changes permanently over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at subsecond temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationship to RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track dynamics of modular brain networks, in three independent datasets (N= 568) of healthy subjects at rest. We show the presence of striking spatiotemporal network pattern consistent over participants. We also show that some RSNs, such as default mode network and temporal network, are not necessary unified units but rather can be divided into multiple sub-networks over time. Using the resting state questionnaire, our results revealed also that brain network dynamics are strongly correlated to mental imagery at rest. These findings add new perspectives to brain dynamic analysis and highlight the importance of tracking fast reconfiguration of electrophysiological networks at rest. Competing Interest Statement The authors have declared no competing interest. The human brain is a dynamic modular network that can be decomposed into a set of modules and its activity changes permanently over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationship to RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track dynamics of modular brain networks, in three independent datasets (N= 568) of healthy subjects at rest. We show the presence of striking spatiotemporal network pattern consistent over participants. We also show that some RSNs, such as default mode network and temporal network, are not necessary ‘unified units’ but rather can be divided into multiple sub-networks over time. Using the resting state questionnaire, our results revealed also that brain network dynamics are strongly correlated to mental imagery at rest. These findings add new perspectives to brain dynamic analysis and highlight the importance of tracking fast reconfiguration of electrophysiological networks at rest. |
| Author | Hassan, Mahmoud Kabbara, Aya Paban, Veronique |
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