The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform

Michael Eakspea - One of the best experts on this subject based on the ideXlab platform.

  • metastable Brain Waves
    Nature Communications, 2019
    Co-Authors: James A Roberts, Leonardo L Gollo, Romesh G Abeysuriya, Gloria Roberts, Philip Mitchell, Mark W Woolrich, Michael Eakspea
    Abstract:

    Traveling patterns of neuronal activity—Brain Waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human Brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-Brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling Waves, spiral Waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical Waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale Waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation. Large-scale Brain activity arises from inter-areal interactions determined by the underlying connectivity. Here, the authors develop a whole-Brain model based on connectivity data that captures activity patterns such as cortical Waves and metastability, relating these to underlying Brain anatomy.

  • metastable Brain Waves
    bioRxiv, 2018
    Co-Authors: James A Roberts, Leonardo L Gollo, Romesh G Abeysuriya, Gloria Roberts, Philip Mitchell, Mark W Woolrich, Michael Eakspea
    Abstract:

    Traveling patterns of neuronal activity -- Brain Waves -- have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human Brain lacks a firm understanding. Here, we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-Brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling Waves, spiral Waves, sources, and sinks. These patterns are metastable, such that system visits multiple spatiotemporal wave patterns in sequence. Transitions between metastable states correspond to reconfigurations of an underlying phase flow, characterized by complex nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical Waves in cortical tissue, the presence of sequential spatiotemporal patterns in resting state MEG data, and large-scale Waves in human electrocorticography. By moving the study of functional networks from a static to an inherently dynamic frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.

Jea Requi - One of the best experts on this subject based on the ideXlab platform.

  • Brain Waves associated with musical incongruities differ for musicians and non musicians
    Neuroscience Letters, 1994
    Co-Authors: Mireille Esso, Frederique Faita, Jea Requi
    Abstract:

    Abstract Musicians and non-musicians were presented with short musical phrases that were either selected from the classical musical repertoire or composed for the experiment. The phrases terminated either in a congruous or a ‘harmonically’, ‘melodically’, or ‘rhythmically’ incongruous note. The Brain Waves produced by the end-notes differed greatly between musicians and non-musicians, and as a function of the subject's familiarity with the melodies and the type of incongruity. The timing of these Brain Waves revealed that musicians are faster than non-musicians in detecting incongruities. This study provides further neurophysiological evidence concerning the mechanisms underlying music perception and the differences between musical and linguistic processing.

Gloria Roberts - One of the best experts on this subject based on the ideXlab platform.

  • metastable Brain Waves
    Nature Communications, 2019
    Co-Authors: James A Roberts, Leonardo L Gollo, Romesh G Abeysuriya, Gloria Roberts, Philip Mitchell, Mark W Woolrich, Michael Eakspea
    Abstract:

    Traveling patterns of neuronal activity—Brain Waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human Brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-Brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling Waves, spiral Waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical Waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale Waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation. Large-scale Brain activity arises from inter-areal interactions determined by the underlying connectivity. Here, the authors develop a whole-Brain model based on connectivity data that captures activity patterns such as cortical Waves and metastability, relating these to underlying Brain anatomy.

  • metastable Brain Waves
    bioRxiv, 2018
    Co-Authors: James A Roberts, Leonardo L Gollo, Romesh G Abeysuriya, Gloria Roberts, Philip Mitchell, Mark W Woolrich, Michael Eakspea
    Abstract:

    Traveling patterns of neuronal activity -- Brain Waves -- have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human Brain lacks a firm understanding. Here, we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-Brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling Waves, spiral Waves, sources, and sinks. These patterns are metastable, such that system visits multiple spatiotemporal wave patterns in sequence. Transitions between metastable states correspond to reconfigurations of an underlying phase flow, characterized by complex nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical Waves in cortical tissue, the presence of sequential spatiotemporal patterns in resting state MEG data, and large-scale Waves in human electrocorticography. By moving the study of functional networks from a static to an inherently dynamic frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.

Philip Mitchell - One of the best experts on this subject based on the ideXlab platform.

  • metastable Brain Waves
    Nature Communications, 2019
    Co-Authors: James A Roberts, Leonardo L Gollo, Romesh G Abeysuriya, Gloria Roberts, Philip Mitchell, Mark W Woolrich, Michael Eakspea
    Abstract:

    Traveling patterns of neuronal activity—Brain Waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human Brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-Brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling Waves, spiral Waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical Waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale Waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation. Large-scale Brain activity arises from inter-areal interactions determined by the underlying connectivity. Here, the authors develop a whole-Brain model based on connectivity data that captures activity patterns such as cortical Waves and metastability, relating these to underlying Brain anatomy.

  • metastable Brain Waves
    bioRxiv, 2018
    Co-Authors: James A Roberts, Leonardo L Gollo, Romesh G Abeysuriya, Gloria Roberts, Philip Mitchell, Mark W Woolrich, Michael Eakspea
    Abstract:

    Traveling patterns of neuronal activity -- Brain Waves -- have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human Brain lacks a firm understanding. Here, we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-Brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling Waves, spiral Waves, sources, and sinks. These patterns are metastable, such that system visits multiple spatiotemporal wave patterns in sequence. Transitions between metastable states correspond to reconfigurations of an underlying phase flow, characterized by complex nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical Waves in cortical tissue, the presence of sequential spatiotemporal patterns in resting state MEG data, and large-scale Waves in human electrocorticography. By moving the study of functional networks from a static to an inherently dynamic frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.

James A Roberts - One of the best experts on this subject based on the ideXlab platform.

  • metastable Brain Waves
    Nature Communications, 2019
    Co-Authors: James A Roberts, Leonardo L Gollo, Romesh G Abeysuriya, Gloria Roberts, Philip Mitchell, Mark W Woolrich, Michael Eakspea
    Abstract:

    Traveling patterns of neuronal activity—Brain Waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human Brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-Brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling Waves, spiral Waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical Waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale Waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation. Large-scale Brain activity arises from inter-areal interactions determined by the underlying connectivity. Here, the authors develop a whole-Brain model based on connectivity data that captures activity patterns such as cortical Waves and metastability, relating these to underlying Brain anatomy.

  • metastable Brain Waves
    bioRxiv, 2018
    Co-Authors: James A Roberts, Leonardo L Gollo, Romesh G Abeysuriya, Gloria Roberts, Philip Mitchell, Mark W Woolrich, Michael Eakspea
    Abstract:

    Traveling patterns of neuronal activity -- Brain Waves -- have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human Brain lacks a firm understanding. Here, we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-Brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling Waves, spiral Waves, sources, and sinks. These patterns are metastable, such that system visits multiple spatiotemporal wave patterns in sequence. Transitions between metastable states correspond to reconfigurations of an underlying phase flow, characterized by complex nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical Waves in cortical tissue, the presence of sequential spatiotemporal patterns in resting state MEG data, and large-scale Waves in human electrocorticography. By moving the study of functional networks from a static to an inherently dynamic frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.