Satellite symposium on 27th “Human brain dynamics research in connectome era”

Recent human connectome researches have revealed macro-scale network organizations of human brain. Using diffusion MRI and resting-state fMRI, whole-brain structural and functional network can be identified and it has been found that identified network can predict personal traits such as age, sex, IQ and mental illness. However dynamic interactions on brain network and their functional role have been largely unknown yet. In this symposium, we will discuss current achievements and future direction of human brain dynamics research in connectome era.

Date            :  27th November Monday, 2017 at ATR
Time            : 13:00-18:00 Symposium (50 mins talk and 10 min discussion)
Location     :  2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto 619-0288 Japan
Organizer   : Okito Yamashita, Ph.D.  (Neural information analysis laboratories, ATR)

Speakers :

  • Sylvain Baillet
    (Professor, Montreal Neurological Institute, McGill University)

    “Mechanisms & dynamical structure of brain rhythms: from rest to perception.”
        One broad objective in neuroscience is to comprehend the mechanisms of large-scale, oscillatory neural dynamics: how they enable functions by shaping communication in brain networks, and how the earliest detection of their alterations in disease can contribute to improved healthcare prevention and interventions. We will review how the ubiquitous polyrhythmic activity of the brain has been approached empirically so far, with underlying mechanisms that remain not understood. This hinders our comprehension of how 1) perception and behaviour emerge from brain network activity, and 2) the pathophysiological developments of brain and mental-health disorders increasingly studied as network diseases, affect large-scale neural communication.
        I will introduce how these difficult questions can benefit from a bottom-up approach: We aim to understand how basic physiological factors of neural integrity and function shape the dynamical structure of oscillatory brain rhythms, such as their interdependence across multiple frequencies through cross-frequency coupling. These phenomena represent a deep source of uncharted markers of neural excitability, activity and connectivity. I will illustrate these principles with our latest results concerning the resting brain, multimodal perception and pathophysiological markers of epilepsy and neurodegenerative syndromes.
  • Roberto D. Pascual-Marqui
    (The KEY Institute for Brain-Mind Research, University of Zurich / Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka, PhD, PD)
  • Keiichi Kitajo (RIKEN Brain Science Institute, PhD)
    “Perturbational and computational approaches to nonlinear human brain dynamics.”
  • Okito Yamashita (ATR)
    “Multi-modal integration approach to understand event-related brain dynamics”
        Recent developments of resting state functional connectivity and diffusion MRI allow for investigating how our brain is organized as a network. These methods have revealed macro-scale network organization of human brain such as existence of functional subnetwork (default mode, attention, sensory and motor systems), hub nodes of structural network, and individual difference of functional connectivity. However investigating dynamics on the brain network is challenging partially because of lack of measurement methodology. To address this issue, we have been developing fMRI-informed MEG/EEG source reconstruction method to visualize brain activities in milli-second temporal resolution (Sato et al. 2004, NeuroImage, MATLAB toolbox available from Effectiveness of the method has been demonstrated with basic neuroscience experiments (Yosioka et al, 2008, NeuroImage, Shibata et al. 2008, Cerebral Cortex) as well as BMI applications (Toda et al. 2011, NeuroImage, Yanagisawa et al, 2016, Nature comm). Recently we further developed extention of VBMEG to describe event-related brain network dynamics (Fukushima et al, NeuroImage, 2015). In this method, a generative dynamics process of MEG is modeled via a current-source network dynamical model whose network structure is constrained by diffusion MRI. Algorithm to infer current sources and the dynamics model parameters from MEG and fMRI data is proposed. This method allow for visulaization how brain activties are generated by interactions on structural network. In this talk I summarize our attempt to understand event-related brain dynamics using multi-modal integration approach.

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