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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model.

Sriniwas Govinda Surampudi1, Joyneel Misra1, Gustavo Deco2

  • 1Center for Visual Information Technology, Kohli Center on Intelligent Systems, International Institute of Information Technology Hyderabad, Hyderabad, 500032, India.

Neuroimage
|September 30, 2018
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel graph-theoretic model (temporal Multiple Kernel Learning, tMKL) to link brain structure and dynamics. This method accurately predicts functional connectivity from resting-state fMRI data, improving upon existing models and showing generalizability.

Keywords:
FCManifoldSCdFCrsfMRItMKL

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Imaging Analysis

Background:

  • Resting-state functional magnetic resonance imaging (rsfMRI) reveals complex spatio-temporal brain dynamics.
  • Characterizing dynamic functional connectivity (dFC) and its relationship to structural connectivity (SC) remains challenging.
  • Existing methods struggle to link latent brain states directly to SC or capture temporal FC evolution.

Purpose of the Study:

  • To develop an innovative method linking brain structural connectivity (SC) with dynamic functional connectivity (dFC).
  • To discover an underlying lower-dimensional manifold representing temporal brain structure.
  • To characterize the SC-dFC-FC relationship within a unifying framework.

Main Methods:

  • Proposed a graph-theoretic model, temporal Multiple Kernel Learning (tMKL), to learn latent state parameters.
  • Utilized a state transition Markov model to predict grand average functional connectivity (FC).
  • Trained and tested the model on rsfMRI data from 46 healthy participants and an independent cohort of 100 HCP participants.

Main Results:

  • The tMKL model significantly outperformed existing methods (DMF, SDK, MKL) in predicting resting-state functional connectivity.
  • The model demonstrated generalizability across different datasets, including the Human Connectome Project (HCP) cohort.
  • The proposed solution retains sensitivity to subject-specific anatomy, offering a holistic approach to SC-FC characterization.

Conclusions:

  • The tMKL approach provides a robust and generally applicable framework for understanding brain dynamics and connectivity.
  • This method successfully links structural and dynamic brain properties, advancing the characterization of SC-dFC-FC relationships.
  • The model's ability to integrate subject-specific anatomy represents a significant contribution to holistic brain connectivity analysis.