Precise Estimation of Resting State Functional Connectivity Using Empirical Mode Decomposition

Sukesh Das, Anil K. Sao, Bharat Biswal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


The estimation of functional connectivity from the observed Blood Oxygen Level-Dependent (BOLD) signal may not be accurate because it is an indirect measure of neuronal activity or the existing deconvolution approaches assume that hemodynamic response function (HRF), which modulates the neuronal activities, is uniform across the brain regions or across the time course. We propose a novel approach using empirical mode decomposition (EMD), to reduce the effect of HRF from estimated neuronal activity signal (NAS) obtained after blind deconvolution for a BOLD time course. The first two intrinsic mode functions (IMFs), obtained during EMD of the neuronal activity signal represent its highest oscillating modes and hence have characteristic of impulses. The sum of the first two IMFs is computed as a refined representation of neuronal activity signal to estimate resting state connectome using the framework of dictionary learning. Usefulness of the proposed method has been demonstrated using two resting state datasets (healthy control and attention deficit hyperactivity disorder) taken from ‘1000 Functional Connectomes’. For quantitative analysis, Jaccard distances are computed between spatial maps obtained using BOLD signals and refined activity signals. Results show that maps obtained using NAS are a subset of that obtained using BOLD signal and hence avoid false acceptance of active voxels, which illustrates the importance of refined NAS.

Original languageEnglish (US)
Title of host publicationBrain Informatics - 13th International Conference, BI 2020, Proceedings
EditorsMufti Mahmud, Stefano Vassanelli, M. Shamim Kaiser, Ning Zhong
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030592769
StatePublished - 2020
Event13th International Conference on Brain Informatics, BI 2020 - Padua, Italy
Duration: Sep 19 2020Sep 19 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12241 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Brain Informatics, BI 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Dictionary learning
  • EMD
  • HRF
  • Neuronal activity signal
  • fMRI


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