Affiliations 

  • 1 Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia
  • 2 Department of Neurosciences, School of Medical Sciences, Hospital Universiti Sains Malaysia, Universiti Sains Malaysia Health Campus, Kota Bharu, Kelantan, Malaysia
  • 3 Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia. Electronic address: tongboon.tang@utp.edu.my
Comput Biol Med, 2017 10 01;89:573-583.
PMID: 28551109 DOI: 10.1016/j.compbiomed.2017.05.005

Abstract

Functional neuroimaging modalities play an important role in deciding the diagnosis and course of treatment of neuronal dysfunction and degeneration. This article presents an analytical tool with visualization by exploiting the strengths of the MEG (magnetoencephalographic) neuroimaging technique. The tool automates MEG data import (in tSSS format), channel information extraction, time/frequency decomposition, and circular graph visualization (connectogram) for simple result inspection. For advanced users, the tool also provides magnitude squared coherence (MSC) values allowing personalized threshold levels, and the computation of default model from MEG data of control population. Default model obtained from healthy population data serves as a useful benchmark to diagnose and monitor neuronal recovery during treatment. The proposed tool further provides optional labels with international 10-10 system nomenclature in order to facilitate comparison studies with EEG (electroencephalography) sensor space. Potential applications in epilepsy and traumatic brain injury studies are also discussed.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.