Ultra-slow frequency bands reflecting potential coherence between neocortical brain regions

X Zhang; Y-T Wang; Y Wang; T P Jung; M Huang; C K Cheng; A J Mandell

doi:10.1016/j.neuroscience.2014.12.050

Ultra-slow frequency bands reflecting potential coherence between neocortical brain regions

Neuroscience. 2015 Mar 19:289:71-84. doi: 10.1016/j.neuroscience.2014.12.050. Epub 2015 Jan 12.

Authors

X Zhang¹, Y-T Wang², Y Wang³, T P Jung⁴, M Huang⁵, C K Cheng⁶, A J Mandell⁷

Affiliations

¹ Institute of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, PR China; Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA.
² Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA; Swartz Center for Computational Neuroscience, Institute of Engineering in Medicine, University of California at San Diego, La Jolla, CA, USA.
³ Swartz Center for Computational Neuroscience, Institute of Engineering in Medicine, University of California at San Diego, La Jolla, CA, USA.
⁴ Swartz Center for Computational Neuroscience, Institute of Engineering in Medicine, University of California at San Diego, La Jolla, CA, USA; Center for Advanced Neurological Engineering, Institute of Engineering in Medicine, University of California at San Diego, La Jolla, CA 92093, USA.
⁵ Department of Radiology, University of California at San Diego, La Jolla, CA 92093, USA.
⁶ Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA. Electronic address: ckcheng@ucsd.edu.
⁷ Fetzer Memorial Trust, Kalamazoo, MI, USA; Department of Psychiatry, University of California at San Diego, La Jolla, CA 92093, USA.

PMID: 25592429
DOI: 10.1016/j.neuroscience.2014.12.050

Abstract

Recent studies of electromagnetic ultra-slow waves (⩽0.1Hz) have suggested that they play a role in the integration of otherwise disassociated brain regions supporting vital functions (Ackermann and Borbely, 1997; Picchioni et al., 2010; Knyazev, 2012; Le Bon et al., 2012). We emphasize this spectral domain in probing sensor coherence issues raised by these studies using Hilbert phase coherences in the human MEG. In addition, we ask: will temporal-spatial phase coherence in regional brain oscillations obtained from the ultraslow spectral bands of multi-channel magnetoencephalograms (MEG) differentiate resting, "task-free" MEG records of normal control and schizophrenic subjects? The goal of the study is a comparison of the relative persistence of intra-regional phase locking values (PLVs), among 10, region-defined, sensors in examined in the resting multichannel, MEG records as a function of spectral frequency bands and diagnostic category. The following comparison of Hilbert-transform-engendered relative phases of each designated spectral band was made using their pair-wise PLVs. This indicated the proportion of shared cycle time in which the phase relations between the index location and reference leads were maintained. Leave one out, bootstrapping of the PLVs via a support vector machine (SVM), classified clinical status with 97.3% accuracy. It was generally the case that spectral bands ⩽1.0Hz generated the highest values of the PLVs and discriminated best between control and patient populations. We conclude that PLV analysis of the oscillatory patterns of MEG recordings in the ultraslow frequency bands suggest their functional significance in intra-regional signal coherence and provide a higher rate of classification of patients and normal subjects than the other spectral domains examined.

Keywords: MEG; PLV; SVM; Ultra Low Frequency; classification; schizophrenia.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adult
Algorithms
Brain Waves*
Female
Humans
Magnetoencephalography / methods
Male
Neocortex / physiology*
Neocortex / physiopathology*
Rest
Schizophrenia / classification*
Schizophrenia / physiopathology*
Signal Processing, Computer-Assisted
Support Vector Machine