Author: Zhang X1, Wang YT2, Wang Y3, Jung TP4, Huang M5, Cheng CK6, Mandell AJ7.
Affiliation:
1Institute 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. 2Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA; Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA. 3Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA. 4Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California 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. 5Department of Radiology, University of California at San Diego, La Jolla, CA 92093, USA. 6Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA. Electronic address: ckcheng@ucsd.edu. 7Fetzer Memorial Trust, Kalamazoo MI and Department of Psychiatry, University of California at San Diego, La Jolla, CA92093, USA.
Conference/Journal: Neuroscience.
Date published: 2015 Jan 12
Other:
Pages: S0306-4522(15)00018-4 , Special Notes: doi: 10.1016/j.neuroscience.2014.12.050. , Word Count: 279
Abstract
Recent studies of electromagnetic ultra-slow waves (⩽ 0.1 Hz) have suggested that they play a role in the integration of otherwise disassociated brain regions supporting vital functions (Picchioni, Horovitz et al, 2011; Ackermann and Borbeley, 1997; Le Bon, Neu, Berquin et al, 2012; Knyazev, 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, PLV, among ten, 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 phase locking values, PLV. 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 ⩽ 1.0 Hz 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 then the other spectral domains examined.
Copyright © 2015. Published by Elsevier Ltd.
KEYWORDS:
Classification; MEG; PLV; SVM; Schizophrenia; Ultra Low Frequency
PMID: 25592429
