LORETA EEG phase reset of the default mode network.

Author: Thatcher RW, North DM, Biver CJ.
Affiliation: EEG and NeuroImaging Laboratory, Applied Neuroscience Research Institute Seminole, FL, USA.
Conference/Journal: Front Hum Neurosci.
Date published: 2014 Jul 23
Other: Volume ID: 8 , Pages: 529 , Special Notes: doi: 10.3389/fnhum.2014.00529 , Word Count: 249



OBJECTIVES:
The purpose of this study was to explore phase reset of 3-dimensional current sources in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG).
METHODS:
The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodmann areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st and 2nd derivatives of the time series of phase differences.
RESULTS:
Phase shift duration exhibited three discrete modes at approximately: (1) 25 ms, (2) 50 ms, and (3) 65 ms. Phase lock duration present primarily at: (1) 300-350 ms and (2) 350-450 ms. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas.
CONCLUSIONS:
The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a "shutter" that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.
KEYWORDS:
EEG phase reset; LORETA; chaos; phase lock; phase shift; self-organized criticality; stability

PMID: 25100976 [PubMed] PMCID: PMC4108033