Effects of age on lagged cross-correlations among neural activities measured by magnetoencephalography (MEG) in the resting state

Author: Margaret Y. Mahan - University of Minnesota, Biomedical Informatics and Computational Biology

Co-Authors: Maren E. Loe - University of Chicago, Mathematics; Arthur C. Leuthold - University of Minnesota, Neuroscience; Apostolos P. Georgopoulos - University of Minnesota, Neuroscience

Abstract: A central effort of our research is focused on investigating brain function across the lifespan. For that purpose, we use magnetoencephalography to record high-fidelity resting-state brain activity at high temporal resolution. This yields 248 sensors x 60,000 ms matrix of neural activity recorded simultaneously from the cerebral cortex. To estimate the strength and direction of neural interactions, we calculate pair-wise cross-correlation functions (CCF) (N = 30,628) between the prewhitened 248 sensor time series for ±50 ms lags. We have found in previous studies that the zero-lag cross-correlations carry sufficient information to discriminate among brain diseases, and change systematically across the lifespan. In this study, we investigated the age-dependent changes in cross-correlations calculated for lags ±50 ms. This yielded 101 cross-correlations for each one of the 30,628 sensor pairs. The strength, sign, and lag of each cross-correlation was noted, and more general patterns of the cross-correlogram were determined and quantified (e.g. contiguous cross-correlations, multiple significant peaks in the cross-correlogram, systematic driving of a sensor on others, etc.). We then regressed each one of these measures and quantitative features of the cross-correlogram against the age of 133 brain-healthy women subjects (28-94 years old). We discovered highly significant associations between CCF attributes and subject age. These served as the basis to construct a model of how brain communication patterns change with age, in such a way that brain function remains healthy, namely, a model of healthy brain aging. This model was further corroborated using longitudinal measurements taken from study subjects every year (http://healthybrain.umn.edu).