Soft Computing

, Volume 22, Issue 7, pp 2341–2368 | Cite as

Examining dynamic functional relationships in a pathological brain using evolutionary computation

Methodologies and Application


Neuropathological conditions often result in abnormal functional relationship between different regions in the brain and are specific to certain spectral bands that are not known in advance. Typically, these abnormalities are spatially and temporally very localized in nature, and detecting these changes can be clinically very useful. In this article, a novel evolutionary computation-based procedure is introduced to discover such localized changes in a data-driven manner. Given a predefined set of regions of interest (ROIs), the procedure automatically detects a subset of ROIs, a time window, and a frequency band, such that the functional relationship among the ROIs significantly differ between controls and neuropathological cases; the procedure makes no prior assumptions regarding the spectral characteristics of the data. To demonstrate the effectiveness of this procedure, a publicly available EEG dataset of 46 alcoholics and 31 controls is used. In all, 100 cross-validation runs are performed. Using the procedure, many weakened inter-hemispheric functional connections, primarily between the left and the right parietal lobe sensors, are detected in chronic alcoholics. For these functional connections, gamma band (35–50 Hz) activity in 200–400 ms window was found to be significantly different between alcoholics and controls. These results are consistent with the existing literature and helps to validate the procedure. In addition, the procedure is also tested via simulation using a graph generation model with known characteristics, and its general utility to brain imaging literature is discussed.


Dynamic functional connectivity Time-frequency analysis Evolutionary computation EEG Alcoholism Feature subset selection Graph simulation 



The data for this research was made available on the web by Dr. Henri Begleiter, Neurodynamics Laboratory, State University of New York Health Center at Brooklyn. I would also like to thank Dr. J. David Schaffer, Institute for Multi-Generational Studies, Binghamton University, Binghamton, NY and Dr. Bharath Sriperumbudur, Department of Statistics, Pennsylvania State University, State College, PA for their valuable comments that greatly helped to improve the manuscript. Finally, I would like to thank Ms. Rachel A. Bernier, Department of Psychology, Pennsylvania State University, State College, PA for her comments regarding clinical utility of this work.

Funding The research was not supported by any funding agency. All programs were written in R 3.2.3, and the experiments were run on a personal laptop computer. The data used in this work are part of a publicly available dataset currently hosted by UCI machine learning repository.

Compliance with ethical standards

Conflict of interest

There are no known conflicts of interest associated with this manuscript.


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© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Applied Physiology and KinesiologyUniversity of FloridaGainesvilleUSA

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