Brain Complexity and Psychiatric Disorders

Ronald Miguel Hernández; Jacqueline Cynthia Ponce-Meza; Miguel Ángel  Saavedra-López; Walter Antonio Campos Ugaz; Roxana Monteza Chanduvi; Walter Campos Monteza

doi:10.18502/ijps.v18i4.13637

Ronald Miguel Hernández Universidad Privada Norbert Wiener, Lima, Perú.
Jacqueline Cynthia Ponce-Meza Universidad Privada del Norte, Lima, Perú.
Miguel Ángel Saavedra-López Universidad Continental, Huancayo, Perú
Walter Antonio Campos Ugaz Universidad Nacional Pedro Ruiz Gallo, Lambayeque, Perú.
Roxana Monteza Chanduvi Universidad Católica Santo Toribio de Mogrovejo, Chiclayo, Perú.
Walter Campos Monteza Universidad de San Martin de Porres, Lima, Perú.

DOI: https://doi.org/10.18502/ijps.v18i4.13637

Keywords: Brain; Complexity Analysis; Neuropathology; Psychiatric Disorders

Abstract

Objective: In recent years, researchers and neuroscientists have begun to use a variety of nonlinear techniques for analyzing neurophysiologic signals derived from fMRI, MEG, and EEG in order to describe the complex dynamical aspects of neural mechanisms. In this work, we first attempted to describe different algorithms to estimate neural complexity in a simple manner understandable for psychiatrists, psychologists, and neuroscientists. Then, we reviewed the findings of the brain complexity analysis in psychiatric disorders and their clinical implications.

Method: A non-systematic comprehensive literature search was conducted for original studies on the complexity analysis of neurophysiological signals such as electroencephalogram, magnetoencephalogram, and blood-oxygen-level-dependent obtained from functional magnetic resonance imaging or functional near infrared spectroscopy. The search encompassed online scientific databases such as PubMed and Google Scholar.

Results: Complexity measures mainly include entropy-based methods, the correlation dimension, fractal dimension, Lempel-Ziv complexity, and the Lyapunov exponent. There are important differences in the physical notions between these measures. Our literature review shows that dementia, autism, and adult ADHD exhibit less complexity in their neurophysiologic signals than healthy controls. However, children with ADHD, drug-naïve young schizophrenic patients with positive symptoms, and patients with mood disorders (i.e., depression and bipolar disorder) exhibit higher complexity in their neurophysiologic signals compared to healthy controls. In addition, contradictory findings still exist in some psychiatric disorders such as schizophrenia regarding brain complexity, which can be due to technical issues, large heterogeneity in psychiatric disorders, and interference of typical factors.

Conclusion: In summary, complexity analysis may present a new dimension to understanding psychiatric disorders. While complexity analysis is still far from having practical applications in routine clinical settings, complexity science can play an important role in comprehending the system dynamics of psychiatric disorders.