Literature DB >> 23089053

Is mental illness complex? From behavior to brain.

Albert C Yang1, Shih-Jen Tsai.   

Abstract

A defining but elusive feature of the human brain is its astonishing complexity. This complexity arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various amazing mental functions. In mentally ill patients, such adaptability is often impaired, leading to either ordered or random patterns of behavior. Quantification and classification of these abnormal human behaviors exhibited during mental illness is one of the major challenges of contemporary psychiatric medicine. In the past few decades, attempts have been made to apply concepts adopted from complexity science to better understand complex human behavior. Although considerable effort has been devoted to studying the abnormal dynamic processes involved in mental illness, unfortunately, the primary features of complexity science are typically presented in a form suitable for mathematicians, physicists, and engineers; thus, they are difficult for practicing psychiatrists or neuroscientists to comprehend. Therefore, this paper introduces recent applications of methods derived from complexity science for examining mental illness. We propose that mental illness is loss of brain complexity and the complexity of mental illness can be studied under a general framework by quantifying the order and randomness of dynamic macroscopic human behavior and microscopic neuronal activity. Additionally, substantial effort is required to identify the link between macroscopic behaviors and microscopic changes in the neuronal dynamics within the brain.
Copyright © 2012 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  BOLD; Complexity; EEG; Entropy; MEG; MSE; Mental illness; Spontaneous brain activity; blood oxygen level dependent; electroencephalogram; fMRI; functional magnetic resonance imaging; magnetoencephalography; multiscale entropy

Mesh:

Year:  2012        PMID: 23089053     DOI: 10.1016/j.pnpbp.2012.09.015

Source DB:  PubMed          Journal:  Prog Neuropsychopharmacol Biol Psychiatry        ISSN: 0278-5846            Impact factor:   5.067


  27 in total

Review 1.  Traditional Chinese medicine: potential approaches from modern dynamical complexity theories.

Authors:  Yan Ma; Kehua Zhou; Jing Fan; Shuchen Sun
Journal:  Front Med       Date:  2016-01-25       Impact factor: 4.592

2.  Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness.

Authors:  Albert C Yang; Chen-Jee Hong; Yin-Jay Liou; Kai-Lin Huang; Chu-Chung Huang; Mu-En Liu; Men-Tzung Lo; Norden E Huang; Chung-Kang Peng; Ching-Po Lin; Shih-Jen Tsai
Journal:  Hum Brain Mapp       Date:  2015-02-09       Impact factor: 5.038

3.  Neural complexity as a potential translational biomarker for psychosis.

Authors:  Brandon Hager; Albert C Yang; Roscoe Brady; Shashwath Meda; Brett Clementz; Godfrey D Pearlson; John A Sweeney; Carol Tamminga; Matcheri Keshavan
Journal:  J Affect Disord       Date:  2016-10-26       Impact factor: 4.839

Review 4.  Applications of dynamical complexity theory in traditional Chinese medicine.

Authors:  Yan Ma; Shuchen Sun; Chung-Kang Peng
Journal:  Front Med       Date:  2014-09-09       Impact factor: 4.592

5.  Uncovering complex central autonomic networks at rest: a functional magnetic resonance imaging study on complex cardiovascular oscillations.

Authors:  Gaetano Valenza; Luca Passamonti; Andrea Duggento; Nicola Toschi; Riccardo Barbieri
Journal:  J R Soc Interface       Date:  2020-03-18       Impact factor: 4.118

6.  Multivariate Classification of Brain Blood-Oxygen Signal Complexity for the Diagnosis of Children with Tourette Syndrome.

Authors:  Xiaoyang Xin; Yixuan Feng; Yufeng Zang; Yuting Lou; Ke Yao; Xiaoqing Gao
Journal:  Mol Neurobiol       Date:  2022-01-03       Impact factor: 5.590

7.  Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project.

Authors:  Ian M McDonough; Kaoru Nashiro
Journal:  Front Hum Neurosci       Date:  2014-06-10       Impact factor: 3.169

8.  Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics.

Authors:  Mimma Nardelli; Gaetano Valenza; Ioana A Cristea; Claudio Gentili; Carmen Cotet; Daniel David; Antonio Lanata; Enzo P Scilingo
Journal:  Front Comput Neurosci       Date:  2015-03-25       Impact factor: 2.380

9.  Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics.

Authors:  Gaetano Valenza; Ronald G Garcia; Luca Citi; Enzo P Scilingo; Carlos A Tomaz; Riccardo Barbieri
Journal:  Front Physiol       Date:  2015-03-13       Impact factor: 4.566

10.  Differences in affect integration in children with and without internalizing difficulties.

Authors:  Charlotte Fiskum; Tonje Grønning Andersen; Unni Tanum Johns; Karl Jacobsen
Journal:  Scand J Child Adolesc Psychiatr Psychol       Date:  2021-07-23
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