# Complexity and psychopathology: from mechanistic science to a physical understanding of the mental conditions based on chaos and complexity

## TL;DR

This paper argues for a new approach to understanding mental conditions using complexity science, suggesting that brain complexity can be measured and may differ in aging, neurodivergence, and mental health.

## Contribution

The paper introduces q-statistics as a novel method to quantify neural complexity and its modulation by age, neurodivergence, and brain states.

## Key findings

- q-statistics can model EEG non-linear behavior and quantify brain complexity.
- Neural complexity may decrease with age and pathology but not in neurodivergence.
- q-statistics may help develop a more precise and comprehensive psychiatry.

## Abstract

The Traditional Scientific Method (TSM), rooted in the Cartesian-Mechanistic Paradigm, is insufficient for the comprehensive study of the human mind and suffering, which are complex phenomena. Thus, psychiatry can pathologize many vital critical processes and neurodivergences when it simplifies subjectivity and diversity through its nosological models. This essay advocates for a shift towards the Complexity Paradigm (CP)to achieve a psychopathology more adequate to human singularity. The CP posits that life and mind are emergent dissipative phenomena arising from the synergy between chaos and order, that enables systems to adapt through self-organization while maintaining their internal stability. Therefore, organic complexity appears to decrease with age and pathologies. We explore Non-Extensive Statistical Mechanics (q-Statistics, qS), proposed by Tsallis to describe neural complexity from EEG signal through the scalar parameter q. We’ve demonstrated that qS, by a q-exponential function, models the EEG non-linear behavior indicating its power for quantify the brain complexity, and also to describe modulation of the complexity by ageing or neurodivergence (attention deficit/hyperactivity condition in children) in the brain as a whole, and by different functional brain states (as in oddball attention paradigm or listening a preferred piece of music) in different brain regions. We hypothesize that neural complexity could be reduced in pathological mental conditions, but not in neurodivergence, neither in mental suffering that are critical processes of transformation. Assessing neural complexity as a property of the brain through q-statistics may be a method to a more comprehensive and precise psychiatry.