Complexity of spontaneous brain activity in mental disorders

Area of Science:

• Neuroscience
• Psychiatry
• Complexity Science

Background:

• Aberrant neural connectivity is increasingly recognized as a core feature of many mental disorders.
• Neural network research highlights the nonlinear dynamical complexity of brain signals across various time scales.
• Nonlinear analytic methods offer quantitative insights into brain signal complexity and aberrant neural connectivity.

Purpose of the Study:

• To explore the inconsistencies in findings related to brain signal complexity in mental disorders.
• To identify potential reasons for the heterogeneity in research outcomes concerning neural connectivity and mental health.

Main Methods:

• Review of recent functional and anatomical studies on neural connectivity.
• Application of nonlinear analytic methods for quantitative description of brain signal complexity.
• Analysis of neurophysiological output signals considering multiple time scales and frequencies.

Main Results:

• Despite evidence linking aberrant neural connectivity to mental disorders, findings on complexity behavior remain inconsistent.
• Inconsistencies may stem from the heterogeneity of mental disorders, analytical challenges, and the influence of typical development and aging.

Conclusions:

• Addressing the heterogeneity of mental disorders is crucial for understanding pathological brain mechanisms.
• Standardizing analytical approaches and accounting for age-related changes in brain complexity are necessary for robust research.
• Future systematic studies are vital to advance knowledge of neural connections and dynamics in mental disorders.