Energy, Information, and the Limits of Neuropsychiatric Classification
Abstract
Importance: The search for reliable neuroimaging biomarkers of mental disorders has remained elusive despite decades of research. Why clinically consequential disorders yield weak neuroimaging separability remains unresolved. Drawing on statistical thermodynamics and the metabolic costs of neural coding, we propose that psychiatric disorders produce constrained deformations of a largely shared repertoire of brain activity patterns, limiting the class-specific information available for classification. Observations: We develop a framework linking physical resource constraints, landscape deformation, distributional separation, and attainable classification, and examine its components using human neuroimaging, synthetic data, and an exactly specified physical model. In resting-state fMRI from 1952 FOR2107 participants (823 with major depressive disorder; 1129 healthy controls), held-out energy-landscape estimates and nonlinear classifiers agreed across 13 comparisons (r = 0.996). Similar results emerged at both FOR2107 centers and in 900 SRPBS participants spanning multiple psychiatric diagnoses. In synthetic data, classification improved with distributional separation and approached the optimum. In an equilibrium three-well model, statistical energy equaled physical potential energy scaled by inverse temperature. Modest deformation redistributed state occupancy while optimal snapshot classification remained weak. A literature map found insufficiently comparable evidence to test the human metabolic constraint. Conclusions: Clinically consequential differences may arise within a largely shared neural repertoire. The thermodynamic framework connects physical deformation to classification under specified assumptions; its metabolic implementation in humans remains untested. Testing this mechanism requires joint measurements of brain activity, bodily physiology, and metabolism.
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BibTeX
@misc{khodadadi2026energy,
title = {{Energy, Information, and the Limits of Neuropsychiatric Classification}},
author = {Farbod Khodadadi and Mina Kheirkhah and Bita Shariatpanahi and Sarah Alizadeh and Madleen Stenger and Amaia Zurinaga Gutierrez and Shakiba Bakhshian and José C. García Alanis and Nils R. Winter and FOR2107 Consortium and Frank Bremmer and Stefan G. Hofmann and Udo Dannlowski and Tilo Kircher and Sharmili Edwin Thanarajah and Igor Nenadić and Tim Hahn and Hamidreza Jamalabadi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14095},
eprint = {2610.14095},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14095},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Energy, Information, and the Limits of Neuropsychiatric Classification
AU - Farbod Khodadadi
AU - Mina Kheirkhah
AU - Bita Shariatpanahi
AU - Sarah Alizadeh
AU - Madleen Stenger
AU - Amaia Zurinaga Gutierrez
AU - Shakiba Bakhshian
AU - José C. García Alanis
AU - Nils R. Winter
AU - FOR2107 Consortium
AU - Frank Bremmer
AU - Stefan G. Hofmann
AU - Udo Dannlowski
AU - Tilo Kircher
AU - Sharmili Edwin Thanarajah
AU - Igor Nenadić
AU - Tim Hahn
AU - Hamidreza Jamalabadi
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.14095
UR - https://arxiv.org/abs/2610.14095
AB - Importance: The search for reliable neuroimaging biomarkers of mental disorders has remained elusive despite decades of research. Why clinically consequential disorders yield weak neuroimaging separability remains unresolved. Drawing on statistical thermodynamics and the metabolic costs of neural coding, we propose that psychiatric disorders produce constrained deformations of a largely shared repertoire of brain activity patterns, limiting the class-specific information available for classification. Observations: We develop a framework linking physical resource constraints, landscape deformation, distributional separation, and attainable classification, and examine its components using human neuroimaging, synthetic data, and an exactly specified physical model. In resting-state fMRI from 1952 FOR2107 participants (823 with major depressive disorder; 1129 healthy controls), held-out energy-landscape estimates and nonlinear classifiers agreed across 13 comparisons (r = 0.996). Similar results emerged at both FOR2107 centers and in 900 SRPBS participants spanning multiple psychiatric diagnoses. In synthetic data, classification improved with distributional separation and approached the optimum. In an equilibrium three-well model, statistical energy equaled physical potential energy scaled by inverse temperature. Modest deformation redistributed state occupancy while optimal snapshot classification remained weak. A literature map found insufficiently comparable evidence to test the human metabolic constraint. Conclusions: Clinically consequential differences may arise within a largely shared neural repertoire. The thermodynamic framework connects physical deformation to classification under specified assumptions; its metabolic implementation in humans remains untested. Testing this mechanism requires joint measurements of brain activity, bodily physiology, and metabolism.
ER -