Structural synaptogenesis superior to functional modulation in a pruning-based recurrent network model of OCD.
Overview
- Independent Researcher, Hong Kong, Hong Kong SAR, China
Abstract
Background: Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive behaviors, with incomplete response to current treatments suggesting limitations in prevailing neurotransmitter-focused
Methods: We developed a modular gated recurrent unit network approximating cortico-striato-thalamo-
Results: At 60% activity-dependent pruning, the untreated network showed impaired accuracy (0.4972) and elevated perseveration (0.5247). In the fixed-parameter comparison, ketamine-like structural repair reduced perseveration to 0.2582, SSRI-like adaptation to 0.3209, and neurosteroid-like inhibition to 0.2654. Relapse vulnerability differed by mechanism: cumulative relapse increased perseveration by +0.1086 after ketamine-like repair, +0.0259 after SSRI-like adaptation, and −0.0017 after neurosteroid-like inhibition in the representative seed. Across five seeds, best acute perseveration was lowest for ketamine-like repair (0.2330 ± 0.0065), followed by neurosteroid-like inhibition (0.2413 ± 0.0021) and SSRI-like adaptation (0.2831 ± 0.0099). SSRI-like adaptation showed the highest mean efficiency because it produced smaller absolute weight changes. Sensitivity analyses showed that untreated perseveration increased with pruning severity, from 0.2627 at 40% pruning to 0.6218 at 70% pruning, while ketamine-like repair remained relatively stable across the same range.
Conclusion: These findings support excessive synaptic pruning as a plausible contributor to OCD-like cognitive inflexibility and illustrate that structural and functional interventions offer different trade-offs within a highly abstract computational model. Structural repair produced the most robust acute rescue and remained resilient across pruning severities, whereas functional mechanisms showed advantages in dose efficiency or relapse stability. The results are hypothesis-generating only and should not be read as clinical evidence for treatment ranking.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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Data
Datasets cited
- github.com/
cheungngo/ , at github.com; found in “Data availability statement”structural-synaptogenesi s-superior-to-functional -modulation-in-a-network -model-of-ocd
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 1 author, 8 keywords, 33 references.
Cite
This paper
Cheung, N. (2026). Structural synaptogenesis superior to functional modulation in a pruning-based recurrent network model of OCD. Frontiers in computational neuroscience, 20, 1799705. https://
BibTeX
@article{cheung2026struc
author = {Cheung, Ngo},
title = {{Structural synaptogenesis superior to functional modulation in a pruning-based recurrent network model of OCD}},
journal = {Frontiers in computational neuroscience},
year = {2026},
month = jul,
volume = {20},
pages = {1799705},
publisher = {Frontiers Media SA},
issn = {1662-5188},
doi = {10.3389/
url = {https://
pmid = {42548786},
pmcid = {PMC13429669}
}
RIS
TY - JOUR
AU - Cheung, Ngo
TI - Structural synaptogenesis superior to functional modulation in a pruning-based recurrent network model of OCD
T2 - Frontiers in computational neuroscience
J2 - Front Comput Neurosci
PY - 2026
DA - 2026/
VL - 20
SP - 1799705
SN - 1662-5188
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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"container-title": "Frontiers in computational neuroscience",
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"given": "Ngo"
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"URL": "https://
"language": "en",
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