OSCR

Structural synaptogenesis superior to functional modulation in a pruning-based recurrent network model of OCD.

Overview

Authors: Ngo Cheung1
  1. Independent Researcher, Hong Kong, Hong Kong SAR, China
Journal: Frontiers in computational neuroscience, volume 20, article 1799705
Dates: received 30 January 2026; accepted 30 June 2026; published online 20 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fncom.2026.1799705 · PMID 42548786 · PMCID PMC13429669 · OpenAlex W7169760065
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: computational modeling (no new data) (modality), none (in silico) (organism), other condition (population), computational (subfield)
Methods: Machine learning
Keywords: computational psychiatry, CSTC, GRU, OCD, pruning, SSRI, neurosteroids, ketamine
Topic: Obsessive-Compulsive Spectrum Disorders (Clinical Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 37 references in the paper

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 models. Epidemiological data indicate lifetime DSM-IV OCD in approximately 2.3% and 12-month OCD in approximately 1.2% of US adults, while subthreshold obsessions or compulsions are substantially more common. One mechanistic possibility is that abnormal synaptic pruning contributes to persistent cortico-striato-thalamo-cortical circuit rigidity. This idea remains indirect in OCD, but is supported by convergent synaptic-marker findings in OCD and by complement-linked pruning mechanisms shown most clearly in schizophrenia.

Methods: We developed a modular gated recurrent unit network approximating cortico-striato-thalamo-cortical dynamics, trained on a rule-switching task sensitive to perseveration. The architecture included a recurrent cortical integration layer, a schematic striatal “habit” module, and a thalamic confidence-gating scalar. Excessive pruning was implemented as 60% activity-dependent pruning, using a composite of low recent gradient-based usage and low weight magnitude, with partial protection of recurrent and habit-related weights. From identical pruned baselines, three mechanistically distinct interventions were simulated: rapid gradient-guided structural reopening, treated as a ketamine-like synaptogenesis motif, prolonged low-learning-rate adaptation with stress-noise annealing (SSRI-like), and tonic inhibitory scaling (neurosteroid-like). An iso-dose pipeline recorded L1 and L2 weight-change norms, synaptic turnover, and change in sparsity; linear interpolation was used when bracketing sweep points existed, and residual mismatch was reported otherwise. Multi-seed statistics and sensitivity analyses were performed.

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

The paper links to its data, not to its authors' code: see the Data section.

The paper's code and data availability statement is in the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://github.com/cheungngo/Structural-Synaptogenesis-Superior-to-Functional-Modulation-in-a-Network-Model-of-OCD.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

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://doi.org/10.3389/fncom.2026.1799705

BibTeX

@article{cheung2026structural,
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/fncom.2026.1799705},
url = {https://doi.org/10.3389/fncom.2026.1799705},
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 - 2026/07/20
VL - 20
SP - 1799705
SN - 1662-5188
PB - Frontiers Media SA
DO - 10.3389/fncom.2026.1799705
UR - https://doi.org/10.3389/fncom.2026.1799705
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fncom.2026.1799705",
"type": "article-journal",
"title": "Structural synaptogenesis superior to functional modulation in a pruning-based recurrent network model of OCD",
"container-title": "Frontiers in computational neuroscience",
"author": [
{
"family": "Cheung",
"given": "Ngo"
}
],
"container-title-short": "Front Comput Neurosci",
"volume": "20",
"page": "1799705",
"DOI": "10.3389/fncom.2026.1799705",
"PMID": "42548786",
"PMCID": "PMC13429669",
"ISSN": "1662-5188",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fncom.2026.1799705",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
20
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/antiox15070908 [code]
Convergent Lower Expression of Redox-Linked Stress-Adaptation and Synaptic-Plasticity Genes in Major Depressive Disorder Across Seven Postmortem dlPFC Cohorts.
Journal: Antioxidants (Basel, Switzerland)
In common: 3 references
[2] doi:10.1038/s41386-026-02406-1 [code]
Functional genomic profiling of schizophrenia-associated genes reveals key microglial regulators.
Journal: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
In common: 2 references
[3] doi:10.1186/s13041-026-01300-7
Hippocampal transcriptome profiling in a 22q11.2 deletion syndrome mouse model: comparison with human schizophrenia.
Journal: Molecular brain
In common: 2 references
[4] doi: [code]
Going deeper with morphologically detailed neural networks by simulation-based gradient propagation
Journal: Frontiers in computational neuroscience
In common: none (in silico), computational, computational modeling (no new data)
[5] doi:10.1371/journal.pcbi.1014617 [code]
An in silico framework for dissecting the mechanistic origins of in vivo recorded neuronal activity.
Journal: PLoS computational biology
In common: none (in silico), computational, computational modeling (no new data)
[6] doi:10.1007/s11571-026-10522-3 [code]
Acetylcholine enhances deviance detection in Hodgkin-Huxley neuronal networks.
Journal: Cognitive neurodynamics
In common: none (in silico), computational, computational modeling (no new data)
[7] doi:10.1038/s41526-026-00644-7 [code]
A computational model of altered neuronal activity in altered gravity.
Journal: NPJ microgravity
In common: none (in silico), computational, computational modeling (no new data)
[8] doi:10.1371/journal.pcbi.1014458 [code]
Neuronal excitability and parameter variability in the Hodgkin-Huxley model.
Journal: PLoS computational biology
In common: none (in silico), computational, computational modeling (no new data)
[9] doi:10.1016/j.isci.2026.116116 [code]
Learning stable radiation boundaries for wave simulations via passive neural state-space models.
Journal: iScience
In common: none (in silico), computational, computational modeling (no new data)
[10] doi:10.1007/s10237-026-02067-5 [code]
Sparse polynomial surrogates for F-actin networks with compliant crosslinkers.
Journal: Biomechanics and modeling in mechanobiology
In common: none (in silico), computational, computational modeling (no new data)

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.