Preliminary testing of a prespecified liability architecture for autism: theory-guided pathogenetic triad models outperform strength-matched alternatives.
The 12 matches
- [1] § Materials and methods › Analytic overview ↔ scripts/_04_pt_triadindex.py, lines 615–724 · score 0.82 · outer training, log loss, model fitting, logistic regression, leakage free, stratified
- [2] § Materials and methods › Constructing the triplet multiverse space ↔ scripts/_05_pt_multiverse.py, lines 4–91 · score 0.82 · ridge logistic regression, repeated domains, distinct domains, full predictor, domain restriction, multiverse
- [3] § Materials and methods › Analytic overview ↔ scripts/_05_pt_multiverse.py, lines 511–571 · score 0.79 · outer training, log loss, leakage free, model fitting, stratified, tuned
- [4] § Results › Does a prespecified PT operationalization show out-of-sample separation? › Primary PT model in triad space ↔ scripts/_03_pt_primary_classifier.py, lines 970–1078 · score 0.69 · bootstrap envelope, ROC curve, Triad space, decision boundary, LR, clustering
- [5] § Materials and methods › Constructing the triplet multiverse space › Matched paired comparisons of PT vs non-PT triplets ↔ scripts/_05_pt_multiverse.py, lines 4–91 · score 0.67 · comparator triplets, PT triplet, univariate AUC, nearest, profile, strength
- [6] § Materials and methods › Data processing and univariate characterization ↔ scripts/_02_pt_preprocessing.py, lines 4–70 · score 0.65 · right skewed, log transformed, oriented, strength, univariate, variables
- [7] § Results › Does a prespecified PT operationalization show out-of-sample separation? › TriadIndex: collapsing triad space onto a single axis ↔ scripts/_03_pt_primary_classifier.py, lines 970–1078 · score 0.60 · calibration curve, ROC curve, decision boundary, sensitivity, triad, space
- [8] § Materials and methods › High-dimensional landscape ↔ scripts/_06_pt_kitchen_sink.py, lines 4–77 · score 0.59 · exhaustive enumeration, 2–3, PT models, landscape, predictor
- [9] § Materials and methods › The Triadindex as a continuous PT risk score ↔ scripts/_04_pt_triadindex.py, lines 1290–1431 · score 0.59 · TriadIndex, equal weight, raw, risk, TI, axis
- [10] § Results › Does a prespecified PT operationalization show out-of-sample separation? › Permutation test and unsupervised clustering ↔ scripts/_03_pt_primary_classifier.py, lines 1294–1345 · score 0.53 · ROC curve, triad space, unsupervised, clustering, BA, model
- [11] § Materials and methods › Measures › Non-PT predictors ↔ scripts/_02_pt_preprocessing.py, lines 4–70 · score 0.52 · domain assignments, preprocessing, MEG, pipelines, variables, NB
- [12] § Results › Does a prespecified PT operationalization show out-of-sample separation? › TriadIndex: collapsing triad space onto a single axis ↔ scripts/_04_pt_triadindex.py, lines 899–1031 · score 0.52 · calibration curve, ROC curve, sensitivity, BA, TriadIndex, AUC
Paper
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The authors' code
Python · 1,435 lines · 49 KB · no license · 3 matches
_04_pt_triadindex.py, no license · at the source
Overview
- Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Paediatric Health Professions and Paediatric Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
Abstract
Background: Neuropsychiatric conditions are heterogeneous and mechanistically diverse, yet predictive modeling studies commonly aggregate features without evaluating prespecified liability architectures. The Pathogenetic Triad (PT) is a multilevel framework proposing that diagnostic outcomes reflect the joint configuration of a trait-related domain, cognitive capacity (CC), and neuropathological burden (NB). In autism, the trait-related domain corresponds to autistic personality (AP). We evaluated this framework using out-of-sample prediction as a structured empirical test of architectural coherence rather than as a purely data-driven exercise in classification.
Methods: We analyzed a case–comparison cohort (n = 42, 21 autistic) with dense multimodal characterization, including behavioral phenotyping, psychometric measures, autonomic physiology, structural MRI morphometry, and magnetoencephalographic indices. AP was indexed by the Autism-Spectrum Quotient, CC by Wechsler’s scales of intelligence, and NB by heart-rate variability as a proxy indicator. This multimodal structure enabled direct comparison of theory-constrained PT models with strength-matched, domain-restricted atheoretical combinations. Predictive discrimination was evaluated using leakage-free nested cross-validation with permutation inference.
Results: Across multiverse specifications, low-dimensional PT models ranked among the strongest models of comparable size and achieved discrimination broadly comparable to higher-dimensional models within this dataset. Matched comparisons indicated that including all three PT domains was associated with systematic advantages relative to alternatives with similar univariate input strength, consistent with complementary configurational information relating to case status.
Conclusions: These findings provide preliminary evidence supporting the Pathogenetic Triad as a multilevel architecture of autism liability. Although based on a small and demographically restricted cohort, this densely characterized dataset permitted explicit comparison of theory-guided and atheoretical model spaces. The results illustrate how prespecified multilevel frameworks can be operationalized and empirically evaluated in neuropsychiatric samples.
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This repository has no license: its authors keep all rights. Read it at the source.
- scripts/
_00_pt_create_synthetic_ — Python, 1,143 lines, not shown heredata.py - scripts/
_01_pt_utils.py — Python, 240 lines, not shown here - scripts/
_02_pt_preprocessing.py — Python, 739 lines, 2 matches, not shown here - scripts/
_03_pt_primary_classifie — Python, 1,603 lines, 3 matches, not shown herer.py - scripts/
_04_pt_triadindex.py — Python, 1,435 lines, 3 matches, not shown here - scripts/
_05_pt_multiverse.py — Python, 1,583 lines, 3 matches, not shown here - scripts/
_06_pt_kitchen_sink.py — Python, 2,543 lines, 1 match, not shown here
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Data availability statement
The datasets presented in this article are not readily available because of ethical/
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Cite
This paper
Sarovic, D. (2026). Preliminary testing of a prespecified liability architecture for autism: theory-guided pathogenetic triad models outperform strength-matched alternatives. Frontiers in psychiatry, 17, 1837909. https://
BibTeX
@article{sarovic2026prel
author = {Sarovic, Darko},
title = {{Preliminary testing of a prespecified liability architecture for autism: theory-guided pathogenetic triad models outperform strength-matched alternatives}},
journal = {Frontiers in psychiatry},
year = {2026},
month = jul,
volume = {17},
pages = {1837909},
publisher = {Frontiers Media SA},
issn = {1664-0640},
doi = {10.3389/
url = {https://
pmid = {42519192},
pmcid = {PMC13381483}
}
RIS
TY - JOUR
AU - Sarovic, Darko
TI - Preliminary testing of a prespecified liability architecture for autism: theory-guided pathogenetic triad models outperform strength-matched alternatives
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J2 - Front Psychiatry
PY - 2026
DA - 2026/
VL - 17
SP - 1837909
SN - 1664-0640
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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