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Peer-reviewedCC BY 4.0Molecular Psychiatry

Machine learning reveals a multimodal, transdiagnostic signature of emotion dysregulation vulnerability across patients, offspring, and controls

  • Luigi F. Saccaro1,2,3
  • Thomas Larrieu4
  • Farnaz Delavari3,5
  • Céline Pellaton6
  • Ben Meuleman1,7
  • Nader Perroud1,2
  • Dimitri Van De Ville3,8
  • Nicolas Toni4
  • Camille Piguet1,9
9 affiliations
  1. 1Psychiatry Department, Faculty of Medicine, University of Geneva, Geneva, Switzerland
  2. 2Psychiatry Department, Geneva University Hospital, Geneva, Switzerland
  3. 3Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
  4. 4Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
  5. 5Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
  6. 6Division of Immunology and Allergy, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
  7. 7Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
  8. 8Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
  9. 9General Pediatric Division, Geneva University Hospital, Geneva, Switzerland
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Abstract

Emotion dysregulation (ED) is a core transdiagnostic feature of several psychiatric disorders, including borderline personality disorder, bipolar disorder, and attention-deficit/hyperactivity disorder. These ED disorders (EDD) exhibit overlapping clinical presentations, shared heritability, and common neurobiological substrates. This study used a transdiagnostic framework to identify early and multimodal markers of vulnerability, particularly in high-risk populations such as the offspring of EDD patients (EDDoff). A total of 237 participants (97 EDD patients, 67 EDDoff, 73 healthy controls; 139 females in total) completed a multimodal assessment including clinical evaluations, diffusion and functional MRI, and immune and neurotrophic serum biomarkers. Dimensionality reduction was performed using principal component analysis (PCA), and exploratory random forest (RF) models were trained for group classification and symptoms prediction. PCA on the full multimodal dataset yielded eight components, two of which significantly differed between groups, one reflecting high ED and altered hippocampal dynamic functional connectivity (dFC), for which EDDoff showed an intermediate phenotype, and another driven by systemic inflammation, increased in EDD patients only. Modality-specific PCA identified significant inter-modality correlations, including reduced white matter integrity with increasing immune dysregulation, and positive correlations between hippocampal dFC and both ED symptoms and inflammation (padj = < 0.01 for all correlations). A RF classifier achieved a balanced accuracy of 80.3% in distinguishing controls from EDD/EDDoff individuals. Multimodal non-clinical features predicted ED symptoms (p < 0.01). This study identifies a specific, clinically relevant, transdiagnostic and multimodal signature of vulnerability to ED, spanning behavioral, neural, and immune systems. This multimodal profile may inform future early intervention strategies targeting at-risk populations, such as EDDoff.

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Cite

BibTeXRIS
BibTeX
@article{saccaro2026machine,
  title = {{Machine learning reveals a multimodal, transdiagnostic signature of emotion dysregulation vulnerability across patients, offspring, and controls}},
  author = {Luigi F. Saccaro and Thomas Larrieu and Farnaz Delavari and Céline Pellaton and Ben Meuleman and Nader Perroud and Dimitri Van De Ville and Nicolas Toni and Camille Piguet},
  journal = {Molecular Psychiatry},
  year = {2026},
  month = jul,
  doi = {10.1038/s41380-026-03767-1},
  url = {https://doi.org/10.1038/s41380-026-03767-1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Machine learning reveals a multimodal, transdiagnostic signature of emotion dysregulation vulnerability across patients, offspring, and controls
AU  - Luigi F. Saccaro
AU  - Thomas Larrieu
AU  - Farnaz Delavari
AU  - Céline Pellaton
AU  - Ben Meuleman
AU  - Nader Perroud
AU  - Dimitri Van De Ville
AU  - Nicolas Toni
AU  - Camille Piguet
PY  - 2026
DA  - 2026/07/27
JO  - Molecular Psychiatry
DO  - 10.1038/s41380-026-03767-1
UR  - https://doi.org/10.1038/s41380-026-03767-1
AB  - Emotion dysregulation (ED) is a core transdiagnostic feature of several psychiatric disorders, including borderline personality disorder, bipolar disorder, and attention-deficit/hyperactivity disorder. These ED disorders (EDD) exhibit overlapping clinical presentations, shared heritability, and common neurobiological substrates. This study used a transdiagnostic framework to identify early and multimodal markers of vulnerability, particularly in high-risk populations such as the offspring of EDD patients (EDDoff). A total of 237 participants (97 EDD patients, 67 EDDoff, 73 healthy controls; 139 females in total) completed a multimodal assessment including clinical evaluations, diffusion and functional MRI, and immune and neurotrophic serum biomarkers. Dimensionality reduction was performed using principal component analysis (PCA), and exploratory random forest (RF) models were trained for group classification and symptoms prediction. PCA on the full multimodal dataset yielded eight components, two of which significantly differed between groups, one reflecting high ED and altered hippocampal dynamic functional connectivity (dFC), for which EDDoff showed an intermediate phenotype, and another driven by systemic inflammation, increased in EDD patients only. Modality-specific PCA identified significant inter-modality correlations, including reduced white matter integrity with increasing immune dysregulation, and positive correlations between hippocampal dFC and both ED symptoms and inflammation (padj = < 0.01 for all correlations). A RF classifier achieved a balanced accuracy of 80.3% in distinguishing controls from EDD/EDDoff individuals. Multimodal non-clinical features predicted ED symptoms (p < 0.01). This study identifies a specific, clinically relevant, transdiagnostic and multimodal signature of vulnerability to ED, spanning behavioral, neural, and immune systems. This multimodal profile may inform future early intervention strategies targeting at-risk populations, such as EDDoff.
SN  - 1359-4184
SN  - 1476-5578
ER  -