Machine Learning Reveals a Multimodal, Transdiagnostic Signature of Emotion Dysregulation Vulnerability Across Patients, Offspring, and Controls
List of authors
Running head: Multimodal Markers of Emotion Dysregulation
LuigiFrancescoSaccaro
MD
1,2,5,10✉EmailLuigiFrancesco.Saccaro@unige.chThomasLarrieu
PhD
3FarnazDelavari
MD, PhD
4,5CélinePellaton
PhD
7BenMeuleman
PhD
1,8NaderPerroud
MD
1,2Dimitri
Van
De VillePhD
5,6NicolasToni
PhD
3CamillePiguet
MD, PhD
1,91
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Psychiatry Department, Faculty of MedicineUniversity of GenevaSwitzerland2Psychiatry DepartmentGeneva University HospitalSwitzerland
3Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University HospitalUniversity of LausannePrillySwitzerland
4Developmental Imaging and Psychopathology LaboratoryUniversity of Geneva School of MedicineGenevaSwitzerland
5Medical Image Processing LaboratoryNeuro-X Institute, École Polytechnique Fédérale de LausanneGenevaSwitzerland
6Department of Radiology and Medical Informatics, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
7Division of Immunology and AllergyLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
8Swiss Center for Affective SciencesUniversity of GenevaGenevaSwitzerland
9General Pediatric DivisionGeneva University HospitalSwitzerland
10Psychiatry DivisionGeneva University HospitalRue Gabrielle-Perret-Gentil 41205GenevaSwitzerland
Luigi F Saccaro, MDa,b,e*; Thomas Larrieu, PhDc; Farnaz Delavari, MD, PhDd,e; Céline Pellaton, PhDg; Ben Meuleman, PhDa,h; Nader Perroud, MDa,b; Dimitri Van De Ville, PhDe,f; Nicolas Toni, PhDc; Camille Piguet, MD, PhDa,i
Affiliations
a Psychiatry Department, Faculty of Medicine, University of Geneva, Switzerland
b Psychiatry Department, Geneva University Hospital, Switzerland
c Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
d Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
e Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
f Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
g Division of Immunology and Allergy, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
h Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
i General Pediatric Division, Geneva University Hospital, Switzerland
*Corresponding author: Luigi Francesco Saccaro, MD, Psychiatry Department, Faculty of Medicine, University of Geneva, Switzerland; Psychiatry Division, Geneva University Hospital, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; email: LuigiFrancesco.Saccaro@unige.ch
Keywords
Emotion dysregulation
Transdiagnostic Multimodal biomarkers
Random forest
Principal component analysis
Immune markers
Magnetic Resonance Imaging
ADHD
Borderline Personality Disorder
Bipolar Disorder
Offspring
High-Risk.
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ABSTRACT
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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
A total of 237 participants (97 EDD patients, 67 EDDoff, 73 healthy controls) 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 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 (p = < .01 for all correlations). A RF classifier accurately distinguished controls from EDD/EDDoff individuals (85.7% accuracy). Multimodal non-clinical features reliably predicted ED symptoms (p < .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, to reduce EDD emergence and progression.A
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INTRODUCTION
Mental disorders account for nearly one-fifth of all years of life lost to disability worldwide, amounting to over 125 million disability-adjusted life years(1). This profound global burden has catalyzed a significant shift in psychiatric research over the past two decades, from reactive treatment to proactive prevention, prioritizing the early identification of individuals at risk. However, despite this shift toward prevention, robust biomarkers that reliably predict vulnerability to psychiatric disorders remain elusive, partly due to the substantial overlap in symptoms(2), genetic factors, and neurobiological features across different psychiatric conditions(3). This overlap challenges traditional diagnostic frameworks, highlighting the need for alternative, transdiagnostic and dimensional approaches(4) that emphasize cross-cutting features and aims to integrate biological, psychological, and behavioral domains to enhance understanding of mental health and illness(5, 6).
Central to this transdiagnostic perspective is emotion dysregulation (ED), defined as difficulty modulating emotional experiences and responses adaptively across contexts(6, 7). ED, proposed as a sixth domain within the National Institute of Mental Health’s Research Domain Criteria framework, manifests through affective instability, heightened emotional reactivity, and reliance on non-adaptive regulation strategies(7). It represents a critical target for dimensional investigation due to its central role across severe and common psychiatric disorders, including borderline personality disorder (BPD)(8), bipolar disorder (BD)(9), and attention-deficit/hyperactivity disorder (ADHD)(6, 10), hereby referred to collectively as emotion dysregulation disorders (EDD). These disorders exhibit shared clinical presentations, substantial heritability(11–14), overlapping neurobiological substrates, and high rates of comorbidity(15–19). Studying individuals at familial risk, such as the offspring of EDD patients (EDDoff), offers a critical opportunity to identify early vulnerability markers that may guide early intervention strategies. Importantly, EDD onset or early prodromal signs can emerge during adolescence and young adulthood, a critical developmental period coinciding with the maturation of emotion regulation fronto-limbic circuits(20). This developmental period represents a critical window of opportunity for identifying early ED-related vulnerability markers. Given the substantial comorbidity and overlapping features across EDD, there is an urgent need for reliable, transdiagnostic vulnerability markers that can guide early detection, risk stratification, and targeted interventions(21, 22). Although early interventions have demonstrated promise(22), identifying robust early markers has proven challenging due to the complex, multifactorial nature of EDD risk.
Previous studies have linked ED to early life stress, which promotes a pro-inflammatory state via dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis(23–28), suggesting a pivotal role in the etiology of EDD(23–30). In addition, stress and inflammatory processes have been shown to disrupt hippocampal structure and function(31–35). The hippocampus, being part of the prefrontal cortical-hippocampal-amygdala emotion-processing circuit, is essential for integrating emotion, memory, and stress responses(9). Thus, from a functional brain network perspective, ED involves alterations not only in the hippocampus(36–39) and in the rest of the limbic network (LN) as well as ventromedial prefrontal and lateral prefrontal regions(9, 40–43), but also in the sensorimotor network (SMN) (29, 44, 45), which plays a role in both cognitive and emotional processing in EDD(4, 44, 46–56). Disruption of SMN connectivity, particularly with the hippocampus, may compromise sensorimotor integration and the appraisal of emotional stimuli, thereby contributing to the core features of ED. These network disruptions may exacerbate ED symptoms, thereby perpetuating a vicious cycle of stress, inflammation, and ED (Supplementary Fig. 1). Indeed, peripheral inflammation is increasingly recognized as a contributor to central white matter alterations through several plausible mechanisms. Inflammatory cytokines can compromise blood-brain barrier integrity, which is already impaired in EDD patients(57), thus facilitating the infiltration of peripheral immune mediators into the central nervous system. These mediators can promote neurotoxic pathways such as the tryptophan-kynurenine axis, impairing oligodendrocyte function and inhibiting remyelination, resulting in microstructural damage(58, 59). Additionally, inflammatory markers have been identified post-mortem within the hippocampi of EDD patients(60), and immunoglobulins derived from the serum of individuals with EDD have demonstrated the ability to hydrolyze human myelin basic protein in vitro(61). Thus, supporting the hypothesis that peripheral immune dysregulation may exert direct neurotoxic effects also on central myelinated pathways relevant to emotional regulation(29, 30, 62, 63).
Despite this growing evidence for shared neurobiological substrates across EDD, and individual studies linking EDD to neural and immune dysfunction(47, 48, 53, 64), no study has simultaneously integrated clinical, structural, functional, and biological data within a transdiagnostic framework, particularly in high-risk populations.
To address this gap, the present study combines clinical symptom assessments, functional and diffusion MRI, and serum biomarkers in a single transdiagnostic and multimodal analysis. Using principal component analysis (PCA) for dimensionality reduction and random forest (RF) machine learning for classification and prediction, we aim to identify integrated markers of ED vulnerability and expression across EDD patients, EDD patients’ offspring, and healthy controls. We hypothesize that both EDD and EDD vulnerability are associated with disruptions in sensorimotor and emotion-processing networks, both structurally and functionally, and with elevated systemic inflammation. Furthermore, we suggest that peripheral inflammation may correlate with impaired white matter integrity, contributing to the neural underpinnings of ED.
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This integrative approach is designed to uncover convergent signatures of ED, highlight transdiagnostic neural and immune associations, and inform early intervention strategies targeting vulnerable individuals.METHODS
Participants
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Participants were recruited into three distinct groups, as previously described(65): adult patients with EDD (including BD, BPD, or ADHD), offspring of other EDD patients (EDDoff), and healthy controls (HC). Detailed exclusion criteria are in the Supplementary Methods. EDD patients fulfilled DSM-IV-TR diagnostic criteria and were recruited from the Mood and ED Clinics at Geneva University Hospitals' Psychiatry Department. EDDoff were individuals who had at least one parent diagnosed with EDD (BD, BPD, or ADHD). To control for familial confounding factors, none of the participants across groups were biologically related. HC participants were recruited through web-based announcements or local research databases.A
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Written informed consent was obtained from all participants, as approved by the Geneva University Ethical Committee (CER 13–081). All participants were evaluated using a comprehensive multimodal assessment battery, incorporating clinical assessment, biological markers, and neuroimaging-derived variables, including structural and dynamic functional connectivity (dFC) metrics. Supplementary Table 1 provides a complete list of all variables acquired and corresponding abbreviations. The details for the acquisition of each of the four blocks of variables are briefly described in the following sections and the Supplementary Materials.Clinical assessment
All participants underwent structured clinical interviews conducted by trained psychologists using the Diagnostic Interview for Genetic Studies (DIGS). ED was assessed via the Affective Lability Scale (ALS) and the Cognitive Emotion Regulation Questionnaire (CERQ). Affective lability, also referred to as emotional instability, represents a core maladaptive ED pattern characterized by rapid, frequent, and intense fluctuations in emotional states(66). Cognitive emotion regulation strategies were specifically evaluated through the CERQ by calculating the ratio of non-adaptive strategies to total emotion regulation strategies (adaptive plus non-adaptive), following established approaches in the literature(91). Additional clinical measures included depression, anxiety, personality, and other clinical, cognitive, and demographic questionnaires (Supplementary Table 1).
Biological markers
Serum markers were quantified using the Procarta multiplex immunoassay platform. These included, for instance, proinflammatory (e.g. C-reactive protein, CRP), anti-inflammatory (e.g. interleukin-10, IL-10), and neurotrophic markers (e.g. Brain Derived Neurotrophic Factor, BDNF). Details of sample collection, assay procedures, and quality control are provided in the Supplementary Materials.
Dynamic functional connectivity markers: acquisition and micro-coactivation patterns (uCAPs) computation
Detailed methods for resting-state fMRI data acquisition, preprocessing, seed-based µCAPs analysis for dynamic functional connectivity (dFC) of hippocampal subregions, and motion correction procedures are provided in the Supplementary Materials and our prior publications(81, 82). Briefly, five large-scale networks (i.e. µCAPs), each connected with a specific data-driven, activity-based hippocampal parcel were identified through µCAPs dFC analysis. Occurrences of each network were computed per each participant, indicating the dFC of that µCAP with the associated hippocampal parcel (Supplementary Fig. 2).
Structural connectivity markers: acquisition and fractional anisotropy (FA) computation
Diffusion-weighted images were acquired using a 3T Siemens TrioTim scanner (TR = 6300 ms, TE = 84 ms, voxel size = 2.3 × 2.3 × 2 mm, 72 diffusion directions, b = 1000 s/mm², 52 slices, FoV read = 230 mm, phase = 100%). Whole-brain FA values were extracted using the ENIGMA-DTI pipeline(67). This included preprocessing with FSL’s tract-based spatial statistics (TBSS) and registration to the ENIGMA-DTI FA template, followed by ROI-wise extraction using the JHU (John Hopkins University) white matter atlas(68). Further details on the preprocessing and TBSS analysis are provided in the Supplementary Materials.
Descriptive analysis: PCA
PCA was conducted in two steps: first, PCA was performed on the full dataset, including all clinical (CL), hippocampal dynamic functional connectivity (dFC), structural connectivity (white matter integrity, FA), and serum biomarkers (BIO) variables, to identify integrated components capturing shared variance across modalities. Retained components (RCs) scores were compared across transdiagnostic groups (EDD, EDDoff, and HC) using ANOVAs followed by Tukey’s HSD post-hoc tests. This analysis aimed to investigate multimodal and transdiagnostic patterns of EDD vulnerability. Second, to explore domain-specific contributions and associations, separate PCAs were conducted on each of the four aforementioned blocks (CL, dFC, FA, and BIO). Pearson correlations were then computed between RCs from different blocks to investigate cross-domain associations.
This two-step approach enabled both the identification of overarching multimodal patterns, and the interpretation of inter-block, domain-specific correlations among retained components from different blocks.
For each PCA, the number of components to retain was determined using the parallel analysis criterion(69, 70), which modifies Kaiser's criterion to adjust for small-sample bias in eigenvalue sizes, and performs a permutation test to evaluate the importance of eigenvalues against the 95th percentile of random eigenvalues. The analyses were carried out in R (version 4.3.3) using the `psych` package, with Varimax rotation and Thurstone’s method applied to compute regression-based component loadings for each block.
Predictive analysis: random forest classification of group and symptoms prediction
The machine learning RF approach was employed to firstly predict group, and secondly ED symptoms (detailed in the Supplementary Materials). Briefly, in the first analysis, all multimodal predictors (clinical, biological, and neuroimaging markers) were used to train a RF model using the randomForest package in R for discriminating HC from individuals with either EDD or familial risk for EDD (EDDoff).
The RF model performance was benchmarked against a baseline linear model, implemented using multinomial ridge regression, with additional analyses performed to ensure robustness against overfitting (see Supplementary Materials and Supplementary Results).
In the second analysis, two RF regressions were trained to predict continuous ED symptom scores (ALS and CERQ) from non-clinical features (serum and neuroimaging markers only). Predictive accuracy was assessed using root-mean square error (RMSE), mean absolute error (MAE), R², and Pearson correlation between predicted and observed values.
Further methodological details for all the aforementioned analyses, including procedures to address potential confounding factors and to minimize overfitting in the RF models, are provided in the Supplementary Materials.
RESULTS
Demographics and clinical characteristics
The final sample consisted of 237 participants: 97 individuals diagnosed with EDD (including 30 with ADHD, 33 with BPD, and 34 with BD), 67 offspring of different EDD patients (EDDoff), and 73 HC. The groups did not differ significantly in terms of educational attainment or handedness (all p > 0.05). A summary of key demographic and clinical characteristics for each group is presented in Supplementary Table 2, as well as in our previous publications(56, 65). Further details on the sample, subgroups, confounding analyses and a list of detailed abbreviations are in the Supplementary Results.
Significant group differences in PCA components related to ED symptoms, inflammation, and hippocampal connectivity
PCA performed on the full multimodal dataset (clinical, neuroimaging, and serum markers) yielded eight retained significant components (RCs). Of these, two RCs, detailed in Supplementary Figs. 3–4, showed significant group differences and are the main focus of this section.
The third component (RC3), characterized by high affective lability, non-adaptive emotion regulation strategies, anxiety, and borderline personality traits, along with reduced hippocampal connectivity with the SMN and increased connectivity with the LN, was labeled “Elevated ED–SMN Hypoconnectivity–LN Hyperconnectivity.” This component was significantly elevated in both EDD patients and EDDoff compared to HC (padj < .00001 for all Tukey-adjusted post-hoc comparisons, Fig. 1).
Fig. 1
Boxplot illustrating significant group differences in the multimodal "High emotion dysregulation-hippocampal dFC" component derived from PCA performed on the full dataset. Notched boxplots display the third retained component (RC3) scores across groups: EDD patients (red), offspring of EDD patients (EDDoff, green), and healthy controls (HC, blue). EDD patients and EDDoff showed significantly higher scores compared to HC (p < .00001 for all Tukey-adjusted post-hoc comparisons). This component was characterized by increased affective lability, anxiety, borderline traits, and non-adaptive emotion regulation strategies, accompanied by reduced hippocampal connectivity with the visual-sensorimotor network (SMN) and increased connectivity with the limbic network (LN). Significant group differences are denoted by asterisks (****, padj < .00001).
The fifth component (RC5), labeled “High Immune Dysregulation,” was predominantly driven by pro-inflammatory immune markers (e.g., MIF, MCP1, CD62E, CRP, IL18, IL17) and included the neurotrophic factor BDNF, as well as some mediators that can have both pro- and anti-inflammatory roles, like IL2-R and EGF. Scores for this component were significantly higher in EDD patients compared to both EDDoff and HC (padj < .02 for all comparisons, Fig. 2).
Fig. 2
Boxplot showing significant group differences in the multimodal "High Immune Dysregulation" component from PCA performed on the full dataset. Notched boxplots display the third retained component (RC5) scores across groups: EDD patients (red), offspring of EDD patients (EDDoff, green), and healthy controls (HC, blue). Scores were significantly elevated in EDD patients relative to both EDDoff and HC (p < .02 for all comparisons). This component was predominantly driven by pro-inflammatory markers (e.g., MIF, MCP1, CRP, IL18, IL17) and the neurotrophic factor BDNF. Significant group differences are denoted by asterisks (*, padj < .05).
Collectively, these multimodal findings demonstrate distinct clinical, functional connectivity, and biological alterations characterizing EDD patients and EDDoff relative to healthy individuals.
Inflammation is inversely correlated with white matter integrity, and hippocampal connectivity positively correlates with ED symptoms and inflammation
To explore the interrelations between PCA-derived components between blocks of variables, we computed PCA separately within each block of variables, revealing a total of 11 significant RCs: four components from biological serum markers, three from FA-based structural connectivity, three from clinical variables, and one from hippocampal dFC.
Pearson correlations between retained components from the block-level PCAs revealed significant inter-block relationships.
A negative correlation emerged between a structural connectivity component (FA-RC3, featuring high FA in CGH, SS, EC, RLIC, PTR, Supplementary Fig. 5) and an immune dysregulation component (BIO-RC1, mainly driven by immune markers, such as IL12, IL4, IFNg, IL10, IL17, IL1b, and TNFa, Supplementary Fig. 6), r = − .15, t(235) = -2.38, p = .01, Fig. 3a. However, a sensitivity analysis removing one influential HC subject reduced the strength of this correlation to a non-significant negative trend.
Fig. 3
Scatterplots illustrating correlations between neuroimaging markers (structural and functional connectivity) and immune dysregulation. (A) Scatterplot showing the significant negative correlation between white matter integrity and immune dysregulation. Higher immune dysregulation (BIO-RC1, driven by immune markers IL12, IL4, IFNg, IL10, IL17, IL1b, and TNFa) transdiagnostically correlated with reduced white matter integrity (fractional anisotropy, FA-RC3, driven by tracts such as CGH, SS, EC, RLIC, PTR), r(235) = − 0.15, p = .018. (B) Scatterplot showing the significant positive correlation between hippocampal dynamic functional connectivity (dFC) and systemic inflammation. Scores on the hippocampal dFC component (dFC-RC1) increase with rising scores on the pro-inflammatory biological component (BIO-RC4), driven by IP-10, GROα, VCAM, IL-18, and IL-17, r(235) = 0.17, p = .0076.
There was only one dFC component (dFC-RC1) that was positively correlated with a clinical “High ED” component (CL-RC1, Supplementary Fig. 7) and with an inflammatory component (BIO-RC4, driven by pro-inflammatory immune markers, such as IP10, GROa, VCAM, IL18, IL17, Supplementary Fig. 8), p < .01 for all comparisons (Figs. 3b-4).
Fig. 4
Scatterplot showing the significant positive correlation between clinical emotion dysregulation (CL-RC1) and hippocampal dynamic functional connectivity (dFC-RC1). Higher clinical symptoms of emotion dysregulation (CL-RC1, mainly driven by affective lability, anxiety, borderline personality traits) correlated with increased disruptions in hippocampal dFC, r(235) = 0.18, p = .0051.
A RF classifier distinguishes HC with strong accuracy
A RF classifier was trained to distinguish HC from the individuals with either EDD or familial EDD risk (EDDoff), using all multimodal features (listed in Supplementary Table 1).
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The model achieved a high classification accuracy of 85.7% (95% CI [75.3%, 92.9%], p = .001), with substantial agreement (Cohen’s κ = 0.64) and non-significant misclassification bias (Mcnemar’s p = .34). Sensitivity was 93.9%, specificity 66.7%, and balanced accuracy 80.3%. Positive predictive value was 86.8%, and negative predictive value was 82.4%. The full confusion matrix is reported in Supplementary Fig. 9.Feature importance analysis (Supplementary Fig. 10) indicated that the most influential variables in predicting group membership were clinical symptom measures related to ED (e.g., affect lability, borderline personality traits, depression, anxiety, and ADHD symptoms), followed by neuroimaging-derived features including structural (e.g. FA of the RLIC and of the ACR) and functional connectivity measures (such as SMN and VN connectivity). CRP, IL2R, and MCP-1 were the most influential serum marker contributor to the model.
RF regression accurately predicts ED symptoms
A RF regression model was trained to predict ED-specific clinical scores, in particular individual scores on the ALS, using only biological and neuroimaging markers as input features. The model demonstrated reliable performance, yielding an RMSE of 0.54, MAE of 0.44, and R² = .23. Predicted ALS scores were highly significantly correlated with observed scores (r = .48, t(235) = 4.45, p < .00005, Supplementary Fig. 11), indicating robust, transdiagnostic convergence between model outputs and actual affective lability severity.
In addition, predicted scores on the CERQ, derived from the same set of biological and imaging predictors, were also significantly correlated with actual values (r = .32, t(235) = 2.73, p = .007, Supplementary Fig. 12), further supporting the predictive capacity of these multimodal markers for ED symptoms and cognitive emotion regulation strategies. In contrast, the same feature set failed to accurately predict other clinical symptom domains (e.g. depression or ADHD symptoms).
Feature importance rankings highlighted neuroimaging features as the most influential in the prediction of ED symptoms (prediction models for ALS in Fig. 5, for CERQ in Supplementary Figs. 13, respectively). In particular, high FA in the PTR and low anterior limb of the internal capsule (ALIC) FA were the most important predictors of affective lability, while low SMN connectivity and high LN connectivity with the hippocampus were the most important functional connectivity predictors of affective lability. Detailed feature importance rankings for CERQ prediction model are presented in Supplementary Figs. 13, but structural connectivity measures were the most influential predictors also in this case.
Fig. 5
Feature importance for predicting affective lability (ALS) from neuroimaging and biological features.
DISCUSSION
This multimodal, transdiagnostic study identified clinically and biologically meaningful signatures of ED vulnerability and disease across EDD patients, EDDoff, and healthy controls.
As detailed above (Fig. 6), PCA conducted on the full dataset identified two transdiagnostic and multimodal components (one associated with ED and hippocampal connectivity, and the other associated with inflammation) that showed significant differences among groups. Subsequent modality-specific PCAs and inter-block correlation analyses revealed clinically relevant patterns of convergence across clinical, neuroimaging, and biological domains. Complementary machine learning analyses further confirmed the predictive utility of specific multimodal features for both group classification and symptom severity, extending prior literature and supporting a dimensional, transdiagnostic view of ED vulnerability.
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Fig. 6
Schematic overview of the study design. A total of 237 participants, 97 with emotion dysregulation disorders (EDD), 67 offspring of EDD patients (EDDoff), and 73 healthy controls, underwent four types of assessment: clinical measures, serum biomarkers, diffusion-weighted MRI (structural connectivity), and resting‐state functional MRI (hippocampal dynamic functional connectivity). Principal component analysis (PCA) was first applied to the full dataset to extract transdiagnostic components that differ by group, then separately within each modality (block‐level PCA) to examine inter‐modality correlations. Random forest machine learning models used these multimodal features to predict groups and emotion dysregulation symptoms.
Multimodal PCA points to ED symptoms and hippocampal connectivity as markers of vulnerability to EDD
Clinically, EDD patients and EDDoff exhibited elevated scores in the “Elevated ED–SMN Hypoconnectivity–LN Hyperconnectivity” component (RC3) characterized by affective lability, anxiety, borderline traits, and non-adaptive emotion regulation strategies, along with reduced hippocampal connectivity with the visual-SMN and increased connectivity with the LN (Fig. 1).
The intermediate phenotype observed in EDDoff supports the hypothesis that disruptions in hippocampal connectivity and ED symptoms may reflect a transdiagnostic vulnerability marker, present even in the absence of full-blown clinical syndromes. This pattern could represent a common liability that does not necessarily lead to disease expression but may increase susceptibility when combined with additional risk factors(56). In fact, this is not only consistent with the conceptualization of ED as a transdiagnostic feature with multiple components across psychiatric disorders and vulnerable individuals(7), but also confirms that opposite patterns of hippocampal connectivity alterations in the SMN and LN may represent an EDD fMRI vulnerability marker. Indeed, also in previous literature(4, 46–55, 71) and in our own prior work on dFC in a subset of this population(56), disruptions in the SMN and LN connectivity were associated with EDD and EDD vulnerability.
Multimodal PCA points to immune dysregulation as a marker of full-blown EDD
Biologically, a distinct "High immune dysregulation" profile was increased in EDD patients compared to the other two groups (Fig. 2). This component (RC5) was primarily driven by classical pro-inflammatory markers (e.g., MIF, MCP1, CD62E, CRP, IL18, IL17) and also by the neurotrophic factor BDNF. This heightened inflammatory profile aligns with established literature on systemic immune dysregulation in EDD(26, 27, 52, 72). In particular, several markers driving RC5, such as peripheral CRP, MCP-1, and IL2R, have been consistently found to be eleveted in BD, ADHD or BPD patients(26, 27, 52, 72).
Interestingly, a minority of the drivers of this multimodal component (RC5) included markers that may also have an anti-inflammatory role (e.g., IL2R and EGF). This indicates that chronic immune dysregulation, characterized by simultaneous activation of both inflammatory and reactive immunomodulatory responses over extended periods, rather than transient purely pro-inflammatory states, may be associated with EDD. This interpretation aligns with evidence highlighting persistent immune imbalance in individuals with EDD, linked for instance with chronic stress(26, 27, 52, 72).
Elevated peripheral BDNF in EDD patients could reflect prior exposure to psychotropic medications, such as mood stabilizers, antidepressants, or antipsychotics, which are known to upregulate BDNF expression(73–75). Alternatively, increased BDNF may reflect a compensatory neurotrophic response to inflammation or stress-induced neuronal damage, consistent with BDNF dual neuroprotective and anti-inflammatory roles(76, 77).
The elevated inflammatory profile characterized by this component indicates that immune dysregulation may mark established EDD, rather than simple EDD vulnerability, which was captured instead by the aforementioned “Elevated ED–SMN Hypoconnectivity–LN Hyperconnectivity” component.
Modality-specific PCA reveals that hippocampal connectivity is positively correlated with ED symptoms and inflammation, while immune dysregulation is inversely associated with white matter integrity
Modality-specific PCA allowed for the exploration of inter-block correlations, revealing positive correlations between hippocampal dFC and both clinical ED symptoms and pro-inflammatory immune markers (BIO-RC4, mainly driven by IP10, GROa, VCAM, IL18, IL17). These findings further implicate hippocampal dFC, more than, for instance, structural connectivity, in ED symptoms. This is consistent with models positing a bidirectional cycle wherein ED symptoms promote stress and inflammation (e.g. via HPA axis dysregulation), which in turn disrupt emotion-regulating neural networks, thereby exacerbating ED and perpetuating the vicious cycle(27, 72). Although this interpretation remains speculative and no causality can be inferred from the present study, exploratory evidence from Mendelian Randomization studies has causally implicated inflammation in the development of EDD, such as BD(78, 79).
Supporting this model, a component driven by high immune dysregulation (BIO-RC1, characterized by IL12, IL4, IFNg, IL10, IL17, IL1b, and TNFa) tended to be negatively associated with a white matter FA component (FA-RC3), driven by FA in tracts such as the CGH first of all, but also the SS, EC, RLIC, and PTR (Fig. 3a). The CGH is a cortico-limbic bundle connecting the parahippocampal gyrus with the cingulate cortex. It plays a crucial role in integrating cognitive, emotional, and memory-related processes. Disruption in the CGH may impair top-down regulation of emotional responses, especially the modulation of limbic activity by the prefrontal cortex(80). It has been implicated in EDD and is often associated with difficulties in emotional control and regulation(80). The other white matter tracts (SS, EC, RLIC, PTR) are involved in visuospatial processing and sensory-motor integration, whose disruption may contribute to impaired processing of emotionally salient stimuli, integration of perceptual and emotional information, exacerbating ED.
This reinforces the hypothesis that immune dysregulation may contribute to microstructural white matter disruption in emotion regulation circuits. Selective vulnerability of emotion regulation tracts, particularly the CGH, to peripheral inflammation has been previously proposed(62, 63, 81, 82). Possible mechanisms include differences in metabolic demand, oligodendrocyte density, and delayed myelination, as these tracts are among the last to complete development(83).
Interestingly, the immune component BIO-RC1 negatively associated with FA was not limited to pro-inflammatory markers but included a minority of anti-inflammatory cytokines (e.g., IL-4, IL-10), suggesting that white matter degradation may result from prolonged immune dysregulation, triggering both inflammatory and immuno-modulating responses over the span of years, rather than acute inflammation alone. This aligns with the aforementioned evidence suggesting that chronic dysregulation of immune signaling, rather than transient inflammatory states, plays a key role in the pathophysiology of EDD and related structural brain changes(77).
Overall, these findings highlight converging disruptions across immune, structural, and functional systems that may underpin transdiagnostic vulnerability to ED and support the continued investigation of multimodal biomarkers in high-risk populations such as EDDoff.
Machine learning analysis accurately predicted group status
Machine learning models further validated the clinical significance of these findings. The RF classifier achieved strong accuracy in distinguishing HC from individuals with either EDD or familial EDD risk (EDDoff). While prior studies have focused on predicting specific diagnoses, for instance, showing that multimodal machine learning models can predict ADHD diagnosis with varying accuracies(84), our study is, to our knowledge, the first to apply a transdiagnostic model that successfully predicts participant status based on multimodal features.
While exploratory and not yet suitable for clinical screening (despite the model’s high sensitivity), the analysis offers two key insights. First, it highlights the discriminative power of multimodal data in identifying EDD risk or diagnosis, supporting dimensional and transdiagnostic frameworks. Second, it enables the identification of the most influential predictors of classification, providing insight into EDD physiopathology. As expected, clinical measures of ED were among the top contributors. Notably, white matter microstructure (particularly FA in the RLIC and ACR) and pro-inflammatory markers (e.g., CRP and MCP-1) also emerged as key features. As mentioned, peripheral CRP and MCP-1 have indeed been consistently found to be elevated in EDD patients(47, 48, 86, 88) and were crucial components in the multimodal PCA described above. Concerning FA markers, RLIC carries fibers from the thalamus (which is strictly connected with the limbic system) to the parietal and occipital lobes, including optic radiations and projections relevant for sensory processing(85). By mediating visual and sensory input to higher-order cortical areas, the RLIC is critical for integrating perceptual and emotional information(86). Alterations could lead to heightened sensitivity to environmental stimuli, a hallmark of emotional lability. Notably, the RLIC was also a central driver of the FA component inversely correlated with systemic inflammation, as discussed earlier.
The ACR, on the other hand, connects the anterior cingulate cortex and prefrontal cortex, key regions for emotion regulation, with subcortical limbic structures such as the thalamus and striatum. This connectivity supports top-down modulation of emotional responses, and reduced structural connectivity (FA) in the ACR has been observed in EDD, such as BD and BPD(87, 88), suggesting that impaired white ACR matter integrity may underlie ED. Notably, interventions aimed at enhancing emotion regulation, such as mindfulness-based practices, have been shown to improve structural connectivity (FA) in the ACR(89).
Collectively, these findings converge with the PCA results, underscoring the significance of white matter tracts, particularly the RLIC and ACR, in EDD, the potential vulnerability of RLIC structural connectivity to inflammation, and their promise as transdiagnostic targets for research and interventions in individuals with or at risk for EDD.
Machine learning models accurately predicted ED symptoms
RF regression analyses showed that multimodal biological and neuroimaging markers specifically predicted individual differences in ED, measured as affective lability (also called emotional lability, a core component of ED(66)) and cognitive emotion regulation strategies, but not other psychopathological dimensions (e.g., depression, ADHD symptoms), highlighting specificity to ED vulnerability.
Feature importance analyses revealed that lower FA in the ALIC, reduced visual-SMN hippocampal connectivity, higher FA in the PTR, and increased LN hippocampal connectivity were particularly influential in predicting higher affective lability.
The ALIC is a critical white matter bundle connecting prefrontal cortical areas to subcortical structures, including parts of the limbic system(85). It supports executive and cognitive control functions essential for effective emotion regulation and adaptive behavioral responses(90). Lower FA in the ALIC, indicative of reduced white matter integrity and disrupted fronto-subcortical connectivity, may therefore compromise the ability of the prefrontal cortex to modulate automatic emotional responses from the limbic system, leading to LN hyperactivity and to more rapid and less controlled emotional shifts, characteristic of affective lability.
The SMN described in our analysis encompassed parietal and occipital regions, involved in processing and integrating multimodal sensory inputs essential for coherent perceptual experiences and visual-sensorimotor integration(91). Impaired integration of visual-sensorimotor information with contextual memory (e.g. in the case of disrupted SMN-hippocampus connectivity) may thus hinder the ability to appropriately appraise emotional situations in light of past emotional experiences, impairing accurate sensory integration and modulation of emotional reactivity and contributing directly to emotional dysregulation(4, 46–55, 71), as we previously discussed(56). Interestingly, as discussed above, also one of the most important predictors of ALS, i.e. RLIC, is involved in sensory processing, underscoring its importance in ED symptoms physiopathology.
Similarly to the RLIC, also the PTR, a major white matter pathway, connects thalamic nuclei to some of the parietal and occipital regions involved in the SMN. Functionally, the PTR supports the relay of visual and somatosensory information from subcortical structures to cortical processing regions involved in sensory integration(92). While decreased FA typically indicates impaired structural integrity, higher FA in the PTR observed in our study may reflect atypical or maladaptive hyper-organization or maturational trajectories(93). We hypothesize that this hyperconnectivity could lead to heightened and less filtered sensory input leading to emotional reactivity to the environment, overwhelming the normal processing capacities of the SMN. Consequently, such maladaptive reorganization might impair efficient sensorimotor integration and sensory gating, increasing emotional reactivity and instability.
Hence, in this unified mechanistic model, higher FA in the PTR may speculatively be associated with compensatory maladaptive disruption of SMN connectivity, ultimately manifesting as increased affective lability and ED, reflected in the higher LN connectivity without a corresponding modulatory activation of the frontal lobes, given the white matter impairment in the ALIC. The convergence of these structural and functional disruptions within the sensorimotor-emotional integration circuits underscores the network-based nature of ED vulnerability, providing an integrated target for future research and early interventions addressing ED symptoms.
Strengths and limitations
The present study has several notable strengths. First, the integration of multimodal data, including clinical, neuroimaging, and biological measures, allows for a comprehensive characterization of ED vulnerability across multiple domains. Second, the inclusion of at-risk offspring provides unique insight into potential intermediate phenotypes, which is valuable for identifying early markers of psychopathology risk. Third, employing advanced machine learning approaches such as RF models significantly enhances the ability to detect complex nonlinear interactions among multimodal data. Finally, the transdiagnostic approach enables the identification of targets suitable for addressing the shared vulnerability among offspring of patients with a certain EDD. Nevertheless, certain limitations should be acknowledged. The cross-sectional design precludes definitive causal inference about the progression from vulnerability to clinical manifestation; longitudinal studies are thus needed to confirm and extend these findings. Additionally, although the sample size is suitable for exploratory machine learning analyses, replication in larger and independent samples is necessary to strengthen generalizability. Finally, although our subanalyses did not reveal significant influence of potential confounding variables (e.g., age, gender, BMI, cognitive scores, handedness, medication, or participant movement during scanning; detailed in the Supplementary Materials), unmeasured confounding variables (e.g., environmental stressors, sleep quality, past medication effects, nutrition etc.) might influence the observed multimodal signatures, warranting consideration in future research.
Conclusions
These findings collectively suggest that hippocampal connectivity alterations and clinical symptoms may represent ED vulnerability markers, as evidenced by their disruption in both EDD patients and EDDoff compared to HC, along with their positive intercorrelation, which suggests their mutual reinforcement in early-stage vulnerability. In contrast, a pronounced pro-inflammatory state appears to emerge specifically in established EDD, suggesting that significant immune dysregulation may be more characteristic of full clinical manifestation rather than prodromal vulnerability stages.
Several multimodal features emerge as potential transdiagnostic EDD markers to be further investigated for early intervention, risk stratification, patient stratification, or diagnosis. These include white matter microstructure in visual/sensorimotor (PTR, RLIC), and emotion-regulating (ALIC, CGH) tracts, functional connectivity disruptions in large-scale networks (particularly -visual-sensorimotor, SMN, and limbic, LN, networks), and elevated levels of systemic biomarkers including BDNF and pro-inflammatory markers (CRP, MCP-1, MIF). The transdiagnostic convergence of ED, visual-sensorimotor and emotion-regulating networks dysfunction, and immune dysregulation provides novel mechanistic insights into the multi-system pathophysiology underlying EDD.
This integrated vulnerability profile carries implications for developing precision psychiatry approaches aimed at early identification and intervention in high-risk populations(22). Future longitudinal studies should systematically evaluate the predictive value and causal relationships of these multimodal markers for early identification and illness progression, while intervention research should investigate targeted approaches capable of modifying these transdiagnostic risk pathways. As a perspective, particularly promising in this regard may be mindfulness-based interventions, given their potential capacity to improve functional connectivity patterns(94, 95), reduce inflammatory markers(96), and enhance white matter integrity(89, 94, 97). Hypothetically, these effects may reflect a fundamental restoration of body-mind integration(98), potentially addressing the observed sensorimotor dysconnectivity and its downstream clinical effects on emotional processing and dissociation symptoms in EDD patients. Hence, a multimodal, transdiagnostic, and targeted intervention of this nature could theoretically interrupt the vicious cycle sustaining ED.
This integrated approach could inform future precision psychiatry strategies, moving beyond diagnosis to the early identification and targeted, early treatment of at-risk individuals, ultimately reducing vulnerability and progression to overt psychopathology among at-risk populations such as EDDoff.
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FINANCIAL DISCLOSURESNone of the authors has any financial disclosure or conflict of interest.
ACKNOWLEDGEMENTS:
We would like to thank all study participants, the team at the Brain and Behavior Laboratory (BBL) of the University of Geneva, where imaging data were acquired, and the team at the Mood and Emotion Dysregulation Disorders Clinics of the Psychiatry Department of Geneva University Hospitals, where patients were recruited. We would like to thank Laura A Kehoe, Medical Communications, Switzerland for proofreading and preparation of the manuscript. We also thank Cecimaria Gutierrez for her contribution in processing the biological samples, and Julien Boccard for his insightful brainstorming on statistical methods that were ultimately not applied in the present work. Finally, we are grateful to Francesca Saviola, Ekansh Sareen, and Noël Suzanne Harris for their support during various phases of this project.
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AUTHORS CONTRIBUTIONS
LFS: Writing - Original Draft, Visualization, Data Curation; TL: Writing - Original Draft; LFS, CP, DVDV: Conceptualization, Writing - Review & Editing; BM, NP, FD, TL, CPe, CP, DVDV: Writing - Review & Editing; LFS, FD, TL, CPe: Methodology, Investigation, Formal Analysis; LFS, CP: Funding Acquisition; BM: Statistical Supervision; NT, CP, DVDV: Resources, Supervision.
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FUNDING
This work was supported by the Swiss National Center of Competence in Research (NCCR); “Synapsy: the Synaptic Basis of Mental Diseases” financed by the Swiss National Science Foundation [Grant Number 51NF40-158776], a grant of the Swiss National Science Foundation [Grant Number 32003B_156914], as well as an MD-PhD grant from the Swiss National Science Foundation [Grant Number 323630_221868].
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DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
SUPPLEMENTARY INFORMATION
Supplementary information is available at MP’s website.
Electronic Supplementary Material
Below is the link to the electronic supplementary material
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FIGURES LEGENDS
Variables are ranked by importance (measured as percentage of increase in Mean Standard Error with permutation, %IncMSE) from the random forest regression model predicting Affective Lability Scale (ALS) scores. Higher %IncMSE values indicate greater predictive contribution. Top predictors included posterior thalamic radiation (PTR) fractional anisotropy (FA), anterior limb of the internal capsule FA (ALIC), sensorimotor network connectivity (SMN), body of the corpus callosum FA (BCC), and retrolenticular internal capsule FA (RLIC). Color coding reflects Spearman correlation (ρ) between each feature and ALS scores, blue for positive and red for negative associations. Please refer to Supplementary Table 1 for the complete list of all variables and corresponding abbreviations.