ArticleSensors (Basel, Switzerland)2023
Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Orbitofrontal cortex and amygdala as key regions for the classification of small animal phobia: A structural MRI machine learning study.Cognitive, affective & behavioral neuroscience · 2026Article
- Lucid Dreaming Frequency Associated With Grey-White Matter Networks: An Exploratory Multimodal MRI Study.Journal of sleep research · 2026Article
- Holding the treatment frame: borderline features, personality functioning, and psychotherapy dropout in a youth mental health service.Research in psychotherapy (Milano) · 2026Article
- Attachment orientations and borderline personality features: the mediating role of difficulties in interpersonal emotion regulation.Borderline personality disorder and emotion dysregulation · 2026Article
- Neural predictors of psychotherapy response in borderline personality disorder: a mini review of neuroimaging studies.Frontiers in human neuroscience · 2026Review
- Latent class analysis of women with borderline personality disorder: the role of adverse childhood experiences in impulsivity, emotional dysregulation, and neurocognitive profiles.Borderline personality disorder and emotion dysregulation · 2025Article
- Narcissistic and Antisocial Personality Traits Are Both Encoded in the Triple Network: Connectomics Evidence.Psychophysiology · 2025Article
- Role of Early Trauma on Defense Mechanisms and Coping Strategies in Personality Disorders.Medicina (Kaunas, Lithuania) · 2025Article
- Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis.Frontiers in human neuroscience · 2025Article
- The phobic brain: Morphometric features correctly classify individuals with small animal phobia.Psychophysiology · 2025Article
- Resting-state BOLD temporal variability of the default mode network predicts spontaneous mind wandering, which is negatively associated with mindfulness skills.Frontiers in human neuroscience · 2025Article
- Gray and white matter alterations in Obsessive-Compulsive Personality Disorder: a data fusion machine learning approach.Frontiers in human neuroscience · 2025Article
- Reduced GM-WM concentration inside the Default Mode Network in individuals with high emotional intelligence and low anxiety: a data fusion mCCA+jICA approach.Social cognitive and affective neuroscience · 2024Article
- A fronto-insular network underlies individual variations in anger expression and control.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- The covariant structural and functional neuro-correlates of cognitive impairments in patients with end-stage renal diseases.Frontiers in neuroscience · 2024Article
- Prediction of borderline personality disorder based on childhood trauma with the mediating role of experiential avoidance.Frontiers in psychiatry · 2024Article
- "Holding in Anger" as a Mediator in the Relationship between Attachment Orientations and Borderline Personality Features.Brain sciences · 2023Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Borderline personality disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study, we applied for the first time a combination of an unsupervised machine learning approach known as multimodal canonical correlation analysis plus joint independent component analysis (mCCA+jICA), in combination with a supervised machine learning approach known as random forest, to possibly find covarying gray matter and white matter (GM-WM) circuits that separate BPD from controls and that are also predictive of this diagnosis. The first analysis was used to decompose the brain into independent circuits of covarying grey and white matter concentrations. The second method was used to develop a predictive model able to correctly classify new unobserved BPD cases based on one or more circuits derived from the first analysis. To this aim, we analyzed the structural images of patients with BPD and matched healthy controls (HCs). The results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in both GM and WM circuits related to early traumatic experiences and specific symptoms.
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