ArticleFrontiers in psychiatry2022
Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- 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
- Machine learning approaches to distinguish bipolar disorder from borderline personality disorder: a scoping review.BMC psychiatry · 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 fingerprint of the dark triad: Resting state BOLD power (fALFF) alterations in executive and default mode networks.Cognitive, affective & behavioral neuroscience · 2026Article
- Narcissistic and Antisocial Personality Traits Are Both Encoded in the Triple Network: Connectomics Evidence.Psychophysiology · 2025Article
- The phobic brain: Morphometric features correctly classify individuals with small animal phobia.Psychophysiology · 2025Article
- Unmasking the Dark Triad: A Data Fusion Machine Learning Approach to Characterize the Neural Bases of Narcissistic, Machiavellian and Psychopathic Traits.The European journal of neuroscience · 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
- Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis.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
- "Holding in Anger" as a Mediator in the Relationship between Attachment Orientations and Borderline Personality Features.Brain sciences · 2023Article
- A Supervised Machine Learning Approach to Classify Brain Morphology of Professional Visual Artists versus Non-Artists.Sensors (Basel, Switzerland) · 2023Article
- Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach.Sensors (Basel, Switzerland) · 2023Article
- Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features.Sensors (Basel, Switzerland) · 2023Article
- Therapist reactions to patient personality: A pilot study of clinicians' emotional and neural responses using three clinical vignettes from in treatment series.Frontiers in human neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
Previous morphometric studies of Borderline Personality Disorder (BPD) reported inconsistent alterations in cortical and subcortical areas. However, these studies have investigated the brain at the voxel level using mass univariate methods or region of interest approaches, which are subject to several artifacts and do not enable detection of more complex patterns of structural alterations that may separate BPD from other clinical populations and healthy controls (HC). Multiple Kernel Learning (MKL) is a whole-brain multivariate supervised machine learning method able to classify individuals and predict an objective diagnosis based on structural features. As such, this method can help identifying objective biomarkers related to BPD pathophysiology and predict new cases. To this aim, we applied MKL to structural images of patients with BPD and matched HCs. Moreover, to ensure that results are specific for BPD and not for general psychological disorders, we also applied MKL to BPD against a group of patients with bipolar disorder, for their similarities in affective instability. Results showed that a circuit, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC (80%). Notably, this circuit positively correlates with the affective sector of the Zanarini questionnaire, thus indicating an involvement of this circuit with affective disturbances. Moreover, by contrasting BPD with BD, the spurious regions were excluded, and a specific circuit for BPD was outlined. These results support that BPD is characterized by anomalies in a cortico-subcortical circuit related to affective instability and that this circuit discriminates BPD from controls and from other clinical populations.
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