Structural features related to affective instability correctly classify patients with Borderline Personality Disorder. A Supervised Machine Learning approach

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 BPD patients and matched healthy controls. 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 Bipolar disorder patients, for their similarities in affective instability. Results showed that a circuit including basal ganglia, amygdala, portions of the temporal lobes and of the orbito-frontal 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 discriminate BPD from controls and from another clinical populations.