Structural MRI Brain Alterations in Borderline Personality Disorder and Bipolar Disorder

The role of the cerebellum in sequencing and predicting social and non-social events in patients with bipolar disorder

Introduction

Advances in the operational mode of the cerebellum indicate a role in sequencing and predicting non-social and social events, crucial for individuals to optimize high-order functions, such as Theory of Mind (ToM). ToM deficits have been described in patients with remitted bipolar disorders (BD). The literature on BD patients' pathophysiology reports cerebellar alterations; however, sequential abilities have never been investigated and no study has previously focused on prediction abilities, which are needed to properly interpret events and to adapt to changes.

Methods

To address this gap, we compared the performance of BD patients in the euthymic phase with healthy controls using two tests that require predictive processing: a ToM test that require implicit sequential processing and a test that explicitly assesses sequential abilities in non-ToM functions. Additionally, patterns of cerebellar gray matter (GM) alterations were compared between BD patients and controls using voxel-based morphometry.

Results

Impaired ToM and sequential skills were detected in BD patients, specifically when tasks required a greater predictive load. Behavioral performances might be consistent with patterns of GM reduction in cerebellar lobules Crus I-II, which are involved in advanced human functions.

Discussion

These results highlight the importance of deepening the cerebellar role in sequential and prediction abilities in patients with BD.

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 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.