Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Two Grey–White Matter Circuits Separate Borderline Personality Disorder From Controls and Mediate the Relationship Between Specific Childhood Traumas and Symptoms. A mCCA+jICA and Random Forest Approach

Alessandro Grecucci
* ORCID logo ,
Harold Dadomo
ORCID logo ,
Gerardo Salvato
ORCID logo ,
Gaia Lapomarda
,
Sara Sorella
,
Irene Messina
Version 1 : Received: 3 February 2023 / Approved: 6 February 2023 / Online: 6 February 2023 (08:47:40 CET)

A peer-reviewed article of this Preprint also exists.

Grecucci, A.; Dadomo, H.; Salvato, G.; Lapomarda, G.; Sorella, S.; Messina, I. Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach. Sensors 2023, 23, 2862. Grecucci, A.; Dadomo, H.; Salvato, G.; Lapomarda, G.; Sorella, S.; Messina, I. Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach. Sensors 2023, 23, 2862.

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 an unsupervised machine learning approach known as mCCA+jICA, in combination with a supervised machine learning approach known as Random Forest, to possibly find covarying GM-WM circuits that separate BPD from controls and that are also predictive of this diagnosis. To this aim, we analyzed the structural images of patients with BPD and matched HCs. 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 GM and WM circuits related to early traumatic experiences and specific symptoms.

Keywords

Borderline personality disorder; machine learning; data fusion; child trauma; symptoms severity

Subject

Social Sciences, Behavior Sciences

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF Download Supplementary

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Supplementary Files Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.