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dc.contributor.authorGrecucci, Alessandro
dc.contributor.authorLapomarda, Gaia
dc.contributor.authorMessina, Irene
dc.contributor.authorMonachesi, Bianca
dc.contributor.authorSorella, Sara
dc.contributor.authorSiugzdaite, Roma
dc.date.accessioned2022-03-14T09:00:31Z
dc.date.available2022-03-14T09:00:31Z
dc.date.issued2022
dc.date.submitted2021-10-29
dc.identifier.issn1664-0640
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334957
dc.description.abstractPrevious 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.
dc.languageen
dc.publisherFrontiers Media SA
dc.subjectPsychiatry
dc.subjectBorderline Personality Disorder
dc.subjectbipolar disorder
dc.subjectmachine learning
dc.subjectmulti-voxel pattern analysis
dc.subjectaffective instability
dc.titleStructural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach.
dc.typeArticle
dc.date.updated2022-03-14T09:00:31Z
prism.publicationNameFront Psychiatry
prism.volume13
dc.identifier.doi10.17863/CAM.82395
dcterms.dateAccepted2022-01-03
rioxxterms.versionofrecord10.3389/fpsyt.2022.804440
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn1664-0640
cam.issuedOnline2022-02-28


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