Common and different gray and white matter alterations in bipolar and borderline personality disorder: A source-based morphometry study

Lapomarda, Gaia; Grecucci, Alessandro; Messina, Irene; Pappaianni, Edoardo; Dadomo, Harold

doi:10.1016/j.brainres.2021.147401

Cited by 19 publications

(38 citation statements)

References 89 publications

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“…In line with previous studies, the set of areas predicting sexual abuse in BPD patients mainly involves subcortical regions such as the Caudate, Putamen and Amygdala ( Herpertz et al, 2001 ; Xu et al, 2016 ). Morphometric alterations of striatum and putamen are associated with several neuropsychiatric disorders characterized by impulsive behavior, affect instability, and substance abuse ( Luo et al, 2019 ; Lapomarda et al, 2021a , b ). Notably, putamen is part of a cortical-striatal-thalamic circuit ( Luo et al, 2019 ) that has been consistently implicated in affective processes of different psychiatric disorders ( Fettes et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…The Borderline Personality Disorder (BPD) is a complex mental disorder with a characteristic pervasive pattern of instability on affect regulation showing different dysphoric states shifting from one interpersonally reactive mood to another with great fluidity ( Zanarini et al, 1998 ; Stiglmayr et al, 2001 ; Dadomo et al, 2016 , 2018 ). Impulsiveness ( Mortensen et al, 2010 ; Lapomarda et al, 2021a , b ), strong feelings of very deep inadequacy, dissociative experiences ( Zanarini et al, 1990 ; Koenigsberg et al, 2002 ), interpersonal relationships and problem with self-image ( Lieb et al, 2004 ; De Panfilis et al, 2019 )are the main characteristics of BPD. This disorder affects approximately 1–3% of the general population ( Lenzenweger et al, 2007 ; Trull et al, 2010 ) up to 10% of outpatient psychiatric patients ( Zimmerman et al, 2005 ) 20% of hospitalized patients and 15–25% of the clinical population ( McGlashan et al, 2000 ).…”

Section: Introductionmentioning

confidence: 99%

“…Up to 2013, the majority of studies indicated that structural differences in the amygdala hub, hippocampus and cingulate cortex are involved in affective deficits ( Minzenberg et al, 2008 ; Nunes et al, 2009 ; Ruocco et al, 2012 ; Piretti et al, 2020 ). More recently, alterations in frontal (e.g., orbifrontal cortex, medial prefrontal cortex; Aguilar-Ortiz et al, 2018 ), cortical and subcortical regions ( Ruocco et al, 2016 ; Stanley et al, 2018 ; Davies et al, 2020 ; Lapomarda et al, 2021a , b ) have also been identified. Yet, if a meta-analysis confirmed this constellation of brain regions ( Yu et al, 2019 ), other extended abnormality in temporal cortex and cerebellum ( Schulze et al, 2016 ) will complete the puzzling picture.…”

Section: Introductionmentioning

confidence: 99%

“…One of the main limitations of the previous neuroimaging studies on BPD concerns the use of mass univariate methods to compare groups (e.g., Minzenberg et al, 2008 ; Grecucci et al, 2015 ; Aguilar-Ortiz et al, 2018 ; Sorella et al, 2019 ; Pappaianni et al, 2020 ; Lapomarda et al, 2021a ). Typically, comparisons of mean imaging indices between patients and healthy controls, across different brain regions using region of interest (ROI) or voxel-based techniques have been performed.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is clear that the high variability of the previous results is probably related to methodological differences and limitations, which in turn influenced the results of the various meta-analyzes. In this background, the use of multivariate methods would instead provide detailed information on how the regions are correlated, identifying naturally grouped circuits ( Grecucci et al, 2016 ; Sorella et al, 2019 ; Saviola et al, 2020 ; Lapomarda et al, 2021a , b ).…”

Section: Introductionmentioning

confidence: 99%

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Structural Features Predict Sexual Trauma and Interpersonal Problems in Borderline Personality Disorder but Not in Controls: A Multi-Voxel Pattern Analysis

Dadomo

Lapomarda

Çiftçi

et al. 2022

Front. Hum. Neurosci.

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Child trauma plays an important role in the etiology of Bordeline Personality Disorder (BPD). Of all traumas, sexual trauma is the most common, severe and most associated with receiving a BPD diagnosis when adult. Etiologic models posit sexual abuse as a prognostic factor in BPD. Here we apply machine learning using Multiple Kernel Regression to the Magnetic Resonance Structural Images of 20 BPD and 13 healthy control (HC) to see whether their brain predicts five sources of traumas: sex abuse, emotion neglect, emotional abuse, physical neglect, physical abuse (Child Trauma Questionnaire; CTQ). We also applied the same analysis to predict symptom severity in five domains: affective, cognitive, impulsivity, interpersonal (Zanarini Rating Scale for Borderline Personality Disorder; Zan-BPD) for BPD patients only. Results indicate that CTQ sexual trauma is predicted by a set of areas including the amygdala, the Heschl area, the Caudate, the Putamen, and portions of the Cerebellum in BPD patients only. Importantly, interpersonal problems only in BPD patients were predicted by a set of areas including temporal lobe and cerebellar regions. Notably, sexual trauma and interpersonal problems were not predicted by structural features in matched healthy controls. This finding may help elucidate the brain circuit affected by traumatic experiences and connected with interpersonal problems BPD suffer from.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Structural Features Predict Sexual Trauma and Interpersonal Problems in Borderline Personality Disorder but Not in Controls: A Multi-Voxel Pattern Analysis

Dadomo

Lapomarda

Çiftçi

et al. 2022

Front. Hum. Neurosci.

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dcSBM: A federated constrained source‐based morphometry approach for multivariate brain structure mapping

Saha,

Silva,

Baker

et al. 2023

Human Brain Mapping

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The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data‐driven source‐based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi‐blind extension of SBM, called constrained source‐based morphometry (constrained SBM), which enables the extraction of maximally independent reference‐alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source‐based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

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Network dysfunction of sadness facial expression processing and morphometry in euthymic bipolar disorder

Miola

Trevisan

Salvucci

et al. 2023

Eur Arch Psychiatry Clin Neurosci

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Facial emotion recognition (FER), including sadness, is altered in bipolar disorder (BD). However, the relationship between this impairment and the brain structure in BD is relatively unexplored. Furthermore, its association with clinical variables and with the subtypes of BD remains to be clarified. Twenty euthymic patients with BD type I (BD-I), 28 BD type II (BD-II), and 45 healthy controls completed a FER test and a 3D-T1-weighted magnetic resonance imaging. Gray matter volume (GMV) of the cortico-limbic regions implicated in emotional processing was estimated and their relationship with FER performance was investigated using network analysis. Patients with BD-I had worse total and sadness-related FER performance relative to the other groups. Total FER performance was significantly negatively associated with illness duration and positively associated with global functioning in patients with BD-I. Sadness-related FER performance was also significantly negatively associated with the number of previous manic episodes. Network analysis showed a reduced association of the GMV of the frontal–insular–occipital areas in patients with BD-I, with a greater edge strength between sadness-related FER performance and amygdala GMV relative to controls. Our results suggest that FER performance, particularly for facial sadness, may be distinctively impaired in patients with BD-I. The pattern of reduced interrelationship in the frontal–insular–occipital regions and a stronger positive relationship between facial sadness recognition and the amygdala GMV in BD may reflect altered cortical modulation of limbic structures that ultimately predisposes to emotional dysregulation. Future longitudinal studies investigating the effect of mood state on FER performance in BD are warranted.

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Common and different gray and white matter alterations in bipolar and borderline personality disorder: A source-based morphometry study

Cited by 19 publications

References 89 publications

Structural Features Predict Sexual Trauma and Interpersonal Problems in Borderline Personality Disorder but Not in Controls: A Multi-Voxel Pattern Analysis

Structural Features Predict Sexual Trauma and Interpersonal Problems in Borderline Personality Disorder but Not in Controls: A Multi-Voxel Pattern Analysis

dcSBM: A federated constrained source‐based morphometry approach for multivariate brain structure mapping

Network dysfunction of sadness facial expression processing and morphometry in euthymic bipolar disorder

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