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

Dadomo, Harold; Lapomarda, Gaia; Çiftçi, Zafer; Messina, Irene; Grecucci, Alessandro

doi:10.3389/fnhum.2022.773593

Cited by 16 publications

(12 citation statements)

References 90 publications

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“…Images were segmented into GM and WM partitions using the segmentation procedure (Ashburner & Friston, 2005) as implemented in SPM12 (Statistical Parametric Mapping software, version 12; http://www.fil.ion.ucl.ac.uk/spm; Wellcome Department of Imaging Neuroscience, London), normalized to MNI space and spatially smoothed (full‐width at half maximum of Gaussian smoothing kernel [8, 8, 8]). Multivariate regression analysis was performed using Kernel ridge regression (KRR), a valid method enabling high predictive accuracy (Chu et al, 2011; Orsenigo & Vercellis, 2012), even in case of relatively small samples and single‐subjects (Dadomo et al, 2022; Schumacher et al, 2019). KRR is a multivariate regression method equivalent to maximum a posteriori approach to Gaussian process regression with fixed prior variance and no explicit noise term.…”

Section: Methodsmentioning

confidence: 99%

Neuroanatomical predictors of real‐time fMRI‐based anterior insula regulation. A supervised machine learning study

Caria

Grecucci

2022

Psychophysiology

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Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modi cations of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including frontooccipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix signi cantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our ndings highlighted speci c neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.

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Section: Methodsmentioning

confidence: 99%

Neuroanatomical predictors of real‐time fMRI‐based anterior insula regulation. A supervised machine learning study

Caria

Grecucci

2022

Psychophysiology

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“…Prospective longitudinal research provided further evidence for the hypothesis that exposure to adverse events in childhood increased the risk of being diagnosed with BPD in adulthood [42; 43; 44; 45]. In a previous attempt to assess the impact of traumatic experiences on the brain of BPD patients, Dadomo and colleagues [46] reported evidence that a circuit including the amygdala, the Heschl area, the Caudate, the Putamen, and portions of the Cerebellum was predictive of sexual abuse. They also reported that another circuit involving temporal and cerebellar regions was predictive of interpersonal problems.…”

Section: The Present Studymentioning

confidence: 99%

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

Grecucci¹,

Dadomo²,

Lapomarda³

et al. 2023

Preprint

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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.

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“…The aim is not only to explore the neural bases of anxiety, but also to extract a neural model to predict trait anxiety. As such, this model can pave the way for the creation of a potential biomarker to predict new cases (Dadomo et al, 2022;Grecucci et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Anxious brains: A combined data fusion machine learning approach to predict trait anxiety from gray and white matter features

Baggio

Grecucci

Meconi

et al. 2022

Preprint

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Trait anxiety relates to the steady propensity to experience and report negative emotions and thoughts such as fear and worries across different situations, along with a stable perception of the environment as characterized by threatening stimuli. Previous studies have tried to investigate neuroanatomical features related to anxiety mostly using univariate analyses and, thus, giving rise to contrasting results. The aim of this study is to find a predictive model of individual differences in trait anxiety from brain structural features, with a combined data fusion machine learning approach to allow generalization to new cases. Additionally, we aimed to perform a Network analysis to test the hypothesis that anxiety-related networks have a central role in modulating other networks not strictly associated with anxiety. Finally, we wanted to test the hypotheses that trait anxiety is associated with specific cognitive emotion regulation strategies, and that it decreases with ageing. Structural brain images of 158 participants were first decomposed into independent covarying gray and white matter networks with a data fusion unsupervised machine learning approach (Parallel ICA). Then, trait anxiety was predicted from these networks via supervised machine learning (Decision Trees) and backward regression. Two covarying gray and white matter independent networks successfully predicted trait anxiety. The first network included mainly parietal and temporal regions, such as the postcentral gyrus, the precuneus, and the middle and superior temporal gyrus, while the second included frontal and parietal regions such as the superior and middle temporal gyrus, the anterior cingulate and the precuneus. We also found that trait anxiety was positively associated with catastrophizing, rumination, other- and self-blame, and negatively associated with positive refocusing and reappraisal, and that it decreased with age. This paper provides new insights regarding the prediction of individual differences in trait anxiety from brain and psychological features and can pave the way for future diagnostic predictive models of anxiety.

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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

Cited by 16 publications

References 90 publications

Neuroanatomical predictors of real‐time fMRI‐based anterior insula regulation. A supervised machine learning study

Neuroanatomical predictors of real‐time fMRI‐based anterior insula regulation. A supervised machine learning study

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

Anxious brains: A combined data fusion machine learning approach to predict trait anxiety from gray and white matter features

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