Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits

Langerbeck, Miriam; Baggio, Teresa; Messina, Irene; Bhat, Salil; Grecucci, Alessandro

doi:10.1016/j.nicl.2023.103530

Cited by 3 publications

(12 citation statements)

References 70 publications

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“…Of note, IC-GM2 largely overlaps with the DMN. The DMN is known to be activated during rest and is associated with anxiety, rumination, personality disorders and other psychopathologies (Vicentini et al, 2017; Zhou et al, 2020; Baggio et al, 2023; Langerbeck et al, 2023). Previous studies have confirmed abnormal functional connectivity within the DMN in psychological disorders (O’Neill et al, 2015; Wolf et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…Scientific investigations into the association between brain irregularities and histrionic traits remain limited. In a recent study, Langerbeck et al (2023) employed supervised learning to construct a predictive model for borderline and histrionic traits using structural brain data. Interestingly, their findings suggested that borderline and histrionic traits do not share the same neural circuitry.…”

Section: Discussionmentioning

confidence: 99%

“…The PSDI is a validated self-report inventory that measures various personality styles and can provide insights into potential personality disorders, particularly when extreme scores are observed. Developed and revised by Kuhl and Kazén (2009), the inventory consists of 140 items organized into 14 subscales. In our study, we considered the scores of the 'narcissist' subscale of the PSDI to examine the hypothesis that individual differences in narcissistic personality traits can be predicted from brain circuits.…”

Section: Personality Styles and Disorders Inventory (Psdi)mentioning

confidence: 99%

“…Test-retest coefficients vary between r = .68 and .83, indicating good testretest reliability. The construct validity of the inventory is considered acceptable for both clinical and non-clinical behavior (Kuhl & Kazén, 2009).…”

Section: Personality Styles and Disorders Inventory (Psdi)mentioning

confidence: 99%

“…Of note these regions should be ascribed to the Default Mode network. The default mode network (DMN) is activated during rest and not only consisted of two major hubs, corresponding to the medial prefrontal cortex and the posterior cingulate cortex (PCC), but also temporo-pariatel regions (Langerbeck et al, 2023). It also includes the hippocampus and the cingulate gyrus, which are known to play a role in semantic memories, internal thoughts, self-monitoring and emotion regulation (Menon, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Narcissus reflected: gray and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Jornkokgoud,

Baggio,

Bakiaj

et al. 2024

Preprint

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Despite the clinical significance of narcissistic personality, its neural bases have not been clear yet, primarily due to methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of gray (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via Parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesize that a fronto-temporo parietal network mainly related to the Default Mode Network, may be involved in NPT and white matter regions related to these regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula, and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could predict other personality traits (i.e., Histrionic, Paranoid, and Avoidant personalities). Notably, this network did not predict these personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model to generalize to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Personality Styles and Disorders Inventory (Psdi)mentioning

confidence: 99%

Section: Personality Styles and Disorders Inventory (Psdi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Narcissus reflected: gray and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Jornkokgoud,

Baggio,

Bakiaj

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Jornkokgoud,

Baggio,

Bakiaj

et al. 2024

Eur J of Neuroscience

Self Cite

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Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto‐temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto‐temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.

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The phobic brain: morphometric features correctly classify individuals with small animal phobia

Scarano,

Fumero,

Baggio

et al. 2024

Preprint

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Specific phobia represents an anxiety disorder category characterized by intense fear generated by distinct stimuli or situational triggers. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscientific literature. Moreover, the few previous studies on this topic have mostly employed univariate analyses, with limited and unbalanced samples, often leading to inconsistent results. To overcome these limitations, and to characterize the neural underpinnings of SAP, this study aims to rely on a machine learning method known as Binary Support Vector Machine (BSVM), to develop a classification model of individuals with small animal phobia based on grey matter features. Moreover, the contribution of specific structural macro-networks, such as the Default Mode, the Salience, the Executive, and the Affective networks, in separating phobic subjects from controls was assessed. Thirty-two subjects with SAP and ninety matched healthy controls were tested to this aim. At a whole-brain level, we found a significant predictive model including brain structures related to emotional regulation, cognitive control, and sensory integration, such as the cerebellum, the temporal pole, the frontal cortex, temporal lobes, the amygdala and the thalamus. Instead, when considering macro-networks analysis, we found the Default Mode, the Affective, the Central Executive and the Sensorimotor networks to significantly outperform the other networks in classifying SAP individuals. In conclusion, this study expands knowledge about the neural basis of small animal phobia, proposing new research directions and potential diagnostic strategies using advanced machine learning methods applied to neuroimaging data.

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Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits

Cited by 3 publications

References 70 publications

Narcissus reflected: gray and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Narcissus reflected: gray and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

The phobic brain: morphometric features correctly classify individuals with small animal phobia

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