首页|University of Trento Reports Findings in Machine Learning (Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits)
University of Trento Reports Findings in Machine Learning (Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Rovereto, Ital y, by NewsRx journalists, research stated, “Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primari ly because of methodological limitations of the previous studies, such as the lo w 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 unsuper vised and supervised machine learning methods, to identify the joint contributio ns of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT).” The news reporters obtained a quote from the research from the University of Tre nto, “After preprocessing, the brain scans of 135 participants were decomposed i nto eight independent networks of covarying GM and WM via parallel ICA. Subseque ntly, stepwise regression and Random Forest were used to predict NPT. We hypothe sized 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 w hether the brain network predicting narcissism could also predict other personal ity traits (i.e., histrionic, paranoid and avoidant personalities). Notably, thi s network did not predict such personality traits. Additionally, a supervised ma chine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predi ct new cases.”
RoveretoItalyEuropeCyborgsEmergi ng TechnologiesMachine Learning
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