Cortical and subcortical microstructure integrity changes after repetitive transcranial magnetic stimulation therapy in cocaine use disorder and relates to clinical outcomes.

Cocaine use disorder (CUD) is a worldwide public health condition that is suggested to induce pathological changes in macrostructure and microstructure. Repetitive transcranial magnetic stimulation (rTMS) has gained attention as a potential treatment for CUD symptoms. Here, we sought to elucidate whether rTMS induces changes in white matter (WM) microstructure in frontostriatal circuits after 2 weeks of therapy in patients with CUD and to test whether baseline WM microstructure of the same circuits affects clinical improvement. This study consisted of a 2-week, parallel-group, double-blind, randomized controlled clinical trial (acute phase) (sham [n = 23] and active [n = 27]), in which patients received two daily sessions of rTMS on the left dorsolateral prefrontal cortex (lDLPFC) as an add-on treatment. T1-weighted and high angular resolution diffusion-weighted imaging (DWI-HARDI) at baseline and 2 weeks after served to evaluate WM microstructure. After active rTMS, results showed a significant increase in neurite density compared with sham rTMS in WM tracts connecting lDLPFC with left and right ventromedial prefrontal cortex (vmPFC). Similarly, rTMS showed a reduction in orientation dispersion in WM tracts connecting lDLPFC with the left caudate nucleus, left thalamus, and left vmPFC. Results also showed a greater reduction in craving Visual Analogue Scale (VAS) after rTMS when baseline intra-cellular volume fraction (ICVF) was low in WM tracts connecting left caudate nucleus with substantia nigra and left pallidum, as well as left thalamus with substantia nigra and left pallidum. Our results evidence rTMS-induced WM microstructural changes in fronto-striato-thalamic circuits and support its efficacy as a therapeutic tool in treating CUD. Further, individual clinical improvement may rely on the patient's individual structural connectivity integrity.

Machine learning detects predictors of symptom severity and impulsivity after dialectical behavior therapy skills training group in borderline personality disorder.

Only 50% of the patients with Borderline Personality Disorder (BPD) respond to psychotherapies, such as Dialectical Behavioral Therapy (DBT), this might be increased by identifying baseline predictors of clinical change. We use machine learning to detect clinical features that could predict improvement/worsening for severity and impulsivity of BPD after DBT skills training group. To predict illness severity, we analyzed data from 125 patients with BPD divided into 17 DBT psychotherapy groups, and for impulsiveness we analyzed 89 patients distributed into 12 DBT groups. All patients were evaluated at baseline using widely self-report tests; ∼70% of the sample were randomly selected and two machine learning models (lasso and Random forest [Rf]) were trained using 10-fold cross-validation and compared to predict the post-treatment response. Models' generalization was assessed in ∼30% of the remaining sample. Relevant variables for DBT (i.e. the mindfulness ability "non-judging", or "non-planning" impulsiveness) measured at baseline, were robust predictors of clinical change after six months of weekly DBT sessions. Using 10-fold cross-validation, the Rf model had significantly lower prediction error than lasso for the BPD severity variable, Mean Absolute Error (MAE) lasso - Rf = 1.55 (95% CI, 0.63-2.48) as well as for impulsivity, MAE lasso - Rf = 1.97 (95% CI, 0.57-3.35). According to Rf and the permutations method, 34/613 significant predictors for severity and 17/613 for impulsivity were identified. Using machine learning to identify the most important variables before starting DBT could be fundamental for personalized treatment and disease prognosis.