Psychiatry Research
July 29, 2023
Mauro Pettorruso, Roberto Guidotti, Giacomo D’andrea et al.
56 citations
Machine learning models predicted which patients with treatment-resistant depression would respond to esketamine nasal spray. In a retrospective study of 149 patients, three random forest classifiers achieved 68.53% accuracy for response at one month and 66.26% at three months, and 68.60% accuracy for remission at three months. Features such as severe anhedonia, anxious distress, mixed symptoms, and bipolarity positively predicted response and remission, while benzodiazepine use and depression severity were linked to delayed responses. The findings suggest machine learning may aid personalized treatment decisions for treatment-resistant depression.
Brain Topography
March 28, 2023
Roberto Guidotti, Antea D’Andrea, Alessio Basti et al.
32 citations
Machine learning applied to fMRI functional connectivity data can distinguish focused attention from open monitoring meditation styles, but only in expert Theravada Buddhist monks, not in novice meditators. The Anterior Salience and Default Mode networks were key for classification, consistent with their roles in emotion and self-regulation during meditation. Specific couplings between areas regulating attention, self-awareness, and somatosensory processing were also important, along with left inter-hemispheric connections. The findings support that extensive meditation practice differentially modulates large-scale brain networks according to meditation style.
Brain sciences
August 18, 2021
Roberto Guidotti, Cosimo Del Gratta, Mauro Gianni Perrucci et al.
27 citations
Long-term meditation practice reshapes functional connectivity patterns in large-scale brain networks, and the specific patterns depend on the type of meditation used. Using fMRI and multivariate pattern analysis, researchers found that connectivity patterns in key brain networks could predict both a meditator's expertise and age. Expertise-related patterns differed between Focused Attention (FA) and Open Monitoring (OM) meditation: FA involved networks for attention, while OM involved networks for cognitive control and emotion regulation. Age-related patterns were unaffected by meditation style. The findings indicate that intensive mental training induces neuroplastic changes in brain network connectivity that are specific to the form of meditation practiced.
Psychiatry Research
August 18, 2025
Ilenia Rosa, L. Padula, Francesco Semeraro et al.
2 citations
Treatment-resistant depression (TRD) challenges standard approaches, prompting a shift toward non-monoaminergic interventions like neuromodulation and glutamatergic agents. This narrative review examines the endocannabinoid system (ECS) as a potential common pathway for these treatments. Evidence indicates that repetitive transcranial magnetic stimulation (rTMS) and electroconvulsive therapy (ECT) increase endocannabinoids anandamide and 2-arachidonoylglycerol, correlating with clinical improvement. Ketamine and esketamine modulate CB1 receptors, while psilocybin restores 2-AG and enhances CB1 expression in mood-related brain regions. These findings suggest ECS modulation may unify diverse antidepressant mechanisms in TRD, offering a promising target for novel therapies.