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.
Frontiers in Neuroscience
February 2, 2024
Antea D’Andrea, Pierpaolo Croce, Jordan O’Byrne et al.
13 citations
Theravada Buddhist monks with extensive meditation experience underwent magnetoencephalography during focused attention meditation, open monitoring meditation, and resting states. Brain microstate coverage and occurrence differed between meditation and rest and between the two meditation styles. The Hurst exponent, a measure of long-range memory in brain dynamics, was lower during both meditation conditions than during rest. Lempel-Ziv complexity, which quantifies signal complexity, increased progressively from rest to focused attention meditation to open monitoring meditation. These changes in brain criticality indices suggest that meditation shifts brain dynamics toward a more critical state, paralleling changes in cognitive state.