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Eduardo Pondé de Sena

3 papers in the library · 13 citations · publishing 2022-2024

Papers

Ketamine in fibromyalgia: a systematic review.

Advances in rheumatology (London, England) July 29, 2024 Jozélio Freire de Carvalho, Eduardo Pondé de Sena 8 citations

A systematic review of six studies involving 115 fibromyalgia patients found that ketamine, an anesthetic drug, appears effective and safe for short-term pain relief. Most patients were female (88-100%), aged 23 to 53, with disease duration from one month to 28 years. Ketamine was given intravenously or subcutaneously at doses of 0.1 to 0.5 mg/kg. Pain scores on a visual analog scale dropped from 59-100 mm before treatment to 2-95 mm after. Short-term studies showed good response, but the only eight-week follow-up study did not. Side effects occurred during infusion but resolved quickly. More long-term studies are needed.

On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

Journal of Physics: Complexity January 22, 2024 Caroline L. Alves, Manuel Ciba, Thaise G. L. de O. Toutain et al. 5 citations

Machine learning applied to EEG data reveals that the psychedelic DMT disrupts the balance between functional segregation and integration in cortical brain networks, making brain activity more entropic. Complex network measures such as closeness centrality best capture these changes, achieving 89% AUC in classifying brain states before and after DMT inhalation. Key connectivity differences involve the temporal and central cortex and the precentral gyrus and lateral occipital cortex, the latter presumably related to emotional, visual, and mystical experiences. Larger communities and longer average path lengths occur under DMT, supporting the view that psychedelics increase brain entropy.

Application of machine learning and complex network measures to an EEG dataset from DMT experiments

medRxiv June 16, 2022 Caroline L. Alves, Thaise G. L. de O. Toutain, Joel Augusto Moura Porto et al. preprint

A machine-learning method using support vector machines classified EEG data from volunteers before and after inhaling the psychedelic DMT. Complex network measures derived from brain connectivity achieved 89% AUC, outperforming raw connectivity matrices. Key distinguishing features included connections between temporal and central cortex regions (TP8-C3) linked to finger movements, and between precentral gyrus and lateral occipital cortex (FC5-P8) potentially related to emotional and mystical experiences. Closeness centrality was the most important network measure. DMT increased community size and average path length, disrupting the balance between functional segregation and integration, supporting the idea that cortical activity becomes more entropic under psychedelics.