Loading...
3 results
Search Results
Now showing 1 - 3 of 3
- Biosensors, biofeedback, and neurofeedbackPublication . Monteiro, Pedro; Tavares, Diana; Mourão, Luís; Nouws, Henri P. A.; Maia, GiselaIn this chapter, the authors write about the processes of biofeedback, giving an insight about the sensors that might be used, the overall concept of biofeedback, as well as the evidence regarding the effectiveness of neurofeedback for the treatment of mental disorders.The main goal is to provide those introducing to the biofeedback as a self-regulation technique, used now for more than 50 years, with concise information about the sensors that might be used to detect the most common measured responses, the main types of physiological biofeedback, and the state-of-the-art evidence about neurofeedback as a form of brain training for individuals with the most prevalent mental disorders. Biofeedback and neurofeedback are guided therapies that include a vast and rowing variety of methodologies aimed to return information to the individual, regarding the physiological functions of the organism itself, in order to enable the modification of those otherwise considered unconscious physiological responses, designed to improve the individual’s health and wellness
- The left-right side-specific neuroendocrine signaling from injured brain: An organizational principlePublication . Watanabe, Hiroyuki; Henrique Maia, Gisela Maria; Kobikov, Yaromir; Nosova, Olga; Sarkisyan, Daniil; Galatenko, Vladimir; Carvalho, Liliana; Maia, Gisela H.; Lukoyanov, Nikolay; Lavrov, Igor; Ossipov, Michael H.; Hallberg, Mathias; Schouenborg, Jens; Zhang, Mengliang; Bakalkin, GeorgyA neurological dogma is that the contralateral effects of brain injury are set through crossed descending neural tracts. We have recently identified a novel topographic neuroendocrine system (T-NES) that operates via a humoral pathway and mediates the left-right side-specific effects of unilateral brain lesions. In rats with completely transected thoracic spinal cords, unilateral injury to the sensorimotor cortex produced contralateral hindlimb flexion, a proxy for neurological deficit. Here, we investigated in acute experiments whether T-NES consists of left and right counterparts and whether they differ in neural and molecular mechanisms. We demonstrated that left- and right-sided hormonal signaling is differentially blocked by the δ-, κ- and µ-opioid antagonists. Left and right neurohormonal signaling differed in targeting the afferent spinal mechanisms. Bilateral deafferentation of the lumbar spinal cord abolished the hormone-mediated effects of the left-brain injury but not the right-sided lesion. The sympathetic nervous system was ruled out as a brain-to-spinal cord-signaling pathway since hindlimb responses were induced in rats with cervical spinal cord transections that were rostral to the preganglionic sympathetic neurons. Analysis of gene–gene co-expression patterns identified the left- and right-side-specific gene co-expression networks that were coordinated via the humoral pathway across the hypothalamus and lumbar spinal cord. The coordination was ipsilateral and disrupted by brain injury. These findings suggest that T-NES is bipartite and that its left and right counterparts contribute to contralateral neurological deficits through distinct neural mechanisms, and may enable ipsilateral regulation of molecular and neural processes across distant neural areas along the neuraxis.
- Comparative evaluation of artificial intelligence chatbots in answering electroencephalography-related questionsPublication . Proença, Soraia; Soares, Joana Isabel; Parra, Joana; Maia, Gisela; Leite, Juliana; Beniczky, Sándor; Jesus-Ribeiro, Joana; Henrique Maia, Gisela MariaAs large language models (LLMs) become more accessible, they may be used to explain challenging EEG concepts to nonspecialists. This study aimed to compare the accuracy, completeness, and readability of EEG-related responses from three LLM-based chatbots and to assess inter-rateragreement. One hundred questions, covering 10 EEG categories, were entered into ChatGPT, Copilot, and Gemini. Six raters from the clinical neurophysiology field (two physicians, two teachers, and two technicians) evaluated the responses. Accuracy was rated on a 6-point scale, completeness on a 3-point scale, and readability was assessed using the Automated Readability Index (ARI). We used a repeated-measures ANOVA for group differences in accuracy and readability, the intraclass correlation coefficient (ICC) for inter-raterreliability, and a two way ANOVA, with chatbot and raters as factors, for completeness. Total accuracy was significantly higher for ChatGPT (mean ± SD 4.54 ± .05) compared with Copilot (mean ± SD 4.11 ± .08) and Gemini (mean ± SD 4.16 ± .13) (p < .001). ChatGPT's lowest performance was in normal variants and patterns of uncertain significance (mean ± SD 3.10 ± .14), while Copilot and Gemini performed lowest in ictal EEG patterns (mean ± SD 2.93 ± .11 and 3.37 ± .24, respectively). Although inter-rater agreement for accuracy was excellent among physicians (ICC = .969) and teachers (ICC = .926), it was poor for technicians in several EEG categories. ChatGPT achieved significantly higher completeness scores than Copilot (p < .001) and Gemini (p = .01). ChatGPT text (ARI − mean ± SD 17.41 ± 2.38) was less readable than Copilot (ARI −mean ± SD 11.14 ± 2.60) (p < .001) and Gemini (ARI − mean ± SD 14.16 ± 3.33). Chatbots achieved relatively high accuracy, but not without flaws, emphasizing that the information provided requires verification. ChatGPT outperformed the other chatbots in accuracy and completeness, though at the expense of readability. The lower inter-rater agreement among technicians may reflect a gap in standardized training or practical experience, potentially impacting the consistency of EEG-related content assessment.