Search Results (3)

Background: Sleep disturbances are a core feature of post-traumatic stress disorder (PTSD), affecting up to 90% of patients and often persisting after standard PTSD treatment. As all the current interventions have limitations, amygdala-targeted neuromodulation may offer a novel treatment pathway. Methods: Secondary analysis of a prospective, single-arm trial (n = 58) was carried out evaluating Prism™ amygdala-derived-EEG-fMRI-Pattern neurofeedback (Amyg-EFP-NF). Sleep outcomes were assessed using the Clinician-Administered PTSD Scale (CAPS-5) sleep item, PTSD Checklist (PCL-5) sleep item, and Patient Health Questionnaire (PHQ-9) sleep items at baseline, post-treatment, and 3-month follow-up. Treatment consisted of 15 sessions over 8 weeks. Results: At 3-months’ follow-up, 63.79% of participants demonstrated clinically meaningful reduction in sleep disturbances (≥1 point reduction in CAPS-5 Item 20). Sleep improvement showed a moderate correlation with overall PTSD symptom reduction (r = 0.484, p < 0.001) and a balanced improvement pattern (−15.1% early, −9.1% late). Sleep responders sustained improvement across multiple measures and showed significant increases in cognitive reappraisal (mean change: +2.57 ± 1.0, p = 0.006), while non-responders showed initial but un-sustained improvement in trauma-related dreams. Conclusions: Amyg-EFP-NF shows preliminary promise for treating PTSD-related sleep disturbances. Our exploratory analyses suggest distinct temporal patterns of sleep improvement and potential associations with enhanced cognitive reappraisal capacity that warrant rigorous investigation in future randomized controlled trials. Full article

Neurodegenerative diseases (NDs), including Parkinson’s and Alzheimer’s disease, pose a significant challenge to global health, and early detection tools are crucial for effective intervention. The adaptation of online screening forms and machine learning methods can lead to better and wider diagnosis, potentially altering the progression of NDs. Therefore, this study examines the diagnostic efficiency of machine learning models using Montreal Cognitive Assessment test results (MoCA) to classify scores of people with Parkinson’s disease (PD) and healthy subjects. For data analysis, we implemented both rule-based modeling using rough set theory (RST) and classic machine learning (ML) techniques such as logistic regression, support vector machines, and random forests. Importantly, the diagnostic accuracy of the best performing model (RST) increased from 80.0% to 93.4% and diagnostic specificity increased from 57.2% to 93.4% when the MoCA score was combined with temporal metrics such as IRT—instrumental reaction time and TTS—submission time. This highlights that online platforms are able to detect subtle signs of bradykinesia (a hallmark symptom of Parkinson’s disease) and use this as a biomarker to provide more precise and specific diagnosis. Despite the constrained number of participants (15 Parkinson’s disease patients and 16 healthy controls), the results suggest that incorporating time-based metrics into cognitive screening algorithms may significantly improve their diagnostic capabilities. Therefore, these findings recommend the inclusion of temporal dynamics in MoCA assessments, which may potentially improve the early detection of NDs. Full article

Evaluation of the rehabilitation efficacy may be an essential indicator of its further implementation and planning. The research aim is to examine whether the estimation of EEG correlates of auditory-verbal processing in a child with overlapping autism spectrum disorder (ASD) and specific language impairment (SLI) symptoms may be a predictor of the treatment efficacy in conditions when behavioral tests do not show improvement during the time course. The prospective case report reports follow-up results in a child aged 36 to 66 months. During continuous integrative therapy, autism risk index, cognitive, speech–language, sensory, and EEG correlates of auditory-verbal information processing are recorded in six test periods, and their mutual interrelation was analyzed. The obtained results show a high statistically significant correlation of all observed functions with EEG correlates related to the difference between the average mean values of theta rhythm in the left (F1, F3, F7) and right (F2, F4, F8) frontal region. The temporal dynamics of the examined processes point to the consistency of the evaluated functions increasing with time flow. These findings indicate that EEG correlates of auditory-verbal processing may be used to diagnose treatment efficacy in children with overlapping ASD and SLI. Full article