Alzheimer s Research & Therapy, Год журнала: 2025, Номер 17(1)
Опубликована: Май 31, 2025
Язык: Английский
Bioengineering, Год журнала: 2025, Номер 12(3), С. 295 - 295
Опубликована: Март 14, 2025
Because of their nature, biomarkers for neuropsychiatric diseases were out the reach medical diagnostic technology until past few decades. In recent years, confluence greater, affordable computer power with need more efficient diagnoses and treatments has increased interest in possibility discovery. This review will focus on progress made over ten years regarding search electroencephalographic diseases. includes algorithms methods analysis, machine learning, quantitative electroencephalography as applied to neurodegenerative neurodevelopmental well traumatic brain injury COVID-19. Our findings suggest that there is a consensus among researchers classification most suit this field; slight disconnection between development increasingly sophisticated analysis what they actually be use clinical setting; finally, are favored type field caveats. The main goal state-of-the-art provide reader general panorama state art field.
Язык: Английский
Процитировано
1
Alzheimer s Research & Therapy, Год журнала: 2025, Номер 17(1)
Опубликована: Апрель 5, 2025
Cognitive decline is a condition affecting almost one sixth of the elder population and widely regarded as first manifestations Alzheimer's disease. Despite extensive body knowledge on condition, there no clear consensus structural defects neurodegeneration processes determining cognitive evolution. Here, we introduce Brain Network Model (BNM) simulating effects neural activity during processing. The model incorporates two key parameters accounting for distinct pathological mechanisms: synaptic degeneration, primarily leading to hyperexcitation, brain disconnection. Through parameter optimization, successfully replicated individual electroencephalography (EEG) responses recorded task execution from 145 participants spanning different stages decline. cohort included healthy controls, patients with subjective (SCD), those mild impairment (MCI) Alzheimer type. inversion, generated personalized BNMs each participant based EEG recordings. These models revealed network configurations corresponding patient's virtual levels directly proportional severity Strikingly, uncovered neurodegeneration-driven phase transition regimes underlying execution. On either side this transition, increasing degeneration induced changes in that closely mirrored experimental observations across stages. This enabled link hyperexcitation severity. Furthermore, pinpointed posterior cingulum fiber driver transition. Our findings highlight potential account evolution while elucidating neurodegenerative mechanisms. approach provides novel framework understanding how functional alterations contribute deterioration along continuum.
Язык: Английский
Процитировано
1
Cognitive Neurodynamics, Год журнала: 2025, Номер 19(1)
Опубликована: Май 10, 2025
Abstract Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: retrospectively analyzed the EEG records 134 and 113 non-AD patients. To generate multilevel features, discrete wavelet transform was used decompose input EEG-signals. devised novel quantum-inspired EEG-signal extraction function 7-distinct different subgraphs Goldner-Harary pattern (GHPat), selectively assigned specific subgraph, using forward-forward distance-based fitness function, each block textural extraction. extracted statistical features standard moments, which we then merged with features. Other components were iterative neighborhood component analysis selection, shallow k-nearest neighbors, as well majority voting greedy algorithm additional voted prediction vectors select best overall results. With leave-one-subject-out cross-validation (LOSO CV), our attained 88.17% accuracy. Accuracy results stratified by channel lead placement brain regions suggested P4 parietal region be most impactful. Comparison existing methods: The proposed outperforms methods achieving higher accuracy approach, ensuring robustness generalizability. Cortex maps generated that allowed visual correlation channel-wise various regions, enhancing explainability.
Язык: Английский
Процитировано
0
Brain Sciences, Год журнала: 2025, Номер 15(6), С. 582 - 582
Опубликована: Май 28, 2025
Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual’s age and education level but not severe enough to significantly interfere with daily activities, variable trajectories that may remain stable, progress dementia, or occasionally revert normal cognition. This systematic review examines neurotechnological approaches rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), training, physical exercise, artificial intelligence (AI) applications. Methods: A following PRISMA guidelines was conducted on 34 empirical studies published between 2014 2024. Studies were identified through comprehensive database searches included if they employed interventions targeting outcomes individuals MCI. Results: Evidence indicates promising across multiple intervention types. Neuromodulation techniques showed beneficial effects memory executive function. EEG analyses characteristic neurophysiological markers of potential early detection monitoring. Virtual enhanced assessment sensitivity engagement ecologically valid environments. training demonstrated the most excellent efficacy multi-domain, adaptive approaches. Physical exercise yielded improvements neurobiological pathways. Emerging AI applications personalized predictive modeling algorithms. Conclusions: Neurotechnological offer avenues rehabilitation, substantial evidence integrated mechanisms. Neurophysiological monitoring provides valuable biomarkers diagnosis treatment response. Future research should focus more extensive trials, standardized protocols, accessible implementation models translate these technological advances into practice.
Язык: Английский
Процитировано
0
Alzheimer s Research & Therapy, Год журнала: 2025, Номер 17(1)
Опубликована: Май 31, 2025
Язык: Английский
Процитировано
0