Resting-State EEG Reveals Regional Brain Activity Correlates in Alzheimer’s and Frontotemporal Dementia

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 6, 2024

Abstract Resting-state EEG records brain activity when awake but not engaged in tasks, analyzing frequency bands linked to cognitive states. Recent studies on Alzheimer’s disease (AD) and frontotemporal dementia (FTD) have found a link between activity, MMSE scores, age, though some findings are conflicting. This study aimed explore regional differences among AD FTD, thereby improving diagnostic strategies. We analyzed recordings from 88 participants OpenNeuro Dataset ds004504, collected at AHEPA General Hospital using Nihon Kohden 2100 device. The used preprocessed recordings, classification algorithms, function assessments (MMSE) identify significant predictors correlations measures variables. revealed that function, show distinct relationships FTD. In AD, scores significantly predicted regions like C3, C4, T4, Fz, with better performance higher power frontal temporal areas. Conversely, age had major influence particularly P3, O1, O2, while did predict activity. P4, Cz, Pz correlated lower function. Thus, the suggest biomarkers can enhance strategies by highlighting different patterns of related

Язык: Английский

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Methods for Measuring Neural Oscillations in Mental Disorders

Springer eBooks, Год журнала: 2024, Номер unknown, С. 1 - 18

Опубликована: Янв. 1, 2024

Язык: Английский

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Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals

Neuropsychiatric Disease and Treatment, Год журнала: 2024, Номер Volume 20, С. 2375 - 2389

Опубликована: Дек. 1, 2024

This study aims to investigate using eyes-open (EO) and eyes-closed (EC) resting-state EEG data diagnose cognitive impairment machine learning methods, enhancing timely intervention cost-effectiveness in dementia research.

Язык: Английский

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Longitudinal assessment of the conversion of mild cognitive impairment into Alzheimer’s dementia: Observations and mechanisms from neuropsychological testing and electrophysiology

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 17, 2024

Abstract INTRODUCTION Elucidating and better understanding functional biomarkers of Alzheimer’s disease (AD) is crucial. By analysing a detailed longitudinal dataset, this study aimed to create model-based toolset characterise understand the conversion mild cognitive impairment (MCI) AD. METHODS EEG, MRI, neuropsychological data were collected from participants in San Marino: AD (n = 10), MCI 20), controls 11). Across two additional years, classified as converters or non-converters. RESULTS We identified Stroop Color Word Test largest differentiator for (ROC AUC 0.795). This was underpinned by disconnectivity working memory attention networks. Unsupervised clustering EEG spectra also differentiated 0.710) reduced excitatory enhanced inhibitory synaptic efficacy (prodromal) Combining electrophysiological assessments increased accuracy differentiation 0.880) comparison each measure considered individually. CONCLUSION assessment with mathematical models can inform development non-invasive, low-cost tools early diagnosis Highlights analysed changes error scores lower The degree found be correlated characterised patterns associated Mathematical modelling revealed Research Context Systematic review: authors used PubMed review literature on use inexpensive modalities, including neurophysiological testing, characterising progression Although promising, existing work suggests full potential these methods prodromal still lacking. Interpretation: A novel application algorithm different patient diagnoses could largely their cluster assignment. differences particular test, Test. Using we there both network mechanisms that underlie differences. Future directions: described herein build markers testing large independent cohort will crucial impact applicability approaches. may ultimately lead characterisation prognosis

Язык: Английский

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Amyloid-β deposition predicts oscillatory slowing of magnetoencephalography signals and a reduction of functional connectivity over time in cognitively unimpaired adults

Brain Communications, Год журнала: 2024, Номер 7(1)

Опубликована: Дек. 24, 2024

Abstract With the ongoing developments in field of anti-amyloid therapy for Alzheimer’s disease, it is crucial to better understand longitudinal associations between amyloid-β deposition and altered network activity living human brain. We included 110 cognitively unimpaired individuals (67.9 ± 5.7 years), who underwent [18F]flutemetamol (amyloid-β)-PET imaging resting-state magnetoencephalography (MEG) recording at baseline 4-year follow-up. tested MEG measures (oscillatory power functional connectivity). Next, we examined relationship measures, as well deposition. Finally, assessed changes both measures. Analyses were performed using linear mixed models corrected age, sex family. At baseline, orbitofrontal-posterior cingulate regions (i.e. early disease regions) was associated with higher theta (4–8 Hz) (β = 0.17, P < 0.01) in- lower connectivity [inverted Joint Permutation Entropy (JPEinv) theta, β −0.24, 0.001] these regions, whole-brain beta (13–30 −0.13, 0.05) (JPEinv −0.18, 0.001). Whole-brain 0.05), −0.21, Baseline also predicted future oscillatory slowing, reflected by increased over time across whole brain 0.11, 0.08, 0.001), decreased −0.04, 0.05). a reduction rest −0.07, 0.01). not Longitudinal −0.19, [corrected amplitude envelope correlations alpha (8–13 Hz), −0.22, 0.05]. relative 0.21, Disruptions appear represent consequences emerging individuals. These findings suggest role neurophysiology monitoring progression potential treatment effects pre-clinical disease.

Язык: Английский

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