Minimum spanning tree analysis of unimpaired individuals at risk of Alzheimer’s disease DOI Creative Commons
Alejandra García‐Colomo, David López‐Sanz, Cornelis J. Stam

et al.

Brain Communications, Journal Year: 2024, Volume and Issue: 6(5)

Published: Jan. 1, 2024

Abstract Identifying early and non-invasive biomarkers to detect individuals in the earliest stages of Alzheimer’s disease continuum is crucial. As a result, electrophysiology plasma are emerging as great candidates this pursuit due their low invasiveness. This first magnetoencephalography study assess relationship between minimum spanning tree parameters, an alternative overcome comparability thresholding problem issues characteristic conventional brain network analyses, phosphorylated tau231 levels unimpaired individuals, with different risk disease. Seventy-six available recordings determination were included. The for theta, alpha beta bands each subject was obtained, leaf fraction, hierarchy diameter calculated. To these topological parameters tau231, we performed correlation whole sample considering two sub-groups separately. Increasing concentrations associated greater fraction values, along lower theta frequency bands. These results emerged higher group, but not group. Our indicate that topology cognitively elevated levels, marker pathology amyloid-β accumulation, already altered, shifting towards more integrated increasing its vulnerability hub-dependency, mostly band. indicated by increases hierarchy, reductions diameter. match initial trajectory proposed theoretical models progression disruption suggest changes function organization begin on.

Language: Английский

GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals DOI Creative Commons
Joseph Paillard, Joerg F. Hipp, Denis A. Engemann

et al.

Patterns, Journal Year: 2025, Volume and Issue: 6(3), P. 101182 - 101182

Published: Feb. 13, 2025

Language: Английский

Citations

2
Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia DOI
Sinead Gaubert, Pilar Garcés, Joerg F. Hipp

et al.

EBioMedicine, Journal Year: 2025, Volume and Issue: 114, P. 105659 - 105659

Published: March 29, 2025

Language: Английский

Citations

1
Different oscillatory mechanisms of dementia-related diseases with cognitive impairment in closed-eye state DOI Creative Commons

Talifu Zikereya,

Yu‐Chen Lin, Zhizhen Zhang

et al.

NeuroImage, Journal Year: 2024, Volume and Issue: unknown, P. 120945 - 120945

Published: Nov. 1, 2024

Language: Английский

Citations

6
Neurologically altered brain activity may not look like aged brain activity: Implications for brain-age modeling and biomarker strategies DOI Creative Commons
Lukas Gemein, Sinead Gaubert, Claire Paquet

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 20, 2025

Abstract Background Brain-age gap (BAG), the difference between predicted age and chronological age, is studied as a biomarker for natural progression of neurodegeneration. The BAG captures brain atrophy measured with structural Magnetic Resonance Imaging (MRI). Electroencephalography (EEG) has also been explored functional means estimating age. However, EEG studies showed mixed results including seemingly paradoxical negative BAG, i.e. younger than in neurological populations. Objectives This study critically examined estimation from spectral power common measure activity two largest public datasets containing cases alongside controls. Methods recordings were analyzed individuals conditions (n=900, TUAB data; n=417 MCI & n=311 dementia, CAU data) controls (n=1254, n=459, data). Results We found that age-prediction models trained on reference population systematically under-predicted people replicating diseased activity. Inspection age-related trends along spectra revealed complex frequency-dependent alterations groups underlying BAG. Conclusions utility an interpretable relies observation MRI progressive neurodegeneration often broadly resembles accelerated aging. assumption can be violated assessments such and, potentially, different psychiatric or therapeutic effects. sign may not meaningfully interpreted deviation normal

Language: Английский

Citations

0
Modern neurophysiological techniques indexing normal or abnormal brain aging DOI Creative Commons
Angelo Pascarella, Lucia Manzo, Edoardo Ferlazzo

et al.

Seizure, Journal Year: 2024, Volume and Issue: unknown

Published: July 1, 2024

Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations neurogenesis, neuronal senescence synaptic plasticity are implicated. Recent years have seen advancements neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) transcranial magnetic stimulation (TMS), offering insights into physiological pathological brain aging. These methods provide real-time information on activity, connectivity network dynamics. Integration Artificial Intelligence (AI) promise tool enhancing diagnosis prognosis age-related decline. Our review highlights recent advances these electrophysiological (focusing EEG, ERP, TMS TMS-EEG methodologies) their application Physiological characterized by changes EEG spectral power connectivity, ERP parameters, indicating neural activity function. Pathological aging, Alzheimer's disease, further disruptions rhythms, components measures, reflecting neurodegenerative processes. Machine learning approaches show classifying impairment predicting disease progression. Standardization integration other modalities crucial for comprehensive understanding disorders. Advanced analysis AI hold potential diagnostic accuracy deepening changes.

Language: Английский

Citations

3
Personalized brain models link cognitive decline progression to underlying synaptic and connectivity degeneration DOI Creative Commons

Lorenzo Gaetano Amato,

Alberto Arturo Vergani, Michael Lassi

et al.

Alzheimer s Research & Therapy, Journal Year: 2025, Volume and Issue: 17(1)

Published: April 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.

Language: Английский

Citations

0
Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer’s disease: a magnetoencephalography study DOI Creative Commons
Anne M van Nifterick, Willem de Haan, Cornelis J. Stam

et al.

Brain Communications, Journal Year: 2024, Volume and Issue: 6(6)

Published: Jan. 1, 2024

Abstract Understanding the nature and onset of neurophysiological changes, selective vulnerability central hub regions in functional network, may aid managing growing impact Alzheimer’s disease on society. However, precise alterations occurring pre-clinical stage human remain controversial. This study aims to provide increased insights quantitative during a true early disease. Using high spatial resolution source-reconstructed magnetoencephalography, we investigated regional whole-brain changes unique cohort 11 cognitively unimpaired individuals with pathogenic mutations presenilin-1 or amyloid precursor protein gene 1:3 matched control group (n = 33) median age 49 years. We examined several magnetoencephalography measures that have been shown robust detecting differences sporadic patients are sensitive excitation-inhibition imbalance. includes spectral power connectivity different frequency bands. also using disruption index. To understand how change as progresses through its stage, correlations between outcomes various clinical variables like were analysed. A comparison mutation carriers controls revealed oscillatory slowing, characterized by widespread higher theta (4–8 Hz) power, lower posterior peak occipital alpha 2 (10–13 power. Functional analyses presented (amplitude-based) (8–13 beta (13–30 bands, predominantly located parieto-temporal regions. Furthermore, found significant index for (phase-based) band, attributed both ‘non-hub’ alongside disruption. Neurophysiological did not correlate indicators progression after multiple comparisons correction. Our findings evidence slowing occur before cognitive impairment autosomal dominant leading The direction these comparable those observed stages disease, suggest an imbalance, fit activity-dependent degeneration hypothesis. These prove useful diagnosis intervention future.

Language: Английский

Citations

1
Minimum spanning tree analysis of unimpaired individuals at risk of Alzheimer’s disease DOI Creative Commons
Alejandra García‐Colomo, David López‐Sanz, Cornelis J. Stam

et al.

Brain Communications, Journal Year: 2024, Volume and Issue: 6(5)

Published: Jan. 1, 2024

Abstract Identifying early and non-invasive biomarkers to detect individuals in the earliest stages of Alzheimer’s disease continuum is crucial. As a result, electrophysiology plasma are emerging as great candidates this pursuit due their low invasiveness. This first magnetoencephalography study assess relationship between minimum spanning tree parameters, an alternative overcome comparability thresholding problem issues characteristic conventional brain network analyses, phosphorylated tau231 levels unimpaired individuals, with different risk disease. Seventy-six available recordings determination were included. The for theta, alpha beta bands each subject was obtained, leaf fraction, hierarchy diameter calculated. To these topological parameters tau231, we performed correlation whole sample considering two sub-groups separately. Increasing concentrations associated greater fraction values, along lower theta frequency bands. These results emerged higher group, but not group. Our indicate that topology cognitively elevated levels, marker pathology amyloid-β accumulation, already altered, shifting towards more integrated increasing its vulnerability hub-dependency, mostly band. indicated by increases hierarchy, reductions diameter. match initial trajectory proposed theoretical models progression disruption suggest changes function organization begin on.

Language: Английский

Citations

0