Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome coupled oscillators model DOI Creative Commons
Felipe Torres Torres, Mónica Otero, Caroline Lea‐Carnall

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

Multi-state metastability in neuroimaging signals reflects the brain's flexibility to transition between network configurations response changing environments or tasks. We modeled these dynamics with a Kuramoto of 90 nodes oscillating at an intrinsic frequency 40 Hz, interconnected using human brain structural connectivity strengths and delays. simulated this model for 30 min generate multi-state metastability. identified global coupling delay parameters that maximize spectral entropy, proxy At operational point, multiple frequency-specific coherent sub-networks spontaneously emerge across oscillatory modes, persisting periods 140 4300 ms, reflecting flexible sustained dynamic states. The topography aligns empirical resting-state data. Additionally, periodic components EEG spectra from young healthy participants correlate maximal metastability, while away point sleep anesthesia spectra. Our findings suggest metastable functional observed data specific interactions connection delays, providing platform study mechanisms underlying cognition.

Language: Английский

Alpha Rhythm and Alzheimer’s Disease: Has Hans Berger’s Dream Come True? DOI Creative Commons
Claudio Babiloni, Xianghong Arakaki, Sandra Báez

et al.

Clinical Neurophysiology, Journal Year: 2025, Volume and Issue: 172, P. 33 - 50

Published: Feb. 14, 2025

Language: Английский

Citations

0
Altered spatiotemporal brain dynamics of interoception in behavioural-variant frontotemporal dementia DOI Creative Commons
Jessica L. Hazelton, Gabriel Della Bella, Pablo Barttfeld

et al.

EBioMedicine, Journal Year: 2025, Volume and Issue: 113, P. 105614 - 105614

Published: Feb. 22, 2025

Language: Английский

Citations

0
EEG biomarkers for Alzheimer's disease: A novel automated pipeline for detecting and monitoring disease progression DOI Creative Commons
Leif Simmatis, Emma E. Russo,

Tayo Steininger

et al.

Journal of Alzheimer s Disease, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Background Alzheimer's disease (AD) is a neurodegenerative disorder that profoundly alters brain function and organization. Currently, there lack of validated functional biomarkers to aid in diagnosing classifying AD. Therefore, pressing need for early, accurate, non-invasive, accessible methods detect characterize progression. Electroencephalography (EEG) has emerged as minimally invasive technique quantify changes neural activity associated with However, challenges such poor signal-to-noise ratio—particularly resting-state (rsEEG) recordings—and issues standardization have hindered its broader application. Objective To conduct pilot analysis our custom automated preprocessing feature extraction pipeline identify indicators AD correlates Methods We analyzed data from 36 individuals 29 healthy participants recorded using standard 19-channel EEG features were processed end-t-end pipeline. Various encompassing amplitude, power, connectivity, complexity, microstates extracted. Unsupervised machine learning (uniform manifold approximation projection) supervised (random forest classifiers nested cross-validation) used the dataset differences between groups. Results Our successfully detected several new previously established EEG-based measures indicative status progression, demonstrating strong external validity. Conclusions findings suggest this approach provides promising initial framework implementing patient population, paving way improved diagnostic monitoring strategies.

Language: Английский

Citations

0
Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses DOI Creative Commons
Carlos Coronel‐Oliveros, Vicente Medel, Grace A Whitaker

et al.

Network Neuroscience, Journal Year: 2023, Volume and Issue: 8(1), P. 275 - 292

Published: Dec. 4, 2023

Abstract High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer’s disease, compromising cognition executive functions. Our study sought to validate high-altitude as a model for assessing activity disruptions akin aging. We collected EEG data from 16 volunteers during acute (at 4,000 masl) at sea level, focusing on relative power aperiodic slope the spectrum due hypoxia. Additionally, we examined functional connectivity using wPLI, segregation integration graph theory tools. High altitude led slower oscillations, that is, increased δ reduced α power, flattened 1/f slope, indicating higher electrophysiological noise, Notably, strengthened θ band, exhibiting unique topographical patterns subnetwork including frontocentral occipitoparietal integration. Moreover, discovered significant correlations between subjects’ age, band integration, observed robust effects after adjusting age. findings shed light how oxygen levels high altitudes influence resembling neurodegenerative disorders aging, making promising comprehending health disease.

Language: Английский

Citations

7
Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations DOI Creative Commons
Sebastián Moguilner, Sandra Báez, Hernán Hernandez

et al.

Nature Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Brain clocks capture diversity and disparities in aging dementia across geographically diverse populationsBrain clocks, which quantify discrepancies between brain age chronological age, hold promise for understanding health disease.However, the impact of (including geographical, socioeconomic, sociodemographic, sex neurodegeneration) on brain-age gap is unknown.We analyzed datasets from 5,306 participants 15 countries (7 Latin American Caribbean (LAC) 8 non-LAC countries).Based higher-order interactions, we developed a deep learning architecture functional magnetic resonance imaging (2,953) electroencephalography (2,353).The comprised healthy controls individuals with mild cognitive impairment, Alzheimer disease behavioral variant frontotemporal dementia.LAC models evidenced older ages (functional imaging: mean directional error = 5.60, root square (r.m.s.e.) 11.91;electroencephalography: 5.34, r.m.s.e.= 9.82) associated frontoposterior networks compared models.Structural socioeconomic inequality, pollution were influential predictors increased gaps, especially LAC (R² 0.37, F² 0.59, 6.9).An ascending to impairment was found.In LAC, observed larger gaps females control groups respective males.The results not explained by variations signal quality, demographics or acquisition methods.These findings provide quantitative framework capturing accelerated aging.The undergoes dynamic changes 1-3 .Accurately mapping trajectory these how they relate critical process, multilevel 4,5 disorders 1 such as Alzheimer's continuum, includes (MCI) related like (bvFTD) 6 .Brain have emerged dimensional, transdiagnostic metrics that measure influenced range factors [7][8][9] , suggesting may be able multimodal 10 .Populations exhibit higher genetic distinct physical, social internal exposomes 11,12 phenotypes 4,13,14 .Income inequality 15,16 high levels air 17 limited access timely effective healthcare 18 rising prevalence communicable noncommunicable diseases 19,20 low education attainment 21,22 are determinants .Thus, although measuring could enhance our risk its 23 there lack research

Language: Английский

Citations

2
Social and non-social working memory in neurodegeneration DOI Creative Commons

Agustina Legaz,

Pavel Prado, Sebastián Moguilner

et al.

Neurobiology of Disease, Journal Year: 2023, Volume and Issue: 183, P. 106171 - 106171

Published: May 30, 2023

Although social functioning relies on working memory, whether a social-specific mechanism exists remains unclear. This undermines the characterization of neurodegenerative conditions with both memory and deficits. We assessed domain-specificity across behavioral, electrophysiological, neuroimaging dimensions in 245 participants. A novel task involving non-social stimuli three load levels was controls different recognized impairments in: cognition (behavioral-variant frontotemporal dementia); general (Alzheimer's disease); unspecific patterns (Parkinson's disease). also examined resting-state theta oscillations functional connectivity correlates domain-specificity. Results all groups together evidenced increased demands for associated frontocinguloparietal salience network connectivity. Canonical frontal executive-default mode anticorrelation indexed stimuli. Behavioral-variant dementia presented generalized deficits related to posterior oscillations, linked In Alzheimer's disease, were temporoparietal executive network. Parkinson's disease showed spared performance canonical brain correlates. Findings support disease-selective pathophysiological mechanisms.

Language: Английский

Citations

6
Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome-coupled oscillators model DOI Creative Commons
Felipe Torres Torres, Mónica Otero, Caroline Lea‐Carnall

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 9, 2024

Abstract Large-scale brain models with biophysical or biophysically inspired parameters generate brain-like dynamics multi-state metastability. Multi-state metastability reflects the capacity of to transition between different network configurations and cognitive states in response changing environments tasks, thus relating flexibility. To study this phenomenon, we used a Kuramoto oscillators corresponding human atlas 90 nodes, each an intrinsic frequency 40 Hz. The network’s nodes were interconnected based on structural connectivity strengths delays found brain. We identified global coupling delay scale maximum spectral entropy, proxy for maximal At point, show that multiple coherent (functional) sub-networks spontaneously emerge across oscillatory modes, persist time periods 140 4389 ms. Most exhibit broad spectra away from their frequency, switch manner similar reported empirical resting-state neuroimaging data. suggest obtained at is suitable model awake Further, yield dynamical features other such as sleep anesthesia. Therefore, entropy also correlates wakefulness synchrony functional networks.

Language: Английский

Citations

1
Brain clocks capture diversity and disparity in aging and dementia DOI Creative Commons
Agustín Ibáñez, Sebastián Moguilner,

Sandra Baez

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 25, 2024

Abstract Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding health disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American -LAC, 8 non-LAC). Based higher-order interactions in signals, developed a BAG deep learning architecture functional magnetic resonance imaging (fMRI=2,953) electroencephalography (EEG=2,353). The comprised healthy controls, individuals with mild cognitive impairment, Alzheimer’s disease, behavioral variant frontotemporal dementia. LAC models evidenced older ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated frontoposterior networks. Structural socioeconomic inequality other disparity-related factors (pollution, disparities) were influential predictors increased gaps, especially (R²=0.37, F²=0.59, RMSE=6.9). A gradient increasing controls impairment disease was found. In LAC, observed larger BAGs females control groups respective males. Results not explained by variations signal quality, demographics, or acquisition methods. Findings provide quantitative framework capturing accelerated aging.

Language: Английский

Citations

1
Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review DOI Creative Commons
Mirko Jerber Rodríguez Mallma, Luis Zuloaga-Rotta, Rubén Borja-Rosales

et al.

Neurology International, Journal Year: 2024, Volume and Issue: 16(6), P. 1285 - 1307

Published: Oct. 29, 2024

In recent years, Artificial Intelligence (AI) methods, specifically Machine Learning (ML) models, have been providing outstanding results in different areas of knowledge, with the health area being one its most impactful fields application. However, to be applied reliably, these models must provide users clear, simple, and transparent explanations about medical decision-making process. This systematic review aims investigate use application explainability ML used brain disease studies. A search was conducted three major bibliographic databases, Web Science, Scopus, PubMed, from January 2014 December 2023. total 133 relevant studies were identified analyzed out a 682 found initial search, which context studied, identifying 11 12 techniques study 20 diseases.

Language: Английский

Citations

1
Applications of Functional Magnetic Resonance Imaging to the Study of Functional Connectivity and Activation in Neurological Disease: A Scoping Review of the Literature DOI
Sandra Leskinen, Souvik Singha, Neel H. Mehta

et al.

World Neurosurgery, Journal Year: 2024, Volume and Issue: 189, P. 185 - 192

Published: June 5, 2024

Language: Английский

Citations

1