ArticleNeurobiology of disease2023
Source space connectomics of neurodegeneration: One-metric approach does not fit all.
Article in Neurobiology of disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 27 citations in OpenAlex.
- Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity.Communications biology · 2026Article
- EEG biomarkers for Alzheimer's disease: A novel automated pipeline for detecting and monitoring disease progression.Journal of Alzheimer's disease : JAD · 2025Article
- Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.Scientific reports · 2025Article
- Alpha rhythm and Alzheimer's disease: Has Hans Berger's dream come true?Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Stepwise connectivity of the entorhinal cortex along connectomic gradients in Alzheimer's disease.Brain communications · 2025Article
- Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome coupled oscillators model.Scientific reports · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations.Nature medicine · 2024Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Structural inequality and temporal brain dynamics across diverse samples.Clinical and translational medicine · 2024Article
- Brain health in diverse settings: How age, demographics and cognition shape brain function.NeuroImage · 2024Article
- Brain clocks capture diversity and disparity in aging and dementia.Research square · 2024Article
- Tau follows principal axes of functional and structural brain organization in Alzheimer's disease.Nature communications · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.bioRxiv : the preprint server for biology · 2024Article
- Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses.Network neuroscience (Cambridge, Mass.) · 2024Article
- The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds.Scientific data · 2023Article
- Social and non-social working memory in neurodegeneration.Neurobiology of disease · 2023Article
- Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.EBioMedicine · 2023Article
- Article
- Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia.Frontiers in neurology · 2023Article
Corrections and comments
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Authors and funding
14 authors at 9 institutions in 6 countries.
Funding
Abstract
Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patients<HCs) involving convergent temporo-parieto-occipital regions in AD, and fronto-temporo-parietal areas in bvFTD. Hyperconnectivity (patients>HCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
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