ArticleScientific reports2016
Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review.Frontiers in aging neuroscience · 2025Pooled it
- Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review.Frontiers in aging neuroscience · 2023Pooled it
- Resting-state EEG connectome topology and social norm processing in healthy young adults.Brain structure & function · 2026Article
- Wavelet event-related EEG phase coherence as a discriminant biomarker of the cognitive status in Parkinson's and Lewy body disease.Frontiers in human neuroscience · 2026Article
- Bridging subjective and neural state transitions in the rubber hand illusion: a neurophenomenological study.Neuroscience of consciousness · 2026Article
- Quantifying Similarity of Dynamic Brain Networks: Two Novel Indices for Structural Change and Temporal Evolution.Bioengineering (Basel, Switzerland) · 2025Article
- Functional Connectivity Is Dominated by Aperiodic, Rather Than Oscillatory, Coupling.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Association of Bidirectional Network Cores in the Brain with Perceptual Awareness and Cognition.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Association of bidirectional network cores in the brain with perceptual awareness and cognition.bioRxiv : the preprint server for biology · 2025Article
- Tell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis.NeuroImage. Clinical · 2025Article
- Audiovisual integration and whole-brain networks in preterm and full-term neonates: A two-layer multiplex network perspective on structural and functional connectivity.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Fatigue Characterization of EEG Brain Networks Under Mixed Reality Stereo Vision.Brain sciences · 2024Article
- Article
- Fingerprints of decreased cognitive performance on fractal connectivity dynamics in healthy aging.GeroScience · 2024Article
- Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals.Frontiers in neuroscience · 2024Article
- Persistent homology reveals robustness loss in inhaled substance abuse rs-fMRI networks.PloS one · 2024Article
- Who is WithMe? EEG features for attention in a visual task, with auditory and rhythmic support.Frontiers in neuroscience · 2024Article
- Reconstructing brain functional networks through identifiability and deep learning.Network neuroscience (Cambridge, Mass.) · 2024Article
- Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis.Frontiers in human neuroscience · 2024Article
- Brain-wide network analysis of resting-state neuromagnetic data.Human brain mapping · 2023Article
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Abstract
The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer's Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.
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