ArticleFrontiers in neuroscience2018
How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters.
Article in Frontiers in neuroscience, 2018. 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, 47 citations in OpenAlex.
- Multiplexity of human brain oscillations as a personal brain signature.Human brain mapping · 2023Article
- Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths.Neuroinformatics · 2023Article
- A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset.NeuroImage · 2022Article
- Impaired functional cortical networks in the theta frequency band of patients with post-traumatic stress disorder during auditory-cognitive processing.Frontiers in psychiatry · 2022Article
- Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder.Frontiers in neuroinformatics · 2022Article
- Dyconnmap: Dynamic connectome mapping-A neuroimaging python module.Human brain mapping · 2021Article
- Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers.Sensors (Basel, Switzerland) · 2021Article
- Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.Human brain mapping · 2021Article
- Functional connectivity of spoken language processing in early-stage Parkinson's disease: An MEG study.NeuroImage. Clinical · 2021Article
- Alterations in resting-state network dynamics along the Alzheimer's disease continuum.Scientific reports · 2020Article
- A Novel Connectome-Based Electrophysiological Study of Subjective Cognitive Decline Related to Alzheimer's Disease by Using Resting-State High-Density EEG EGI GES 300.Brain sciences · 2020Article
- Reduced parietal alpha power and psychotic symptoms: Test-retest reliability of resting-state magnetoencephalography in schizophrenia and healthy controls.Schizophrenia research · 2020Article
- Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease.Frontiers in neuroscience · 2020Article
- Optimization of graph construction can significantly increase the power of structural brain network studies.NeuroImage · 2019Article
- Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury.Frontiers in computational neuroscience · 2019Article
- Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease.NeuroImage. Clinical · 2019Article
- Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment.Frontiers in neuroscience · 2019Article
- Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-Beamformed Connectivity Analysis.Frontiers in neuroscience · 2018Article
- Article
- Can Long-Term Regular Practice of Physical Exercises Including Taichi Improve Finger Tapping of Patients Presenting With Mild Cognitive Impairment?Frontiers in physiology · 2018Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α
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