ArticleFrontiers in neuroscience2021
Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data.Scientific reports · 2025Article
- Repetitive training enhances the pattern recognition capability of cultured neural networks.PLoS computational biology · 2025Article
- MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings.Cell reports methods · 2024Article
- MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures.bioRxiv : the preprint server for biology · 2024Article
- Investigating the interplay between segregation and integration in developing cortical assemblies.Frontiers in cellular neuroscience · 2024Article
- Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends.Bioengineering (Basel, Switzerland) · 2023Review
- The expanding horizons of network neuroscience: From description to prediction and control.NeuroImage · 2022Review
- Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
3 authors.
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Abstract
The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron
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