ArticleStatistics surveys2013
Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain
Article in Statistics surveys, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
57 citing papers in PubMed, 130 citations in OpenAlex.
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- Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network.Health information science and systems · 2024Article
- Identifying covariate-related subnetworks for whole-brain connectome analysis.Biostatistics (Oxford, England) · 2024Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study.Aging brain · 2024Article
- The architecture of functional brain network modulated by driving under train running noise exposure.PloS one · 2024Article
- Parameter Estimation Procedures for Exponential-family Random Graph Models on Count-valued Networks: A Comparative Simulation Study.Social networks · 2024Article
- The robustness of persistent homology of brain networks to data acquisition-related non-neural variability in resting state fMRI.Human brain mapping · 2023Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- R-Mixup: Riemannian Mixup for Biological Networks.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2023Article
- Brain Network Analysis: A Review on Multivariate Analytical Methods.Brain connectivity · 2023Review
- 3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.Network neuroscience (Cambridge, Mass.) · 2023Article
- The expanding horizons of network neuroscience: From description to prediction and control.NeuroImage · 2022Review
- FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation.Proceedings of machine learning research · 2022Article
- A mixed-modeling framework for whole-brain dynamic network analysis.Network neuroscience (Cambridge, Mass.) · 2022Article
- A hands-on tutorial on network and topological neuroscience.Brain structure & function · 2022Review
- A regression framework for brain network distance metrics.Network neuroscience (Cambridge, Mass.) · 2022Article
- Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices.PloS one · 2022Article
- Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome.Frontiers in robotics and AI · 2022Article
- Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.