ArticleIEEE transactions on medical imaging2018
Connectivity in fMRI: Blind Spots and Breakthroughs.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Longitudinal evaluation of common and unique brain-networks in variants of primary progressive aphasia.Alzheimer's research & therapy · 2025Article
- Monotonicity in graph theoretic summaries of fMRI data acquired during human learning.Frontiers in human neuroscience · 2025Article
- Sliding window functional connectivity inference with nonstationary autocorrelations and cross-correlations.bioRxiv : the preprint server for biology · 2024Article
- Unveiling hidden sources of dynamic functional connectome through a novel regularized blind source separation approach.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Altered topological structure of the brain white matter in maltreated children through topological data analysis.Network neuroscience (Cambridge, Mass.) · 2024Article
- Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.NeuroImage · 2023Article
- LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.The annals of applied statistics · 2023Article
- Article
- Single-index models with functional connectivity network predictors.Biostatistics (Oxford, England) · 2022Article
- On predictability of individual functional connectivity networks from clinical characteristics.Human brain mapping · 2022Article
- Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction.IEEE transactions on medical imaging · 2022Article
- Functional Connectivity Methods and Their Applications in fMRI Data.Entropy (Basel, Switzerland) · 2022Review
- Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Analysis of Brain Functional Connectivity Neural Circuits in Children With Autism Based on Persistent Homology.Frontiers in human neuroscience · 2021Article
- A survey on exponential random graph models: an application perspective.PeerJ. Computer science · 2020Article
- Decoding Task-Specific Cognitive States with Slow, Directed Functional Networks in the Human Brain.eNeuroArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.
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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.