ArticleBrain imaging and behavior2024
Edge time series components of functional connectivity and cognitive function in Alzheimer's disease.
Article in Brain imaging and behavior, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 4 citations in OpenAlex.
- Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.Nature communications · 2026Article
- The dual interpretation of edge time series: Time-varying connectivity versus statistical interaction.iScience · 2026Article
- Sex-Based Mechanisms of Stress-Induced Alcohol-Seeking.Behavioral sciences (Basel, Switzerland) · 2026Article
- Regional activity and interregional functional connectivity uniquely contribute to social cognitive judgments during movie-watching.Network neuroscience (Cambridge, Mass.) · 2026Article
- Brain functional connectivity analysis of fMRI-based Alzheimer's disease data.Frontiers in medicine · 2025Article
- Temporal variability of brain-behavior relationships in fine-scale dynamics of edge time series.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Mapping high-amplitude fMRI edge time series events across space and time.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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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.