ArticleFrontiers in neuroscience2021
Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification.
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.
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
9 citing papers in PubMed, 13 citations in OpenAlex.
- Brain Graph Sparsification for fMRI-based Connectome Analysis: A Methodological Review.Neuroinformatics · 2026Review
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Normalized group activations based feature extraction technique using heterogeneous data for Alzheimer's disease classification.PeerJ. Computer science · 2024Article
- Resting-State Functional MRI Adaptation with Attention Graph Convolution Network for Brain Disorder Identification.Brain sciences · 2022Article
- Multi-band network fusion for Alzheimer's disease identification with functional MRI.Frontiers in psychiatry · 2022Article
- Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection.Frontiers in neuroinformatics · 2022Article
- Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network.Frontiers in aging neuroscience · 2022Article
- Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI.Frontiers in neuroinformatics · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so as to improve classification performance and help us understand the pathological mechanism of AD-related brain disorders. Previous studies usually extract node statistics or edge weights from FBNs to represent each subject. However, these methods generally ignore the
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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.