ArticleIEEE journal of selected topics in signal processing2016
Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction.
Article in IEEE journal of selected topics in signal processing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- Predictive Models for the Transition from Mild Neurocognitive Disorder to Major Neurocognitive Disorder: Insights from Clinical, Demographic, and Neuropsychological Data.Biomedicines · 2024Article
- Prediction of cognitive performance differences in older age from multimodal neuroimaging data.GeroScience · 2024Article
- Task Cortical Connectivity Reveals Different Network Reorganizations between Mild Stroke Patients with Cortical and Subcortical Lesions.Brain sciences · 2023Article
- Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging.Neurobiology of aging · 2022Article
- Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification.Frontiers in neuroscience · 2022Article
- Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins.Frontiers in aging neuroscience · 2022Article
- Integrating multiple brain imaging modalities does not boost prediction of subclinical atherosclerosis in midlife adults.NeuroImage. Clinical · 2022Article
- Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability.PLoS computational biology · 2021Article
- Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging.Frontiers in neuroscience · 2018Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomarker of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce
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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.