ArticleBrain connectivity2014
Frequent and discriminative subnetwork mining for mild cognitive impairment classification.
Article in Brain connectivity, 2014. 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, 28 citations in OpenAlex.
- Bridging the gap: Translating fetal, infant, and toddler neuroimaging insights into clinical practice.Developmental cognitive neuroscience · 2026Review
- Brain functional network modeling and analysis based on fMRI: a systematic review.Cognitive neurodynamics · 2021Review
- A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection.Radiology. Artificial intelligence · 2019Article
- Hybrid Functional Brain Network With First-Order and Second-Order Information for Computer-Aided Diagnosis of Schizophrenia.Frontiers in neuroscience · 2019Article
- Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI.Biomedical engineering online · 2018Article
- Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states.Scientific reports · 2018Article
- Integrating the Local Property and Topological Structure in the Minimum Spanning Tree Brain Functional Network for Classification of Early Mild Cognitive Impairment.Frontiers in neuroscience · 2018Article
- Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.NeuroImage. Clinical · 2018Article
- Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease.Frontiers in neuroscience · 2017Article
- Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.Computational and mathematical methods in medicine · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies on brain networks have suggested that many brain diseases, such as Alzheimer's disease and mild cognitive impairment (MCI), are related to a large-scale brain network, rather than individual brain regions. However, it is challenging to find such a network from the whole brain network due to the complexity of brain networks. In this article, the authors propose a novel method to mine the discriminative subnetworks for classifying MCI patients from healthy controls (HC). Specifically, the authors first extract a set of frequent subnetworks from each of the two groups (i.e., MCI and HC), respectively. Then, measure the discriminative ability of those frequent subnetworks using the graph kernel-based classification method and select the most discriminative subnetworks for subsequent classification. The results on the functional connectivity networks of 12 MCI and 25 HC show that this method can obtain competitive results compared with state-of-the-art methods on MCI classification.
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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.