Deep Fusion of Multi-Template Using Spatio-Temporal Weighted Multi-Hypergraph Convolutional Networks for Brain Disease Analysis

Liu, Jingyu; Cui, Weigang; Chen, Yipeng; Ma, Yulan; Dong, Qunxi; Cai, Ran; Li, Yang; Hu, Bin

doi:10.1109/tmi.2023.3325261

Cited by 5 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, hypergraph modeling networks are very noise sensitive limiting its applications ( Dai and Gao, 2023 ). Recently several attempts have been made toward this direction ( Liu et al, 2024a , b ). Concern has also been raised with the choice of atlas on result variability as different brain atlases lead to different partitions.…”

Section: Conclusion and Limitation Of The Studymentioning

confidence: 99%

“…These findings suggest the potential for highly accurate early detection of AD. Arguing that FC networks based on pairwise correlations may rather follow a higher-order relationships, attempts have been made to propose hyperconnectivity network (HCN) models ( Guo et al, 2017 ; Li et al, 2018 ; Liu et al, 2024a , b ). Very recently, many novel methods such as spatio-temporal weighted multi-hypergraph convolutional network (STW-MHGCN), directed hypergraph convolutional network (DHGCN) etc., have been proposed and tested for MCI, AD and Major depressive disorders (MDD) with an impressive success ( Liu et al, 2024a , b ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying discriminative features of brain network for prediction of Alzheimer’s disease using graph theory and machine learning

Karim,

Fahad,

Rathore

2024

Front. Neuroinform.

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21–76 years) and the Open Access Series of Imaging Studies (OASIS, age 64–95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82–92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

show abstract

Section: Conclusion and Limitation Of The Studymentioning

confidence: 99%