Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data

2021 ◽  
Author(s):  
Mansu Kim ◽  
Jaesik Kim ◽  
Jeffrey Qu ◽  
Heng Huang ◽  
Qi Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neuroimaging Data ◽  
Temporal Graph ◽  
Prognostic Prediction

scholarly journals Graph neural network based heterogeneous propagation scheme for classifying alzheimer’s disease

2021 ◽  
Author(s):  
Jiyoung Byun ◽  
Yong Jeong
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Heterogeneous Data ◽  
Disease Classification ◽  
Imaging Features ◽  
Neuroimaging Data ◽  
Individual Features ◽  
The Individual

ABSTRACTDeep learning frameworks for disease classification using neuroimaging and non-imaging information require the capability of capturing individual features as well as associative information among subjects. Graphs represent the interactions among nodes, which contain the individual features, through the edges in order to incorporate the inter-relatedness among heterogeneous data. Previous graph-based approaches for disease classification have focused on the similarities among subjects by establishing customized functions or solely based on imaging features. The purpose of this paper is to propose a novel graph-based deep learning architecture for classifying Alzheimer’s disease (AD) by combining the resting-state functional magnetic resonance imaging and demographic measures without defining any study-specific function. We used the neuroimaging data from the ADNI and OASIS databases to test the robustness of our proposed model. We combined imaging-based and non-imaging information of individuals by categorizing them into distinctive nodes to construct a subject–demographic bipartite graph. The approximate personalized propagation of neural predictions, a recently developed graph neural network model, was used to classify the AD continuum from cognitively unimpaired individuals. The results showed that our model successfully captures the heterogeneous relations among subjects and improves the quality of classification when compared with other classical and deep learning models, thus outperforming the other models.

scholarly journals IC-P-144: BLAZER: A VERSATILE AND EFFICIENT WORKFLOW FOR ANALYZING PET/MR NEUROIMAGING DATA IN ALZHEIMER'S DISEASE

2019 ◽  
Vol 15 ◽  
pp. P117-P118
Author(s):  
Fabio Raman ◽  
Sameera Grandhi ◽  
Charles F. Murchison ◽  
Richard E. Kennedy ◽  
Susan M. Landau ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neuroimaging Data

P3-417: INDIVIDUALIZED STRUCTURAL CONNECTOME FOR DIAGNOSTIC AND PROGNOSTIC PREDICTION OF ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_23) ◽  
pp. P1266-P1267
Author(s):  
Yun Wang ◽  
Chenxiao Xu ◽  
Seonjoo Lee ◽  
Yaakov Stern ◽  
Jong Hun Kim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Connectome ◽  
Prognostic Prediction

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1574
Author(s):  
Shabana Urooj ◽  
Satya P. Singh ◽  
Areej Malibari ◽  
Fadwa Alrowais ◽  
Shaeen Kalathil
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Differential Evolution ◽  
Early Stage ◽  
Selection Process ◽  
Parameter Tuning ◽  
Wavelet Neural Network ◽  
Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

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