scholarly journals Sensitive biomarkers to Alzheimer's disease progression in longitudinal MRI from deep learning

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Mengjin Dong ◽  
Long Xie ◽  
Jiancong Wang ◽  
Sandhitsu R Das ◽  
David A Wolk ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Progression ◽  
Longitudinal Mri

scholarly journals Predicting Alzheimer’s disease progression using multi-modal deep learning approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Garam Lee ◽  
◽  
Kwangsik Nho ◽  
Byungkon Kang ◽  
Kyung-Ah Sohn ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Progression ◽  
Learning Approach

scholarly journals Detecting biological changes in longitudinal MRI scans of Alzheimer’s disease patients in the hippocampus region with deep learning

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Mengjin Dong ◽  
Long Xie ◽  
Sandhitsu R. Das ◽  
David A. Wolk ◽  
Paul A. Yushkevich
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Longitudinal Mri ◽  
Mri Scans

scholarly journals Interpretable Deep Temporal Structure Learning Model for Early Detection of Alzheimer’s Disease

2019 ◽  
Author(s):  
Xiaoqian Wang ◽  
Dinggang Shen ◽  
Heng Huang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Progression ◽  
Structure Learning ◽  
Temporal Structure ◽  
Temporal Correlation ◽  
Intermediate Stage ◽  
Learning Model ◽  
Learning Framework

AbstractIn Alzheimer’s research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer’s disease. How to distinguish MCI samples that finally convert to AD from those do not is an essential problem in the prevention and diagnosis of Alzheimer’s. Traditional methods use various classification models to distinguish MCI converters from non-converters, while the performance is usually limited by the small number of available data. Moreover, previous methods only use the data at baseline time for training but ignore the longitudinal information at other time points along the disease progression. To tackle with these problems, we propose a novel deep learning framework that uncovers the temporal correlation structure of the longitudinal neuroimaing data in the disease progression. In the meantime, we formulate our new deep learning model in an interpretable style such that it provides insights on the important features Alzheimer’s research. We conduct extensive experiments on the ADNI cohort and outperform the related methods with significant margin.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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