scholarly journals Predict Alzheimer’s disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sreevani Katabathula ◽  
Qinyong Wang ◽  
Rong Xu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Model ◽  
State Of The Art ◽  
Shape Features ◽  
Convolutional Network ◽  
Shape Representations ◽  
Global Shape ◽  
Deep Convolutional Network ◽  
Better Than

Abstract Background Alzheimer’s disease (AD) is a progressive and irreversible brain disorder. Hippocampus is one of the involved regions and its atrophy is a widely used biomarker for AD diagnosis. We have recently developed DenseCNN, a lightweight 3D deep convolutional network model, for AD classification based on hippocampus magnetic resonance imaging (MRI) segments. In addition to the visual features of the hippocampus segments, the global shape representations of the hippocampus are also important for AD diagnosis. In this study, we propose DenseCNN2, a deep convolutional network model for AD classification by incorporating global shape representations along with hippocampus segmentations. Methods The data was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and was T1-weighted structural MRI from initial screening or baseline, including ADNI 1,2/GO and 3. DenseCNN2 was trained and evaluated with 326 AD subjects and 607 CN hippocampus MRI using 5-fold cross-validation strategy. DenseCNN2 was compared with other state-of-the-art machine learning approaches for the task of AD classification. Results We showed that DenseCNN2 with combined visual and global shape features performed better than deep learning models with visual or global shape features alone. DenseCNN2 achieved an average accuracy of 0.925, sensitivity of 0.882, specificity of 0.949, and area under curve (AUC) of 0.978, which are better than or comparable to the state-of-the-art methods in AD classification. Data visualization analysis through 2D embedding of UMAP confirmed that global shape features improved class discrimination between AD and normal. Conclusion DenseCNN2, a lightweight 3D deep convolutional network model based on combined hippocampus segmentations and global shape features, achieved high performance and has potential as an efficient diagnostic tool for AD classification.

Combination of convolutional networks with a learning based ensemble strategy for hippocampus subfield segmentation

2020 ◽  
Author(s):  
Guanghua He ◽  
Tianzhe Lu ◽  
Hancan Zhu
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
State Of The Art ◽  
Brain Disorders ◽  
Convolutional Network ◽  
Hippocampal Subfields ◽  
Convolutional Networks ◽  
Ensemble Strategy

Abstract Background Accurate segmentation of hippocampal subfields from magnetic resonance (MR) brain images is an important step for studying brain disorders, including epilepsy, Alzheimer’s Disease (AD) and Parkinson’s disease. However, it is a difficult task because of the low signal contrast and small structural size.Methods Many advanced convolutional networks have been proposed and have achieved state-of-the-art performances in various applications. To take advantage of these advanced convolutional networks, in this paper, we propose a learning based ensemble strategy to integrate the results of different convolutional networks for hippocampus subfield segmentation. Our ensemble strategy is implemented by using a convolutional network. We have validated the proposed method based on a publicly available dataset.Results The experiment results have showed that the proposed ensemble strategy can significantly improve the performance of each single convolutional network, and outperform the state-of-the-art hippocampus subfield segmentation method.ConclusionThe proposed ensemble strategy is effective for combining multiple different convolutional networks in hippocampus subfield segmentation.Background Hippocampus is a bilateral brain structure, involved in many brain disorders, such as epilepsy, Alzheimer’s disease (AD), and Parkinson's disease 1. It consists of several histologically and functionally specialized subfields: the subiculum (SUB), the cornu ammonis sectors (CA) 1–3, and the dentate gyrus (DG) 2. The studies have shown that different diseases affect different subfields, which suggest that hippocampal subfields may provide more precise information for earlier disease diagnosis than using the whole hippocampus 3.

scholarly journals APOE: The New Frontier in the Development of a Therapeutic Target towards Precision Medicine in Late-Onset Alzheimer’s

2021 ◽  
Vol 22 (3) ◽  
pp. 1244
Author(s):  
Anna Yang ◽  
Boris Kantor ◽  
Ornit Chiba-Falek
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Precision Medicine ◽  
Antisense Oligonucleotides ◽  
State Of The Art ◽  
Late Onset ◽  
Base Editing ◽  
Medical Need ◽  
New Frontier ◽  
Amyloid Cascade

Alzheimer’s disease (AD) has a critical unmet medical need. The consensus around the amyloid cascade hypothesis has been guiding pre-clinical and clinical research to focus mainly on targeting beta-amyloid for treating AD. Nevertheless, the vast majority of the clinical trials have repeatedly failed, prompting the urgent need to refocus on other targets and shifting the paradigm of AD drug development towards precision medicine. One such emerging target is apolipoprotein E (APOE), identified nearly 30 years ago as one of the strongest and most reproduceable genetic risk factor for late-onset Alzheimer’s disease (LOAD). An exploration of APOE as a new therapeutic culprit has produced some very encouraging results, proving that the protein holds promise in the context of LOAD therapies. Here, we review the strategies to target APOE based on state-of-the-art technologies such as antisense oligonucleotides, monoclonal antibodies, and gene/base editing. We discuss the potential of these initiatives in advancing the development of novel precision medicine therapies to LOAD.

scholarly journals State‐of‐the‐art of lumbar puncture and its place in the journey of patients with Alzheimer's disease

2021 ◽  
Author(s):  
Harald Hampel ◽  
Leslie M. Shaw ◽  
Paul Aisen ◽  
Christopher Chen ◽  
Alberto Lleó ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lumbar Puncture ◽  
State Of The Art

An Informant Questionnaire for Detecting Alzheimer's Disease: Are Some Items Better Than Others?

2011 ◽  
Vol 17 (4) ◽  
pp. 674-681 ◽  
Author(s):  
Sietske A.M. Sikkes ◽  
Dirk L. Knol ◽  
Mark T. van den Berg ◽  
Elly S.M. de Lange-de Klerk ◽  
Philip Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Equation ◽  
Structural Model ◽  
The Elderly ◽  
Equation Modeling ◽  
Or Education ◽  
Healthy Elderly ◽  
Patient Groups ◽  
Better Than

AbstractA decline in everyday cognitive functioning is important for diagnosing dementia. Informant questionnaires, such as the informant questionnaire on cognitive decline in the elderly (IQCODE), are used to measure this. Previously, conflicting results on the IQCODEs ability to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI), and cognitively healthy elderly were found. We aim to investigate whether specific groups of items are more useful than others in discriminating between these patient groups. Informants of 180 AD, 59 MCI, and 89 patients with subjective memory complaints (SMC) completed the IQCODE. To investigate the grouping of questionnaire items, we used a two-dimensional graded response model (GRM).The association between IQCODE, age, gender, education, and diagnosis was modeled using structural equation modeling. The GRM with two groups of items fitted better than the unidimensional model. However, the high correlation between the dimensions (r=.90) suggested unidimensionality. The structural model showed that the IQCODE was able to differentiate between all patient groups. The IQCODE can be considered as unidimensional and as a useful addition to diagnostic screening in a memory clinic setting, as it was able to distinguish between AD, MCI, and SMC and was not influenced by gender or education. (JINS, 2011, 17, 674–681)

scholarly journals Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1260
Author(s):  
Savanna Denega Machado ◽  
João Elison da Rosa Tavares ◽  
Márcio Garcia Martins ◽  
Jorge Luis Victória Barbosa ◽  
Gabriel Villarrubia González ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
State Of The Art ◽  
Physiological Data ◽  
Data Sets ◽  
Scientific Contribution ◽  
Daily Lives ◽  
Iot Applications ◽  
Methodological Aspects ◽  
Dangerous Behaviors

New Internet of Things (IoT) applications are enabling the development of projects that help with monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s disease (AD). This paper presents an ontology-based computational model that receives physiological data from external IoT applications, allowing identification of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of context histories and context prediction, which, considering the state of the art, is the only one that uses analysis of context histories to perform predictions. In this research, we also propose a simulator to generate activities of the daily life of patients, allowing the creation of data sets. These data sets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1026 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical, and methodological aspects that are recorded in this article.

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