A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer’s Disease

Aim and Objective: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the development of deep learning, impressive progress has also been made in the automatic diagnosis of AD. Most existing studies on automatic diagnosis are concerned with a single base network, whose accuracy for disease diagnosis still needs to be improved. This study was undertaken to propose a method to improve the accuracy of automatic diagnosis of AD. Materials and Methods: MRI image data from the Alzheimer’s Disease Neuroimaging Initiative were used to train a deep learning model to achieve computer-aided diagnosis of Alzheimer's disease. The data consisted of 138 with AD, 280 with mild cognitive impairment, and 138 normal controls. Here, a new deeply-fused net is proposed, which combines several deep convolutional neural networks, thereby avoiding the error of a single base network and increasing the classification accuracy and generalization capacity. Results: Experiments show that when differentiating between patients with AD, mild cognitive impairment, and normal controls on a subset of the ADNI database without data leakage, the new architecture improves the accuracy by about 4 percentage points as compared to a single standard base network. Conclusion: This new approach exhibits better performance, but there is still much to be done before its clinical application. In the future, greater research effort will be devoted to improving the performance of the deeply-fused net.

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Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer’s Disease

Journal of Alzheimer s Disease ◽

10.3233/jad-201438 ◽

2021 ◽

Vol 80 (3) ◽

pp. 1079-1090

Author(s):

Sanjay Nagaraj ◽

Tim Q. Duong

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Learning Algorithm ◽

Selection Methods ◽

Deep Learning Algorithm ◽

Neurocognitive Tests ◽

Risk Stratification Score

Background: Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD) from cognitive normal (CN). This can make it challenging for clinicians to make efficient and objective clinical diagnoses. It is possible to reduce the number of variables needed to make a reasonably accurate classification using machine learning. Objective: The goal of this study was to develop a deep learning algorithm to identify a few significant neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for diagnosis. Methods: Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer’s Disease Neuroimaging Initiative database. A neural network algorithm was trained on data split 90% for training and 10% testing using 10-fold cross-validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. We also evaluated five different feature selection methods. Results: The five feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes, Delayed total recall, Modified Preclinical Alzheimer Cognitive Composite with Trails test, Modified Preclinical Alzheimer Cognitive Composite with Digit test, and Mini-Mental State Examination. The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset. Conclusion: The deep-learning algorithm and simplified risk score accurately classifies EMCI, LMCI, AD and CN patients using a few common neurocognitive tests.

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Development of a Deep Learning Model for Early Alzheime's Disease Detection from Structural MRIs and External Validation on an Independent Cohort

10.1101/2021.05.28.21257318 ◽

2021 ◽

Author(s):

Sheng Liu ◽

Arjun Masurkar ◽

Henry Rusinek ◽

Jingyun Chen ◽

Ben Zhang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Neural Networks ◽

Alzheimer's Disease ◽

Deep Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Learning Model ◽

Deep Learning Model ◽

Alzheimer’S Disease Dementia ◽

Independent Cohort

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is more accurate and significantly faster than the volume/thickness model. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer's disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.

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Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment

Behavioural Neurology ◽

10.1155/2021/3359103 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Lanlan Li ◽

Yeying Yang ◽

Qi Zhang ◽

Jiao Wang ◽

Jiehui Jiang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Association Studies ◽

Genomic Research ◽

Genome Wide Association Studies ◽

Genetic Biomarkers ◽

Gwas Analysis

Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50 % , 98.39 % ± 2.50 % , and 99.44 % ± 1.11 % using the DLG model, while 71.38 % ± 0.63 % , 63.13 % ± 2.87 % , and 85.59 % ± 6.66 % using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

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P1-325: A HYBRID MACHINE LEARNING APPROACH FOR PREDICTION OF CONVERSION FROM MILD COGNITIVE IMPAIRMENT TO ALZHEIMER'S DISEASE

Alzheimer s & Dementia ◽

10.1016/j.jalz.2019.06.880 ◽

2019 ◽

Vol 15 ◽

pp. P368-P368

Author(s):

Magda Bucholc ◽

Sofya Titarenko ◽

KongFatt Wong-Ling ◽

Xuemei Ding ◽

Paula McClean ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Learning Approach ◽

Machine Learning Approach ◽

Hybrid Machine

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A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion: further evidence of its accuracy via a transfer learning approach

International Psychogeriatrics ◽

10.1017/s1041610218001618 ◽

2018 ◽

Vol 31 (07) ◽

pp. 937-945 ◽

Cited By ~ 5

Author(s):

Massimiliano Grassi ◽

David A. Loewenstein ◽

Daniela Caldirola ◽

Koen Schruers ◽

Ranjan Duara ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Transfer Learning ◽

Learning Algorithm ◽

Support Vector ◽

Learning Approach ◽

Machine Learning Algorithm

ABSTRACTBackground:In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer’s disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach.Methods:We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study.Results:Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705–0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706).Conclusions:These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.

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Deep Learning of resting-state electroencephalogram signals for 3-class classification of Alzheimer’s Disease, Mild Cognitive Impairment and Healthy Ageing

Journal of Neural Engineering ◽

10.1088/1741-2552/ac05d8 ◽

2021 ◽

Author(s):

Cameron James Huggins ◽

Javier Escudero ◽

Mario A Parra ◽

Brian Scally ◽

Renato Anghinah ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Resting State ◽

Healthy Ageing

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Data-Limited Deep Learning Methods for Mild Cognitive Impairment Classification in Alzheimer’s Disease Patients

10.1109/embc46164.2021.9630598 ◽

2021 ◽

Author(s):

Ashley De Luna ◽

Roummel F. Marcia

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Learning Methods

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A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease

10.1101/383687 ◽

2018 ◽

Cited By ~ 2

Author(s):

Simeon Spasov ◽

Luca Passamonti ◽

Andrea Duggento ◽

Pietro Liò ◽

Nicola Toschi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Clinical Data ◽

Medical Condition ◽

Structural Mri ◽

Learning Model ◽

Similar Data

AbstractSome forms of mild cognitive impairment (MCI) can be the clinical precursor of severe dementia like Alzheimer’s disease (AD), while other types of MCI tend to remain stable over-time and do not progress to AD pathology. To choose an effective and personalized treatment for AD, we need to identify which MCI patients are at risk of developing AD and which are not.Here, we present a novel deep learning architecture, based on dual learning and an ad hoc layer for 3D separable convolutions, which aims at identifying those people with MCI who have a high likelihood of developing AD. Our deep learning procedures combine structural magnetic resonance imaging (MRI), demographic, neuropsychological, and APOe4 genotyping data as input measures. The most novel characteristics of our machine learning model compared to previous ones are as follows: 1) multi-tasking, in the sense that our deep learning model jointly learns to simultaneously predict both MCI to AD conversion, and AD vs healthy classification which facilitates the relevant feature extraction for prognostication; 2) the neural network classifier employs relatively few parameters compared to other deep learning architectures (we use ~550,000 network parameters, orders of magnitude lower than other network designs) without compromising network complexity and hence significantly limits data-overfitting; 3) both structural MRI images and warp field characteristics, which quantify the amount of volumetric change compared to the common template, were used as separate input streams to extract as much information as possible from the MRI data. All the analyses were performed on a subset of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for a total of n=785 participants (192 AD, 409 MCI, and184 healthy controls (HC)).We found that the most predictive combination of inputs included the structural MRI images and the demographic, neuropsychological, and APOe4 data, while the warp field metric added little predictive value. We achieved an area under the ROC curve (AUC) of 0.925 with a 10-fold cross-validated accuracy of 86%, a sensitivity of 87.5% and specificity of 85% in classifying MCI patients who developed AD in three years’ time from those individuals showing stable MCI over the same time-period. To the best of our knowledge, this is the highest performance reported on a test set achieved in the literature using similar data. The same network provided an AUC of 1 and 100% accuracy, sensitivity and specificity when classifying NC from AD. We also demonstrated that our classification framework was robust to different co-registration templates and possibly irrelevant features / image sections.Our approach is flexible and can in principle integrate other imaging modalities, such as PET, and a more diverse group of clinical data. The convolutional framework is potentially applicable to any 3D image dataset and gives the flexibility to design a computer-aided diagnosis system targeting the prediction of any medical condition utilizing multi-modal imaging and tabular clinical data.

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