Dataset Information

Detection and Prediction of Mild Cognitive Impairment in Alzheimer's Disease Mice.

ABSTRACT: BACKGROUND:The recent failure of clinical trials to treat Alzheimer's disease (AD) indicates that the current approach of modifying disease is either wrong or is too late to be efficient. Mild cognitive impairment (MCI) denotes the phase between the preclinical phase and clinical overt dementia. AD mouse models that overexpress human amyloid-? (A?) are used to study disease pathogenesis and to conduct drug development/testing. However, there is no direct correlation between the A? deposition, the age of onset, and the severity of cognitive dysfunction. OBJECTIVE:To detect and predict MCI when A? plaques start to appear in the hippocampus of an AD mouse. METHODS:We trained wild-type and AD mice in a Morris water maze (WM) task with different inter-trial intervals (ITI) at 3 months of age and assessed their WM performance. Additionally, we used a classification algorithm to predict the genotype (APPtg versus wild-type) of an individual mouse from their respective WM data. RESULTS:MCI can be empirically detected using a short-ITI protocol. We show that the ITI modulates the spatial learning of AD mice without affecting the formation of spatial memory. Finally, a simple classification algorithm such as logistic regression on WM data can give an accurate prediction of the cognitive dysfunction of a specific mouse. CONCLUSION:MCI can be detected as well as predicted simultaneously with the onset of A? deposition in the hippocampus in AD mouse model. The mild cognitive impairment prediction can be used for assessing the efficacy of a treatment.

SUBMITTER: Rai SP

PROVIDER: S-EPMC7683054 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Detection and Prediction of Mild Cognitive Impairment in Alzheimer's Disease Mice.

Rai Surya Prakash SP Bascuñana Pablo P Brackhan Mirjam M Krohn Markus M Möhle Luisa L Paarmann Kristin K Pahnke Jens J

Journal of Alzheimer's disease : JAD 20200101 3

<h4>Background</h4>The recent failure of clinical trials to treat Alzheimer's disease (AD) indicates that the current approach of modifying disease is either wrong or is too late to be efficient. Mild cognitive impairment (MCI) denotes the phase between the preclinical phase and clinical overt dementia. AD mouse models that overexpress human amyloid-β (Aβ) are used to study disease pathogenesis and to conduct drug development/testing. However, there is no direct correlation between the Aβ deposi ...[more]

PMID: 32831204

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Project description:Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.

Dataset Information

Detection and Prediction of Mild Cognitive Impairment in Alzheimer's Disease Mice.

Publications

Detection and Prediction of Mild Cognitive Impairment in Alzheimer's Disease Mice.

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