Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion

Skolariki, Konstantina; Terrera, G Muniz; Danso, Samuel

doi:10.1007/978-3-030-32622-7_8

Cited by 8 publications

(6 citation statements)

References 39 publications

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“…Particularly, early diagnosis of AD is pivotal in therapeutic development and ultimately for effective patient care [2]. ML methods can be used to identify clinical features and characteristic MR images and patterns for diagnostic predictions [59,60]. This approach can potentially help with dementia predisposition identi cation in those who develop cognitive complaints [61-64], as this is a common question faced by clinicians.…”

Section: Discussionmentioning

confidence: 99%

Machine learning of cerebello-cerebral functional networks for mild cognitive impairment detection

Yao

Song

et al. 2023

Preprint

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Background: Early identification of degenerative processes in Alzheimer’s disease (AD) is essential. Cerebello-cerebral network changes can be used for early diagnosis of dementia and its stages, namely mild cognitive impairment (MCI) and AD. Methods: Features of cortical thickness (CT) and cerebello-cerebral functional connectivity (FC) extracted from MRI data were used to analyze structural and functional changes, and machine learning for the disease progression classification. Results: CT features have an accuracy of 92.05% for AD vs. HC, 88.64% for MCI vs. HC, and 83.13% for MCI vs. AD. Additionally, combined with convolutional CT and cerebello-cerebral FC features, the accuracy of the classifier reached 94.12% for MCI vs. HC, 90.91% for AD vs. HC, and 89.16% for MCI vs. AD, evaluated using support vector machines. Conclusions: The proposed pipeline offers a promising low-cost alternative for the diagnosis of preclinical AD and can be useful for other degenerative brain disorders.

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Section: Discussionmentioning

confidence: 99%

Machine learning of cerebello-cerebral functional networks for mild cognitive impairment detection

Yao

Song

et al. 2023

Preprint

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“…The EEG signals were preprocessed on the MATLAB version R2021a environment software (https://kr.mathworks.com/) using the EEGlab version 2010 toolbox (https://sccn.ucsd. edu/eeglab/index.php) by applying FFT to obtain qEEG time-frequency (TF) images with a dimension of 875×656 for ECR with sub-bands (delta [1-4 Hz], theta [4][5][6][7][8], alpha [8][9][10][11][12], beta [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and gamma [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]) from each EEG channel and TF images colormap was normalized in the range of [− 20 20] dB. EEGlab is an interactive MATLAB toolkit for analyzing continuous and event-related EEG signals as well as other electrophysiological data.…”

Section: Eeg Recordings and Preprocessingmentioning

confidence: 99%

“…The branch of AI known as machine learning (ML) has been successfully implemented in medical research and used to predict the conversion of MCI-to-AD, 26 , 27 as with most studies for early diagnosis of MCI and other types of dementia only, EEG was used as a biomarker focus on a group study. 28 The goal of ML algorithms is to obtain elements of the data that are not visible using traditional statistical analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Simfukwe¹,

Youn²,

Kim³

et al. 2023

NDT

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Purpose: Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR). Participants and Methods:The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN). Results: The trained models', HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively. Conclusion:The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.

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“…Regarding the application of ML algorithms in AD using longitudinal data, the most frequent objective was the development of prediction models that determine the risk/time of conversion from MCI to AD [ 22 , 30 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ], or the course of the disease in terms of severity [ 50 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ]. These models are based on time, which adds another layer of complexity.…”

Section: Main Applications Of Ai In Ad Researchmentioning

confidence: 99%

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

et al. 2022

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Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.

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Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion

Cited by 8 publications

References 39 publications

Machine learning of cerebello-cerebral functional networks for mild cognitive impairment detection

Machine learning of cerebello-cerebral functional networks for mild cognitive impairment detection

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

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