Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects

By Elaheh Moradi, Antonietta Pepe, Christian Gaser, Heikki Huttunen, Jussi Tohka. NeuroImage, 2014

694 citations recorded by the source corpus.

Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer’s disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.

Subfields: Physiology, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e5990ab10adfaea19f505109a8bb4b4372d4f291

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Citations in the corpus

Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review (2017, methodology): “…role because it could assess which of the features contribute most to the prediction by also providing a correspondence to anatomical regions or structures with a biologically plausible connection to pathology (Gray et al., 2013; Lebedev et al., 2014; Moradi et al., 2015; Ardekani et al., 2017).”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, positive): “We highly encourage reporting the results derived from public datasets, and appropriate listing of the subjects, as had been done in a few studies (Moradi et al., 2015; Zhang et al., 2011).”
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review (2020, background): “diagnosis of Alzheimer disease [64], prediction of epilepsy [65], and diagnosis of diabetic retinopathy in retinal images [66].”
Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment (2018, methodology): “The sensitivity of the proposed method is a little lower than the method of Moradi et al. (2015) but much higher than the method of Tong et al. (2017), and the speciﬁcity of the proposed method is between other two methods.”
Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images (2017, methodology): “We conducted the sMCI vs. pMCI experiment to be able to compare the classification accuracy of our proposed novel method with published and comparable state-of-the-art methods 18,20,21,28,40–46 .”
A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease (2018, methodology): “…(Filipovych and Davatzikos, 2011; Moradi et al., 2015; Mosconi et al., 2007; Tong et al., 2017, Young et al., 2013), cognitive testing (Casanova et al., 2011; Moradi et al., 2015), and CSF biomarkers (Davatzikos et al., 2011; Hansson et al., 2006; Riemenschneider et al., 2002; Sonnen et al., 2010).”
Deep ensemble learning of sparse regression models for brain disease diagnosis (2017, methodology): “’s method (Moradi et al., 2015) that combined MRI features with age and cognitive 570 measures for classification showed the highest accuracy and sensitivity.”
Your evidence? Machine learning algorithms for medical diagnosis and prediction (2019, background): “Additionally, machine-learning frameworks for early MRI-based Alzheimer's conversion prediction in MCI subjects are being explored (Moradi et al., 2015).”