A Multimodal Neuroimaging Approach for Classification and Prediction of Alzheimer\u27s Disease Using Machine Learning

Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders among the elderly population. It is progressive, irreversible in nature, and is considered the main cause of dementia. AD has become a world health problem affecting developed and developing nations alike, with the number of diagnosed AD patients increasing rather dramatically as both the life span of humans and the earth’s population continue to increase. Therefore, AD diagnosis in its earliest manifestations, preferably at the presymptomatic stage is critical for the timely planning of treatment and therapeutic interventions. We introduce new machine learning algorithms to detect and predict Alzheimer’s disease in the early phase to include the presymptomatic stage where no manifestation of cognitive decline is yet apparent. An investigation is carried out in search of optimal feature selection methods on different machine learning platforms with the intent to address the challenging classification and regression analysis. This research endeavor introduces three machine learning platforms that are based on (1) deep neural network, (2) support vector machine (SVM), and (3) Gaussian-based model classifiers all optimized in order to delineate the different stages of the disease as well as a regression framework to predict future cognitive scores as means to gauge disease progression, which could play an important role in pre-and post-treatment evaluations. The input data to these machine learning architectures included magnetic resonance imaging (MRI), positron emission tomography (PET), the metabolic fluorodeoxyglucose (FDG)-PET, cognitive scores, cerebrospinal fluid (CSF), and the apolipoprotein E4 (APOE4) gene. An investigation is carried out on the transition phases of AD through regression analysis by predicting cognitive tests including Alzheimer’s disease assessment scale cognitive subscale (ADAS-Cog), Mini-mental state examination (MMSE), and Rey’s auditory verbal learning (RAVLT) that have been designed and used as important criteria to evaluate cognitive status of AD patients. We formulated the prediction of disease progression as a multimodal multitask regression problem across six time points in a 4-year longitudinal study. Major findings of this work reveal that for binary classification, the highest accuracy of 84% for delineating the challenging group of early mild cognitively impaired individuals (EMCI) from the cognitively normal (CN) group is obtained. With multiclass classification using deep neural network methodology, especially when early and late MCI (EMCI and LMCI) groups are included, the accuracy does not exceed 70%, which clearly explains the many nuances in the transition phases of the disease, especially in the early stages. Moreover, the episodic tests like RVALT as used in this study were shown to be effective for selecting the at-risk groups. MRI morphometry was found to be the most sensitive biomarker to predict disease conversion and observed that parietal and prefrontal cortices are also associated with episodic memory in addition to the temporal lobe. Although adding the modalities of FDG-PET, CSF, and APOE allele gene improved the prediction error significantly at 4 time points, multimodal neuroimaging does not statistically enhance the prediction performance at some time points due to the inherent challenge of missing data. It is clear that for longitudinal studies of such duration (4-year), beyond the variability and interrelatedness of features, the missing data challenge remains the most difficult to overcome

A survey on applications and analysis methods of functional magnetic resonance imaging for Alzheimer’s disease

•Discussions of methods have been used in functional magnetic resonance imaging analysis.•Investigation of pre-clinical and clinical applications of functional magnetic resonance imaging in Alzheimer’s disease.•Investigation of functional brain connectivity in mild cognitive impairment and Alzheimer’s disease subjects.•There is a need for new methods to understand the functional pattern alternation in early stage of Alzheimer’s disease.•Importance of multimodal neuroimaging in early diagnosis of mild cognitive impairment. Functional magnetic resonance imaging (fMRI) is an MRI-based neuroimaging technique that measures brain activity on the basis of blood oxygenation level. This study reviews the main fMRI methods reported in the literature and their related applications in clinical and preclinical studies, focusing on relating functional brain networks in the prodromal stages of Alzheimer’s disease (AD), with a focus on the transition phases from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to AD. The purpose of this study is to present and compare different approaches of supervised and unsupervised fMRI analyses and to highlight the different applications of fMRI in the diagnosis of MCI and AD. Survey article asserts that brain network disruptions of a given dysfunction or in relation to disease prone areas of the brain in neurodegenerative dementias could be extremely useful in ascertaining the extent of cognitive deficits at the different stages of the disease. Identifying the earliest changes in these activity patterns is essential for the early planning of treatment and therapeutic protocols. Analysis methods such as independent component analysis (ICA) and graph theory-based approaches are strong analytical techniques most suitable for functional connectivity investigations. However, graph theory-based approaches have received more attention due to the higher performance they achieve in both functional and effective connectivity studies. This article shows that the disruption of brain connectivity patterns of MCI and AD could be associated with cognitive decline, an interesting finding that could augment the prospects for early diagnosis. More importantly, results reveal that changes in functional connectivity as obtained through fMRI precede detection of cortical thinning in structural MRI and amyloid deposition in positron emission tomography (PET). However, a major challenge in using fMRI as a single imaging modality, like all other imaging modalities used in isolation, is in relating a particular disruption in functional connectivity in relation to a specific disease. This is a challenge that requires more thorough investigation, and one that could perhaps be overcome through multimodal neuroimaging by consolidating the strengths of these individual imaging modalities