JMIR Publications

ABSTRACT

Background:

Alzheimer’s disease (AD) is a degenerative progressive brain disorder where symptoms of dementia and cognitive impairment intensify over time. Numerous factors exist which may or may not be related to the lifestyle of a patient, can trigger off a higher risk for AD. Diagnosing the disorder in its beginning period is of incredible significance and several techniques are used to diagnose AD. A number of studies have been conducted for the detection and diagnosis of AD. This paper reports the empirical study performed on the longitudinal-based MRI OASIS data set. Furthermore, the study highlights several factors which influence in the prediction of AD.

Objective:

This study aims to examine the effect of longitudinal MRI data in demented and non-demented older adults. The purpose of this study is to investigate and report the correlation among various MRI features, in particular, the role of different scores obtained while MR image acquisition.

Methods:

In this study, we attempted to establish the role of the longitudinal magnetic resonance imaging (MRI) in exploratory data analysis (EDA) of AD patients. EDA was performed on the dataset of 150 patients for 343 MRI sessions [Mean age ± SD = 77.01 ± 7.64]. T1-weighted MRI of each subject on a 1.5-T Vision scanner was used for the image acquisition. Scores of three features, viz.- mini-mental state examination (MMSE), clinical dementia rating (CDR), and atlas scaling factor (ASF) were used to characterize the AD patients included in this study. We assessed the role of various features i.e. age, gender, education, socioeconomic status, MMSE, CDR, estimated total intracranial volume, normalized whole brain volume and ASF in the prognosis of AD.

Results:

The analysis further establishes the role of gender in prevalence and development of AD in older people. Moreover, a considerable relationship has been observed between education and socioeconomic position on the progression of AD. Also, outliers and linearity of each feature were determined to rule out the extreme values in measuring the skewness. The differences in nWBV between CDR = 0 (non-demented), CDR = 0.5 (very mild dementia), CDR = 1 (mild dementia) comes out to be significant i.e. p<0.01.

Conclusions:

A substantial correlation has been observed between pattern and other related features of longitudinal MRI data that can significantly assist in the diagnosis and determination of AD in older patients.