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Currently accepted at: JMIR Biomedical Engineering

Date Submitted: Apr 15, 2019
Open Peer Review Period: Apr 23, 2019 - Jun 18, 2019
Date Accepted: Feb 9, 2020
(closed for review but you can still tweet)

This paper has been accepted and is currently in production.

It will appear shortly on 10.2196/14389

The final accepted version (not copyedited yet) is in this tab.

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Longitudinal Magnetic Resonance Imaging as a Potential Correlate of Exploratory Data Analysis in the Diagnosis of Alzheimer Disease

  • Afreen Khan; 
  • Swaleha Zubair

Background:

Alzheimer disease (AD) is a degenerative progressive brain disorder where symptoms of dementia and cognitive impairment intensify over time. Numerous factors exist that may or may not be related to the lifestyle of a patient that result in a higher risk for AD. Diagnosing the disorder in its beginning period is important, and several techniques are used to diagnose AD. A number of studies have been conducted on the detection and diagnosis of AD. This paper reports the empirical study performed on the longitudinal-based magnetic resonance imaging (MRI) Open Access Series of Brain Imaging dataset. Furthermore, the study highlights several factors that influence the prediction of AD.

Objective:

This study aimed to correlate the effect of various factors such as age, gender, education, and socioeconomic background of patients with the development of AD. The effect of patient-related factors on the severity of AD was assessed on the basis of MRI features, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated total intracranial volume (eTIV), normalized whole brain volume (nWBV), and Atlas Scaling Factor (ASF).

Methods:

In this study, we attempted to establish the role of the longitudinal MRI in exploratory data analysis (EDA) of AD patients. EDA was performed on the dataset of 150 patients for 343 MRI sessions (mean age 77.01 [SD 7.64] years). The T1-weighted MRI of each subject on a 1.5-Tesla Vision (Siemens) scanner was used for image acquisition. Scores of three features, MMSE, CDR, and ASF, were used to characterize the AD patients included in this study. We assessed the role of various features (ie, age, gender, education, socioeconomic status, MMSE, CDR, eTIV, nWBV, and ASF) on 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 (nondemented), CDR=0.5 (very mild dementia), and CDR=1 (mild dementia) are significant (ie, P<.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.


 Citation

Please cite as:

Khan A, Zubair S

Longitudinal Magnetic Resonance Imaging as a Potential Correlate of Exploratory Data Analysis in the Diagnosis of Alzheimer Disease

DOI: 10.2196/preprints.14389

URL: https://preprints.jmir.org/preprint/14389

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