BHARAT: An Integrated Big Data Analytic Model for Early Diagnostic Biomarker of Alzheimer's Disease.

Ankita Sharma,Deepika Shukla,Pravat Kumar Mandal,Tripti Goel

doi:10.3389/fneur.2019.00009

Abstract

Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting millions of people worldwide. Progressive and relentless efforts are being made for therapeutic development by way of advancing understanding of non-invasive imaging modalities for the causal molecular process of AD. We present a Hadoop-based big data framework integrating non-invasive magnetic resonance imaging (MRI), MR spectroscopy (MRS) as well as neuropsychological test outcomes to identify early diagnostic biomarkers of AD. This big data framework for AD incorporates the three “V”s (volume, variety, velocity) with advanced data mining, machine learning, and statistical modeling algorithms. A large volume of longitudinal information from non-invasive imaging modalities with colligated parametric variety and speed for both data acquisition and processing as velocity complete the fundamental requirements of this big data framework for early AD diagnosis. Brain structural, neurochemical, and behavioral features are extracted from MRI, MRS, and neuropsychological scores, respectively. Subsequently, feature selection and ensemble-based classification are proposed and their outputs are fused based on the combination rule for final accurate classification and validation from clinicians. A multi-modality-based decision framework (BHARAT) for classification of early AD will be immensely helpful.

Highlights

Alzheimer’s disease (AD) is a neurodegenerative disorder affecting elderly people and no cure is available to date
Diagnostic biomarkers originating from the combined analysis of the information derived from multi-modal data (MRI, MR spectroscopy (MRS), and neuropsychological) can provide insights to the actual cause of AD
We have tested this scheme involving 128 magnetic resonance imaging (MRI) images, 128 MRS data points, and 128 neuro-psychological data points in a Scalable Hadoop cluster consisting of two nodes with 36 + 2 cores

Summary

INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative disorder affecting elderly people and no cure is available to date. Comprehending the big data challenges in AD research [21], a new and specific Hadoop [20]-based platform is proposed which incorporates clinical data management, processing, and analysis of the diverse multi-modal imaging, neuro-chemical, and neuropsychological data. Data processing includes quality checks, feature extraction, selection, and decision incorporation These features are used for classification of subjects into HO, MCI, and AD; followed by statistical analysis and verification. MapReduce, Spark, and YARN The proposed framework classifies subject categories between HO, MCI, and AD using data from three different modalities Such high-dimensional datasets have problems with storage, analysis, and visualization.

Findings

DISCUSSION

CONCLUSIONS