A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data

Abdullah, Ahsan; Hussain, Amir; Khan, Imtiaz Hussain

Abstract

Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analytic calorie-based cognitive studies, especially using non-invasive near infrared spectroscopy (NIRS) data that predict obesity using predictive data mining. Obesity is linked with neurodegenerative diseases, diabetes, and cardiovascular diseases. Thus, understanding, predicting, preventing, and managing obesity have the potential to save the lives of millions. Behavioral studies suggest that overeating in obese individuals is triggered by exaggerated brain reward center (BRC) activity to high-calorie food stimuli (Shefer et al., Neurosci Biobehav Rev 37(10):2489–503, 2013). In this paper, details of a novel research methodology are presented for a 24-month longitudinal study using a 44-channel NIRS device with the subjects in a natural environment. The proposed methodology consists of using visual stimuli of low/high calorie food items under fasting and satiated conditions for three types of subjects. The experiments consist of block design, longitudinal plan, data smoothing, BRC activation mapping, stereotactic normalization, generating paired t-test maps under fasting and non-fasting conditions and subsequently using Naïve Bayes modeling to generate obesity prediction maps for the control subjects. The simulated results consist of generation of Bayesian prediction maps using layers of paired t-test cerebral activity maps for the four BRC functional regions considered for three types of subjects, i.e., obese, control, and control subjects fed high calorie diet. We have demonstrated how cerebral functional activity data in response to visual food stimuli can be used to predict obesity in the non-obese, thus offering a non-invasive preventive measure.

• Journal Article Type: Article
• Acceptance Date: Dec 28, 2017
• Online Publication Date: Jan 30, 2018
• Publication Date: 2018-08
• Deposit Date: Jul 25, 2019
• Journal: Cognitive Computation
• Print ISSN: 1866-9956
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 4
• Pages: 591-609
• DOI: https://doi.org/10.1007/s12559-017-9541-x
• Keywords: Prediction, Data mining, Noise, Preventing obesity, NIRS, Naïve Bayes, Paired t-test, Calorie
• Public URL: http://researchrepository.napier.ac.uk/Output/1792141
• Related Public URLs: https://www.storre.stir.ac.uk/handle/1893/26788