A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data
A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data
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DOI：
10.1007/s12559-017-9541-x
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发表时间：
2018-01
影响因子：
5.4
通讯作者：
A. Abdullah;A. Hussain;Imtiaz Hussain Khan
中科院分区：
文献类型：
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作者：
A. Abdullah;A. Hussain;Imtiaz Hussain Khan
在全球范围内，肥胖率急剧上升，男性和女性的患病率预计将分别增加到18%和21%(非传染性疾病风险因素合作，《柳叶刀》第387(10026)：1377-96,2016年)。然而，几乎没有任何基于卡路里的数据分析认知研究，特别是使用非侵入性近红外光谱(NIRS)数据，使用预测性数据挖掘预测肥胖。肥胖与神经退行性疾病、糖尿病和心血管疾病有关。因此，了解、预测、预防和管理肥胖有可能挽救数百万人的生命。行为研究表明，肥胖者过度进食是由大脑奖励中心(BRC)对高卡路里食物刺激的夸大活动引发的(Shefer等人，Neurosci BioBehaviv Rev 37(10)：2489-503,2013)。在这篇论文中，提出了一种新的研究方法的细节，该研究方法使用44个通道的近红外光谱设备在自然环境中进行了为期24个月的纵向研究。所提出的方法包括对三种类型的受试者在禁食和饱食条件下使用低/高卡路里食物的视觉刺激。实验包括区组设计、纵向计划、数据平滑、BRC激活图、立体定向归一化，在禁食和非禁食条件下生成配对t检验图，然后使用朴素贝叶斯建模为对照对象生成肥胖预测图。模拟结果包括使用三种类型的受试者，即肥胖受试者、对照受试者和高热量饮食受试者的四个BRC功能区的多层配对t检验脑活动图来生成贝叶斯预测图。我们已经展示了如何利用大脑功能活动数据对视觉食物刺激的反应来预测非肥胖者的肥胖，从而提供了一种非侵入性的预防措施。
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 pairedt-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 pairedt-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.