Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26788
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dc.contributor.authorAbdullah, Ahsanen_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.authorKhan, Imtiaz Hussainen_UK
dc.date.accessioned2018-02-22T23:34:50Z-
dc.date.available2018-02-22T23:34:50Z-
dc.date.issued2018-08-31en_UK
dc.identifier.urihttp://hdl.handle.net/1893/26788-
dc.description.abstractGlobally, 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.en_UK
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.relationAbdullah A, Hussain A & Khan IH (2018) A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Data. Cognitive Computation, 10 (4), pp. 591-609. https://doi.org/10.1007/s12559-017-9541-xen_UK
dc.rightsThis item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. The final publication is available at Springer via https://doi.org/10.1007/s12559-017-9541-xen_UK
dc.subjectPredictionen_UK
dc.subjectData miningen_UK
dc.subjectNoiseen_UK
dc.subjectPreventing obesityen_UK
dc.subjectNIRSen_UK
dc.subjectNaïve Bayesen_UK
dc.subjectPaired t-testen_UK
dc.subjectCalorieen_UK
dc.titleA Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity Using Near Infrared Spectroscopy (NIRS) Cerebral Functional Activity Dataen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2019-09-01en_UK
dc.rights.embargoreason[NIRS obesity AA AHU IHK-f.pdf] Publisher requires embargo of 12 months after formal publication.en_UK
dc.identifier.doi10.1007/s12559-017-9541-xen_UK
dc.citation.jtitleCognitive Computationen_UK
dc.citation.issn1866-9964en_UK
dc.citation.issn1866-9956en_UK
dc.citation.volume10en_UK
dc.citation.issue4en_UK
dc.citation.spage591en_UK
dc.citation.epage609en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailahu@cs.stir.ac.uken_UK
dc.contributor.affiliationFoundation University, Islamabaden_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationKing Abdulaziz University, Saudi Arabiaen_UK
dc.identifier.isiWOS:000441015100005en_UK
dc.identifier.scopusid2-s2.0-85041115072en_UK
dc.identifier.wtid498436en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.accepted2017-12-28en_UK
dcterms.dateAccepted2017-12-28en_UK
dc.date.filedepositdate2018-02-22en_UK
dc.relation.funderprojectTowards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devicesen_UK
dc.relation.funderrefEP/M026981/1en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionAMen_UK
local.rioxx.authorAbdullah, Ahsan|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.authorKhan, Imtiaz Hussain|en_UK
local.rioxx.projectEP/M026981/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2019-09-01en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2019-08-31en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2019-09-01|en_UK
local.rioxx.filenameNIRS obesity AA AHU IHK-f.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1866-9956en_UK
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