{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T01:51:29Z","timestamp":1773280289843,"version":"3.50.1"},"reference-count":17,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2017,1,25]],"date-time":"2017-01-25T00:00:00Z","timestamp":1485302400000},"content-version":"vor","delay-in-days":24,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Annals of the New York Academy of Sciences"],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:p>This paper presents a video\u2010based eye\u2010tracking method, ideally deployed via a mobile device or laptop\u2010based webcam, as a tool for measuring brain function. Eye movements and pupillary motility are tightly regulated by brain circuits, are subtly perturbed by many disease states, and are measurable using video\u2010based methods. Quantitative measurement of eye movement by readily available webcams may enable early detection and diagnosis, as well as remote\/serial monitoring, of neurological and neuropsychiatric disorders. We successfully extracted computational and semantic features for 14 testing sessions, comprising 42 individual video blocks and approximately 17,000 image frames generated across several days of testing. Here, we demonstrate the feasibility of collecting video\u2010based eye\u2010tracking data from a standard webcam in order to assess psychomotor function. Furthermore, we were able to demonstrate through systematic analysis of this data set that eye\u2010tracking features (in particular, radial and tangential variance on a circular visual\u2010tracking paradigm) predict performance on well\u2010validated psychomotor tests.<\/jats:p>","DOI":"10.1111\/nyas.13305","type":"journal-article","created":{"date-parts":[[2017,1,25]],"date-time":"2017-01-25T20:37:26Z","timestamp":1485376646000},"page":"145-152","source":"Crossref","is-referenced-by-count":13,"title":["Video\u2010based eye tracking for neuropsychiatric assessment"],"prefix":"10.1111","volume":"1387","author":[{"given":"Sam","family":"Adhikari","sequence":"first","affiliation":[{"name":"School of Medicine Stanford University  Stanford California"},{"name":"Department of Management Science and Information Systems Rutgers University  Newark New Jersey"},{"name":"Division of Intelligent Systems Sysoft Corporation  Whitehouse Station New Jersey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David E.","family":"Stark","sequence":"additional","affiliation":[{"name":"Institute for Next Generation Healthcare, and Department of Health System Design and Global Health, Icahn School of Medicine at Mount Sinai  New York New York"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2017,1,25]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(07)61238-0"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.mp.4001777"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.4338\/ACI-2015-12-LE-0176"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2013.281078"},{"key":"e_1_2_7_6_1","article-title":"Internet trends","author":"Meeker M.","year":"2013","journal-title":"D11 Conference"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1159\/000358786"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature12715"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2011.06.027"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.3357\/ASEM.3882.2014"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.3758\/s13428-012-0248-3"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.7205\/MILMED-D-13-00420"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/HTR.0b013e3181e67936"},{"key":"e_1_2_7_14_1","unstructured":"Vision.EdgeDetector System object.http:\/\/www.mathworks.com\/help\/vision\/ref\/vision.edgedetector-class.html."},{"key":"e_1_2_7_15_1","unstructured":"Matlab imfindcircles object. Accessed December 9 2016.https:\/\/www.mathworks.com\/help\/images\/ref\/imfindcircles.html?requestedDomain=www.mathworks.com."},{"key":"e_1_2_7_16_1","unstructured":"Template matching approaches using Matlab Image Processing Toolbox.http:\/\/www.mathworks.com\/help\/vision\/ref\/templatematching.html."},{"key":"e_1_2_7_17_1","first-page":"11","article-title":"An approach to image segmentation using K means clustering algorithm","volume":"1","author":"Chandhok C.","year":"2012","journal-title":"Int. J. Inform. Technol."},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.9790\/2834-0122445"}],"container-title":["Annals of the New York Academy of Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2Fnyas.13305","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/nyaspubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/nyas.13305","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T21:14:22Z","timestamp":1694466862000},"score":1,"resource":{"primary":{"URL":"https:\/\/nyaspubs.onlinelibrary.wiley.com\/doi\/10.1111\/nyas.13305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1]]},"references-count":17,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"alternative-id":["10.1111\/nyas.13305"],"URL":"https:\/\/doi.org\/10.1111\/nyas.13305","archive":["Portico"],"relation":{},"ISSN":["0077-8923","1749-6632"],"issn-type":[{"value":"0077-8923","type":"print"},{"value":"1749-6632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1]]}}}