{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T15:12:11Z","timestamp":1777648331369,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001332","name":"Council for Scientific and Industrial Research, South Africa","doi-asserted-by":"publisher","award":["CSIR\u201009\/079(2697)\/2016\u2010EMR\u2010I"],"award-info":[{"award-number":["CSIR\u201009\/079(2697)\/2016\u2010EMR\u2010I"]}],"id":[{"id":"10.13039\/501100001332","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 MH084315"],"award-info":[{"award-number":["R01 MH084315"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","award":["EMR\/2017\/004155"],"award-info":[{"award-number":["EMR\/2017\/004155"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Hippocampus"],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Most commonly used behavioral measures for testing learning and memory in the Morris water maze (MWM) involve comparisons of an animal's residence time in different quadrants of the pool. Such measures are limited in their ability to test different aspects of the animal's performance. Here, we describe novel measures of performance in the MWM that use vector fields to capture the motion of mice as well as their search pattern in the maze. Using these vector fields, we develop quantitative measures of performance that are intuitive and more sensitive than classical measures. First, we describe search patterns in terms of vector field properties and use these properties to define three metrics of spatial memory namely Spatial Accuracy, Uncertainty and, Intensity of Search. We demonstrate the usefulness of these measures using four different data sets including comparisons between different strains of mice, an analysis of two mouse models of Noonan syndrome (NS;\n                    <jats:italic>Ptpn11<\/jats:italic>\n                    D61G and\n                    <jats:italic>Ptpn11<\/jats:italic>\n                    N308D\/+), and a study of goal reversal training. Importantly, besides highlighting novel aspects of performance in this widely used spatial task, our measures were able to uncover previously undetected differences, including in an animal model of NS, which we rescued with the mitogen activated protein kinase kinase (MEK) inhibitor SL327. Thus, our results show that our approach breaks down performance in the MWM into sensitive measurable independent components that highlight differences in spatial learning and memory in the MWM that were undetected by conventional measures.\n                  <\/jats:p>","DOI":"10.1002\/hipo.23404","type":"journal-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T10:10:37Z","timestamp":1642068637000},"page":"264-285","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Novel measures of Morris water maze performance that use vector field maps to assess accuracy, uncertainty, and intention of navigational searches"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0711-5762","authenticated-orcid":false,"given":"Prabod Kumar","family":"Meenakshi","sequence":"first","affiliation":[{"name":"Centre for Neuroscience Indian Institute of Science  Bengaluru 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