{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T16:16:12Z","timestamp":1787156172981,"version":"build-2736575974"},"reference-count":24,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2013,4,17]],"date-time":"2013-04-17T00:00:00Z","timestamp":1366156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes Brain and Behavior"],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p><jats:bold>Data comparability between different laboratories strongly depends on the individually applied analysis method. This factor is often a critical source of variation in rodent phenotyping and has never been systematically investigated in Pavlovian fear conditioning paradigms. In rodents, fear is typically quantified in terms of freezing duration via manual observation or automated systems. While manual analysis includes biases such as tiredness or inter\u2010personal scoring variability, computer\u2010assisted systems are unable to distinguish between freezing and immobility. Consequently, the novel software called <jats:styled-content style=\"fixed-case\">MOVE<\/jats:styled-content> follows a semi\u2010automatized approach that prefilters video sequences of interest for the final human judgment. Furthermore, <jats:styled-content style=\"fixed-case\">MOVE<\/jats:styled-content> allows integrating additional data sources (e.g. force\u2010sensitive platform, <jats:styled-content style=\"fixed-case\">EEG<\/jats:styled-content>) to reach the most accurate and precise results. <jats:styled-content style=\"fixed-case\">MOVE<\/jats:styled-content> directly supports multi\u2010angle video recordings with webcams or standard laboratory equipment. The integrated manual key logger and internal video player complement this all\u2010in\u2010one software solution. Calculating the interlaboratory variability of manual freezing evaluation revealed significantly different freezing scores in two out of six laboratories. This difference was minimized when all experiments were analyzed with <jats:styled-content style=\"fixed-case\">MOVE<\/jats:styled-content>. Applied to a genetically modified mouse model, <jats:styled-content style=\"fixed-case\">MOVE<\/jats:styled-content> revealed higher fear responses of <jats:styled-content style=\"fixed-case\">CB1<\/jats:styled-content> deficient mice compared to their wild\u2010type littermates after foreground context fear conditioning. Multi\u2010angle video analysis compared to the single\u2010camera approach reached up to 15% higher accuracy and two fold higher precision. Multidimensional analysis provided by integration of additional data sources further improved the overall result. We conclude that the widespread usage of <jats:styled-content style=\"fixed-case\">MOVE<\/jats:styled-content> could substantially improve the comparability of results from different laboratories.<\/jats:bold><\/jats:p>","DOI":"10.1111\/gbb.12040","type":"journal-article","created":{"date-parts":[[2013,4,8]],"date-time":"2013-04-08T17:44:39Z","timestamp":1365443079000},"page":"583-592","source":"Crossref","is-referenced-by-count":14,"title":["Standardizing the analysis of conditioned fear in rodents: a multidimensional software approach"],"prefix":"10.1111","volume":"12","author":[{"given":"P.","family":"Meuth","sequence":"first","affiliation":[{"name":"Institute of Physiology I Westf\u00e4lische Wilhelms\u2010Universit\u00e4t M\u00fcnster  M\u00fcnster Germany"},{"name":"Department of Neurology \u2013 Inflammatory Disorders of the Nervous System and Neurooncology Westf\u00e4lische Wilhelms\u2010Universit\u00e4t M\u00fcnster  M\u00fcnster 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