Loading…

Unbiased classification of spatial strategies in the Barnes maze

Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial l...

Full description

Saved in:
Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2016-11, Vol.32 (21), p.3314-3320
Main Authors: Illouz, Tomer, Madar, Ravit, Clague, Charlotte, Griffioen, Kathleen J, Louzoun, Yoram, Okun, Eitan
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c422t-86a44ce64d6f2a64c033667d892725c745d42492cc28ff371491fc8e8b33ed613
cites cdi_FETCH-LOGICAL-c422t-86a44ce64d6f2a64c033667d892725c745d42492cc28ff371491fc8e8b33ed613
container_end_page 3320
container_issue 21
container_start_page 3314
container_title Bioinformatics (Oxford, England)
container_volume 32
creator Illouz, Tomer
Madar, Ravit
Clague, Charlotte
Griffioen, Kathleen J
Louzoun, Yoram
Okun, Eitan
description Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze. Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis. Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application. eitan.okun@biu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.
doi_str_mv 10.1093/bioinformatics/btw376
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1826712449</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1826712449</sourcerecordid><originalsourceid>FETCH-LOGICAL-c422t-86a44ce64d6f2a64c033667d892725c745d42492cc28ff371491fc8e8b33ed613</originalsourceid><addsrcrecordid>eNpVkMtKAzEUhoMotlYfQcnSzdjcJsns1OINCm7sOmQyiUbmUnNSRJ_ekdaCq_Mf-C_wIXROyRUlFZ_XcYh9GFJnc3Qwr_MnV_IATSmXqhCa0sO9JnyCTgDeCSElKeUxmjDFlWZVOUXXq76OFnyDXWsBYohuLBx6PAQM61HaFkNONvvX6AHHHuc3j29t6sevs9_-FB0F24I_290ZWt3fvSwei-Xzw9PiZlk4wVgutLRCOC9FIwOzUjjCuZSq0RVTrHRKlI1gomLOMR0CV1RUNDjtdc25byTlM3S57V2n4WPjIZsugvNta3s_bMBQzaSiTIhqtJZbq0sDQPLBrFPsbPoylJhfeOY_PLOFN-YudhObuvPNPvVHi_8AU8VwDQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1826712449</pqid></control><display><type>article</type><title>Unbiased classification of spatial strategies in the Barnes maze</title><source>Oxford University Press Open Access</source><source>PubMed Central</source><creator>Illouz, Tomer ; Madar, Ravit ; Clague, Charlotte ; Griffioen, Kathleen J ; Louzoun, Yoram ; Okun, Eitan</creator><creatorcontrib>Illouz, Tomer ; Madar, Ravit ; Clague, Charlotte ; Griffioen, Kathleen J ; Louzoun, Yoram ; Okun, Eitan</creatorcontrib><description>Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze. Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis. Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application. eitan.okun@biu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btw376</identifier><identifier>PMID: 27378295</identifier><language>eng</language><publisher>England</publisher><subject>Algorithms ; Animals ; Maze Learning ; Memory ; Support Vector Machine</subject><ispartof>Bioinformatics (Oxford, England), 2016-11, Vol.32 (21), p.3314-3320</ispartof><rights>The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c422t-86a44ce64d6f2a64c033667d892725c745d42492cc28ff371491fc8e8b33ed613</citedby><cites>FETCH-LOGICAL-c422t-86a44ce64d6f2a64c033667d892725c745d42492cc28ff371491fc8e8b33ed613</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27378295$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Illouz, Tomer</creatorcontrib><creatorcontrib>Madar, Ravit</creatorcontrib><creatorcontrib>Clague, Charlotte</creatorcontrib><creatorcontrib>Griffioen, Kathleen J</creatorcontrib><creatorcontrib>Louzoun, Yoram</creatorcontrib><creatorcontrib>Okun, Eitan</creatorcontrib><title>Unbiased classification of spatial strategies in the Barnes maze</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze. Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis. Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application. eitan.okun@biu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Maze Learning</subject><subject>Memory</subject><subject>Support Vector Machine</subject><issn>1367-4803</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNpVkMtKAzEUhoMotlYfQcnSzdjcJsns1OINCm7sOmQyiUbmUnNSRJ_ekdaCq_Mf-C_wIXROyRUlFZ_XcYh9GFJnc3Qwr_MnV_IATSmXqhCa0sO9JnyCTgDeCSElKeUxmjDFlWZVOUXXq76OFnyDXWsBYohuLBx6PAQM61HaFkNONvvX6AHHHuc3j29t6sevs9_-FB0F24I_290ZWt3fvSwei-Xzw9PiZlk4wVgutLRCOC9FIwOzUjjCuZSq0RVTrHRKlI1gomLOMR0CV1RUNDjtdc25byTlM3S57V2n4WPjIZsugvNta3s_bMBQzaSiTIhqtJZbq0sDQPLBrFPsbPoylJhfeOY_PLOFN-YudhObuvPNPvVHi_8AU8VwDQ</recordid><startdate>20161101</startdate><enddate>20161101</enddate><creator>Illouz, Tomer</creator><creator>Madar, Ravit</creator><creator>Clague, Charlotte</creator><creator>Griffioen, Kathleen J</creator><creator>Louzoun, Yoram</creator><creator>Okun, Eitan</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20161101</creationdate><title>Unbiased classification of spatial strategies in the Barnes maze</title><author>Illouz, Tomer ; Madar, Ravit ; Clague, Charlotte ; Griffioen, Kathleen J ; Louzoun, Yoram ; Okun, Eitan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c422t-86a44ce64d6f2a64c033667d892725c745d42492cc28ff371491fc8e8b33ed613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Maze Learning</topic><topic>Memory</topic><topic>Support Vector Machine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Illouz, Tomer</creatorcontrib><creatorcontrib>Madar, Ravit</creatorcontrib><creatorcontrib>Clague, Charlotte</creatorcontrib><creatorcontrib>Griffioen, Kathleen J</creatorcontrib><creatorcontrib>Louzoun, Yoram</creatorcontrib><creatorcontrib>Okun, Eitan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Illouz, Tomer</au><au>Madar, Ravit</au><au>Clague, Charlotte</au><au>Griffioen, Kathleen J</au><au>Louzoun, Yoram</au><au>Okun, Eitan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unbiased classification of spatial strategies in the Barnes maze</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2016-11-01</date><risdate>2016</risdate><volume>32</volume><issue>21</issue><spage>3314</spage><epage>3320</epage><pages>3314-3320</pages><issn>1367-4803</issn><eissn>1367-4811</eissn><abstract>Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze. Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis. Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application. eitan.okun@biu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pmid>27378295</pmid><doi>10.1093/bioinformatics/btw376</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1367-4803
ispartof Bioinformatics (Oxford, England), 2016-11, Vol.32 (21), p.3314-3320
issn 1367-4803
1367-4811
language eng
recordid cdi_proquest_miscellaneous_1826712449
source Oxford University Press Open Access; PubMed Central
subjects Algorithms
Animals
Maze Learning
Memory
Support Vector Machine
title Unbiased classification of spatial strategies in the Barnes maze
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T10%3A18%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Unbiased%20classification%20of%20spatial%20strategies%20in%20the%20Barnes%20maze&rft.jtitle=Bioinformatics%20(Oxford,%20England)&rft.au=Illouz,%20Tomer&rft.date=2016-11-01&rft.volume=32&rft.issue=21&rft.spage=3314&rft.epage=3320&rft.pages=3314-3320&rft.issn=1367-4803&rft.eissn=1367-4811&rft_id=info:doi/10.1093/bioinformatics/btw376&rft_dat=%3Cproquest_cross%3E1826712449%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c422t-86a44ce64d6f2a64c033667d892725c745d42492cc28ff371491fc8e8b33ed613%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1826712449&rft_id=info:pmid/27378295&rfr_iscdi=true