Aleksey E Matukhno, Mikhail V Petrushan, Valery N Kiroy, Fedor V Arsenyev, Larisa V Lysenko
{"title":"通过比对个体间气味图来评估大鼠嗅球肾小球模式局部排列的方法。","authors":"Aleksey E Matukhno, Mikhail V Petrushan, Valery N Kiroy, Fedor V Arsenyev, Larisa V Lysenko","doi":"10.1007/s10827-023-00858-8","DOIUrl":null,"url":null,"abstract":"<p><p>The comparison of odor functional maps in rodents demonstrates a high degree of inter-individual variability in glomerular activity patterns. There are substantial methodological difficulties in the interindividual assessment of local permutations in the glomerular patterns, since the position of anatomical extracranial landmarks, as well as the size, shape and angular orientation of olfactory bulbs can vary significantly. A new method for defining anatomical coordinates of active glomeruli in the rat olfactory bulb has been developed. The method compares the interindividual odor functional maps and calculates probabilistic maps of glomerular activity with adjustment. This adjustment involves rotation, scaling and shift of the functional map relative to its expected position in probabilistic map, computed according to the anatomical coordinates. The calculation of the probabilistic map of the odorant-specific response compensates for potential anatoamical errors due to individual variability in olfactory bulb dimensions and angular orientation. We show its efficiency on real data from a large animal sample recorded by two-photon calcium imaging in dorsal surface of the rat olfactory bulb. The proposed method with probabilistic map calculation enables the spatial consistency of the effects of individual odorants in different rats to be assessed and allow stereotypical positions of odor-specific clusters in the glomerular layer of the olfactory bulb to be identified.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":"433-444"},"PeriodicalIF":1.5000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The method for assessment of local permutations in the glomerular patterns of the rat olfactory bulb by aligning interindividual odor maps.\",\"authors\":\"Aleksey E Matukhno, Mikhail V Petrushan, Valery N Kiroy, Fedor V Arsenyev, Larisa V Lysenko\",\"doi\":\"10.1007/s10827-023-00858-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The comparison of odor functional maps in rodents demonstrates a high degree of inter-individual variability in glomerular activity patterns. There are substantial methodological difficulties in the interindividual assessment of local permutations in the glomerular patterns, since the position of anatomical extracranial landmarks, as well as the size, shape and angular orientation of olfactory bulbs can vary significantly. A new method for defining anatomical coordinates of active glomeruli in the rat olfactory bulb has been developed. The method compares the interindividual odor functional maps and calculates probabilistic maps of glomerular activity with adjustment. This adjustment involves rotation, scaling and shift of the functional map relative to its expected position in probabilistic map, computed according to the anatomical coordinates. The calculation of the probabilistic map of the odorant-specific response compensates for potential anatoamical errors due to individual variability in olfactory bulb dimensions and angular orientation. We show its efficiency on real data from a large animal sample recorded by two-photon calcium imaging in dorsal surface of the rat olfactory bulb. The proposed method with probabilistic map calculation enables the spatial consistency of the effects of individual odorants in different rats to be assessed and allow stereotypical positions of odor-specific clusters in the glomerular layer of the olfactory bulb to be identified.</p>\",\"PeriodicalId\":54857,\"journal\":{\"name\":\"Journal of Computational Neuroscience\",\"volume\":\" \",\"pages\":\"433-444\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10827-023-00858-8\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/8/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10827-023-00858-8","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/25 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
The method for assessment of local permutations in the glomerular patterns of the rat olfactory bulb by aligning interindividual odor maps.
The comparison of odor functional maps in rodents demonstrates a high degree of inter-individual variability in glomerular activity patterns. There are substantial methodological difficulties in the interindividual assessment of local permutations in the glomerular patterns, since the position of anatomical extracranial landmarks, as well as the size, shape and angular orientation of olfactory bulbs can vary significantly. A new method for defining anatomical coordinates of active glomeruli in the rat olfactory bulb has been developed. The method compares the interindividual odor functional maps and calculates probabilistic maps of glomerular activity with adjustment. This adjustment involves rotation, scaling and shift of the functional map relative to its expected position in probabilistic map, computed according to the anatomical coordinates. The calculation of the probabilistic map of the odorant-specific response compensates for potential anatoamical errors due to individual variability in olfactory bulb dimensions and angular orientation. We show its efficiency on real data from a large animal sample recorded by two-photon calcium imaging in dorsal surface of the rat olfactory bulb. The proposed method with probabilistic map calculation enables the spatial consistency of the effects of individual odorants in different rats to be assessed and allow stereotypical positions of odor-specific clusters in the glomerular layer of the olfactory bulb to be identified.
期刊介绍:
The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.