Sh K Abdulaev, D. Tarumov, K. Markin, Aleksandra А. Ustyuzhina
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In this regard, it is relevant to conduct a study with a comparative assessment of various statistical methods of ROI-analysis in processing resting state fMRI data. \nAIM: to assess the functional connectivity of the main resting state networks of the brain using ROI-analysis using various statistical approaches. \nMATERIALS AND METHODS: We analyzed data from 15 resting-state fMRI studies of the brain of patients without neurological and mental pathology. fMRI scanning was performed on a Phillips Ingenia 1.5 T scanner using a gradient echo-planar imaging (EPI-BOLD) sequence. ROI-analysis was used to build networks. Statistical data processing was performed using methods: functional network connectivity, randomization/permutation spatial pairwise clustering statistics, and threshold-free cluster enhancement. \nRESULTS: The number of connections between the structures of brain networks recorded using the method of functional network connectivity is 280, spatial pairwise clustering — 186, threshold-free cluster enhancement — 182. An interesting fact is that negative connections were identified only when using parametric statistics. \nCONCLUSION: A comparative assessment of methods for statistical processing of fMRI data during ROI-analysis was carried out. The functional network connectivity method based on multivariate parametric statistics turned out to be more informative than randomization/permutation spatial pairwise clustering statistics and the method based on threshold-free cluster enhancement. Despite the growing popularity in recent years of resting-state fMRI in the study of functional activity and connectivity of the brain, there are no standardized algorithms for constructing networks of the brain.","PeriodicalId":167099,"journal":{"name":"Russian Military Medical Academy Reports","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Resting-state functional magnetic resonance imaging: features of statistical processing of ROI-analysis data\",\"authors\":\"Sh K Abdulaev, D. Tarumov, K. Markin, Aleksandra А. Ustyuzhina\",\"doi\":\"10.17816/rmmar623485\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND: In many works, to study intra- and inter-network connections, a method for constructing networks is used — ROI-analysis (region of interest analysis). The conflicting results obtained when assessing brain connectivity using ROI-analysis can be explained by methodological differences associated with the statistical processing of fMRI data. In this regard, it is relevant to conduct a study with a comparative assessment of various statistical methods of ROI-analysis in processing resting state fMRI data. \\nAIM: to assess the functional connectivity of the main resting state networks of the brain using ROI-analysis using various statistical approaches. \\nMATERIALS AND METHODS: We analyzed data from 15 resting-state fMRI studies of the brain of patients without neurological and mental pathology. fMRI scanning was performed on a Phillips Ingenia 1.5 T scanner using a gradient echo-planar imaging (EPI-BOLD) sequence. ROI-analysis was used to build networks. 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引用次数: 0
摘要
背景:在许多研究中,为了研究网络内部和网络之间的连接,都会使用一种构建网络的方法--ROI 分析(感兴趣区分析)。使用 ROI 分析法评估大脑连接性时获得的结果相互矛盾,这可能是由于对 fMRI 数据进行统计处理的方法存在差异。因此,有必要开展一项研究,对处理静息状态 fMRI 数据的各种 ROI 分析统计方法进行比较评估。目的:利用 ROI 分析法评估大脑主要静息态网络的功能连通性,并采用不同的统计方法。材料与方法:我们分析了 15 项静息状态 fMRI 研究的数据,这些数据来自无神经和精神病变的患者大脑。fMRI 扫描在 Phillips Ingenia 1.5 T 扫描仪上进行,使用梯度回波平面成像(EPI-BOLD)序列。采用 ROI 分析法构建网络。统计数据处理方法包括:功能网络连通性、随机化/畸变空间成对聚类统计和无阈值聚类增强。结果:使用功能网络连通性方法记录的大脑网络结构之间的连接数量为 280 个,空间成对聚类为 186 个,无阈值聚类增强为 182 个。一个有趣的事实是,只有在使用参数统计时才会发现负连接。结论:在 ROI 分析过程中,对 fMRI 数据统计处理方法进行了比较评估。结果表明,基于多元参数统计的功能网络连通性方法比随机化/畸变空间成对聚类统计和基于无阈值聚类增强的方法更有信息量。尽管近年来静息态 fMRI 在大脑功能活动和连接性研究中越来越受欢迎,但目前还没有构建大脑网络的标准化算法。
Resting-state functional magnetic resonance imaging: features of statistical processing of ROI-analysis data
BACKGROUND: In many works, to study intra- and inter-network connections, a method for constructing networks is used — ROI-analysis (region of interest analysis). The conflicting results obtained when assessing brain connectivity using ROI-analysis can be explained by methodological differences associated with the statistical processing of fMRI data. In this regard, it is relevant to conduct a study with a comparative assessment of various statistical methods of ROI-analysis in processing resting state fMRI data.
AIM: to assess the functional connectivity of the main resting state networks of the brain using ROI-analysis using various statistical approaches.
MATERIALS AND METHODS: We analyzed data from 15 resting-state fMRI studies of the brain of patients without neurological and mental pathology. fMRI scanning was performed on a Phillips Ingenia 1.5 T scanner using a gradient echo-planar imaging (EPI-BOLD) sequence. ROI-analysis was used to build networks. Statistical data processing was performed using methods: functional network connectivity, randomization/permutation spatial pairwise clustering statistics, and threshold-free cluster enhancement.
RESULTS: The number of connections between the structures of brain networks recorded using the method of functional network connectivity is 280, spatial pairwise clustering — 186, threshold-free cluster enhancement — 182. An interesting fact is that negative connections were identified only when using parametric statistics.
CONCLUSION: A comparative assessment of methods for statistical processing of fMRI data during ROI-analysis was carried out. The functional network connectivity method based on multivariate parametric statistics turned out to be more informative than randomization/permutation spatial pairwise clustering statistics and the method based on threshold-free cluster enhancement. Despite the growing popularity in recent years of resting-state fMRI in the study of functional activity and connectivity of the brain, there are no standardized algorithms for constructing networks of the brain.