Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions
To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariat...
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| description | To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value |
| doi_str_mv | 10.1002/hbm.25124 |
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This paper presents kernel‐reweighted logistic regression (KELLER) to estimate dFC in resting state functional Magnetic Resonance Imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. The proposed method is compared with SWC and tapered‐SWC with different window lengths using simulated and real rs‐fMRI data.</description><identifier>ISSN: 1065-9471</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.25124</identifier><identifier>PMID: 32643845</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Bivariate analysis ; Brain ; Brain mapping ; Computer simulation ; dynamic connection detectability ; dynamic functional connectivity ; Functional magnetic resonance imaging ; hypothesis testing ; Magnetic resonance imaging ; multivariate dependencies ; Neural networks ; Neuroimaging ; Observational weighting ; resting state functional magnetic resonance imaging ; Similarity ; Statistical analysis ; surrogate data</subject><ispartof>Human brain mapping, 2020-10, Vol.41 (15), p.4264-4287</ispartof><rights>2020 The Authors. published by Wiley Periodicals LLC.</rights><rights>2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>2020. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5104-b287bfc12d0cf55b41b046c5247ae360bb74f39c6b33944e7c8239a37a6312db3</citedby><cites>FETCH-LOGICAL-c5104-b287bfc12d0cf55b41b046c5247ae360bb74f39c6b33944e7c8239a37a6312db3</cites><orcidid>0000-0002-5238-2448 ; 0000-0002-7302-6856</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7502846/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7502846/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,1417,11562,27924,27925,45574,45575,46052,46476,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32643845$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Maleki Balajoo, Somayeh</creatorcontrib><creatorcontrib>Asemani, Davud</creatorcontrib><creatorcontrib>Khadem, Ali</creatorcontrib><creatorcontrib>Soltanian‐Zadeh, Hamid</creatorcontrib><title>Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions</title><title>Human brain mapping</title><addtitle>Hum Brain Mapp</addtitle><description>To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value <.001), respectively. Results of these different methods applied on real rs‐fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T‐SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC‐based method was unable to detect these dynamic connections.
This paper presents kernel‐reweighted logistic regression (KELLER) to estimate dFC in resting state functional Magnetic Resonance Imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. The proposed method is compared with SWC and tapered‐SWC with different window lengths using simulated and real rs‐fMRI data.</description><subject>Algorithms</subject><subject>Bivariate analysis</subject><subject>Brain</subject><subject>Brain mapping</subject><subject>Computer simulation</subject><subject>dynamic connection detectability</subject><subject>dynamic functional connectivity</subject><subject>Functional magnetic resonance imaging</subject><subject>hypothesis testing</subject><subject>Magnetic resonance imaging</subject><subject>multivariate dependencies</subject><subject>Neural networks</subject><subject>Neuroimaging</subject><subject>Observational weighting</subject><subject>resting state functional magnetic resonance imaging</subject><subject>Similarity</subject><subject>Statistical analysis</subject><subject>surrogate data</subject><issn>1065-9471</issn><issn>1097-0193</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp1ks9u1DAQhyMEoqVw4AVQJC5wyNb_EicXpFIBrVTEBc6W7Uy2rhI72Mmu9j14YCbdZdUiUA5xnO_3WZ6ZLHtNyYoSws5vzbBiJWXiSXZKSSMLQhv-dFlXZdEISU-yFyndEUJpSejz7ISzSvBalKfZr-thjGEDbd7uvB6czW3wHuzkgs9bmA6rMWwh5s7nkCY36OkB383-ntH9Mbpx0275SK6F6Pw6H-Yed3V0mETrCL4Fbx2k3MC0BfC5iRrtEdZoSi-zZ53uE7w6vM-yH58_fb-8Km6-fbm-vLgpbEmJKAyrpeksZS2xXVkaQQ0RlS2ZkBp4RYyRouONrQznjRAgbc14o7nUFceQ4WfZh713nM0ArQU_Rd2rMeIV404F7dTjP97dqnXYKFkSVosKBe8Oghh-zlgbNbhkoe-1hzAnxQRjhEhZlYi-_Qu9C3PEqi2UEKyiFfbkSK11D8r5LuC5dpGqC4ntpKypKVKrf1D4tIAdCR46h_uPAu_3ARtDShG64x0pUcsIKRwhdT9CyL55WJQj-WdmEDjfA1s8Zfd_k7r6-HWv_A2qHtNB</recordid><startdate>20201015</startdate><enddate>20201015</enddate><creator>Maleki Balajoo, Somayeh</creator><creator>Asemani, Davud</creator><creator>Khadem, Ali</creator><creator>Soltanian‐Zadeh, Hamid</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-5238-2448</orcidid><orcidid>https://orcid.org/0000-0002-7302-6856</orcidid></search><sort><creationdate>20201015</creationdate><title>Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions</title><author>Maleki Balajoo, Somayeh ; Asemani, Davud ; Khadem, Ali ; Soltanian‐Zadeh, Hamid</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5104-b287bfc12d0cf55b41b046c5247ae360bb74f39c6b33944e7c8239a37a6312db3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Bivariate analysis</topic><topic>Brain</topic><topic>Brain mapping</topic><topic>Computer simulation</topic><topic>dynamic connection detectability</topic><topic>dynamic functional connectivity</topic><topic>Functional magnetic resonance imaging</topic><topic>hypothesis testing</topic><topic>Magnetic resonance imaging</topic><topic>multivariate dependencies</topic><topic>Neural networks</topic><topic>Neuroimaging</topic><topic>Observational weighting</topic><topic>resting state functional magnetic resonance imaging</topic><topic>Similarity</topic><topic>Statistical analysis</topic><topic>surrogate data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Maleki Balajoo, Somayeh</creatorcontrib><creatorcontrib>Asemani, Davud</creatorcontrib><creatorcontrib>Khadem, Ali</creatorcontrib><creatorcontrib>Soltanian‐Zadeh, Hamid</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Free Content</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Human brain mapping</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Maleki Balajoo, Somayeh</au><au>Asemani, Davud</au><au>Khadem, Ali</au><au>Soltanian‐Zadeh, Hamid</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions</atitle><jtitle>Human brain mapping</jtitle><addtitle>Hum Brain Mapp</addtitle><date>2020-10-15</date><risdate>2020</risdate><volume>41</volume><issue>15</issue><spage>4264</spage><epage>4287</epage><pages>4264-4287</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><abstract>To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value <.001), respectively. Results of these different methods applied on real rs‐fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T‐SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC‐based method was unable to detect these dynamic connections.
This paper presents kernel‐reweighted logistic regression (KELLER) to estimate dFC in resting state functional Magnetic Resonance Imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. The proposed method is compared with SWC and tapered‐SWC with different window lengths using simulated and real rs‐fMRI data.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>32643845</pmid><doi>10.1002/hbm.25124</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0002-5238-2448</orcidid><orcidid>https://orcid.org/0000-0002-7302-6856</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Bivariate analysis Brain Brain mapping Computer simulation dynamic connection detectability dynamic functional connectivity Functional magnetic resonance imaging hypothesis testing Magnetic resonance imaging multivariate dependencies Neural networks Neuroimaging Observational weighting resting state functional magnetic resonance imaging Similarity Statistical analysis surrogate data |
| title | Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions |
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