Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction
The aim of this study was to analyze the application value of functional magnetic resonance imaging (FMRI) optimized by the fast independent component correlation algorithm (ICA algorithm) in the diagnosis of brain functional areas in patients with lumbar disc herniation (LDH). An optimized fast ICA...
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| creator | Du, Naiyi Zhang, Zhao Xiao, Yao Jiang, Lijie |
| description | The aim of this study was to analyze the application value of functional magnetic resonance imaging (FMRI) optimized by the fast independent component correlation algorithm (ICA algorithm) in the diagnosis of brain functional areas in patients with lumbar disc herniation (LDH). An optimized fast ICA algorithm was established based on the ICA algorithm. 50 patients with cerebral infarction were selected as the research objects, and 30 healthy people were selected as the control group. The 50 patients from the observation group were examined by fMRI based on Fast ICA algorithm, while the control group was tested by fMRI based on the routine ICA algorithm. The performances of the two algorithms, the analysis results of the two groups of brain functional areas, cerebral blood flow (CBF), resting state functional connectivity (rsFC), behavioral data, and image data correlation of patients were compared. The results showed that the sensitivity, specificity, and accuracy of Fast ICA algorithm were 97.83%, 89.52%, and 96.27%, respectively, which in the experimental group were greatly better than the control group (88.73%, 72.19%, and 89.72%), showing statistically significant differences (P |
| doi_str_mv | 10.1155/2021/5177037 |
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| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8645397</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2610913061</sourcerecordid><originalsourceid>FETCH-LOGICAL-c420t-d70817a85f832c86a17b5fd7d28a1396fbe58d3c0cb3a383535a6ac52e91cf193</originalsourceid><addsrcrecordid>eNp9kU9v1DAQxSMEoqVw44x8RCqh_hPHyQVpu3RhpSIkBGdr4kwSo8Re7CyoX4jPidNdVuXCZTya99ObkV-WvWT0LWNSXnHK2ZVkSlGhHmXnaSTzQjD1-NTT-ix7FuN3SotC1OJpdiaKmnFW8fPs9wbiTLauxR2m4may9tPOu6VbORjvoo1kNfY-2HmY8muI2JLN3pnZ-iSTT9A7nK0hXzCmgTNIthP01vXEOjIPSN7bhPjFxndkPYDrMS7adYBUH1itAsIBwoBNSJOt6yDcy8-zJx2MEV8c34vs2-bm6_pjfvv5w3a9us1Nwemct4pWTEElu0pwU5XAVCO7VrW8AibqsmtQVq0w1DQCRCWkkFCCkRxrZjpWi4vs3cF3t28mbE36hnSI3gU7QbjTHqz-V3F20L3_qauykKJWyeD10SD4H3uMs55sNDiO4NDvo-YlozUTtGQJfXNATfAxBuxOaxjVS7R6iVYfo034q4enneC_WSbg8gAM1rXwy_7f7g-0O67y</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2610913061</pqid></control><display><type>article</type><title>Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction</title><source>MEDLINE</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><source>PubMed Central Open Access</source><creator>Du, Naiyi ; Zhang, Zhao ; Xiao, Yao ; Jiang, Lijie</creator><contributor>Teekaraman, Yuvaraja</contributor><creatorcontrib>Du, Naiyi ; Zhang, Zhao ; Xiao, Yao ; Jiang, Lijie ; Teekaraman, Yuvaraja</creatorcontrib><description>The aim of this study was to analyze the application value of functional magnetic resonance imaging (FMRI) optimized by the fast independent component correlation algorithm (ICA algorithm) in the diagnosis of brain functional areas in patients with lumbar disc herniation (LDH). An optimized fast ICA algorithm was established based on the ICA algorithm. 50 patients with cerebral infarction were selected as the research objects, and 30 healthy people were selected as the control group. The 50 patients from the observation group were examined by fMRI based on Fast ICA algorithm, while the control group was tested by fMRI based on the routine ICA algorithm. The performances of the two algorithms, the analysis results of the two groups of brain functional areas, cerebral blood flow (CBF), resting state functional connectivity (rsFC), behavioral data, and image data correlation of patients were compared. The results showed that the sensitivity, specificity, and accuracy of Fast ICA algorithm were 97.83%, 89.52%, and 96.27%, respectively, which in the experimental group were greatly better than the control group (88.73%, 72.19%, and 89.72%), showing statistically significant differences (P<0.05). The maximum Dice coefficient of FAST ICA algorithm was 0.967, and FAST ICA algorithm was better obviously than the traditional ICA algorithm (P<0.05). The cerebral blood flow of the healthy superior frontal gyrus (SFG) and healthy superior marginal gyrus (SMG) of the observation group with good motor function recovery were 1.02 ± 0.22 and 1.53 ± 0.61, respectively; both indicators showed an increasing trend, and those in the experimental group were much higher in contrast to the control group, showing statistically obvious differences (P<0.05). Besides, the detection results of cerebral blood flow (CBF) in the healthy SFG and healthy SMG were negatively correlated with the results of connection test B. In summary, the fMRI based on the Fast ICA algorithm showed a good diagnostic effect in the changes of brain functional areas in patients with cerebral infarction. The experimental results showed that the cerebral blood flow in the brain area was related to motor or cognitive function. The results of this study provided a reliable reference for the examination and diagnosis of brain functional areas in patients with cerebral infarction.</description><identifier>ISSN: 1555-4309</identifier><identifier>EISSN: 1555-4317</identifier><identifier>DOI: 10.1155/2021/5177037</identifier><identifier>PMID: 34912182</identifier><language>eng</language><publisher>England: Hindawi</publisher><subject>Algorithms ; Brain - diagnostic imaging ; Brain Mapping - methods ; Cerebral Infarction - diagnostic imaging ; Humans ; Magnetic Resonance Imaging - methods</subject><ispartof>Contrast media and molecular imaging, 2021-11, Vol.2021, p.5177037-10</ispartof><rights>Copyright © 2021 Naiyi Du et al.</rights><rights>Copyright © 2021 Naiyi Du et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c420t-d70817a85f832c86a17b5fd7d28a1396fbe58d3c0cb3a383535a6ac52e91cf193</citedby><cites>FETCH-LOGICAL-c420t-d70817a85f832c86a17b5fd7d28a1396fbe58d3c0cb3a383535a6ac52e91cf193</cites><orcidid>0000-0003-4553-982X ; 0000-0001-7963-1945 ; 0000-0002-7438-3169 ; 0000-0003-0224-8424</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/PMC8645397/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8645397/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34912182$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Teekaraman, Yuvaraja</contributor><creatorcontrib>Du, Naiyi</creatorcontrib><creatorcontrib>Zhang, Zhao</creatorcontrib><creatorcontrib>Xiao, Yao</creatorcontrib><creatorcontrib>Jiang, Lijie</creatorcontrib><title>Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction</title><title>Contrast media and molecular imaging</title><addtitle>Contrast Media Mol Imaging</addtitle><description>The aim of this study was to analyze the application value of functional magnetic resonance imaging (FMRI) optimized by the fast independent component correlation algorithm (ICA algorithm) in the diagnosis of brain functional areas in patients with lumbar disc herniation (LDH). An optimized fast ICA algorithm was established based on the ICA algorithm. 50 patients with cerebral infarction were selected as the research objects, and 30 healthy people were selected as the control group. The 50 patients from the observation group were examined by fMRI based on Fast ICA algorithm, while the control group was tested by fMRI based on the routine ICA algorithm. The performances of the two algorithms, the analysis results of the two groups of brain functional areas, cerebral blood flow (CBF), resting state functional connectivity (rsFC), behavioral data, and image data correlation of patients were compared. The results showed that the sensitivity, specificity, and accuracy of Fast ICA algorithm were 97.83%, 89.52%, and 96.27%, respectively, which in the experimental group were greatly better than the control group (88.73%, 72.19%, and 89.72%), showing statistically significant differences (P<0.05). The maximum Dice coefficient of FAST ICA algorithm was 0.967, and FAST ICA algorithm was better obviously than the traditional ICA algorithm (P<0.05). The cerebral blood flow of the healthy superior frontal gyrus (SFG) and healthy superior marginal gyrus (SMG) of the observation group with good motor function recovery were 1.02 ± 0.22 and 1.53 ± 0.61, respectively; both indicators showed an increasing trend, and those in the experimental group were much higher in contrast to the control group, showing statistically obvious differences (P<0.05). Besides, the detection results of cerebral blood flow (CBF) in the healthy SFG and healthy SMG were negatively correlated with the results of connection test B. In summary, the fMRI based on the Fast ICA algorithm showed a good diagnostic effect in the changes of brain functional areas in patients with cerebral infarction. The experimental results showed that the cerebral blood flow in the brain area was related to motor or cognitive function. The results of this study provided a reliable reference for the examination and diagnosis of brain functional areas in patients with cerebral infarction.</description><subject>Algorithms</subject><subject>Brain - diagnostic imaging</subject><subject>Brain Mapping - methods</subject><subject>Cerebral Infarction - diagnostic imaging</subject><subject>Humans</subject><subject>Magnetic Resonance Imaging - methods</subject><issn>1555-4309</issn><issn>1555-4317</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kU9v1DAQxSMEoqVw44x8RCqh_hPHyQVpu3RhpSIkBGdr4kwSo8Re7CyoX4jPidNdVuXCZTya99ObkV-WvWT0LWNSXnHK2ZVkSlGhHmXnaSTzQjD1-NTT-ix7FuN3SotC1OJpdiaKmnFW8fPs9wbiTLauxR2m4may9tPOu6VbORjvoo1kNfY-2HmY8muI2JLN3pnZ-iSTT9A7nK0hXzCmgTNIthP01vXEOjIPSN7bhPjFxndkPYDrMS7adYBUH1itAsIBwoBNSJOt6yDcy8-zJx2MEV8c34vs2-bm6_pjfvv5w3a9us1Nwemct4pWTEElu0pwU5XAVCO7VrW8AibqsmtQVq0w1DQCRCWkkFCCkRxrZjpWi4vs3cF3t28mbE36hnSI3gU7QbjTHqz-V3F20L3_qauykKJWyeD10SD4H3uMs55sNDiO4NDvo-YlozUTtGQJfXNATfAxBuxOaxjVS7R6iVYfo034q4enneC_WSbg8gAM1rXwy_7f7g-0O67y</recordid><startdate>20211128</startdate><enddate>20211128</enddate><creator>Du, Naiyi</creator><creator>Zhang, Zhao</creator><creator>Xiao, Yao</creator><creator>Jiang, Lijie</creator><general>Hindawi</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><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><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4553-982X</orcidid><orcidid>https://orcid.org/0000-0001-7963-1945</orcidid><orcidid>https://orcid.org/0000-0002-7438-3169</orcidid><orcidid>https://orcid.org/0000-0003-0224-8424</orcidid></search><sort><creationdate>20211128</creationdate><title>Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction</title><author>Du, Naiyi ; Zhang, Zhao ; Xiao, Yao ; Jiang, Lijie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-d70817a85f832c86a17b5fd7d28a1396fbe58d3c0cb3a383535a6ac52e91cf193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Brain - diagnostic imaging</topic><topic>Brain Mapping - methods</topic><topic>Cerebral Infarction - diagnostic imaging</topic><topic>Humans</topic><topic>Magnetic Resonance Imaging - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Du, Naiyi</creatorcontrib><creatorcontrib>Zhang, Zhao</creatorcontrib><creatorcontrib>Xiao, Yao</creatorcontrib><creatorcontrib>Jiang, Lijie</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><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><collection>PubMed Central (Full Participant titles)</collection><jtitle>Contrast media and molecular imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Du, Naiyi</au><au>Zhang, Zhao</au><au>Xiao, Yao</au><au>Jiang, Lijie</au><au>Teekaraman, Yuvaraja</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction</atitle><jtitle>Contrast media and molecular imaging</jtitle><addtitle>Contrast Media Mol Imaging</addtitle><date>2021-11-28</date><risdate>2021</risdate><volume>2021</volume><spage>5177037</spage><epage>10</epage><pages>5177037-10</pages><issn>1555-4309</issn><eissn>1555-4317</eissn><abstract>The aim of this study was to analyze the application value of functional magnetic resonance imaging (FMRI) optimized by the fast independent component correlation algorithm (ICA algorithm) in the diagnosis of brain functional areas in patients with lumbar disc herniation (LDH). An optimized fast ICA algorithm was established based on the ICA algorithm. 50 patients with cerebral infarction were selected as the research objects, and 30 healthy people were selected as the control group. The 50 patients from the observation group were examined by fMRI based on Fast ICA algorithm, while the control group was tested by fMRI based on the routine ICA algorithm. The performances of the two algorithms, the analysis results of the two groups of brain functional areas, cerebral blood flow (CBF), resting state functional connectivity (rsFC), behavioral data, and image data correlation of patients were compared. The results showed that the sensitivity, specificity, and accuracy of Fast ICA algorithm were 97.83%, 89.52%, and 96.27%, respectively, which in the experimental group were greatly better than the control group (88.73%, 72.19%, and 89.72%), showing statistically significant differences (P<0.05). The maximum Dice coefficient of FAST ICA algorithm was 0.967, and FAST ICA algorithm was better obviously than the traditional ICA algorithm (P<0.05). The cerebral blood flow of the healthy superior frontal gyrus (SFG) and healthy superior marginal gyrus (SMG) of the observation group with good motor function recovery were 1.02 ± 0.22 and 1.53 ± 0.61, respectively; both indicators showed an increasing trend, and those in the experimental group were much higher in contrast to the control group, showing statistically obvious differences (P<0.05). Besides, the detection results of cerebral blood flow (CBF) in the healthy SFG and healthy SMG were negatively correlated with the results of connection test B. In summary, the fMRI based on the Fast ICA algorithm showed a good diagnostic effect in the changes of brain functional areas in patients with cerebral infarction. The experimental results showed that the cerebral blood flow in the brain area was related to motor or cognitive function. The results of this study provided a reliable reference for the examination and diagnosis of brain functional areas in patients with cerebral infarction.</abstract><cop>England</cop><pub>Hindawi</pub><pmid>34912182</pmid><doi>10.1155/2021/5177037</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-4553-982X</orcidid><orcidid>https://orcid.org/0000-0001-7963-1945</orcidid><orcidid>https://orcid.org/0000-0002-7438-3169</orcidid><orcidid>https://orcid.org/0000-0003-0224-8424</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Brain - diagnostic imaging Brain Mapping - methods Cerebral Infarction - diagnostic imaging Humans Magnetic Resonance Imaging - methods |
| title | Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction |
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