The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy
Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and...
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description | Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ.
The flowchart of the complexity of spontaneous brain activity by using multi‐types entropies. The raw rs‐fMRI images of the MSC dataset and UCLA dataset underwent preprocessing, time‐series extraction and then were used to compute entropy values using seven entropy methods. The entropy analysis was conducted based on whole‐brain gray matter voxels. Test–retest reliability was finally examined by using the intraclass correlation coefficient (ICC). Besides, altered complexity of spontaneous brain activity and classification results were explored. |
doi_str_mv | 10.1002/hbm.26129 |
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The flowchart of the complexity of spontaneous brain activity by using multi‐types entropies. The raw rs‐fMRI images of the MSC dataset and UCLA dataset underwent preprocessing, time‐series extraction and then were used to compute entropy values using seven entropy methods. The entropy analysis was conducted based on whole‐brain gray matter voxels. Test–retest reliability was finally examined by using the intraclass correlation coefficient (ICC). Besides, altered complexity of spontaneous brain activity and classification results were explored.</description><identifier>ISSN: 1065-9471</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.26129</identifier><identifier>PMID: 36358029</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>(multiscale)entropies ; ADHD/BP/SCHZ ; Adult ; Attention Deficit Disorder with Hyperactivity - diagnostic imaging ; Attention deficit hyperactivity disorder ; Bipolar disorder ; Bipolar Disorder - diagnostic imaging ; Brain ; Brain - diagnostic imaging ; Classification ; Consortia ; Correlation coefficient ; Correlation coefficients ; Datasets ; different atlases ; Entropy ; Humans ; Magnetic resonance imaging ; Mathematical analysis ; Mental disorders ; Methods ; Permutations ; Random variables ; Reliability analysis ; Reproducibility of Results ; resting‐state fMRI ; Schizophrenia ; Schizophrenia - diagnostic imaging ; Signs and symptoms ; test–retest reliability ; Young adults</subject><ispartof>Human brain mapping, 2023-01, Vol.44 (1), p.94-118</ispartof><rights>2022 The Authors. published by Wiley Periodicals LLC.</rights><rights>2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.</rights><rights>2022. This article is published under http://creativecommons.org/licenses/by-nc-nd/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-c4439-f1b113466b361e87f9f8c2e4a7064a5a5b6bfbae064c6d7f90e5aaef7a0af4073</citedby><cites>FETCH-LOGICAL-c4439-f1b113466b361e87f9f8c2e4a7064a5a5b6bfbae064c6d7f90e5aaef7a0af4073</cites><orcidid>0000-0002-3710-3500 ; 0000-0003-0145-6627</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/PMC9783493/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783493/$$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/36358029$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Guan, Sihai</creatorcontrib><creatorcontrib>Wan, Dongyu</creatorcontrib><creatorcontrib>Zhao, Rong</creatorcontrib><creatorcontrib>Canario, Edgar</creatorcontrib><creatorcontrib>Meng, Chun</creatorcontrib><creatorcontrib>Biswal, Bharat B.</creatorcontrib><title>The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy</title><title>Human brain mapping</title><addtitle>Hum Brain Mapp</addtitle><description>Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ.
The flowchart of the complexity of spontaneous brain activity by using multi‐types entropies. The raw rs‐fMRI images of the MSC dataset and UCLA dataset underwent preprocessing, time‐series extraction and then were used to compute entropy values using seven entropy methods. The entropy analysis was conducted based on whole‐brain gray matter voxels. Test–retest reliability was finally examined by using the intraclass correlation coefficient (ICC). 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Wan, Dongyu ; Zhao, Rong ; Canario, Edgar ; Meng, Chun ; Biswal, Bharat B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4439-f1b113466b361e87f9f8c2e4a7064a5a5b6bfbae064c6d7f90e5aaef7a0af4073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>(multiscale)entropies</topic><topic>ADHD/BP/SCHZ</topic><topic>Adult</topic><topic>Attention Deficit Disorder with Hyperactivity - diagnostic imaging</topic><topic>Attention deficit hyperactivity disorder</topic><topic>Bipolar disorder</topic><topic>Bipolar Disorder - diagnostic imaging</topic><topic>Brain</topic><topic>Brain - diagnostic imaging</topic><topic>Classification</topic><topic>Consortia</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Datasets</topic><topic>different atlases</topic><topic>Entropy</topic><topic>Humans</topic><topic>Magnetic resonance imaging</topic><topic>Mathematical analysis</topic><topic>Mental disorders</topic><topic>Methods</topic><topic>Permutations</topic><topic>Random variables</topic><topic>Reliability analysis</topic><topic>Reproducibility of Results</topic><topic>resting‐state fMRI</topic><topic>Schizophrenia</topic><topic>Schizophrenia - diagnostic imaging</topic><topic>Signs and symptoms</topic><topic>test–retest reliability</topic><topic>Young adults</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guan, Sihai</creatorcontrib><creatorcontrib>Wan, Dongyu</creatorcontrib><creatorcontrib>Zhao, Rong</creatorcontrib><creatorcontrib>Canario, Edgar</creatorcontrib><creatorcontrib>Meng, Chun</creatorcontrib><creatorcontrib>Biswal, Bharat B.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</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>Guan, Sihai</au><au>Wan, Dongyu</au><au>Zhao, Rong</au><au>Canario, Edgar</au><au>Meng, Chun</au><au>Biswal, Bharat B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy</atitle><jtitle>Human brain mapping</jtitle><addtitle>Hum Brain Mapp</addtitle><date>2023-01</date><risdate>2023</risdate><volume>44</volume><issue>1</issue><spage>94</spage><epage>118</epage><pages>94-118</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><abstract>Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ.
The flowchart of the complexity of spontaneous brain activity by using multi‐types entropies. The raw rs‐fMRI images of the MSC dataset and UCLA dataset underwent preprocessing, time‐series extraction and then were used to compute entropy values using seven entropy methods. The entropy analysis was conducted based on whole‐brain gray matter voxels. Test–retest reliability was finally examined by using the intraclass correlation coefficient (ICC). Besides, altered complexity of spontaneous brain activity and classification results were explored.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>36358029</pmid><doi>10.1002/hbm.26129</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0002-3710-3500</orcidid><orcidid>https://orcid.org/0000-0003-0145-6627</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | (multiscale)entropies ADHD/BP/SCHZ Adult Attention Deficit Disorder with Hyperactivity - diagnostic imaging Attention deficit hyperactivity disorder Bipolar disorder Bipolar Disorder - diagnostic imaging Brain Brain - diagnostic imaging Classification Consortia Correlation coefficient Correlation coefficients Datasets different atlases Entropy Humans Magnetic resonance imaging Mathematical analysis Mental disorders Methods Permutations Random variables Reliability analysis Reproducibility of Results resting‐state fMRI Schizophrenia Schizophrenia - diagnostic imaging Signs and symptoms test–retest reliability Young adults |
title | The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy |
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