Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code

This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic label...
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Autor*in:	Xinyu Zhao [verfasserIn] D. Rangaprakash [verfasserIn] Thomas S. Denney, Jr. [verfasserIn] Jeffrey S. Katz [verfasserIn] Michael N. Dretsch [verfasserIn] Gopikrishna Deshpande [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	In: Data in Brief - Elsevier, 2015, 22(2019), Seite 570-573
Übergeordnetes Werk:	volume:22 ; year:2019 ; pages:570-573

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.dib.2018.01.080

Katalog-ID:	DOAJ060858370

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allfields	10.1016/j.dib.2018.01.080 doi (DE-627)DOAJ060858370 (DE-599)DOAJe88caaad2e03485a910f3684e6b20fc3 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Xinyu Zhao verfasserin aut Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders Computer applications to medicine. Medical informatics Science (General) D. Rangaprakash verfasserin aut Thomas S. Denney, Jr. verfasserin aut Jeffrey S. Katz verfasserin aut Michael N. Dretsch verfasserin aut Gopikrishna Deshpande verfasserin aut In Data in Brief Elsevier, 2015 22(2019), Seite 570-573 (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:22 year:2019 pages:570-573 https://doi.org/10.1016/j.dib.2018.01.080 kostenfrei https://doaj.org/article/e88caaad2e03485a910f3684e6b20fc3 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340918300830 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 22 2019 570-573
spelling	10.1016/j.dib.2018.01.080 doi (DE-627)DOAJ060858370 (DE-599)DOAJe88caaad2e03485a910f3684e6b20fc3 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Xinyu Zhao verfasserin aut Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders Computer applications to medicine. Medical informatics Science (General) D. Rangaprakash verfasserin aut Thomas S. Denney, Jr. verfasserin aut Jeffrey S. Katz verfasserin aut Michael N. Dretsch verfasserin aut Gopikrishna Deshpande verfasserin aut In Data in Brief Elsevier, 2015 22(2019), Seite 570-573 (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:22 year:2019 pages:570-573 https://doi.org/10.1016/j.dib.2018.01.080 kostenfrei https://doaj.org/article/e88caaad2e03485a910f3684e6b20fc3 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340918300830 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 22 2019 570-573
allfields_unstemmed	10.1016/j.dib.2018.01.080 doi (DE-627)DOAJ060858370 (DE-599)DOAJe88caaad2e03485a910f3684e6b20fc3 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Xinyu Zhao verfasserin aut Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders Computer applications to medicine. Medical informatics Science (General) D. Rangaprakash verfasserin aut Thomas S. Denney, Jr. verfasserin aut Jeffrey S. Katz verfasserin aut Michael N. Dretsch verfasserin aut Gopikrishna Deshpande verfasserin aut In Data in Brief Elsevier, 2015 22(2019), Seite 570-573 (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:22 year:2019 pages:570-573 https://doi.org/10.1016/j.dib.2018.01.080 kostenfrei https://doaj.org/article/e88caaad2e03485a910f3684e6b20fc3 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340918300830 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 22 2019 570-573
allfieldsGer	10.1016/j.dib.2018.01.080 doi (DE-627)DOAJ060858370 (DE-599)DOAJe88caaad2e03485a910f3684e6b20fc3 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Xinyu Zhao verfasserin aut Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders Computer applications to medicine. Medical informatics Science (General) D. Rangaprakash verfasserin aut Thomas S. Denney, Jr. verfasserin aut Jeffrey S. Katz verfasserin aut Michael N. Dretsch verfasserin aut Gopikrishna Deshpande verfasserin aut In Data in Brief Elsevier, 2015 22(2019), Seite 570-573 (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:22 year:2019 pages:570-573 https://doi.org/10.1016/j.dib.2018.01.080 kostenfrei https://doaj.org/article/e88caaad2e03485a910f3684e6b20fc3 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340918300830 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 22 2019 570-573
allfieldsSound	10.1016/j.dib.2018.01.080 doi (DE-627)DOAJ060858370 (DE-599)DOAJe88caaad2e03485a910f3684e6b20fc3 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Xinyu Zhao verfasserin aut Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders Computer applications to medicine. Medical informatics Science (General) D. Rangaprakash verfasserin aut Thomas S. Denney, Jr. verfasserin aut Jeffrey S. Katz verfasserin aut Michael N. Dretsch verfasserin aut Gopikrishna Deshpande verfasserin aut In Data in Brief Elsevier, 2015 22(2019), Seite 570-573 (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:22 year:2019 pages:570-573 https://doi.org/10.1016/j.dib.2018.01.080 kostenfrei https://doaj.org/article/e88caaad2e03485a910f3684e6b20fc3 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340918300830 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 22 2019 570-573
language	English
source	In Data in Brief 22(2019), Seite 570-573 volume:22 year:2019 pages:570-573
sourceStr	In Data in Brief 22(2019), Seite 570-573 volume:22 year:2019 pages:570-573
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics Science (General)
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container_title	Data in Brief
authorswithroles_txt_mv	Xinyu Zhao @@aut@@ D. Rangaprakash @@aut@@ Thomas S. Denney, Jr. @@aut@@ Jeffrey S. Katz @@aut@@ Michael N. Dretsch @@aut@@ Gopikrishna Deshpande @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	797838937
id	DOAJ060858370
language_de	englisch
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callnumber-first	R - Medicine
author	Xinyu Zhao
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topic_title	R858-859.7 Q1-390 Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code
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title	Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code
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title_full	Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code
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title_sort	identifying neuropsychiatric disorders using unsupervised clustering methods: data and code
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title_auth	Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code
abstract	This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders
abstractGer	This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders
abstract_unstemmed	This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders
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title_short	Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code
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Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code

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