Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent

Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learni...
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Autor*in:	Wu, Yunfan [verfasserIn] Ma, Xiaofen [verfasserIn] Zhou, Zhihua [verfasserIn] Yan, Jianhao [verfasserIn] Xu, Shoujun [verfasserIn] Li, Meng [verfasserIn] Fang, Jing [verfasserIn] Li, Guoming [verfasserIn] Zeng, Shaoqing [verfasserIn] Lin, Chulan [verfasserIn] Li, Chunlong [verfasserIn] Huang, Shumei [verfasserIn] Jiang, Guihua [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Cough Syrup Human connectome Impulsive behavior Machine learning

Übergeordnetes Werk:	Enthalten in: Journal of psychiatric research - Amsterdam [u.a.] : Elsevier Science, 1961, 130, Seite 333-341
Übergeordnetes Werk:	volume:130 ; pages:333-341

DOI / URN:	10.1016/j.jpsychires.2020.08.001

Katalog-ID:	ELV004783883

Internformat


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245	1	0	\|a Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent
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520			\|a Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future.
650		4	\|a Cough
650		4	\|a Syrup
650		4	\|a Human connectome
650		4	\|a Impulsive behavior
650		4	\|a Machine learning
700	1		\|a Ma, Xiaofen \|e verfasserin \|4 aut
700	1		\|a Zhou, Zhihua \|e verfasserin \|4 aut
700	1		\|a Yan, Jianhao \|e verfasserin \|4 aut
700	1		\|a Xu, Shoujun \|e verfasserin \|4 aut
700	1		\|a Li, Meng \|e verfasserin \|4 aut
700	1		\|a Fang, Jing \|e verfasserin \|4 aut
700	1		\|a Li, Guoming \|e verfasserin \|4 aut
700	1		\|a Zeng, Shaoqing \|e verfasserin \|4 aut
700	1		\|a Lin, Chulan \|e verfasserin \|4 aut
700	1		\|a Li, Chunlong \|e verfasserin \|4 aut
700	1		\|a Huang, Shumei \|e verfasserin \|4 aut
700	1		\|a Jiang, Guihua \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Journal of psychiatric research \|d Amsterdam [u.a.] : Elsevier Science, 1961 \|g 130, Seite 333-341 \|h Online-Ressource \|w (DE-627)30666111X \|w (DE-600)1500641-4 \|w (DE-576)081986718 \|x 1879-1379 \|7 nnns
773	1	8	\|g volume:130 \|g pages:333-341
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912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
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912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
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936	b	k	\|a 44.91 \|j Psychiatrie \|j Psychopathologie
951			\|a AR
952			\|d 130 \|h 333-341

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publishDate	2020
allfields	10.1016/j.jpsychires.2020.08.001 doi (DE-627)ELV004783883 (ELSEVIER)S0022-3956(20)30912-2 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl Wu, Yunfan verfasserin aut Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future. Cough Syrup Human connectome Impulsive behavior Machine learning Ma, Xiaofen verfasserin aut Zhou, Zhihua verfasserin aut Yan, Jianhao verfasserin aut Xu, Shoujun verfasserin aut Li, Meng verfasserin aut Fang, Jing verfasserin aut Li, Guoming verfasserin aut Zeng, Shaoqing verfasserin aut Lin, Chulan verfasserin aut Li, Chunlong verfasserin aut Huang, Shumei verfasserin aut Jiang, Guihua verfasserin aut Enthalten in Journal of psychiatric research Amsterdam [u.a.] : Elsevier Science, 1961 130, Seite 333-341 Online-Ressource (DE-627)30666111X (DE-600)1500641-4 (DE-576)081986718 1879-1379 nnns volume:130 pages:333-341 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie AR 130 333-341
spelling	10.1016/j.jpsychires.2020.08.001 doi (DE-627)ELV004783883 (ELSEVIER)S0022-3956(20)30912-2 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl Wu, Yunfan verfasserin aut Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future. Cough Syrup Human connectome Impulsive behavior Machine learning Ma, Xiaofen verfasserin aut Zhou, Zhihua verfasserin aut Yan, Jianhao verfasserin aut Xu, Shoujun verfasserin aut Li, Meng verfasserin aut Fang, Jing verfasserin aut Li, Guoming verfasserin aut Zeng, Shaoqing verfasserin aut Lin, Chulan verfasserin aut Li, Chunlong verfasserin aut Huang, Shumei verfasserin aut Jiang, Guihua verfasserin aut Enthalten in Journal of psychiatric research Amsterdam [u.a.] : Elsevier Science, 1961 130, Seite 333-341 Online-Ressource (DE-627)30666111X (DE-600)1500641-4 (DE-576)081986718 1879-1379 nnns volume:130 pages:333-341 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie AR 130 333-341
allfields_unstemmed	10.1016/j.jpsychires.2020.08.001 doi (DE-627)ELV004783883 (ELSEVIER)S0022-3956(20)30912-2 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl Wu, Yunfan verfasserin aut Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future. Cough Syrup Human connectome Impulsive behavior Machine learning Ma, Xiaofen verfasserin aut Zhou, Zhihua verfasserin aut Yan, Jianhao verfasserin aut Xu, Shoujun verfasserin aut Li, Meng verfasserin aut Fang, Jing verfasserin aut Li, Guoming verfasserin aut Zeng, Shaoqing verfasserin aut Lin, Chulan verfasserin aut Li, Chunlong verfasserin aut Huang, Shumei verfasserin aut Jiang, Guihua verfasserin aut Enthalten in Journal of psychiatric research Amsterdam [u.a.] : Elsevier Science, 1961 130, Seite 333-341 Online-Ressource (DE-627)30666111X (DE-600)1500641-4 (DE-576)081986718 1879-1379 nnns volume:130 pages:333-341 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie AR 130 333-341
allfieldsGer	10.1016/j.jpsychires.2020.08.001 doi (DE-627)ELV004783883 (ELSEVIER)S0022-3956(20)30912-2 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl Wu, Yunfan verfasserin aut Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future. Cough Syrup Human connectome Impulsive behavior Machine learning Ma, Xiaofen verfasserin aut Zhou, Zhihua verfasserin aut Yan, Jianhao verfasserin aut Xu, Shoujun verfasserin aut Li, Meng verfasserin aut Fang, Jing verfasserin aut Li, Guoming verfasserin aut Zeng, Shaoqing verfasserin aut Lin, Chulan verfasserin aut Li, Chunlong verfasserin aut Huang, Shumei verfasserin aut Jiang, Guihua verfasserin aut Enthalten in Journal of psychiatric research Amsterdam [u.a.] : Elsevier Science, 1961 130, Seite 333-341 Online-Ressource (DE-627)30666111X (DE-600)1500641-4 (DE-576)081986718 1879-1379 nnns volume:130 pages:333-341 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie AR 130 333-341
allfieldsSound	10.1016/j.jpsychires.2020.08.001 doi (DE-627)ELV004783883 (ELSEVIER)S0022-3956(20)30912-2 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl Wu, Yunfan verfasserin aut Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future. Cough Syrup Human connectome Impulsive behavior Machine learning Ma, Xiaofen verfasserin aut Zhou, Zhihua verfasserin aut Yan, Jianhao verfasserin aut Xu, Shoujun verfasserin aut Li, Meng verfasserin aut Fang, Jing verfasserin aut Li, Guoming verfasserin aut Zeng, Shaoqing verfasserin aut Lin, Chulan verfasserin aut Li, Chunlong verfasserin aut Huang, Shumei verfasserin aut Jiang, Guihua verfasserin aut Enthalten in Journal of psychiatric research Amsterdam [u.a.] : Elsevier Science, 1961 130, Seite 333-341 Online-Ressource (DE-627)30666111X (DE-600)1500641-4 (DE-576)081986718 1879-1379 nnns volume:130 pages:333-341 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie AR 130 333-341
language	English
source	Enthalten in Journal of psychiatric research 130, Seite 333-341 volume:130 pages:333-341
sourceStr	Enthalten in Journal of psychiatric research 130, Seite 333-341 volume:130 pages:333-341
format_phy_str_mv	Article
bklname	Psychiatrie Psychopathologie
institution	findex.gbv.de
topic_facet	Cough Syrup Human connectome Impulsive behavior Machine learning
dewey-raw	610
isfreeaccess_bool	false
container_title	Journal of psychiatric research
authorswithroles_txt_mv	Wu, Yunfan @@aut@@ Ma, Xiaofen @@aut@@ Zhou, Zhihua @@aut@@ Yan, Jianhao @@aut@@ Xu, Shoujun @@aut@@ Li, Meng @@aut@@ Fang, Jing @@aut@@ Li, Guoming @@aut@@ Zeng, Shaoqing @@aut@@ Lin, Chulan @@aut@@ Li, Chunlong @@aut@@ Huang, Shumei @@aut@@ Jiang, Guihua @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	30666111X
dewey-sort	3610
id	ELV004783883
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV004783883</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524155457.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230503s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jpsychires.2020.08.001</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV004783883</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0022-3956(20)30912-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.91</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Yunfan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cough</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Syrup</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Human connectome</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Impulsive behavior</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Xiaofen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Zhihua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, 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author	Wu, Yunfan
spellingShingle	Wu, Yunfan ddc 610 bkl 44.91 misc Cough misc Syrup misc Human connectome misc Impulsive behavior misc Machine learning Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent
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topic_title	610 DE-600 44.91 bkl Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent Cough Syrup Human connectome Impulsive behavior Machine learning
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title	Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent
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title_full	Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent
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author_browse	Wu, Yunfan Ma, Xiaofen Zhou, Zhihua Yan, Jianhao Xu, Shoujun Li, Meng Fang, Jing Li, Guoming Zeng, Shaoqing Lin, Chulan Li, Chunlong Huang, Shumei Jiang, Guihua
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title_sort	functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent
title_auth	Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent
abstract	Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future.
abstractGer	Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future.
abstract_unstemmed	Purpose: Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future.
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title_short	Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent
remote_bool	true
author2	Ma, Xiaofen Zhou, Zhihua Yan, Jianhao Xu, Shoujun Li, Meng Fang, Jing Li, Guoming Zeng, Shaoqing Lin, Chulan Li, Chunlong Huang, Shumei Jiang, Guihua
author2Str	Ma, Xiaofen Zhou, Zhihua Yan, Jianhao Xu, Shoujun Li, Meng Fang, Jing Li, Guoming Zeng, Shaoqing Lin, Chulan Li, Chunlong Huang, Shumei Jiang, Guihua
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doi_str	10.1016/j.jpsychires.2020.08.001
up_date	2024-07-07T00:08:11.328Z
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Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier.Methods: 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects.Results: The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%.Conclusions: These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cough</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Syrup</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Human connectome</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Impulsive behavior</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Xiaofen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Zhihua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, 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