Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor

BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET....
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Autor*in:	Xueyan Zhang [verfasserIn] Li Tao [verfasserIn] Huiyue Chen [verfasserIn] Xiaoyu Zhang [verfasserIn] Hansheng Wang [verfasserIn] Wanlin He [verfasserIn] Qin Li [verfasserIn] Fajin Lv [verfasserIn] Tianyou Luo [verfasserIn] Jin Luo [verfasserIn] Yun Man [verfasserIn] Zheng Xiao [verfasserIn] Jun Cao [verfasserIn] Weidong Fang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	essential tremor depression regional homogeneity resting-state functional magnetic resonance imaging machine learning

Übergeordnetes Werk:	In: Frontiers in Neurology - Frontiers Media S.A., 2010, 13(2022)
Übergeordnetes Werk:	volume:13 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fneur.2022.847650

Katalog-ID:	DOAJ041494350

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520			\|a BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.
650		4	\|a essential tremor
650		4	\|a depression
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650		4	\|a resting-state functional magnetic resonance imaging
650		4	\|a machine learning
653		0	\|a Neurology. Diseases of the nervous system
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allfields	10.3389/fneur.2022.847650 doi (DE-627)DOAJ041494350 (DE-599)DOAJ4cf29ce083134b028d045f3af1217b8c DE-627 ger DE-627 rakwb eng RC346-429 Xueyan Zhang verfasserin aut Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients. essential tremor depression regional homogeneity resting-state functional magnetic resonance imaging machine learning Neurology. Diseases of the nervous system Li Tao verfasserin aut Huiyue Chen verfasserin aut Xiaoyu Zhang verfasserin aut Hansheng Wang verfasserin aut Wanlin He verfasserin aut Qin Li verfasserin aut Fajin Lv verfasserin aut Tianyou Luo verfasserin aut Jin Luo verfasserin aut Yun Man verfasserin aut Zheng Xiao verfasserin aut Jun Cao verfasserin aut Weidong Fang verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 13(2022) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:13 year:2022 https://doi.org/10.3389/fneur.2022.847650 kostenfrei https://doaj.org/article/4cf29ce083134b028d045f3af1217b8c kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2022.847650/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
spelling	10.3389/fneur.2022.847650 doi (DE-627)DOAJ041494350 (DE-599)DOAJ4cf29ce083134b028d045f3af1217b8c DE-627 ger DE-627 rakwb eng RC346-429 Xueyan Zhang verfasserin aut Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients. essential tremor depression regional homogeneity resting-state functional magnetic resonance imaging machine learning Neurology. Diseases of the nervous system Li Tao verfasserin aut Huiyue Chen verfasserin aut Xiaoyu Zhang verfasserin aut Hansheng Wang verfasserin aut Wanlin He verfasserin aut Qin Li verfasserin aut Fajin Lv verfasserin aut Tianyou Luo verfasserin aut Jin Luo verfasserin aut Yun Man verfasserin aut Zheng Xiao verfasserin aut Jun Cao verfasserin aut Weidong Fang verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 13(2022) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:13 year:2022 https://doi.org/10.3389/fneur.2022.847650 kostenfrei https://doaj.org/article/4cf29ce083134b028d045f3af1217b8c kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2022.847650/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfields_unstemmed	10.3389/fneur.2022.847650 doi (DE-627)DOAJ041494350 (DE-599)DOAJ4cf29ce083134b028d045f3af1217b8c DE-627 ger DE-627 rakwb eng RC346-429 Xueyan Zhang verfasserin aut Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients. essential tremor depression regional homogeneity resting-state functional magnetic resonance imaging machine learning Neurology. Diseases of the nervous system Li Tao verfasserin aut Huiyue Chen verfasserin aut Xiaoyu Zhang verfasserin aut Hansheng Wang verfasserin aut Wanlin He verfasserin aut Qin Li verfasserin aut Fajin Lv verfasserin aut Tianyou Luo verfasserin aut Jin Luo verfasserin aut Yun Man verfasserin aut Zheng Xiao verfasserin aut Jun Cao verfasserin aut Weidong Fang verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 13(2022) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:13 year:2022 https://doi.org/10.3389/fneur.2022.847650 kostenfrei https://doaj.org/article/4cf29ce083134b028d045f3af1217b8c kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2022.847650/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfieldsGer	10.3389/fneur.2022.847650 doi (DE-627)DOAJ041494350 (DE-599)DOAJ4cf29ce083134b028d045f3af1217b8c DE-627 ger DE-627 rakwb eng RC346-429 Xueyan Zhang verfasserin aut Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients. essential tremor depression regional homogeneity resting-state functional magnetic resonance imaging machine learning Neurology. Diseases of the nervous system Li Tao verfasserin aut Huiyue Chen verfasserin aut Xiaoyu Zhang verfasserin aut Hansheng Wang verfasserin aut Wanlin He verfasserin aut Qin Li verfasserin aut Fajin Lv verfasserin aut Tianyou Luo verfasserin aut Jin Luo verfasserin aut Yun Man verfasserin aut Zheng Xiao verfasserin aut Jun Cao verfasserin aut Weidong Fang verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 13(2022) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:13 year:2022 https://doi.org/10.3389/fneur.2022.847650 kostenfrei https://doaj.org/article/4cf29ce083134b028d045f3af1217b8c kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2022.847650/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfieldsSound	10.3389/fneur.2022.847650 doi (DE-627)DOAJ041494350 (DE-599)DOAJ4cf29ce083134b028d045f3af1217b8c DE-627 ger DE-627 rakwb eng RC346-429 Xueyan Zhang verfasserin aut Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients. essential tremor depression regional homogeneity resting-state functional magnetic resonance imaging machine learning Neurology. Diseases of the nervous system Li Tao verfasserin aut Huiyue Chen verfasserin aut Xiaoyu Zhang verfasserin aut Hansheng Wang verfasserin aut Wanlin He verfasserin aut Qin Li verfasserin aut Fajin Lv verfasserin aut Tianyou Luo verfasserin aut Jin Luo verfasserin aut Yun Man verfasserin aut Zheng Xiao verfasserin aut Jun Cao verfasserin aut Weidong Fang verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 13(2022) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:13 year:2022 https://doi.org/10.3389/fneur.2022.847650 kostenfrei https://doaj.org/article/4cf29ce083134b028d045f3af1217b8c kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2022.847650/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
language	English
source	In Frontiers in Neurology 13(2022) volume:13 year:2022
sourceStr	In Frontiers in Neurology 13(2022) volume:13 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	essential tremor depression regional homogeneity resting-state functional magnetic resonance imaging machine learning Neurology. Diseases of the nervous system
isfreeaccess_bool	true
container_title	Frontiers in Neurology
authorswithroles_txt_mv	Xueyan Zhang @@aut@@ Li Tao @@aut@@ Huiyue Chen @@aut@@ Xiaoyu Zhang @@aut@@ Hansheng Wang @@aut@@ Wanlin He @@aut@@ Qin Li @@aut@@ Fajin Lv @@aut@@ Tianyou Luo @@aut@@ Jin Luo @@aut@@ Yun Man @@aut@@ Zheng Xiao @@aut@@ Jun Cao @@aut@@ Weidong Fang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	631498753
id	DOAJ041494350
language_de	englisch
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We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. 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callnumber-first	R - Medicine
author	Xueyan Zhang
spellingShingle	Xueyan Zhang misc RC346-429 misc essential tremor misc depression misc regional homogeneity misc resting-state functional magnetic resonance imaging misc machine learning misc Neurology. Diseases of the nervous system Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor
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topic_title	RC346-429 Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor essential tremor depression regional homogeneity resting-state functional magnetic resonance imaging machine learning
topic	misc RC346-429 misc essential tremor misc depression misc regional homogeneity misc resting-state functional magnetic resonance imaging misc machine learning misc Neurology. Diseases of the nervous system
topic_unstemmed	misc RC346-429 misc essential tremor misc depression misc regional homogeneity misc resting-state functional magnetic resonance imaging misc machine learning misc Neurology. Diseases of the nervous system
topic_browse	misc RC346-429 misc essential tremor misc depression misc regional homogeneity misc resting-state functional magnetic resonance imaging misc machine learning misc Neurology. Diseases of the nervous system
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title	Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor
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title_full	Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor
author_sort	Xueyan Zhang
journal	Frontiers in Neurology
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author_browse	Xueyan Zhang Li Tao Huiyue Chen Xiaoyu Zhang Hansheng Wang Wanlin He Qin Li Fajin Lv Tianyou Luo Jin Luo Yun Man Zheng Xiao Jun Cao Weidong Fang
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doi_str_mv	10.3389/fneur.2022.847650
author2-role	verfasserin
title_sort	combined intrinsic local functional connectivity with multivariate pattern analysis to identify depressed essential tremor
callnumber	RC346-429
title_auth	Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor
abstract	BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.
abstractGer	BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.
abstract_unstemmed	BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.
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title_short	Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor
url	https://doi.org/10.3389/fneur.2022.847650 https://doaj.org/article/4cf29ce083134b028d045f3af1217b8c https://www.frontiersin.org/articles/10.3389/fneur.2022.847650/full https://doaj.org/toc/1664-2295
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author2	Li Tao Huiyue Chen Xiaoyu Zhang Hansheng Wang Wanlin He Qin Li Fajin Lv Tianyou Luo Jin Luo Yun Man Zheng Xiao Jun Cao Weidong Fang
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up_date	2024-07-03T20:32:48.824Z
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Nicht das Richtige dabei?