Functional connectivity markers of depression in advanced Parkinson's disease
Background: Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients. Obje...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | NeuroImage: Clinical |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158219304772 |
| _version_ | 1818176191964643328 |
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| author | Hai Lin Xiaodong Cai Doudou Zhang Jiali Liu Peng Na Weiping Li |
| author_facet | Hai Lin Xiaodong Cai Doudou Zhang Jiali Liu Peng Na Weiping Li |
| author_sort | Hai Lin |
| collection | DOAJ |
| description | Background: Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients. Objectives: This study is to explore functional connectivity markers of depression in PD patients using resting-state fMRI and help diagnose whether patients have depression or not. Methods: We reviewed 156 advanced PD patients (duration > 5 years; 59 depressed ones) and 45 healthy control subjects who underwent a resting-state fMRI scanning. Functional connectivity analysis was employed to characterize intrinsic connectivity networks using group independent component analysis and extract connectivity features. Features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for classification. Random forest classifiers were built for depression diagnosis, on the basis of identified features. Results: 42 intrinsic connectivity networks were identified and arranged into subcortical, auditory, somatomotor, visual, cognitive control, default-mode and cerebellar networks. Six features were significantly relevant to classification. They were connectivity within posterior cingulate cortex, within insula, between posterior cingulate cortex and insula/hippocampus+amygdala, between insula and precuneus, and between superior parietal lobule and medial prefrontal cortex. The mean accuracy achieved with classifiers to discriminate depressed patients from the non-depressed was 82.4%. Conclusions: Our findings provide preliminary evidence that resting-state functional connectivity can characterize depressed PD patients and help distinguish them from non-depressed ones. Keywords: Parkinson's disease, Depression, Resting-state fMRI, Intrinsic connectivity network, Functional connectivity |
| first_indexed | 2024-12-11T20:12:16Z |
| format | Article |
| id | doaj.art-00e44749a7994e79933ec1e44d527a43 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-11T20:12:16Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-00e44749a7994e79933ec1e44d527a432022-12-22T00:52:17ZengElsevierNeuroImage: Clinical2213-15822020-01-0125Functional connectivity markers of depression in advanced Parkinson's diseaseHai Lin0Xiaodong Cai1Doudou Zhang2Jiali Liu3Peng Na4Weiping Li5Department of Functional Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China; Brain Centre, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China; School of Medicine, Shenzhen University, Shenzhen, Guangdong, ChinaDepartment of Functional Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China; School of Medicine, Shenzhen University, Shenzhen, Guangdong, ChinaDepartment of Functional Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China; School of Medicine, Shenzhen University, Shenzhen, Guangdong, ChinaDepartment of Functional Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China; School of Medicine, Shenzhen University, Shenzhen, Guangdong, ChinaDepartment of Functional Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China; Brain Centre, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China; School of Medicine, Shenzhen University, Shenzhen, Guangdong, ChinaBrain Centre, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China; School of Medicine, Shenzhen University, Shenzhen, Guangdong, China; Corresponding author at: Brain Centre, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, 3002# Sungang West Road, Futian District, Shenzhen 518035, Guangdong, China.Background: Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients. Objectives: This study is to explore functional connectivity markers of depression in PD patients using resting-state fMRI and help diagnose whether patients have depression or not. Methods: We reviewed 156 advanced PD patients (duration > 5 years; 59 depressed ones) and 45 healthy control subjects who underwent a resting-state fMRI scanning. Functional connectivity analysis was employed to characterize intrinsic connectivity networks using group independent component analysis and extract connectivity features. Features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for classification. Random forest classifiers were built for depression diagnosis, on the basis of identified features. Results: 42 intrinsic connectivity networks were identified and arranged into subcortical, auditory, somatomotor, visual, cognitive control, default-mode and cerebellar networks. Six features were significantly relevant to classification. They were connectivity within posterior cingulate cortex, within insula, between posterior cingulate cortex and insula/hippocampus+amygdala, between insula and precuneus, and between superior parietal lobule and medial prefrontal cortex. The mean accuracy achieved with classifiers to discriminate depressed patients from the non-depressed was 82.4%. Conclusions: Our findings provide preliminary evidence that resting-state functional connectivity can characterize depressed PD patients and help distinguish them from non-depressed ones. Keywords: Parkinson's disease, Depression, Resting-state fMRI, Intrinsic connectivity network, Functional connectivityhttp://www.sciencedirect.com/science/article/pii/S2213158219304772 |
| spellingShingle | Hai Lin Xiaodong Cai Doudou Zhang Jiali Liu Peng Na Weiping Li Functional connectivity markers of depression in advanced Parkinson's disease NeuroImage: Clinical |
| title | Functional connectivity markers of depression in advanced Parkinson's disease |
| title_full | Functional connectivity markers of depression in advanced Parkinson's disease |
| title_fullStr | Functional connectivity markers of depression in advanced Parkinson's disease |
| title_full_unstemmed | Functional connectivity markers of depression in advanced Parkinson's disease |
| title_short | Functional connectivity markers of depression in advanced Parkinson's disease |
| title_sort | functional connectivity markers of depression in advanced parkinson s disease |
| url | http://www.sciencedirect.com/science/article/pii/S2213158219304772 |
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