Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level
Background: Recent studies using resting-state functional connectivity and machine-learning to distinguish patients with neurodegenerative diseases from other groups of subjects show promising results. This approach has not been tested to discriminate between Parkinson's disease (PD) and multip...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-01-01
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| Series: | NeuroImage: Clinical |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158219300701 |
| _version_ | 1818059164373483520 |
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| author | Hugo C. Baggio Alexandra Abos Barbara Segura Anna Campabadal Carme Uribe Darly M. Giraldo Alexandra Perez-Soriano Esteban Muñoz Yaroslau Compta Carme Junque Maria Jose Marti |
| author_facet | Hugo C. Baggio Alexandra Abos Barbara Segura Anna Campabadal Carme Uribe Darly M. Giraldo Alexandra Perez-Soriano Esteban Muñoz Yaroslau Compta Carme Junque Maria Jose Marti |
| author_sort | Hugo C. Baggio |
| collection | DOAJ |
| description | Background: Recent studies using resting-state functional connectivity and machine-learning to distinguish patients with neurodegenerative diseases from other groups of subjects show promising results. This approach has not been tested to discriminate between Parkinson's disease (PD) and multiple system atrophy (MSA) patients. Objectives: Our first aim is to characterize possible abnormalities in resting-state functional connectivity between the cerebellum and a set of intrinsic-connectivity brain networks and between the cerebellum and different regions of the striatum in PD and MSA. The second objective of this study is to assess the potential of cerebellar connectivity measures to distinguish between PD and MSA patients at the single-patient level. Methods: Fifty-nine healthy controls, 62 PD patients, and 30 MSA patients underwent resting-state functional MRI with a 3T scanner. Independent component analysis and dual regression were used to define seven resting-state networks of interest. To assess striatal connectivity, a seed-to-voxel approach was used after dividing the striatum into six regions bilaterally. Measures of cerebellar-brain network and cerebellar-striatal connectivity were then used as features in a support vector machine to discriminate between PD and MSA patients. Results: MSA patients displayed reduced cerebellar connectivity with different brain networks and with the striatum compared with PD patients and with controls. The classification procedure achieved an overall accuracy of 77.17% with 83.33% of the MSA subjects and 74.19% of the PD patients correctly classified. Conclusion: Our findings suggest that measures of cerebellar functional connectivity have the potential to distinguish between PD and MSA patients. Keywords: Machine learning, Parkinson's disease, Multiple system atrophy, Functional connectivity, Resting state, Cerebellum |
| first_indexed | 2024-12-10T13:12:10Z |
| format | Article |
| id | doaj.art-a512e65ad06e4ce19db6da3e350b8406 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-10T13:12:10Z |
| publishDate | 2019-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-a512e65ad06e4ce19db6da3e350b84062022-12-22T01:47:38ZengElsevierNeuroImage: Clinical2213-15822019-01-0122Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient levelHugo C. Baggio0Alexandra Abos1Barbara Segura2Anna Campabadal3Carme Uribe4Darly M. Giraldo5Alexandra Perez-Soriano6Esteban Muñoz7Yaroslau Compta8Carme Junque9Maria Jose Marti10Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, SpainMedical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, SpainMedical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, SpainMedical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, SpainMedical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, SpainCentro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain; Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, SpainCentro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain; Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, SpainCentro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain; Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, SpainCentro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain; Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, SpainMedical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, SpainCentro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain; Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Corresponding author at: Parkinson's Disease and Movement Disorders Unit of the Neurology Service of Hospital Clinic, Carrer de Villarroel 170, 08036 Barcelona, Catalonia, Spain.Background: Recent studies using resting-state functional connectivity and machine-learning to distinguish patients with neurodegenerative diseases from other groups of subjects show promising results. This approach has not been tested to discriminate between Parkinson's disease (PD) and multiple system atrophy (MSA) patients. Objectives: Our first aim is to characterize possible abnormalities in resting-state functional connectivity between the cerebellum and a set of intrinsic-connectivity brain networks and between the cerebellum and different regions of the striatum in PD and MSA. The second objective of this study is to assess the potential of cerebellar connectivity measures to distinguish between PD and MSA patients at the single-patient level. Methods: Fifty-nine healthy controls, 62 PD patients, and 30 MSA patients underwent resting-state functional MRI with a 3T scanner. Independent component analysis and dual regression were used to define seven resting-state networks of interest. To assess striatal connectivity, a seed-to-voxel approach was used after dividing the striatum into six regions bilaterally. Measures of cerebellar-brain network and cerebellar-striatal connectivity were then used as features in a support vector machine to discriminate between PD and MSA patients. Results: MSA patients displayed reduced cerebellar connectivity with different brain networks and with the striatum compared with PD patients and with controls. The classification procedure achieved an overall accuracy of 77.17% with 83.33% of the MSA subjects and 74.19% of the PD patients correctly classified. Conclusion: Our findings suggest that measures of cerebellar functional connectivity have the potential to distinguish between PD and MSA patients. Keywords: Machine learning, Parkinson's disease, Multiple system atrophy, Functional connectivity, Resting state, Cerebellumhttp://www.sciencedirect.com/science/article/pii/S2213158219300701 |
| spellingShingle | Hugo C. Baggio Alexandra Abos Barbara Segura Anna Campabadal Carme Uribe Darly M. Giraldo Alexandra Perez-Soriano Esteban Muñoz Yaroslau Compta Carme Junque Maria Jose Marti Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level NeuroImage: Clinical |
| title | Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level |
| title_full | Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level |
| title_fullStr | Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level |
| title_full_unstemmed | Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level |
| title_short | Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level |
| title_sort | cerebellar resting state functional connectivity in parkinson s disease and multiple system atrophy characterization of abnormalities and potential for differential diagnosis at the single patient level |
| url | http://www.sciencedirect.com/science/article/pii/S2213158219300701 |
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