Multimodal Imaging Signatures of Parkinson’s Disease
Parkinson's disease (PD) is a complex neurodegenerative disorder that manifests through hallmark motor symptoms, often accompanied by a range of non-motor symptoms. There is a putative delay between the onset of the neurodegenerative process, marked by the death of dopamine-producing cells, an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-04-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00131/full |
| _version_ | 1818842208916209664 |
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| author | DuBois eBowman Daniel eDrake Daniel eHuddleston |
| author_facet | DuBois eBowman Daniel eDrake Daniel eHuddleston |
| author_sort | DuBois eBowman |
| collection | DOAJ |
| description | Parkinson's disease (PD) is a complex neurodegenerative disorder that manifests through hallmark motor symptoms, often accompanied by a range of non-motor symptoms. There is a putative delay between the onset of the neurodegenerative process, marked by the death of dopamine-producing cells, and the onset of motor symptoms, creating an urgent need to develop biomarkers that may yield early PD detection. Neuroimaging offers a non-invasive approach to examining the potential utility of a vast number of functional and structural brain characteristics as biomarkers. We present a statistical framework for analyzing neuroimaging data from multiple modalities to determine features that reliably distinguish PD patients from healthy control (HC) subjects. Our approach builds on elastic net, performing regularization and variable selection, while introducing additional criteria centering on parsimony and reproducibility. We apply our method to data from 42 subjects (28 PD patients and 14 HC). Our approach demonstrates extremely high accuracy, assessed via cross-validation, and isolates brain regions that are implicated in the neurodegenerative PD process. |
| first_indexed | 2024-12-19T04:38:20Z |
| format | Article |
| id | doaj.art-a72bfb90ee0f483d994b6245c3fae08e |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-19T04:38:20Z |
| publishDate | 2016-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-a72bfb90ee0f483d994b6245c3fae08e2022-12-21T20:35:41ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2016-04-011010.3389/fnins.2016.00131173368Multimodal Imaging Signatures of Parkinson’s DiseaseDuBois eBowman0Daniel eDrake1Daniel eHuddleston2Columbia UniversityColumbia UniversityEmory UniversityParkinson's disease (PD) is a complex neurodegenerative disorder that manifests through hallmark motor symptoms, often accompanied by a range of non-motor symptoms. There is a putative delay between the onset of the neurodegenerative process, marked by the death of dopamine-producing cells, and the onset of motor symptoms, creating an urgent need to develop biomarkers that may yield early PD detection. Neuroimaging offers a non-invasive approach to examining the potential utility of a vast number of functional and structural brain characteristics as biomarkers. We present a statistical framework for analyzing neuroimaging data from multiple modalities to determine features that reliably distinguish PD patients from healthy control (HC) subjects. Our approach builds on elastic net, performing regularization and variable selection, while introducing additional criteria centering on parsimony and reproducibility. We apply our method to data from 42 subjects (28 PD patients and 14 HC). Our approach demonstrates extremely high accuracy, assessed via cross-validation, and isolates brain regions that are implicated in the neurodegenerative PD process.http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00131/fullClassificationMRIpredictionbiomarkersMultimodal Imagingpenalized regression |
| spellingShingle | DuBois eBowman Daniel eDrake Daniel eHuddleston Multimodal Imaging Signatures of Parkinson’s Disease Frontiers in Neuroscience Classification MRI prediction biomarkers Multimodal Imaging penalized regression |
| title | Multimodal Imaging Signatures of Parkinson’s Disease |
| title_full | Multimodal Imaging Signatures of Parkinson’s Disease |
| title_fullStr | Multimodal Imaging Signatures of Parkinson’s Disease |
| title_full_unstemmed | Multimodal Imaging Signatures of Parkinson’s Disease |
| title_short | Multimodal Imaging Signatures of Parkinson’s Disease |
| title_sort | multimodal imaging signatures of parkinson s disease |
| topic | Classification MRI prediction biomarkers Multimodal Imaging penalized regression |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00131/full |
| work_keys_str_mv | AT duboisebowman multimodalimagingsignaturesofparkinsonsdisease AT danieledrake multimodalimagingsignaturesofparkinsonsdisease AT danielehuddleston multimodalimagingsignaturesofparkinsonsdisease |