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Multimodal Imaging Signatures of Parkinson's Disease.

Bowman FD, Drake DF, Huddleston DE - Front Neurosci (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, The Mailman School of Public Health, Columbia University New York, NY, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Models achieving perfect separation between PD patients and HC subjects with a minimum number of variables. Each three feature model is adjusted for age, sex, and head coil. The models are comprised of eight distinct features.
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Figure 6: Models achieving perfect separation between PD patients and HC subjects with a minimum number of variables. Each three feature model is adjusted for age, sex, and head coil. The models are comprised of eight distinct features.

Mentions: The 24 features contribute extremely strong predictive power. Using logistic regression, still controlling for head coil, sex, and age, one can achieve perfect separation between PD patients and HC using subsets of as few as three of these multimodal imaging features. In fact, out of all possible three-feature models, three of them achieve perfect separation between the groups, and comprise an aggregate of eight separate features. The three models and the associated map of features are presented in Figure 6. No model of less than three features achieves perfect separation; however many such models exist when more than three out of the 24 features are considered.


Multimodal Imaging Signatures of Parkinson's Disease.

Bowman FD, Drake DF, Huddleston DE - Front Neurosci (2016)

Models achieving perfect separation between PD patients and HC subjects with a minimum number of variables. Each three feature model is adjusted for age, sex, and head coil. The models are comprised of eight distinct features.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834347&req=5

Figure 6: Models achieving perfect separation between PD patients and HC subjects with a minimum number of variables. Each three feature model is adjusted for age, sex, and head coil. The models are comprised of eight distinct features.
Mentions: The 24 features contribute extremely strong predictive power. Using logistic regression, still controlling for head coil, sex, and age, one can achieve perfect separation between PD patients and HC using subsets of as few as three of these multimodal imaging features. In fact, out of all possible three-feature models, three of them achieve perfect separation between the groups, and comprise an aggregate of eight separate features. The three models and the associated map of features are presented in Figure 6. No model of less than three features achieves perfect separation; however many such models exist when more than three out of the 24 features are considered.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, The Mailman School of Public Health, Columbia University New York, NY, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus