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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

(A) AUC for different tuning parameters, with each point averaged over 100 applications of two-fold cross-validation. The point A reflects the tuning parameter value yielding the maximum AUC, and is depicted in the curves in (B). The traces define a restricted space of tuning parameters. Above and to the right of the white trace yields no more than an average of 75 predictors, and below and to the left of the black trace reflects at least 0.90 AUC on average. (B) ROC curve (in black) reflecting high prediction accuracy based on 271 imaging predictors; AUC is 0.989. The colored curves highlight the variability associated with each separate CV sample.
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Figure 4: (A) AUC for different tuning parameters, with each point averaged over 100 applications of two-fold cross-validation. The point A reflects the tuning parameter value yielding the maximum AUC, and is depicted in the curves in (B). The traces define a restricted space of tuning parameters. Above and to the right of the white trace yields no more than an average of 75 predictors, and below and to the left of the black trace reflects at least 0.90 AUC on average. (B) ROC curve (in black) reflecting high prediction accuracy based on 271 imaging predictors; AUC is 0.989. The colored curves highlight the variability associated with each separate CV sample.

Mentions: The resulting average AUC values in the (α, λ) grid are shown in Figure 4A. Point A indicates the (α, λ) combination with the maximum average area under the curve, AUC = 0.989. The corresponding average ROC curve (black) is shown in Figure 4B, along with the individual ROC curves from each cross-validation fit, indicating the degree of variability across samples. Point A, at α = 0.02, is very close to ridge regression and, correspondingly, there is only a slight degree of feature selection. The average number of nonzero coefficients over the 200 training samples is 245.3 (out of 271). Moreover, no feature is consistently excluded over the 200 samples. So, while on average the models achieve remarkable accuracy in distinguishing PD patients from healthy controls, the large number of contributing variables involved does not advance our goal of identifying potential biomarkers that can be considered in future research to explore possible biological mechanisms. Therefore, despite attaining high prediction accuracy, our pursuit of potential markers prompts us to seek additional parsimony.


Multimodal Imaging Signatures of Parkinson's Disease.

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

(A) AUC for different tuning parameters, with each point averaged over 100 applications of two-fold cross-validation. The point A reflects the tuning parameter value yielding the maximum AUC, and is depicted in the curves in (B). The traces define a restricted space of tuning parameters. Above and to the right of the white trace yields no more than an average of 75 predictors, and below and to the left of the black trace reflects at least 0.90 AUC on average. (B) ROC curve (in black) reflecting high prediction accuracy based on 271 imaging predictors; AUC is 0.989. The colored curves highlight the variability associated with each separate CV sample.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 4: (A) AUC for different tuning parameters, with each point averaged over 100 applications of two-fold cross-validation. The point A reflects the tuning parameter value yielding the maximum AUC, and is depicted in the curves in (B). The traces define a restricted space of tuning parameters. Above and to the right of the white trace yields no more than an average of 75 predictors, and below and to the left of the black trace reflects at least 0.90 AUC on average. (B) ROC curve (in black) reflecting high prediction accuracy based on 271 imaging predictors; AUC is 0.989. The colored curves highlight the variability associated with each separate CV sample.
Mentions: The resulting average AUC values in the (α, λ) grid are shown in Figure 4A. Point A indicates the (α, λ) combination with the maximum average area under the curve, AUC = 0.989. The corresponding average ROC curve (black) is shown in Figure 4B, along with the individual ROC curves from each cross-validation fit, indicating the degree of variability across samples. Point A, at α = 0.02, is very close to ridge regression and, correspondingly, there is only a slight degree of feature selection. The average number of nonzero coefficients over the 200 training samples is 245.3 (out of 271). Moreover, no feature is consistently excluded over the 200 samples. So, while on average the models achieve remarkable accuracy in distinguishing PD patients from healthy controls, the large number of contributing variables involved does not advance our goal of identifying potential biomarkers that can be considered in future research to explore possible biological mechanisms. Therefore, despite attaining high prediction accuracy, our pursuit of potential markers prompts us to seek additional parsimony.

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