Limits...
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) Plots of the mean absolute coefficient (standardized) vs. the proportion of times the feature is retained over 200 training samples at (α, λ) corresponding to points (B–E) in Figure 4A. The enlarged plot shown in (B) is point E from Figure 4A, with colors depicting the modality. The reference lines in all plots reveal the 10% of values with strongest predictive power over the training samples. At point E, modalities FC, SC, and VBM yield the most predictive features.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4834347&req=5

Figure 5: (A) Plots of the mean absolute coefficient (standardized) vs. the proportion of times the feature is retained over 200 training samples at (α, λ) corresponding to points (B–E) in Figure 4A. The enlarged plot shown in (B) is point E from Figure 4A, with colors depicting the modality. The reference lines in all plots reveal the 10% of values with strongest predictive power over the training samples. At point E, modalities FC, SC, and VBM yield the most predictive features.

Mentions: From the previously described elastic net with repeated two-fold cross-validation, Figure 5A shows scatter plots of the mean absolute coefficient of each standardized feature vs. the proportion of instances the feature is retained (i.e., has a nonzero coefficient) over the 200 training samples. Each plot corresponds to operating points A, B, C, and D in Figure 4A. At point A, we see that the mean absolute coefficient values are relatively large, and that every feature is selected 75% or more of the 200 trials. Points B, C, and D explore different extremes of our bounded search region. As alpha increases, the rate at which features are selected decreases. At large lambda (point B), the mean coefficient values are small. In each panel, the horizontal line indicates the threshold ξ0.10 signifying the top 10% with the strongest predictive features (based on mean absolute coefficient value). Figure 5B shows an enlarged plot at point E, a representative point near the middle of the search region. Using color, the plot illustrates the distribution associated with the different modalities. At point E, modalities FC, SC, and VBM yield the most predictive features.


Multimodal Imaging Signatures of Parkinson's Disease.

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

(A) Plots of the mean absolute coefficient (standardized) vs. the proportion of times the feature is retained over 200 training samples at (α, λ) corresponding to points (B–E) in Figure 4A. The enlarged plot shown in (B) is point E from Figure 4A, with colors depicting the modality. The reference lines in all plots reveal the 10% of values with strongest predictive power over the training samples. At point E, modalities FC, SC, and VBM yield the most predictive features.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 5: (A) Plots of the mean absolute coefficient (standardized) vs. the proportion of times the feature is retained over 200 training samples at (α, λ) corresponding to points (B–E) in Figure 4A. The enlarged plot shown in (B) is point E from Figure 4A, with colors depicting the modality. The reference lines in all plots reveal the 10% of values with strongest predictive power over the training samples. At point E, modalities FC, SC, and VBM yield the most predictive features.
Mentions: From the previously described elastic net with repeated two-fold cross-validation, Figure 5A shows scatter plots of the mean absolute coefficient of each standardized feature vs. the proportion of instances the feature is retained (i.e., has a nonzero coefficient) over the 200 training samples. Each plot corresponds to operating points A, B, C, and D in Figure 4A. At point A, we see that the mean absolute coefficient values are relatively large, and that every feature is selected 75% or more of the 200 trials. Points B, C, and D explore different extremes of our bounded search region. As alpha increases, the rate at which features are selected decreases. At large lambda (point B), the mean coefficient values are small. In each panel, the horizontal line indicates the threshold ξ0.10 signifying the top 10% with the strongest predictive features (based on mean absolute coefficient value). Figure 5B shows an enlarged plot at point E, a representative point near the middle of the search region. Using color, the plot illustrates the distribution associated with the different modalities. At point E, modalities FC, SC, and VBM yield the most predictive features.

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