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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

Overview of the multiple modalities generating data, estimates obtained from each reflecting particular structural or functional properties, spatial scale for summary data representations, and ultimately the features constituting the global set of potential neuroimaging markers of PD.
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Figure 3: Overview of the multiple modalities generating data, estimates obtained from each reflecting particular structural or functional properties, spatial scale for summary data representations, and ultimately the features constituting the global set of potential neuroimaging markers of PD.

Mentions: We generate data representations (or features) for each imaging modality and specify the spatial scale. Figure 3 provides a conceptual overview describing the multiple modalities generating data, estimates obtained from each reflecting particular structural or functional properties of the brain, the spatial scale for each summary, and ultimately the features constituting the global set of potential neuroimaging markers of PD. We use 290 regions from the extended AAL map (AAL-290) to compute regional averages of local volumetric MRI measures, specifically from voxel-based morphometry (VBM) (see Table 1). We use rs-fMRI data to generate both localized and connectivity features. To quantify the power concentrated at low frequencies for fMRI data, we use fractional amplitude of low frequency fluctuation (fALFF), which calculates the ratio of the power spectrum at low-frequencies (0.01–0.10 Hz) to that of the entire frequency range (Zou et al., 2008). We compute fALFF at a voxel level, for all voxels, and average within each of the 290 subregions. We quantify functional connectivity (FC) by calculating pairwise correlations between the average time courses within each pair of the 290 subregions. We compute fractional anisotropy (FA) for each voxel and obtain regional summaries by averaging over each of the AAL-90 regions. Thus our summary measure will increase both as a function of the restricted diffusion in the regional white matter and the proportion of white matter within a region. We calculate structural connectivity (SC) derived from DTI, using anisotropy to constrain tracking. We use FSL to perform estimation of the diffusion tensor (BEDPOSTX) and tractography (PROBTRACKX) (Behrens et al., 2007).


Multimodal Imaging Signatures of Parkinson's Disease.

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

Overview of the multiple modalities generating data, estimates obtained from each reflecting particular structural or functional properties, spatial scale for summary data representations, and ultimately the features constituting the global set of potential neuroimaging markers of PD.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Overview of the multiple modalities generating data, estimates obtained from each reflecting particular structural or functional properties, spatial scale for summary data representations, and ultimately the features constituting the global set of potential neuroimaging markers of PD.
Mentions: We generate data representations (or features) for each imaging modality and specify the spatial scale. Figure 3 provides a conceptual overview describing the multiple modalities generating data, estimates obtained from each reflecting particular structural or functional properties of the brain, the spatial scale for each summary, and ultimately the features constituting the global set of potential neuroimaging markers of PD. We use 290 regions from the extended AAL map (AAL-290) to compute regional averages of local volumetric MRI measures, specifically from voxel-based morphometry (VBM) (see Table 1). We use rs-fMRI data to generate both localized and connectivity features. To quantify the power concentrated at low frequencies for fMRI data, we use fractional amplitude of low frequency fluctuation (fALFF), which calculates the ratio of the power spectrum at low-frequencies (0.01–0.10 Hz) to that of the entire frequency range (Zou et al., 2008). We compute fALFF at a voxel level, for all voxels, and average within each of the 290 subregions. We quantify functional connectivity (FC) by calculating pairwise correlations between the average time courses within each pair of the 290 subregions. We compute fractional anisotropy (FA) for each voxel and obtain regional summaries by averaging over each of the AAL-90 regions. Thus our summary measure will increase both as a function of the restricted diffusion in the regional white matter and the proportion of white matter within a region. We calculate structural connectivity (SC) derived from DTI, using anisotropy to constrain tracking. We use FSL to perform estimation of the diffusion tensor (BEDPOSTX) and tractography (PROBTRACKX) (Behrens et al., 2007).

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