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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

Depiction of AAL-90 parcellation and a hierarchical subparcellation with 290 brain regions. The subregions are constructed from resting state fMRI data of healthy controls (outside of the current sample) based on functional characteristics with anatomical constraints to keep subregions contiguous and bounded within a single region.
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Figure 2: Depiction of AAL-90 parcellation and a hierarchical subparcellation with 290 brain regions. The subregions are constructed from resting state fMRI data of healthy controls (outside of the current sample) based on functional characteristics with anatomical constraints to keep subregions contiguous and bounded within a single region.

Mentions: The first step is to determine the spatial scale for data representations. The imaging data from MRI, rs-fMRI, and DTI are acquired at a voxel level. We utilize a popular neuranatomic parcellation of the brain, the Automated Anatomical Labeling (AAL) (Tzourio-Mazoyer et al., 2002) system, to define 90 brain regions. For MRI and rs-fMRI, we further refine the standard AAL parcellation by defining subregions to yield more homogeneous collections of voxels within subregions. This refinement of the AAL-90 parcellation uses a hierarchical clustering algorithm to subdivide each region based on a metric that combines distance, structural and functional connectivity, and tissue type to identify homogeneous subregions of the encompassing region. The resulting extended parcellation produces 290 subregions (AAL-290), with a given subregion falling entirely within a single AAL region. The regional parcellations appear in Figure 2.


Multimodal Imaging Signatures of Parkinson's Disease.

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

Depiction of AAL-90 parcellation and a hierarchical subparcellation with 290 brain regions. The subregions are constructed from resting state fMRI data of healthy controls (outside of the current sample) based on functional characteristics with anatomical constraints to keep subregions contiguous and bounded within a single region.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Depiction of AAL-90 parcellation and a hierarchical subparcellation with 290 brain regions. The subregions are constructed from resting state fMRI data of healthy controls (outside of the current sample) based on functional characteristics with anatomical constraints to keep subregions contiguous and bounded within a single region.
Mentions: The first step is to determine the spatial scale for data representations. The imaging data from MRI, rs-fMRI, and DTI are acquired at a voxel level. We utilize a popular neuranatomic parcellation of the brain, the Automated Anatomical Labeling (AAL) (Tzourio-Mazoyer et al., 2002) system, to define 90 brain regions. For MRI and rs-fMRI, we further refine the standard AAL parcellation by defining subregions to yield more homogeneous collections of voxels within subregions. This refinement of the AAL-90 parcellation uses a hierarchical clustering algorithm to subdivide each region based on a metric that combines distance, structural and functional connectivity, and tissue type to identify homogeneous subregions of the encompassing region. The resulting extended parcellation produces 290 subregions (AAL-290), with a given subregion falling entirely within a single AAL region. The regional parcellations appear in Figure 2.

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