Temporal trajectory and progression score estimation from voxelwise longitudinal imaging measures: Application to amyloid imaging

Murat Bilgel, Bruno Jedynak, Dean Foster Wong, Susan M. Resnick, Jerry Ladd Prince

Research output: Contribution to journalArticle

Abstract

Cortical β-amyloid deposition begins in Alzheimer’s disease (AD) years before the onset of any clinical symptoms. It is therefore important to determine the temporal trajectories of amyloid deposition in these earliest stages in order to better understand their associations with progression to AD. A method for estimating the temporal trajectories of voxelwise amyloid as measured using longitudinal positron emission tomography (PET) imaging is presented. The method involves the estimation of a score for each subject visit based on the PET data that reflects their amyloid progression. This amyloid progression score allows subjects with similar progressions to be aligned and analyzed together. The estimation of the progression scores and the amyloid trajectory parameters are performed using an expectation-maximization algorithm. The correlations among the voxel measures of amyloid are modeled to reflect the spatial nature of PET images. Simulation results show that model parameters are captured well at a variety of noise and spatial correlation levels. The method is applied to longitudinal amyloid imaging data considering each cerebral hemisphere separately. The results are consistent across the hemispheres and agree with a global index of brain amyloid known as mean cortical DVR. Unlike mean cortical DVR, which depends on a priori defined regions, the progression score extracted by the method is data-driven and does not make assumptions about regional longitudinal changes. Compared to regressing on age at each voxel, the longitudinal trajectory slopes estimated using the proposed method show better localized longitudinal changes.

Fingerprint

Progression
Trajectories
Imaging
Trajectory
Imaging techniques
Positron Emission Tomography
Positron emission tomography
Alzheimer's Disease
Hemisphere
Voxel
Expectation-maximization Algorithm
Spatial Correlation
Data-driven
Amyloid
Slope
Brain
Simulation

Keywords

  • Amyloid
  • Longitudinal image analysis
  • PiB
  • Pittsburgh compound B
  • Progression score

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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title = "Temporal trajectory and progression score estimation from voxelwise longitudinal imaging measures: Application to amyloid imaging",
abstract = "Cortical β-amyloid deposition begins in Alzheimer’s disease (AD) years before the onset of any clinical symptoms. It is therefore important to determine the temporal trajectories of amyloid deposition in these earliest stages in order to better understand their associations with progression to AD. A method for estimating the temporal trajectories of voxelwise amyloid as measured using longitudinal positron emission tomography (PET) imaging is presented. The method involves the estimation of a score for each subject visit based on the PET data that reflects their amyloid progression. This amyloid progression score allows subjects with similar progressions to be aligned and analyzed together. The estimation of the progression scores and the amyloid trajectory parameters are performed using an expectation-maximization algorithm. The correlations among the voxel measures of amyloid are modeled to reflect the spatial nature of PET images. Simulation results show that model parameters are captured well at a variety of noise and spatial correlation levels. The method is applied to longitudinal amyloid imaging data considering each cerebral hemisphere separately. The results are consistent across the hemispheres and agree with a global index of brain amyloid known as mean cortical DVR. Unlike mean cortical DVR, which depends on a priori defined regions, the progression score extracted by the method is data-driven and does not make assumptions about regional longitudinal changes. Compared to regressing on age at each voxel, the longitudinal trajectory slopes estimated using the proposed method show better localized longitudinal changes.",
keywords = "Amyloid, Longitudinal image analysis, PiB, Pittsburgh compound B, Progression score",
author = "Murat Bilgel and Bruno Jedynak and Wong, {Dean Foster} and Resnick, {Susan M.} and Prince, {Jerry Ladd}",
year = "2015",
doi = "10.1007/978-3-319-19992-4_33",
language = "English (US)",
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pages = "424--436",
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T1 - Temporal trajectory and progression score estimation from voxelwise longitudinal imaging measures

T2 - Application to amyloid imaging

AU - Bilgel, Murat

AU - Jedynak, Bruno

AU - Wong, Dean Foster

AU - Resnick, Susan M.

AU - Prince, Jerry Ladd

PY - 2015

Y1 - 2015

N2 - Cortical β-amyloid deposition begins in Alzheimer’s disease (AD) years before the onset of any clinical symptoms. It is therefore important to determine the temporal trajectories of amyloid deposition in these earliest stages in order to better understand their associations with progression to AD. A method for estimating the temporal trajectories of voxelwise amyloid as measured using longitudinal positron emission tomography (PET) imaging is presented. The method involves the estimation of a score for each subject visit based on the PET data that reflects their amyloid progression. This amyloid progression score allows subjects with similar progressions to be aligned and analyzed together. The estimation of the progression scores and the amyloid trajectory parameters are performed using an expectation-maximization algorithm. The correlations among the voxel measures of amyloid are modeled to reflect the spatial nature of PET images. Simulation results show that model parameters are captured well at a variety of noise and spatial correlation levels. The method is applied to longitudinal amyloid imaging data considering each cerebral hemisphere separately. The results are consistent across the hemispheres and agree with a global index of brain amyloid known as mean cortical DVR. Unlike mean cortical DVR, which depends on a priori defined regions, the progression score extracted by the method is data-driven and does not make assumptions about regional longitudinal changes. Compared to regressing on age at each voxel, the longitudinal trajectory slopes estimated using the proposed method show better localized longitudinal changes.

AB - Cortical β-amyloid deposition begins in Alzheimer’s disease (AD) years before the onset of any clinical symptoms. It is therefore important to determine the temporal trajectories of amyloid deposition in these earliest stages in order to better understand their associations with progression to AD. A method for estimating the temporal trajectories of voxelwise amyloid as measured using longitudinal positron emission tomography (PET) imaging is presented. The method involves the estimation of a score for each subject visit based on the PET data that reflects their amyloid progression. This amyloid progression score allows subjects with similar progressions to be aligned and analyzed together. The estimation of the progression scores and the amyloid trajectory parameters are performed using an expectation-maximization algorithm. The correlations among the voxel measures of amyloid are modeled to reflect the spatial nature of PET images. Simulation results show that model parameters are captured well at a variety of noise and spatial correlation levels. The method is applied to longitudinal amyloid imaging data considering each cerebral hemisphere separately. The results are consistent across the hemispheres and agree with a global index of brain amyloid known as mean cortical DVR. Unlike mean cortical DVR, which depends on a priori defined regions, the progression score extracted by the method is data-driven and does not make assumptions about regional longitudinal changes. Compared to regressing on age at each voxel, the longitudinal trajectory slopes estimated using the proposed method show better localized longitudinal changes.

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