- Email: [email protected]
A voxel-based morphometry comparison of the 3.0T ADNI-1 and ADNI-2 MPRAGE protocols
Poster Presentations: P3 amyloidosis and tau pathology, on the frontal lobe, brain metabolism possibly depends on additional factors rather than AD pathology. P3-084
A VOXEL-BASED MORPHOMETRY COMPARISON OF THE 3.0T ADNI-1 AND ADNI-2 MPRAGE PROTOCOLS
Simon Brunton1, Cerisse Gunasinghe1, Nigel Jones1, Matthew Kempton2, Eric Westman3, Andrew Simmons4, 1King’s College London, London, United Kingdom; 2Kings College London, London, United Kingdom; 3 Karolinska University, Stockholm, Sweden; 4King’s College London, London, United Kingdom. Contact e-mail: [email protected] Background: The Alzheimer’s Disease Neuroimaging Initiative 3.0T MRI image acquisition scheme changed between the original ADNI-1 grant and the two subsequent grants (ADNI-GO and ADNI-2). The aim of the current study was to investigate the compatibility of the 3.0T ADNI-1 and ADNI-2 T1-w volumes using voxel-based morphometry (VBM). Methods: 3D T1weighted MPRAGE images of 30 subjects(15-male mean age 32.2 years and 15-female mean age 25.1) were acquired on a 3T GE scanner using the following sequences: ADNI-1: Sagittal 3D-IR-FSPGR, 8-channel coil, TR¼650ms, TE¼min full, flip-angle¼8 o, slice thickness¼1.2mm, resolution¼256x256mm, FOV¼26cm. ADNI-2: Sagittal 3D-IR-SPGR, 8-channel coil, TR¼400ms, TE¼min full, flip-angle¼11 o, slice thickness¼1.2mm, resolution¼256x256mm, FOV¼26cm. Images were pre-processed and analysed using SPM8. We compared global grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF), as well as voxel-by-voxel differences in GM and WM. Results: Correlation coefficients and percentage differences for each tissue type between ADNI-1 and ADNI-2 were as follows: ((GM R 2 ¼0.78, ADNI-1 4.55% < ADNI-2) (WM R 2 ¼ 0.85, ADNI-1 3.41% > ADNI-2) (CSF R 2 ¼0.81, ADNI-1 0.34% > ADNI-GO)). ADNI-2: widespread increases in GM most notably in the cerebellum and pre-central gyrus, and localised decreases along the midline and temporal lobes. ADNI-1: widespread increases in WM, particularly in the cerebellum and pre-central gyrus, and localised decreases in the temporal gyrus. Conclusions: A widespread increase in GM and localised decrease in WM in ADNI-2 compared to ADNI-1 MPRAGE images suggests that the image acquisition protocols are not directly comparable. Total volumes of GM, WM and CSF also differed between the protocols in the following order of magnitude: GM > CSF > WM. This has implications for studies aiming to analyse images acquired using two different protocols using VBM. P3-085
HIPPOCAMPAL TEXTURE PREDICTS CONVERSION FROM MCI TO ALZHEIMER’S DISEASE 1
2
1
1 1
Lauge Sørensen , Akshay Pai , Christian Igel , Mads Nielsen , University of Copenhagen, Copenhagen, Denmark; 2Biomediq, Copenhagen, Denmark. Contact e-mail: [email protected] Background: The purpose of this study was to investigate whether baseline hippocampal MRI texture predicts conversion from MCI to AD after one year. Methods: A standardized subset of the ADNI database recently released by ADNI, comprising 169 normal controls (CTRL), 233 MCI, and 101 AD, was considered. The MCIs were further subdivided into AD converters (MCI-C, 41) and non-converts (MCI-NC, 192) after one year. Segmentations of the hippocampi obtained using cross-sectional FreeSurfer (v5.1.0) were used to define the region of interest (ROI) in each baseline 1.5T T1-weighted MRI scan. A texture-based marker that has demonstrated good diagnostic capabilities in a previous study was trained to separate CTRL from AD, and it was subsequently applied to score the MCI-Cs and the MCI-NCs. The hippocampal fraction (HF) defined as hippocampal volume divided by intracranial volume (ICV) was also computed based on the same ROI and on FreeSurfers estimate of ICV. Two markers were evaluated, hippocampal texture in isolation and a logistic regression model combining texture, HF, and age. Results were reported by ROC-analysis of MCI-C vs MCI-NC, testing for significance using DeLong, DeLong and Clark-Pearson’s test (P<0.05 was regarded as significant). Results: ROCcurves for prognosis of conversion for the two markers are shown in the Figure, and the corresponding AUCs were for texture in isolation 0.731
P581
(P<0.001) and for the combined marker 0.754 (P<0.001). Texture, HF, and age were all significant in the logistic regression model with the following P-values: 0.00001, 0.00166, and 0.00011. Conclusions: A novel texture-based MRI marker was able to predict conversion to AD after one year in MCI subjects, demonstrating that hippocampal MRI texture at baseline was related to future cognition. Combining texture with HF and age increased the prognostic accuracy while texture maintained the highest significance in the combined model. Texture may detect the summarized effect of several sub-voxel resolution events and may thereby precede structural changes, making it a promising marker for early detection of AD. Combining texture with other markers from MRI relying on structural information, such as HF, also has promising perspectives.
P3-086
AMYLOID HUBS IN INDIVIDUAL PiB-PET IMAGING
Jorge Sepulcre1, John Becker2, Reisa Sperling3, Keith Johnson2, 1Harvard Medical School, Boston, Massachusetts, United States; 2Massachusetts General Hospital, Boston, Massachusetts, United States; 3Brigham and Women’s Hospital, Boston, Massachusetts, United States. Contact e-mail: [email protected] Background: Much is known about regional brain atrophy in Alzheimer’s disease (AD), yet our knowledge about the network nature of ADassociated Ab accumulation is limited. In this study, we hypothesized that PIB binding during individual PET imaging acquisitions may hold information about temporo-spatial relationships between cerebral regions. We think that significant association between amyloid accumulations of distributed regions may point out to underlying temporal relationships. For instance, specific regions may predict the amyloid deposits of other regions in the brain. The aim of this study was to describe the amyloid hubs that are either affecting or being affected by other amyloid regions of the brain at the individual level. Methods: We used PIB-PET images from a cognitive normal sample of elderly controls (N¼159; age¼74.27) and a Granger causality strategy to study the forecasting properties of the PIB dynamical signal during individual acquisitions (Fig. 1-A). Granger causality test is used here for statistically determining whether time series of PIB signal in brain voxels are valuable in forecasting another PIB time series of the brain. Different lags were used in order to optimize the approach. For each subject, we computed two connectivity matrices: 1) one that includes all the Granger out-going associations and 2) another that includes all the Granger in-coming associations between voxels. Finally, we computed the degree of connectivity of each voxel in the brain by summing the out-going or in-coming associations. Results: We identified regions that accumulate a high number of associations in both, Granger out-going and Granger in-coming relationships. Amyloid hubs that influence the uptake
A VOXEL-BASED MORPHOMETRY COMPARISON OF THE 3.0T ADNI-1 AND ADNI-2 MPRAGE PROTOCOLS
Simon Brunton1, Cerisse Gunasinghe1, Nigel Jones1, Matthew Kempton2, Eric Westman3, Andrew Simmons4, 1King’s College London, London, United Kingdom; 2Kings College London, London, United Kingdom; 3 Karolinska University, Stockholm, Sweden; 4King’s College London, London, United Kingdom. Contact e-mail: [email protected] Background: The Alzheimer’s Disease Neuroimaging Initiative 3.0T MRI image acquisition scheme changed between the original ADNI-1 grant and the two subsequent grants (ADNI-GO and ADNI-2). The aim of the current study was to investigate the compatibility of the 3.0T ADNI-1 and ADNI-2 T1-w volumes using voxel-based morphometry (VBM). Methods: 3D T1weighted MPRAGE images of 30 subjects(15-male mean age 32.2 years and 15-female mean age 25.1) were acquired on a 3T GE scanner using the following sequences: ADNI-1: Sagittal 3D-IR-FSPGR, 8-channel coil, TR¼650ms, TE¼min full, flip-angle¼8 o, slice thickness¼1.2mm, resolution¼256x256mm, FOV¼26cm. ADNI-2: Sagittal 3D-IR-SPGR, 8-channel coil, TR¼400ms, TE¼min full, flip-angle¼11 o, slice thickness¼1.2mm, resolution¼256x256mm, FOV¼26cm. Images were pre-processed and analysed using SPM8. We compared global grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF), as well as voxel-by-voxel differences in GM and WM. Results: Correlation coefficients and percentage differences for each tissue type between ADNI-1 and ADNI-2 were as follows: ((GM R 2 ¼0.78, ADNI-1 4.55% < ADNI-2) (WM R 2 ¼ 0.85, ADNI-1 3.41% > ADNI-2) (CSF R 2 ¼0.81, ADNI-1 0.34% > ADNI-GO)). ADNI-2: widespread increases in GM most notably in the cerebellum and pre-central gyrus, and localised decreases along the midline and temporal lobes. ADNI-1: widespread increases in WM, particularly in the cerebellum and pre-central gyrus, and localised decreases in the temporal gyrus. Conclusions: A widespread increase in GM and localised decrease in WM in ADNI-2 compared to ADNI-1 MPRAGE images suggests that the image acquisition protocols are not directly comparable. Total volumes of GM, WM and CSF also differed between the protocols in the following order of magnitude: GM > CSF > WM. This has implications for studies aiming to analyse images acquired using two different protocols using VBM. P3-085
HIPPOCAMPAL TEXTURE PREDICTS CONVERSION FROM MCI TO ALZHEIMER’S DISEASE 1
2
1
1 1
Lauge Sørensen , Akshay Pai , Christian Igel , Mads Nielsen , University of Copenhagen, Copenhagen, Denmark; 2Biomediq, Copenhagen, Denmark. Contact e-mail: [email protected] Background: The purpose of this study was to investigate whether baseline hippocampal MRI texture predicts conversion from MCI to AD after one year. Methods: A standardized subset of the ADNI database recently released by ADNI, comprising 169 normal controls (CTRL), 233 MCI, and 101 AD, was considered. The MCIs were further subdivided into AD converters (MCI-C, 41) and non-converts (MCI-NC, 192) after one year. Segmentations of the hippocampi obtained using cross-sectional FreeSurfer (v5.1.0) were used to define the region of interest (ROI) in each baseline 1.5T T1-weighted MRI scan. A texture-based marker that has demonstrated good diagnostic capabilities in a previous study was trained to separate CTRL from AD, and it was subsequently applied to score the MCI-Cs and the MCI-NCs. The hippocampal fraction (HF) defined as hippocampal volume divided by intracranial volume (ICV) was also computed based on the same ROI and on FreeSurfers estimate of ICV. Two markers were evaluated, hippocampal texture in isolation and a logistic regression model combining texture, HF, and age. Results were reported by ROC-analysis of MCI-C vs MCI-NC, testing for significance using DeLong, DeLong and Clark-Pearson’s test (P<0.05 was regarded as significant). Results: ROCcurves for prognosis of conversion for the two markers are shown in the Figure, and the corresponding AUCs were for texture in isolation 0.731
P581
(P<0.001) and for the combined marker 0.754 (P<0.001). Texture, HF, and age were all significant in the logistic regression model with the following P-values: 0.00001, 0.00166, and 0.00011. Conclusions: A novel texture-based MRI marker was able to predict conversion to AD after one year in MCI subjects, demonstrating that hippocampal MRI texture at baseline was related to future cognition. Combining texture with HF and age increased the prognostic accuracy while texture maintained the highest significance in the combined model. Texture may detect the summarized effect of several sub-voxel resolution events and may thereby precede structural changes, making it a promising marker for early detection of AD. Combining texture with other markers from MRI relying on structural information, such as HF, also has promising perspectives.
P3-086
AMYLOID HUBS IN INDIVIDUAL PiB-PET IMAGING
Jorge Sepulcre1, John Becker2, Reisa Sperling3, Keith Johnson2, 1Harvard Medical School, Boston, Massachusetts, United States; 2Massachusetts General Hospital, Boston, Massachusetts, United States; 3Brigham and Women’s Hospital, Boston, Massachusetts, United States. Contact e-mail: [email protected] Background: Much is known about regional brain atrophy in Alzheimer’s disease (AD), yet our knowledge about the network nature of ADassociated Ab accumulation is limited. In this study, we hypothesized that PIB binding during individual PET imaging acquisitions may hold information about temporo-spatial relationships between cerebral regions. We think that significant association between amyloid accumulations of distributed regions may point out to underlying temporal relationships. For instance, specific regions may predict the amyloid deposits of other regions in the brain. The aim of this study was to describe the amyloid hubs that are either affecting or being affected by other amyloid regions of the brain at the individual level. Methods: We used PIB-PET images from a cognitive normal sample of elderly controls (N¼159; age¼74.27) and a Granger causality strategy to study the forecasting properties of the PIB dynamical signal during individual acquisitions (Fig. 1-A). Granger causality test is used here for statistically determining whether time series of PIB signal in brain voxels are valuable in forecasting another PIB time series of the brain. Different lags were used in order to optimize the approach. For each subject, we computed two connectivity matrices: 1) one that includes all the Granger out-going associations and 2) another that includes all the Granger in-coming associations between voxels. Finally, we computed the degree of connectivity of each voxel in the brain by summing the out-going or in-coming associations. Results: We identified regions that accumulate a high number of associations in both, Granger out-going and Granger in-coming relationships. Amyloid hubs that influence the uptake