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Relationship between GM atrophy and WM disintegration in MRI of Alzheimer's disease patients
P346
Poster Presentations: P2
of this work was to develop a fully automated method to quantify the change in MTL volume over time that maximizes measurement precision at the expense of anatomical accuracy. Methods: The Expectation Maximization (EM) segmentation is a model-based classification method used to define gray matter, white matter, and cerebrospinal fluid in baseline images. An atlas-based registration method was then used to define the MTL region of interest (ROI) on the gray matter pixels. A seeded region-growing algorithm was applied to the MTL ROI to smooth strongly-visible boundaries. Baseline images were registered to 24 months follow-up images using a deformable registration algorithm to propagate the MTL ROI defined on the baseline images and measure the change in volume. T1-weighted MP-RAGE MRI images acquired at baseline and at 24 months in 24 normal elderly subjects, 25 subjects with mild cognitive impairment (MCI) and 25 subjects with AD were randomly selected from the A lzheimer Disease Neuroimaging Initiative (ADNI) database to test the algorithm. The accuracy and precision were determined using 100 volumetric image sets of a cube phantom with known volume. Results: The known volume (mean 6 STD) of the cube phantom was underestimated by 3% 6 0.04%. The repeated measures t- test showed that there was a significant decrease (p <0.0001) in MTL volume in AD subjects. The MTL volume changes between baseline and 24 months (Mean6SEM, 75 6 46 mm 3 in the normal elderly, 45 6 38 mm 3 in subjects with MCI, and 242 6 43 mm 3 in subjects with AD) were significantly different between groups (P <0.01). The Tukey post-hoc analysis showed a significant difference between AD and normal elderly (P <0.05); AD and MCI subjects (P <0.01). Conclusions: A fully automated segmentation method to measure MTL volume changes detected increased atrophy in the MTL in subjects with AD compared to MCI and control subjects.
P2-243
RELATIONSHIP BETWEEN GM ATROPHY AND WM DISINTEGRATION IN MRI OF ALZHEIMER’S DISEASE PATIENTS
Rahyeong Juh, Heeyoung Kim, Un Jung Cho, Jae Hong Lee, Seong Yoon Kim, Asan Medical Center, Seoul, South Korea. Background: Neuronal loss seems to be the core pathology of Alzheimer’s disease (AD) brain, but white matter tract disintegration is also a frequently observed change. We evaluated the relationship between white matter (WM) tract disintegration and gray matter (GM) atrophy in patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI) and controls, using diffusion tensor imaging (DTI) and an optimized voxel-based analysis. Methods: Two hundred thirty one individuals (61 controls, 116 MCI and 54 AD) were included. Voxel-based WM tract statistics were used to obtain whole-brain maps of WM bundles for FA. Voxel-based morphometry (VBM) was conducted to detect regions of gray matter (GM) atrophy in the AD, MCI group relative to the control group. FA maps were processed to make voxel-wise comparison of tract based analysis in whole brain between each the two groups. The relationship between locations of abnormalities in the WM and GM were examined. Results: Patients with AD showed significant GM atrophy at the posterior cingulate gyrus (BA31, 32), the precuneus, the middle temporal lobe (BA19), the superior frontal (BA9) to the anterior cingulate (BA 32), the medial frontal lobe (BA 11, BA25), the hippocampus, the parahippocampal gyrus (BA30/34) and the insula area. The WM tracts of AD subjects were more disintegrated in the uncinate fasciculus, posterior cingulate fasciculus and fornix area compared with the control and MCI groups. These abnormalities in the AD group were explained by either structural atrophy in GM or neural dysfunction related with functional disconnections in the WM tracts. Conclusions: Even though the areas of GM atrophy and WM disintegration show high topographical correlation, it is still not certain which process is primary and more fundamental in AD progress. Comparison between AD and MCI subjects who later convert to AD would clarify this GM-WM relationship more clearly. Using tract based spatial statistics and voxel based analysis, are useful neuroimaging analysis tool investigating GM and WM changes in neurodegenerative disorders like AD.
P2-244
DIAGNOSING MILD COGNITIVE IMPAIRMENT DUE TO ALZHEIMER’S DISEASE USING BIOMARKERS OF BETA-AMYLOID PROTEIN AND NEURONAL INJURY
Shizuo Hatashita, Hidetomo Yamasaki, Shonan-Atsugi Hospital, Atsugi, Japan. Background: We have recently demonstrated that a diagnostic framework with a Ab deposition by [11C]-PIB PET allows for an earlier and more specific AD diagnosis. This study is to define “mild cognitive impairment (MCI) due to Alzheimer’s disease (AD)” using beta-amyloid protein (Ab) and neuronal injury as biomarkers. Furthermore, we sought to determine whether episodic memory impairment, age and apolipoprotein-E (APOE) genotype have an effect on progression from MCI to AD dementia. Methods: Fifty-six MCI patients underwent cognitive testing, 60-min dynamic [11C]-PIB PET and 20-min static [18F]-FDG PET at baseline, 12 months and 24 months, and APOE genotype assessment at baseline. Regions of interest were defined on co-registered MRI. PIB distribution volume ratios (DVR) were calculated using Logan graphical analysis, and quantitative analysis for [18F]-FDG used the standardized uptake value ratio (SUVR) on the same regions. Results: Twenty-eight (50%) of all 56 MCI patients (MMSE: 27.0 6 1.6, CDR: 0.5, CDR SB: 0.8 6 0.3) converted to AD (MMSE: 22.7 6 2.3, CDR: 0.6 6 0.2, CDR SB: 2.7 6 1.0) over 17.5 6 8.0 months, whereas 28 (63.6%) of 44 MCI patients with positive Ab biomarker (DVR1.49) converted. In addition, for 43 MCI patients with both positive biomarkers of Ab and neuronal injury (SUVR0.99), the rate of conversion was 65.1%. In contrast, of 39 MCI patients with impaired paragraph delayed recall (WMS-R Logical Memory II) and both positive biomarkers, 27 (69.2%) converted to AD. In 14 MCI patients aged 75-89 years with impaired paragraph recall and both positive biomarkers, the rate of converters increased to 84.6% compared to 61.9% of 21 MCI patients aged 65-74 years. All (100%) of 4 APOE E4/4 carriers with positive Ab biomarker converted to AD while 52.6% of the 19 APOE E4/3 carriers converted. All of 7 MCI patients with negative biomarkers of both Ab and neuronal injury did not convert to AD. Conclusions: A biomarker of Ab, in addition to neuronal injury, is most important to accurately diagnose “MCI due to AD.” Furthermore, MCI in these individuals who have APOE e4/4 allele or are older than 75 years old is more likely to convert to AD dementia.
P2-245
DYNAMIC BIOMARKER MODEL IN ALZHEIMER’S DISEASE: LONGITUDINAL ANALYSIS OF HIPPOCAMPAL VOLUME SHOWS LINEAR DECLINE
Abderazzak Mouiha, Simon Duchesne, Institut Universitaire de Sante Mentale de Quebec, Quebec, Quebec, Canada. Background: Statistical analysis of longitudinal biomarker data is necessary to prove or disprove hypotheses regarding Alzheimer’s disease (AD) biomarker trajectories in preclinical to clinical disease stages. The most prevalent hypothesis, as proposed in Jack et al. (Lancet Neurol., 2010), is for a sigmoidal relationship between biomarkers and disease severity. Preliminary evidence from models based on baseline ADNI data (Caroli et al., Neurobiol. Aging, 2010; Mouiha et al., J. Alz. Dis., 2012) have not confirmed this hypothesis for major AD biomarkers. The objective of this study was to investigate the longitudinal shape of this association between a well-known structural biomarker of AD and disease severity. Methods: We selected 135 mild cognitive impairment (MCI) subjects from the ADNI dataset (49 females, 86 males) who converted to probable AD within 36 months. Left and right h ippocampal volumes (HC) were measured using FreeSurfer software at every six months from 1.5T v olumetric MP-RAGE MR scans. For modeling purposes we used the average of left and right HC volumes. An analysis for repeated measures was used to compare volumes and MMSE between time points. All subjects were ordered based on their score on the Mini-Mental State Examination (MMSE) as a surrogate marker
Poster Presentations: P2
of this work was to develop a fully automated method to quantify the change in MTL volume over time that maximizes measurement precision at the expense of anatomical accuracy. Methods: The Expectation Maximization (EM) segmentation is a model-based classification method used to define gray matter, white matter, and cerebrospinal fluid in baseline images. An atlas-based registration method was then used to define the MTL region of interest (ROI) on the gray matter pixels. A seeded region-growing algorithm was applied to the MTL ROI to smooth strongly-visible boundaries. Baseline images were registered to 24 months follow-up images using a deformable registration algorithm to propagate the MTL ROI defined on the baseline images and measure the change in volume. T1-weighted MP-RAGE MRI images acquired at baseline and at 24 months in 24 normal elderly subjects, 25 subjects with mild cognitive impairment (MCI) and 25 subjects with AD were randomly selected from the A lzheimer Disease Neuroimaging Initiative (ADNI) database to test the algorithm. The accuracy and precision were determined using 100 volumetric image sets of a cube phantom with known volume. Results: The known volume (mean 6 STD) of the cube phantom was underestimated by 3% 6 0.04%. The repeated measures t- test showed that there was a significant decrease (p <0.0001) in MTL volume in AD subjects. The MTL volume changes between baseline and 24 months (Mean6SEM, 75 6 46 mm 3 in the normal elderly, 45 6 38 mm 3 in subjects with MCI, and 242 6 43 mm 3 in subjects with AD) were significantly different between groups (P <0.01). The Tukey post-hoc analysis showed a significant difference between AD and normal elderly (P <0.05); AD and MCI subjects (P <0.01). Conclusions: A fully automated segmentation method to measure MTL volume changes detected increased atrophy in the MTL in subjects with AD compared to MCI and control subjects.
P2-243
RELATIONSHIP BETWEEN GM ATROPHY AND WM DISINTEGRATION IN MRI OF ALZHEIMER’S DISEASE PATIENTS
Rahyeong Juh, Heeyoung Kim, Un Jung Cho, Jae Hong Lee, Seong Yoon Kim, Asan Medical Center, Seoul, South Korea. Background: Neuronal loss seems to be the core pathology of Alzheimer’s disease (AD) brain, but white matter tract disintegration is also a frequently observed change. We evaluated the relationship between white matter (WM) tract disintegration and gray matter (GM) atrophy in patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI) and controls, using diffusion tensor imaging (DTI) and an optimized voxel-based analysis. Methods: Two hundred thirty one individuals (61 controls, 116 MCI and 54 AD) were included. Voxel-based WM tract statistics were used to obtain whole-brain maps of WM bundles for FA. Voxel-based morphometry (VBM) was conducted to detect regions of gray matter (GM) atrophy in the AD, MCI group relative to the control group. FA maps were processed to make voxel-wise comparison of tract based analysis in whole brain between each the two groups. The relationship between locations of abnormalities in the WM and GM were examined. Results: Patients with AD showed significant GM atrophy at the posterior cingulate gyrus (BA31, 32), the precuneus, the middle temporal lobe (BA19), the superior frontal (BA9) to the anterior cingulate (BA 32), the medial frontal lobe (BA 11, BA25), the hippocampus, the parahippocampal gyrus (BA30/34) and the insula area. The WM tracts of AD subjects were more disintegrated in the uncinate fasciculus, posterior cingulate fasciculus and fornix area compared with the control and MCI groups. These abnormalities in the AD group were explained by either structural atrophy in GM or neural dysfunction related with functional disconnections in the WM tracts. Conclusions: Even though the areas of GM atrophy and WM disintegration show high topographical correlation, it is still not certain which process is primary and more fundamental in AD progress. Comparison between AD and MCI subjects who later convert to AD would clarify this GM-WM relationship more clearly. Using tract based spatial statistics and voxel based analysis, are useful neuroimaging analysis tool investigating GM and WM changes in neurodegenerative disorders like AD.
P2-244
DIAGNOSING MILD COGNITIVE IMPAIRMENT DUE TO ALZHEIMER’S DISEASE USING BIOMARKERS OF BETA-AMYLOID PROTEIN AND NEURONAL INJURY
Shizuo Hatashita, Hidetomo Yamasaki, Shonan-Atsugi Hospital, Atsugi, Japan. Background: We have recently demonstrated that a diagnostic framework with a Ab deposition by [11C]-PIB PET allows for an earlier and more specific AD diagnosis. This study is to define “mild cognitive impairment (MCI) due to Alzheimer’s disease (AD)” using beta-amyloid protein (Ab) and neuronal injury as biomarkers. Furthermore, we sought to determine whether episodic memory impairment, age and apolipoprotein-E (APOE) genotype have an effect on progression from MCI to AD dementia. Methods: Fifty-six MCI patients underwent cognitive testing, 60-min dynamic [11C]-PIB PET and 20-min static [18F]-FDG PET at baseline, 12 months and 24 months, and APOE genotype assessment at baseline. Regions of interest were defined on co-registered MRI. PIB distribution volume ratios (DVR) were calculated using Logan graphical analysis, and quantitative analysis for [18F]-FDG used the standardized uptake value ratio (SUVR) on the same regions. Results: Twenty-eight (50%) of all 56 MCI patients (MMSE: 27.0 6 1.6, CDR: 0.5, CDR SB: 0.8 6 0.3) converted to AD (MMSE: 22.7 6 2.3, CDR: 0.6 6 0.2, CDR SB: 2.7 6 1.0) over 17.5 6 8.0 months, whereas 28 (63.6%) of 44 MCI patients with positive Ab biomarker (DVR1.49) converted. In addition, for 43 MCI patients with both positive biomarkers of Ab and neuronal injury (SUVR0.99), the rate of conversion was 65.1%. In contrast, of 39 MCI patients with impaired paragraph delayed recall (WMS-R Logical Memory II) and both positive biomarkers, 27 (69.2%) converted to AD. In 14 MCI patients aged 75-89 years with impaired paragraph recall and both positive biomarkers, the rate of converters increased to 84.6% compared to 61.9% of 21 MCI patients aged 65-74 years. All (100%) of 4 APOE E4/4 carriers with positive Ab biomarker converted to AD while 52.6% of the 19 APOE E4/3 carriers converted. All of 7 MCI patients with negative biomarkers of both Ab and neuronal injury did not convert to AD. Conclusions: A biomarker of Ab, in addition to neuronal injury, is most important to accurately diagnose “MCI due to AD.” Furthermore, MCI in these individuals who have APOE e4/4 allele or are older than 75 years old is more likely to convert to AD dementia.
P2-245
DYNAMIC BIOMARKER MODEL IN ALZHEIMER’S DISEASE: LONGITUDINAL ANALYSIS OF HIPPOCAMPAL VOLUME SHOWS LINEAR DECLINE
Abderazzak Mouiha, Simon Duchesne, Institut Universitaire de Sante Mentale de Quebec, Quebec, Quebec, Canada. Background: Statistical analysis of longitudinal biomarker data is necessary to prove or disprove hypotheses regarding Alzheimer’s disease (AD) biomarker trajectories in preclinical to clinical disease stages. The most prevalent hypothesis, as proposed in Jack et al. (Lancet Neurol., 2010), is for a sigmoidal relationship between biomarkers and disease severity. Preliminary evidence from models based on baseline ADNI data (Caroli et al., Neurobiol. Aging, 2010; Mouiha et al., J. Alz. Dis., 2012) have not confirmed this hypothesis for major AD biomarkers. The objective of this study was to investigate the longitudinal shape of this association between a well-known structural biomarker of AD and disease severity. Methods: We selected 135 mild cognitive impairment (MCI) subjects from the ADNI dataset (49 females, 86 males) who converted to probable AD within 36 months. Left and right h ippocampal volumes (HC) were measured using FreeSurfer software at every six months from 1.5T v olumetric MP-RAGE MR scans. For modeling purposes we used the average of left and right HC volumes. An analysis for repeated measures was used to compare volumes and MMSE between time points. All subjects were ordered based on their score on the Mini-Mental State Examination (MMSE) as a surrogate marker