- Email: [email protected]
Combination of predictors to classify cognitive decline in individual subjects with mild cognitive impairment
S298 P1-419
Poster Presentations P1 COMBINATION OF PREDICTORS TO CLASSIFY COGNITIVE DECLINE IN INDIVIDUAL SUBJECTS WITH MILD COGNITIVE IMPAIRMENT
Frank Thiele1,2, Laura Yee3, Fabian Wenzel4, Xiao-Hua Zhou3, Satoshi Minoshima2, 1Philips Research North America, Briarcliff Manor, NY, USA; 2Radiology, University of Washington, Seattle, WA, USA; 3Biostatistics, University of Washington, Seattle, WA, USA; 4Philips Research, Hamburg, Germany. Contact e-mail: [email protected] Background: The combination of biomarkers and risk factors has been shown to improve the prediction of cognitive decline in mild cognitive impairment (MCI) (Devanand et al, 2008). Most studies have relied on logistic regression (LR) to combine predictors. However, multivariate LR has known shortcomings if predictors are correlated and if sample sizes are small. The objective of this study was to investigate partial least squares (PLS) regression as an alternative to LR for combining predictors of cognitive decline in individual subjects with MCI. Methods: 140 MCI subjects with MMSE25, FDG PET at baseline and 24 months follow-up were included from the Alzheimer’s Disease Neuroimaging Initiative ADNI (age ¼ 75 6 7y, MMSE ¼ 27.5 6 1.4). Subjects were divided into two categories: Cognitively ‘stable’ (MMSE25 after 24 months, n ¼ 99), and ‘progressive’ (MMSE<25 at follow-up, n ¼ 41). The following measures at baseline were considered as predictors a priori: 4 FDG PET regional values, MMSE, ADAS-cog, APOE4 status, age, gender, and education. Observerindependent classifications of ‘stable’ vs ‘progressive’ were obtained using LR and PLS for various combinations of predictors. Leave-one-out cross validation was performed to obtain unbiased measures of accuracies. Results: For single predictors, PLS classification of stable vs progressive MCI yielded an area under the ROC curve (AUC) between 0.55(frontal region FDG) and 0.80(ADAS-cog). AUC of LR was on average 0.04 (7%) lower. Age, gender, and APOE4 were not included in these results as they were not predictive individually (AUC<0.5). For all 13 tested combinations of predictors, AUC of LR was lower than that of PLS, on average 0.02 (3%). Combining all 10 predictors gave AUC of 0.84 for PLS and 0.82 for LR, with maximum classification accuracies of 84% and 81%, respectively. For this combination, LR found MMSE, ADAS-cog, and posterior cingulate hypometabolism most significant (p < 0.05). MMSE, ADAS-cog, and parietal hypometabolism were most discriminative with PLS. Conclusions: Combination of several predictors improves individual classification of stable vs progressive MCI. PLS consistently showed slightly better discrimination than logistic regression. Next to MMSE and ADAS-cog, hypometabolism in brain regions known to be affected by Alzheimer’s Disease were most predictive of cognitive decline. P1-420
LONGITUDINAL CHANGES IN AMYLOID DEPOSITION IN NORMAL ELDERLY, MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE
William E. Klunk1, Ann Cohen1, Julie Price1, Chet Mathis1, Robert Nebes1, Judith Saxton1, Beth Snitz1, Howard Aizenstein1, Lisa Weissfeld1, Steven DeKosky2, 1University of Pittsburgh, Pittsburgh, PA, USA; 2University of Virginia, Charlottesville, VA, USA. Contact e-mail: klunkwe@ upmc.edu Background: Postmortem studies of amyloid deposition have shown variations in amyloid load at different levels of severity. In vivo amyloid imaging opens an opportunity to follow the deposition of amyloid within individuals over time. Our goal is to determine the change in amyloid deposition over time in normal controls (NC), Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). Methods: 22 subjects (6-AD, 7NC, 9-MCI) received two PiB-PET scans (15mCi, 40-60min; ECAT HR+) within 28 days to determine test-retest variability. The changes in these subjects were compared to that in 55 NC [24 (44%) PiB(+)], 27 MCI [20 (74%) PiB(+)] and 15 AD patients [100% PiB(+)]imaged 1 or 2 years after baseline. Tissue ratios were calculated for regions-ofinterest (ROIs) in anterior cingulate, frontal, lateral temporal, parietal, and precuneus cortex and a global ROI composed of these 5 ROIs.
Data was normalized to cerebellum after atrophy correction (SUVR). The delta-SUVR (follow-up minus baseline) was calculated. Results: Test-retest variability was -0.001 6 0.14 SUVR units. An increase above 1.645 standard deviations (0.1431.645 ¼ 0.23 SUVR units) in any ROI was defined as a significant increase (p < 0.05). Subjects were divided into those PiB(+) (at their most recent scan) and those ‘‘stably’’ PiB(-) during the study. Of the PiB(+) group, 14/24 (58%) NC; 11/20 (55%) MCI; and 9/15 (60%) AD subjects showed significant increases in PiB retention. Of the stably PiB(-) group, 4/31 (13%) NC and 2/7 (29%) MCI increased [no AD was PiB(-)]. 8 NC and 4 MCI converted from PiB(-) to PiB(+) during the study. No subject reverted from PiB(+) to PiB(-). About 2/3 of all subjects who showed a significant increase in PiB retention in any ROI also showed a global increase. The mean increase in global SUVR across all subjects over 1-2 years was 0.09 6 0.16 (p ¼ 0.014) for NC, 0.15 6 0.21 (p ¼ 0.005) for MCI and 0.14 6 0.18 (p ¼ 0.02) for AD. Conclusions: A progressive accumulation of Ab deposits was detectable with PiB-PET in 58% of PiB(+), but only 16% of stably PiB(-) subjects. The frequency and degree of increased PiB retention was similar across diagnoses. Information regarding the natural history of amyloid deposition is critical to the interpretation of the effects of anti-amyloid therapies. P1-421
TOPOGRAPHIC EXTENT OF CEREBRAL HYPOMETABOLISM PREDICTS TIME OF CONVERSION FROM AMCI TO ALZHEIMER’S DISEASE: DATA FROM THE ALZHEIMER’S DISEASE NEUROIMAGING INITIATIVE
Norman L. Foster1, Angela Y. Wang1, P. Thomas Fletcher1, Sarang Joshi1, Satoshi Minoshima2, William J. Jagust3, Kewei Chen4, Eric M. Reiman4, Michael W. Weiner5, 1University of Utah, Salt Lake City, UT, USA; 2University of Washington, Seattle, WA, USA; 3University of California, Berkeley, Berkeley, CA, USA; 4Banner Alzheimer’s Institute, Phoenix, AZ, USA; 5 University of California, San Francisco, San Francisco, CA, USA. Contact e-mail: [email protected] Background: Clinical trials to prevent Alzheimer’s disease (AD) would be more efficient with a biomarker that could determine whether and when dementia is likely to develop in subjects with memory loss but no functional impairment. Hypometabolism measured with fluorodeoxyglucose positron emission tomography (FDG-PET) begins before deficits are apparent and becomes more extensive as cognitive deficits evolve from amnestic mild cognitive impairment (aMCI) to AD. Consequently, quantitative FDG-PET measures may help identify patients with prodromal AD who are likely to become demented over the brief course of a clinical trial. Objective: Examine the relationship between the topographic extent of glucose hypometabolism and timing of conversion to AD in subjects with aMCI. Methods: We evaluated all 161 baseline FDG-PET scans in subjects with clinically diagnosed aMCI followed for at least 1 year as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Each scan was analyzed with Neurostat, providing 3D-stereotactic surface projection (3D-SSP) maps of glucose metabolism relative to pons and corresponding Z-score maps compared to 27 cognitively normal subjects (14 men, 13 women, mean age 69.6 6 7.7). The number of pixels with metabolism 3 or more standard deviations below normal subjects was compared to the clinical judgment, independent of imaging results, of progression to AD at 6 and 12 months. Results: 14.3% of aMCI subjects (14 men, 9 women, mean age 75.3 6 6.5) converted from MCI to AD over 12 months. Gender and age did not differ from those who continued to have aMCI (92 men, 46 women, mean age 75.4 6 6.5). FDG-PET scans in converters had significantly more hypometabolic pixels (511 6 627 vs. 168 6 283, p < 0.02). The extent of hypometabolism was greater in the 5 who converted after 6 months (1128 6 1000 pixels) than the 18 that converted after 12 months (340 6 363 pixels). Conclusions: The topographic extent of glucose hypometabolism on FDG-PET predicts the likelihood of conversion to AD in aMCI subjects. More extensive hypometabolism indicates conversion will occur sooner. Outcomes would be more predictable and clinical trials of prodromal AD more efficient using FDG-PET for subject selection.
Poster Presentations P1 COMBINATION OF PREDICTORS TO CLASSIFY COGNITIVE DECLINE IN INDIVIDUAL SUBJECTS WITH MILD COGNITIVE IMPAIRMENT
Frank Thiele1,2, Laura Yee3, Fabian Wenzel4, Xiao-Hua Zhou3, Satoshi Minoshima2, 1Philips Research North America, Briarcliff Manor, NY, USA; 2Radiology, University of Washington, Seattle, WA, USA; 3Biostatistics, University of Washington, Seattle, WA, USA; 4Philips Research, Hamburg, Germany. Contact e-mail: [email protected] Background: The combination of biomarkers and risk factors has been shown to improve the prediction of cognitive decline in mild cognitive impairment (MCI) (Devanand et al, 2008). Most studies have relied on logistic regression (LR) to combine predictors. However, multivariate LR has known shortcomings if predictors are correlated and if sample sizes are small. The objective of this study was to investigate partial least squares (PLS) regression as an alternative to LR for combining predictors of cognitive decline in individual subjects with MCI. Methods: 140 MCI subjects with MMSE25, FDG PET at baseline and 24 months follow-up were included from the Alzheimer’s Disease Neuroimaging Initiative ADNI (age ¼ 75 6 7y, MMSE ¼ 27.5 6 1.4). Subjects were divided into two categories: Cognitively ‘stable’ (MMSE25 after 24 months, n ¼ 99), and ‘progressive’ (MMSE<25 at follow-up, n ¼ 41). The following measures at baseline were considered as predictors a priori: 4 FDG PET regional values, MMSE, ADAS-cog, APOE4 status, age, gender, and education. Observerindependent classifications of ‘stable’ vs ‘progressive’ were obtained using LR and PLS for various combinations of predictors. Leave-one-out cross validation was performed to obtain unbiased measures of accuracies. Results: For single predictors, PLS classification of stable vs progressive MCI yielded an area under the ROC curve (AUC) between 0.55(frontal region FDG) and 0.80(ADAS-cog). AUC of LR was on average 0.04 (7%) lower. Age, gender, and APOE4 were not included in these results as they were not predictive individually (AUC<0.5). For all 13 tested combinations of predictors, AUC of LR was lower than that of PLS, on average 0.02 (3%). Combining all 10 predictors gave AUC of 0.84 for PLS and 0.82 for LR, with maximum classification accuracies of 84% and 81%, respectively. For this combination, LR found MMSE, ADAS-cog, and posterior cingulate hypometabolism most significant (p < 0.05). MMSE, ADAS-cog, and parietal hypometabolism were most discriminative with PLS. Conclusions: Combination of several predictors improves individual classification of stable vs progressive MCI. PLS consistently showed slightly better discrimination than logistic regression. Next to MMSE and ADAS-cog, hypometabolism in brain regions known to be affected by Alzheimer’s Disease were most predictive of cognitive decline. P1-420
LONGITUDINAL CHANGES IN AMYLOID DEPOSITION IN NORMAL ELDERLY, MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE
William E. Klunk1, Ann Cohen1, Julie Price1, Chet Mathis1, Robert Nebes1, Judith Saxton1, Beth Snitz1, Howard Aizenstein1, Lisa Weissfeld1, Steven DeKosky2, 1University of Pittsburgh, Pittsburgh, PA, USA; 2University of Virginia, Charlottesville, VA, USA. Contact e-mail: klunkwe@ upmc.edu Background: Postmortem studies of amyloid deposition have shown variations in amyloid load at different levels of severity. In vivo amyloid imaging opens an opportunity to follow the deposition of amyloid within individuals over time. Our goal is to determine the change in amyloid deposition over time in normal controls (NC), Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). Methods: 22 subjects (6-AD, 7NC, 9-MCI) received two PiB-PET scans (15mCi, 40-60min; ECAT HR+) within 28 days to determine test-retest variability. The changes in these subjects were compared to that in 55 NC [24 (44%) PiB(+)], 27 MCI [20 (74%) PiB(+)] and 15 AD patients [100% PiB(+)]imaged 1 or 2 years after baseline. Tissue ratios were calculated for regions-ofinterest (ROIs) in anterior cingulate, frontal, lateral temporal, parietal, and precuneus cortex and a global ROI composed of these 5 ROIs.
Data was normalized to cerebellum after atrophy correction (SUVR). The delta-SUVR (follow-up minus baseline) was calculated. Results: Test-retest variability was -0.001 6 0.14 SUVR units. An increase above 1.645 standard deviations (0.1431.645 ¼ 0.23 SUVR units) in any ROI was defined as a significant increase (p < 0.05). Subjects were divided into those PiB(+) (at their most recent scan) and those ‘‘stably’’ PiB(-) during the study. Of the PiB(+) group, 14/24 (58%) NC; 11/20 (55%) MCI; and 9/15 (60%) AD subjects showed significant increases in PiB retention. Of the stably PiB(-) group, 4/31 (13%) NC and 2/7 (29%) MCI increased [no AD was PiB(-)]. 8 NC and 4 MCI converted from PiB(-) to PiB(+) during the study. No subject reverted from PiB(+) to PiB(-). About 2/3 of all subjects who showed a significant increase in PiB retention in any ROI also showed a global increase. The mean increase in global SUVR across all subjects over 1-2 years was 0.09 6 0.16 (p ¼ 0.014) for NC, 0.15 6 0.21 (p ¼ 0.005) for MCI and 0.14 6 0.18 (p ¼ 0.02) for AD. Conclusions: A progressive accumulation of Ab deposits was detectable with PiB-PET in 58% of PiB(+), but only 16% of stably PiB(-) subjects. The frequency and degree of increased PiB retention was similar across diagnoses. Information regarding the natural history of amyloid deposition is critical to the interpretation of the effects of anti-amyloid therapies. P1-421
TOPOGRAPHIC EXTENT OF CEREBRAL HYPOMETABOLISM PREDICTS TIME OF CONVERSION FROM AMCI TO ALZHEIMER’S DISEASE: DATA FROM THE ALZHEIMER’S DISEASE NEUROIMAGING INITIATIVE
Norman L. Foster1, Angela Y. Wang1, P. Thomas Fletcher1, Sarang Joshi1, Satoshi Minoshima2, William J. Jagust3, Kewei Chen4, Eric M. Reiman4, Michael W. Weiner5, 1University of Utah, Salt Lake City, UT, USA; 2University of Washington, Seattle, WA, USA; 3University of California, Berkeley, Berkeley, CA, USA; 4Banner Alzheimer’s Institute, Phoenix, AZ, USA; 5 University of California, San Francisco, San Francisco, CA, USA. Contact e-mail: [email protected] Background: Clinical trials to prevent Alzheimer’s disease (AD) would be more efficient with a biomarker that could determine whether and when dementia is likely to develop in subjects with memory loss but no functional impairment. Hypometabolism measured with fluorodeoxyglucose positron emission tomography (FDG-PET) begins before deficits are apparent and becomes more extensive as cognitive deficits evolve from amnestic mild cognitive impairment (aMCI) to AD. Consequently, quantitative FDG-PET measures may help identify patients with prodromal AD who are likely to become demented over the brief course of a clinical trial. Objective: Examine the relationship between the topographic extent of glucose hypometabolism and timing of conversion to AD in subjects with aMCI. Methods: We evaluated all 161 baseline FDG-PET scans in subjects with clinically diagnosed aMCI followed for at least 1 year as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Each scan was analyzed with Neurostat, providing 3D-stereotactic surface projection (3D-SSP) maps of glucose metabolism relative to pons and corresponding Z-score maps compared to 27 cognitively normal subjects (14 men, 13 women, mean age 69.6 6 7.7). The number of pixels with metabolism 3 or more standard deviations below normal subjects was compared to the clinical judgment, independent of imaging results, of progression to AD at 6 and 12 months. Results: 14.3% of aMCI subjects (14 men, 9 women, mean age 75.3 6 6.5) converted from MCI to AD over 12 months. Gender and age did not differ from those who continued to have aMCI (92 men, 46 women, mean age 75.4 6 6.5). FDG-PET scans in converters had significantly more hypometabolic pixels (511 6 627 vs. 168 6 283, p < 0.02). The extent of hypometabolism was greater in the 5 who converted after 6 months (1128 6 1000 pixels) than the 18 that converted after 12 months (340 6 363 pixels). Conclusions: The topographic extent of glucose hypometabolism on FDG-PET predicts the likelihood of conversion to AD in aMCI subjects. More extensive hypometabolism indicates conversion will occur sooner. Outcomes would be more predictable and clinical trials of prodromal AD more efficient using FDG-PET for subject selection.