- Email: [email protected]
GREY MATTER CONNECTIVITY IS ASSOCIATED WITH THE RATE OF COGNITIVE DECLINE IN MILD COGNITIVE IMPAIRMENT
P1102
Poster Presentations: Tuesday, July 18, 2017
detection. Our machine learning FDG PET classifier quantifies expression of a neurodegenerative pattern that is predictive of decline (Figure 1). Tau PET has enabled assessment of pathology that may underlie variability in pattern expression and rates of decline in Am+ subjects. Methods: The AV1451 tau PET scans of 101 ADNI2 subjects (45 NL/SMC, 19 EMCI, 28 LMCI, 9 AD; 76+/-7 yrs, 40% F, 39% APOEe4 carriers) were spatially normalized along with their temporally closest FDG, MRI, and AV45 scans. FDG preceded tau scans by 1-4 years. Tau scan regions of interest were measured in structures aligned with Braak stages I– VI (Scholl, 2016) referenced to cerebellar cortex. The degree to which each subject’s FDG scan expressed a pattern reflecting AD progression was quantified using the AD Progression classifier (CV1). CV1 scores were compared across groups defined by clinical diagnosis, amyloid status, and tau Braak stage (0-2, 3, or 46). SPM t-tests were performed for the FDG scans of a) NL/ EMCI Am+Tau stage 0 to 2, b) NL/EMCI/LMCI subjects Am+Tau stage 0 to 3, and c) Am+ subjects at Tau stages 4-6 compared to NL Am-Tau- subjects. Results: Am-Tau- subjects had CV1 scores corresponding to a lack of AD-like pattern regardless of clinical diagnosis. CV1 scores increased with Braak stage for each diagnostic group, and across diagnostic groups at the same tau Braak stage
Figure 3. SPM t-test comparison (p<0.0001, uncorrected) of 12 MCI and AD Am+ subjects, tau Braak stage 4-6, vs. 16 Am-Tau- normal subjects, referenced to pons. Only hypometabolism was detected, corresponding to the FDG CV1 pattern.
(NL
Figure 1. Pattern of hypometabolism and preservation or hypermetabolism relative to whole brain that reflects AD progression. At right, the mean CV1 scores of independent test subjects across the spectrum of disease from NL Am- to AD Am+, where higher scores correspond to greater expression of the pattern at left.
Figure 2. Top row: SPM-t test comparison (p<0.001, uncorrected) of 14 NL and MCI Am+ subjects, tau Braak stage 0-3, vs. 16 Am-Tau- normal subjects, referenced to pons. Bottom row: SPM-test comparison (p<0.01) of a subset of 6 NL and 3 MCI subjects Am+ tau Braak stage 0-2, vs. 16 Am-Tau- normal subjects, referenced to pons.
GREY MATTER CONNECTIVITY IS ASSOCIATED WITH THE RATE OF COGNITIVE DECLINE IN MILD COGNITIVE IMPAIRMENT
Ellen Dicks1,2, Betty M. Tijms1,2, Alida A. Gouw1,2, Marije R. Benedictus1,2, Frederik Barkhof1,2,3, Philip Scheltens1,4, Wiesje M. van der Flier1,2, 1VU University Medical Center, Amsterdam, Netherlands; 2Neuroscience Campus Amsterdam, Amsterdam, Netherlands; 3 UCL, London, United Kingdom; 4Neuroscience, Amsterdam, Netherlands. Contact e-mail: [email protected] Background: For clinical trials markers are needed that can be used
to identify those subjects at risk for Alzheimer’s disease (AD) who will show fast progression. Grey matter connectivity is disrupted in AD and more severe disruptions of grey matter connectivity have been cross-sectionally associated with worse cognitive functioning. We studied whether grey matter connectivity measures are associated with the rate of decline in specific cognitive domains in patients with mild cognitive impairment (MCI). Methods: We included 258 MCI subjects (mean age: 6768 years; 41% females) with baseline MRI and at least 1 year of neuropsychological followup (average assessments: 3, range 2-11) from the Amsterdam Dementia Cohort. Single-subject grey matter networks were extracted from baseline structural MRI and the network properties size, degree, connectivity density, betweenness centrality (BC), clustering coefficient, path length and their normalized versions (gamma and lambda) and small-world coefficient were computed at whole-brain level and for 90 anatomical areas. We calculated composite zscores of memory, attention, executive function, language and visuospatial domains and assessed global cognition with the MMSE. Linear mixed models were used to determine effects of baseline network measures on cognitive decline, adjusted for sex, age, education and intracranial volume. Results: Linear mixed models showed that higher path length, lambda and BC values
Poster Presentations: Tuesday, July 18, 2017
were cross-sectionally related to worse memory functioning (p<0.05). Lower small-world coefficient values were related to a steeper decline in MMSE, memory, attention and executive functioning (p<0.05). Lower gamma values were associated with a faster decline in memory, attention and executive functioning and lower BC values correlated with a steeper decline in MMSE and memory functioning (p<0.05). At a regional level, lower BC values were most strongly associated with a steeper decline in MMSE and memory in the precuneus, medial frontal and temporal cortex (pFDR<0.05). Conclusions: Aberrant grey matter connectivity measures, indicating a more random network topology as often found in AD, were associated with a steeper decline in memory, attention, executive and general cognitive functioning. These results show that network measures might be useful to identify subjects who will show fast cognitive decline.
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P1103
QRISK2) were significantly correlated with WMH. FHS-CVD and FHS-Stroke risk scores also correlated with lateral ventricle size, a measure of central atrophy. Higher FHSCVD predicted significantly lower scores on the PACC. We observed no significant relationships between any CV risk scores and PET measures of amyloid burden. Conclusions: We observed significant (but modest) cross-sectional relationships between well-validated measures of CV risk and imaging biomarkers commonly used to stratify risk of decline in preclinical AD, but no relationship between elevated CV risk and amyloid burden. Though significant relationships between CV risk scores and WMH were present, these were also relatively weak, suggesting measures of WMH may have limited overlap with vascular risk as assessed in other medical contexts. These results support considering CV risk in studies of preclinical AD, and suggest WMH measures may not completely account for CV risk.
QRISK2 AND FRAMINGHAM CARDIOVASCULAR RISK SCORES SIGNIFICANTLY CORRELATE WITH IMAGING BIOMARKERS OF PRECLINICAL AD: FINDINGS FROM THE HARVARD AGING BRAIN STUDY
Emily P. Kilpatrick1, Rachel F. Buckley1,2,3,4, Gad A. Marshall1,4,5, Hannah Klein1, Michael Properzi6, Aaron P. Schultz4,6,7, Vaishnavi Rao8, Jennifer S. Rabin1,4, Bernard J. Hanseeuw1,4,7,9, Dorene M. Rentz4,5,6, Trey Hedden1,4,7, Reisa A. Sperling1,4,5,7, Keith Johnson4,5,6,7, Jasmeer P. Chhatwal1,4,7, 1Massachusetts General Hospital, Boston, MA, USA; 2University of Melbourne, Melbourne, Australia; 3The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; 4Harvard Medical School, Boston, MA, USA; 5Brigham and Women’s Hospital, Boston, MA, USA; 6Massachusetts General Hospital, Charlestown, MA, USA; 7Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA; 8Massachusetts Institute of Technology, Cambridge, MA, USA; 9 Institute of Neuroscience, Universite Catholique de Louvain, Brussels, Belgium. Contact e-mail: [email protected] Background: Neuropathological studies suggest many cases of
late onset dementia may be due to mixed pathologies, most commonly a combination of Alzheimer’s disease (AD) pathology and cerebrovascular disease. The amount of white matter disease (WM hyperintensities, WMH) is often used as a proxy measure for the impact of vascular disease on the brain, but lacks the extensive validation of commonly used vascular risk (CV Risk) scores such as the QRISK2, Framingham Heart Study (FHS) cardiovascular disease (FHS-CVD) and stroke (FHS-Stroke) metrics. Accordingly, we examined the crosssectional relationship of these CV risk metrics to commonly used imaging biomarkers, including 18F- fludeoxyglucose (FDG) PET, Pittsburgh Compound B (PiB) PET, hippocampal volume, ventricle size, and WMH. Methods: MRI, PiB PET, and FDG PET data from 195 clinically normal older participants in the Harvard Aging Brain Study (HABS) were compared to CV risk scores calculated from available demographic, blood pressure, and cholesterol data. Linear regression models (corrected for age and sex) were used to assess relationships between CV Risk and imaging biomarkers and with the Preclinical Alzheimer’s Cognitive Composite (PACC). Results: QRISK2 and FHS-CVD risk scores were significantly correlated with AD-typical hypometabolism on FDG PET (Figure 1). Higher QRISK2 was associated with lower hippocampal volume. FHS-CVD and FHS-Stroke risk scores (but not
Figure 1. Associations Between Cardiovascular Risk Scores and CrossSectional AD Biomarkers in the Harvard Aging Brain Study. The heatmap shown depicts partial correlations between cardiovascular risk scores and baseline imaging and cognitive measures, controlling forage and sex. (CVD ¼ Cardiovascular Disease; CHD ¼ Coronary Heart Disease; CVD Death and CHD Death refer to risk of death from CVD or CHD, respectively; *, ** , and *** correspond to p < 0.05, < 0.005, and < 0.0005, respectively).
P3-377
BRAIN MRI FINDINGS WHICH MAY PREDICT PROGRESSION OF MILD COGNITIVE IMPAIRMENT
Eric M. Peters, Udochukwu Uyoyo, Daniel Kido, Adina Achiriloaie, Loma Linda University, Loma Linda, CA, USA. Contact e-mail: [email protected] Background: 19% of people older than 65 develop mild cognitive impairment (MCI), and 46% of these patients will progress to dementia within 3 years. Not many prognostic indicators are available. Metabolic syndrome has been suggested as a possible risk factor for dementia. Methods: In a cohort of 96 total subjects with MCI with and without progression to dementia and normal controls, the earliest available axial FLAIR images for each subject were analyzed using Olea Sphere software (Olea Medical, France)
Poster Presentations: Tuesday, July 18, 2017
detection. Our machine learning FDG PET classifier quantifies expression of a neurodegenerative pattern that is predictive of decline (Figure 1). Tau PET has enabled assessment of pathology that may underlie variability in pattern expression and rates of decline in Am+ subjects. Methods: The AV1451 tau PET scans of 101 ADNI2 subjects (45 NL/SMC, 19 EMCI, 28 LMCI, 9 AD; 76+/-7 yrs, 40% F, 39% APOEe4 carriers) were spatially normalized along with their temporally closest FDG, MRI, and AV45 scans. FDG preceded tau scans by 1-4 years. Tau scan regions of interest were measured in structures aligned with Braak stages I– VI (Scholl, 2016) referenced to cerebellar cortex. The degree to which each subject’s FDG scan expressed a pattern reflecting AD progression was quantified using the AD Progression classifier (CV1). CV1 scores were compared across groups defined by clinical diagnosis, amyloid status, and tau Braak stage (0-2, 3, or 46). SPM t-tests were performed for the FDG scans of a) NL/ EMCI Am+Tau stage 0 to 2, b) NL/EMCI/LMCI subjects Am+Tau stage 0 to 3, and c) Am+ subjects at Tau stages 4-6 compared to NL Am-Tau- subjects. Results: Am-Tau- subjects had CV1 scores corresponding to a lack of AD-like pattern regardless of clinical diagnosis. CV1 scores increased with Braak stage for each diagnostic group, and across diagnostic groups at the same tau Braak stage
Figure 3. SPM t-test comparison (p<0.0001, uncorrected) of 12 MCI and AD Am+ subjects, tau Braak stage 4-6, vs. 16 Am-Tau- normal subjects, referenced to pons. Only hypometabolism was detected, corresponding to the FDG CV1 pattern.
(NL
Figure 1. Pattern of hypometabolism and preservation or hypermetabolism relative to whole brain that reflects AD progression. At right, the mean CV1 scores of independent test subjects across the spectrum of disease from NL Am- to AD Am+, where higher scores correspond to greater expression of the pattern at left.
Figure 2. Top row: SPM-t test comparison (p<0.001, uncorrected) of 14 NL and MCI Am+ subjects, tau Braak stage 0-3, vs. 16 Am-Tau- normal subjects, referenced to pons. Bottom row: SPM-test comparison (p<0.01) of a subset of 6 NL and 3 MCI subjects Am+ tau Braak stage 0-2, vs. 16 Am-Tau- normal subjects, referenced to pons.
GREY MATTER CONNECTIVITY IS ASSOCIATED WITH THE RATE OF COGNITIVE DECLINE IN MILD COGNITIVE IMPAIRMENT
Ellen Dicks1,2, Betty M. Tijms1,2, Alida A. Gouw1,2, Marije R. Benedictus1,2, Frederik Barkhof1,2,3, Philip Scheltens1,4, Wiesje M. van der Flier1,2, 1VU University Medical Center, Amsterdam, Netherlands; 2Neuroscience Campus Amsterdam, Amsterdam, Netherlands; 3 UCL, London, United Kingdom; 4Neuroscience, Amsterdam, Netherlands. Contact e-mail: [email protected] Background: For clinical trials markers are needed that can be used
to identify those subjects at risk for Alzheimer’s disease (AD) who will show fast progression. Grey matter connectivity is disrupted in AD and more severe disruptions of grey matter connectivity have been cross-sectionally associated with worse cognitive functioning. We studied whether grey matter connectivity measures are associated with the rate of decline in specific cognitive domains in patients with mild cognitive impairment (MCI). Methods: We included 258 MCI subjects (mean age: 6768 years; 41% females) with baseline MRI and at least 1 year of neuropsychological followup (average assessments: 3, range 2-11) from the Amsterdam Dementia Cohort. Single-subject grey matter networks were extracted from baseline structural MRI and the network properties size, degree, connectivity density, betweenness centrality (BC), clustering coefficient, path length and their normalized versions (gamma and lambda) and small-world coefficient were computed at whole-brain level and for 90 anatomical areas. We calculated composite zscores of memory, attention, executive function, language and visuospatial domains and assessed global cognition with the MMSE. Linear mixed models were used to determine effects of baseline network measures on cognitive decline, adjusted for sex, age, education and intracranial volume. Results: Linear mixed models showed that higher path length, lambda and BC values
Poster Presentations: Tuesday, July 18, 2017
were cross-sectionally related to worse memory functioning (p<0.05). Lower small-world coefficient values were related to a steeper decline in MMSE, memory, attention and executive functioning (p<0.05). Lower gamma values were associated with a faster decline in memory, attention and executive functioning and lower BC values correlated with a steeper decline in MMSE and memory functioning (p<0.05). At a regional level, lower BC values were most strongly associated with a steeper decline in MMSE and memory in the precuneus, medial frontal and temporal cortex (pFDR<0.05). Conclusions: Aberrant grey matter connectivity measures, indicating a more random network topology as often found in AD, were associated with a steeper decline in memory, attention, executive and general cognitive functioning. These results show that network measures might be useful to identify subjects who will show fast cognitive decline.
P3-376
P1103
QRISK2) were significantly correlated with WMH. FHS-CVD and FHS-Stroke risk scores also correlated with lateral ventricle size, a measure of central atrophy. Higher FHSCVD predicted significantly lower scores on the PACC. We observed no significant relationships between any CV risk scores and PET measures of amyloid burden. Conclusions: We observed significant (but modest) cross-sectional relationships between well-validated measures of CV risk and imaging biomarkers commonly used to stratify risk of decline in preclinical AD, but no relationship between elevated CV risk and amyloid burden. Though significant relationships between CV risk scores and WMH were present, these were also relatively weak, suggesting measures of WMH may have limited overlap with vascular risk as assessed in other medical contexts. These results support considering CV risk in studies of preclinical AD, and suggest WMH measures may not completely account for CV risk.
QRISK2 AND FRAMINGHAM CARDIOVASCULAR RISK SCORES SIGNIFICANTLY CORRELATE WITH IMAGING BIOMARKERS OF PRECLINICAL AD: FINDINGS FROM THE HARVARD AGING BRAIN STUDY
Emily P. Kilpatrick1, Rachel F. Buckley1,2,3,4, Gad A. Marshall1,4,5, Hannah Klein1, Michael Properzi6, Aaron P. Schultz4,6,7, Vaishnavi Rao8, Jennifer S. Rabin1,4, Bernard J. Hanseeuw1,4,7,9, Dorene M. Rentz4,5,6, Trey Hedden1,4,7, Reisa A. Sperling1,4,5,7, Keith Johnson4,5,6,7, Jasmeer P. Chhatwal1,4,7, 1Massachusetts General Hospital, Boston, MA, USA; 2University of Melbourne, Melbourne, Australia; 3The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; 4Harvard Medical School, Boston, MA, USA; 5Brigham and Women’s Hospital, Boston, MA, USA; 6Massachusetts General Hospital, Charlestown, MA, USA; 7Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA; 8Massachusetts Institute of Technology, Cambridge, MA, USA; 9 Institute of Neuroscience, Universite Catholique de Louvain, Brussels, Belgium. Contact e-mail: [email protected] Background: Neuropathological studies suggest many cases of
late onset dementia may be due to mixed pathologies, most commonly a combination of Alzheimer’s disease (AD) pathology and cerebrovascular disease. The amount of white matter disease (WM hyperintensities, WMH) is often used as a proxy measure for the impact of vascular disease on the brain, but lacks the extensive validation of commonly used vascular risk (CV Risk) scores such as the QRISK2, Framingham Heart Study (FHS) cardiovascular disease (FHS-CVD) and stroke (FHS-Stroke) metrics. Accordingly, we examined the crosssectional relationship of these CV risk metrics to commonly used imaging biomarkers, including 18F- fludeoxyglucose (FDG) PET, Pittsburgh Compound B (PiB) PET, hippocampal volume, ventricle size, and WMH. Methods: MRI, PiB PET, and FDG PET data from 195 clinically normal older participants in the Harvard Aging Brain Study (HABS) were compared to CV risk scores calculated from available demographic, blood pressure, and cholesterol data. Linear regression models (corrected for age and sex) were used to assess relationships between CV Risk and imaging biomarkers and with the Preclinical Alzheimer’s Cognitive Composite (PACC). Results: QRISK2 and FHS-CVD risk scores were significantly correlated with AD-typical hypometabolism on FDG PET (Figure 1). Higher QRISK2 was associated with lower hippocampal volume. FHS-CVD and FHS-Stroke risk scores (but not
Figure 1. Associations Between Cardiovascular Risk Scores and CrossSectional AD Biomarkers in the Harvard Aging Brain Study. The heatmap shown depicts partial correlations between cardiovascular risk scores and baseline imaging and cognitive measures, controlling forage and sex. (CVD ¼ Cardiovascular Disease; CHD ¼ Coronary Heart Disease; CVD Death and CHD Death refer to risk of death from CVD or CHD, respectively; *, ** , and *** correspond to p < 0.05, < 0.005, and < 0.0005, respectively).
P3-377
BRAIN MRI FINDINGS WHICH MAY PREDICT PROGRESSION OF MILD COGNITIVE IMPAIRMENT
Eric M. Peters, Udochukwu Uyoyo, Daniel Kido, Adina Achiriloaie, Loma Linda University, Loma Linda, CA, USA. Contact e-mail: [email protected] Background: 19% of people older than 65 develop mild cognitive impairment (MCI), and 46% of these patients will progress to dementia within 3 years. Not many prognostic indicators are available. Metabolic syndrome has been suggested as a possible risk factor for dementia. Methods: In a cohort of 96 total subjects with MCI with and without progression to dementia and normal controls, the earliest available axial FLAIR images for each subject were analyzed using Olea Sphere software (Olea Medical, France)