- Email: [email protected]
ASSESSING REGIONAL CORTICAL THICKNESS FOR PREDICTING MCI CONVERSION TO ALZHEIMER'S DISEASE
P1096
Poster Presentations: Wednesday, July 27, 2016
Table 1 Demographic and clinical characteristics for the total sample and each diagnostic group total
non-demented
demented
N diagnosis (n)
511 -
347 AD (347)
age sex (% male) education (scale: 1-7)a MMSE TIV W-score (whole-brain)
66.5 (7.3) 51.7 5.0 (1-7)
164 SCD (56) MCI (108) 66.6 (7.3) 57.3 6.0 (1-7)
66.4 (7.3) 49.0 5.0 (1-7)
22.8 (4.6) 1.5 (.17) ,0 (.47)
27.0 (2.2) 1.5 (.16) .13 (.47)
20.9 (4.2) 1.5 (.17) -.06 (.46)
Data are presented as mean (SD) unless indicated otherwise. MMSE ¼ Mini-Mental State Examination. TIY ¼ total intracranial volume in dm3. SCD ¼ subjective cognitive decline, MCI ¼ mild cognitive impairment, AD ¼ Alzheimer’s disease dementia. a Data represent median (range).
clinical severity (i.e. demented, n¼347, vs. non-demented, n¼164). Results: We observed significant correlations between education and mean W-scores (indicating that higher education related to more CR) in the TP (r¼-.163, p<.001) and whole-brain mask (r¼-.120, p<.01). In the voxel-wise analysis, this effect was most prominent in the right superior lateral occipital, bilateral inferior parietal and inferior and middle temporal cortex (p<.05, corrected for multiple comparisons [see Figure 3]). Conclusions: This novel neuroimaging approach captures CR in high anatomical detail at the individual level. Our methods yields a standardized measure and can be modified (using different neuroimaging and cognitive parameters) and broadly applied (to various types of pathology and CR proxies), making it a promising tool for future studies.
lated as the annual percentage rate of change of HI. Results: A scattergram of HI vs rHI is shown in Figure 1. The mean HI was significantly lower in AD as compared to CN (0.65660.083 vs 0.87660.048; p¼.000). The mean rHI was also significantly different between groups (CN: -0.4260.89 %/yr; AD: -5.6563.06 %/yr; p¼.000). An SVM classifier was used to test the discriminatory power of these measures yielding an accuracy of 97%. Conclusions: There was no significant difference between values for males and females in the CN group. There was a significant sex difference for values in the AD group, with females exhibiting significantly faster atrophy. The rHI value for males was -4.3162.12 %/yr and for females -6.6163.30 %/yr for which p¼0.008. This result remained significant after correction for disease status as measured by average MMSE (p¼0.013). P4-193
P4-192
A SEX DIFFERENCE IN THE RATE OF HIPPOCAMPAL ATROPHY IN ALZHEIMER’S DISEASE 1,2
1 1
Babak Ardekani , Alvin Bachman , Nathan Kline Institute, Orangeburg, NY, USA; 2New York University, New York, NY, USA. Contact e-mail: [email protected] Background: Hippocampal atrophy is associated with the memory deficits in Alzheimer’s Disease (AD). We developed a new measure for characterizing the hippocampus (volumetric) integrity (HI) which can be computed rapidly (w1 minute) from 3D T1-weighted raw MRI scans without any preprocessing. We studied the associations between HI and age, sex and AD. Methods: We downloaded T1-weighted MRI scans of 22 cognitively normal (CN) subjects (11M, 11F) and 43 mild to moderate AD patients (25F, 18M) from the MIRIAD database. Baseline and a 1-year follow-up scans were registered (using an inverse-consistent algorithm) and a mean image computed. An atlas-based algorithm was used to locate the left and right hippocampal regions on the mean image and then projected back onto the original images. Image intensities for these regions were analyzed and the fraction of parenchymal tissue determined. Left and right values were averaged. We designate this quantity as the Hippocampal Integrity (HI). The method is more fully described in Ardekani, Convit, & Bachman (Journal of Alzheimer’s Disease, in press). Using regression of HI vs Age for the CN group, we corrected all HI values to a common age value. An index of the rate of hippocampal atrophy (rHI), is calcu-
ASSESSING REGIONAL CORTICAL THICKNESS FOR PREDICTING MCI CONVERSION TO ALZHEIMER’S DISEASE
Jo€el Schaerer1, Mehul Sampat2, Florent Roche1, Joonmi Oh2, Luc Bracoud1, Joyce Suhy2 the Alzheimer’s Disease Neuroimaging Initiative, 1BioClinica, Inc., Lyon, France; 2BioClinica, Inc., Newark, CA, USA. Contact e-mail: [email protected] Background: Brain cortical thickness is gaining popularity as an
endpoint in AD clinical trials. While a global approach provides valuable insights, a regional analysis may be more sensitive. The aim of this work is to determine if a regional approach is preferable and potentially which aggregation of regions is the most sensitive for prediction of conversion to AD. Methods: Cortical thickness measurements were performed on the Baseline 3DT1-weighted sequences of 232 MCI subjects (116 who did not convert to AD within 36 months, 116 who did) from the ADNI-1 database (http://adni.loni.ucla.edu). FreeSurfer v5.3 (http://surfer.nmr.mgh. harvard.edu/) was used for cortical parcellation. Cortical thickness was assessed by solving Laplace’s equation, building the set of paths between the inner and outer cortical surfaces and deriving the average thickness from them, for each cortical region provided by FreeSurfer, as well as overall. The Lasso method (Tibshirani, J. Royal. Statist. Soc B. 1996) was used in order to optimally combine regions. Threefold cross-validation, run 100 times, was used to estimate an AUC for this model. Then the model was fitted on the whole database in order to determine which aggregation of regions was best suited for discriminating between converters and non-
Poster Presentations: Wednesday, July 27, 2016
P1097
Table 1 Optimal aggregation of cortical regions for the detection of conversion from MCI to AD
Middelheim and Hoge Beuken, Antwerp, Belgium. Contact e-mail: [email protected]
Left Caudal Anterior Cingulate Left Frontal Pole Left Fusiform Left Inferior Parietal Left Posterior Cingulate Left Precentral Left Rostral Anterior Cingulate Left Temporal Pole Right Banks of the Superior Temporal Sulcus Right Caudal Anterior Cingulate Right Entorhinal Right Inferior Temporal Right Parsopercularis Right Postcentral Right Precuneus
visualized by PET is a key biomarker in Alzheimer’s disease (AD). Static SUV-ratio (SUVr) measures Aß plaque load but is sensitive to heterogeneous blood flow changes between target and reference region1. Therefore, we determined volume of distribution (VT) with full kinetic modeling for more accurate discrimination between diagnostic groups and correlated model based relative [18F]-AV45 delivery (R1) with [15O]-H2O perfusion scans. Methods: Dynamic 60-min [18F]-AV45 (292669 MBq) and 1-min [15O]-H2O (370 MBq) scans were obtained in 6 probable AD dementia patients (ADD; 7265y; MMSE 2263), 18 amnestic mild cognitively impairment patients (aMCI; 7368y; MMSE 2563) and 10 cognitively healthy control subjects (HC; 6966y; MMSE 2961). The [18F]AV45 arterial input function was measured continuously complemented with 7 manual samples for HPLC metabolite analysis. [18F]-AV45 tissue time activity curves were modeled using the two tissue compartmental model to calculate VT and the delivery of [18F]-AV45 to the target tissue relative to the cerebellum (R1¼K1/ K’1). Static SUVr (reference: cerebellar grey) was calculated from 50-60 min p.i.. R1 was correlated with perfusion as quantified by [15O]-H2O SUVr. All data were corrected for partial volume effects
Table 2 Group separation (AUC) AUC Whole cortex Right Inferior Temporal Left Fusiform Left Interior Parietal Left Middle Temporal Right Middle Temporal Left Precuneus Lasso method
0.664 0.698 0.691 0.684 0.674 0.659 0.659 0.700
converters. Results: Average cortical thickness over the whole cortex yielded a 0.664 AUC to distinguish between MCI converters and non-converters. Of the available regions from the FreeSurfer atlas, the Right Inferior Temporal was the most sensitive (AUC ¼ 0.698). Results for the next best other individual regions are shown in Table 2. The Lasso method selected a composite region made of 15 subregions (see Table 1), showing marginally higher sensitivity (AUC ¼ 0.700). Conclusions: While a regional analysis of cortical thickness best predicts conversion to AD, compared to whole cortex analysis, looking for an optimal aggregation did not provide significantly better results. Nevertheless, such approach may be more robust to image quality issues, which is to be further confirmed on other datasets. A similar analysis will be performed on longitudinal data in order to determine which single or composite region would provide the best effect size when measuring change in cortical thickness over time.
P4-194
18
Background: Increased brain uptake of [ F]-AV45 (Florbetapir)
Figure 1. Regional [15O]-H20 SUVr (mean 6 std) values for the three groups. Stars denote significant differences with the HC group (****: p<0.0001).
BLOOD FLOW–INDEPENDENT QUANTIFICATION OF [18F]-AV45 PET USING MODEL-BASED KINETICS WITH A METABOLITE-CORRECTED ARTERIAL INPUT FUNCTION
Julie Ottoy1, Jeroen Verhaeghe1, Ellis Niemantsverdriet2, Leonie Wyffels3, Charisse Somers2, Ellen Elisa De Roeck2, Hanne Struyfs2, Steven Deleye1, Sarah Ceyssens3, Sigrid Stroobants3, Sebastiaan Engelborghs2,4, Steven Staelens1, 1Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium; 2Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium; 3 University Hospital of Antwerp, Antwerp, Belgium; 4Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA)
Figure 2. Average spatially normalized [15O]-H2O SUVr images for the three groups. White arrows indicate regions with reduced blood flow compared to HC (precuneus, parietal and temporal cortex).
Poster Presentations: Wednesday, July 27, 2016
Table 1 Demographic and clinical characteristics for the total sample and each diagnostic group total
non-demented
demented
N diagnosis (n)
511 -
347 AD (347)
age sex (% male) education (scale: 1-7)a MMSE TIV W-score (whole-brain)
66.5 (7.3) 51.7 5.0 (1-7)
164 SCD (56) MCI (108) 66.6 (7.3) 57.3 6.0 (1-7)
66.4 (7.3) 49.0 5.0 (1-7)
22.8 (4.6) 1.5 (.17) ,0 (.47)
27.0 (2.2) 1.5 (.16) .13 (.47)
20.9 (4.2) 1.5 (.17) -.06 (.46)
Data are presented as mean (SD) unless indicated otherwise. MMSE ¼ Mini-Mental State Examination. TIY ¼ total intracranial volume in dm3. SCD ¼ subjective cognitive decline, MCI ¼ mild cognitive impairment, AD ¼ Alzheimer’s disease dementia. a Data represent median (range).
clinical severity (i.e. demented, n¼347, vs. non-demented, n¼164). Results: We observed significant correlations between education and mean W-scores (indicating that higher education related to more CR) in the TP (r¼-.163, p<.001) and whole-brain mask (r¼-.120, p<.01). In the voxel-wise analysis, this effect was most prominent in the right superior lateral occipital, bilateral inferior parietal and inferior and middle temporal cortex (p<.05, corrected for multiple comparisons [see Figure 3]). Conclusions: This novel neuroimaging approach captures CR in high anatomical detail at the individual level. Our methods yields a standardized measure and can be modified (using different neuroimaging and cognitive parameters) and broadly applied (to various types of pathology and CR proxies), making it a promising tool for future studies.
lated as the annual percentage rate of change of HI. Results: A scattergram of HI vs rHI is shown in Figure 1. The mean HI was significantly lower in AD as compared to CN (0.65660.083 vs 0.87660.048; p¼.000). The mean rHI was also significantly different between groups (CN: -0.4260.89 %/yr; AD: -5.6563.06 %/yr; p¼.000). An SVM classifier was used to test the discriminatory power of these measures yielding an accuracy of 97%. Conclusions: There was no significant difference between values for males and females in the CN group. There was a significant sex difference for values in the AD group, with females exhibiting significantly faster atrophy. The rHI value for males was -4.3162.12 %/yr and for females -6.6163.30 %/yr for which p¼0.008. This result remained significant after correction for disease status as measured by average MMSE (p¼0.013). P4-193
P4-192
A SEX DIFFERENCE IN THE RATE OF HIPPOCAMPAL ATROPHY IN ALZHEIMER’S DISEASE 1,2
1 1
Babak Ardekani , Alvin Bachman , Nathan Kline Institute, Orangeburg, NY, USA; 2New York University, New York, NY, USA. Contact e-mail: [email protected] Background: Hippocampal atrophy is associated with the memory deficits in Alzheimer’s Disease (AD). We developed a new measure for characterizing the hippocampus (volumetric) integrity (HI) which can be computed rapidly (w1 minute) from 3D T1-weighted raw MRI scans without any preprocessing. We studied the associations between HI and age, sex and AD. Methods: We downloaded T1-weighted MRI scans of 22 cognitively normal (CN) subjects (11M, 11F) and 43 mild to moderate AD patients (25F, 18M) from the MIRIAD database. Baseline and a 1-year follow-up scans were registered (using an inverse-consistent algorithm) and a mean image computed. An atlas-based algorithm was used to locate the left and right hippocampal regions on the mean image and then projected back onto the original images. Image intensities for these regions were analyzed and the fraction of parenchymal tissue determined. Left and right values were averaged. We designate this quantity as the Hippocampal Integrity (HI). The method is more fully described in Ardekani, Convit, & Bachman (Journal of Alzheimer’s Disease, in press). Using regression of HI vs Age for the CN group, we corrected all HI values to a common age value. An index of the rate of hippocampal atrophy (rHI), is calcu-
ASSESSING REGIONAL CORTICAL THICKNESS FOR PREDICTING MCI CONVERSION TO ALZHEIMER’S DISEASE
Jo€el Schaerer1, Mehul Sampat2, Florent Roche1, Joonmi Oh2, Luc Bracoud1, Joyce Suhy2 the Alzheimer’s Disease Neuroimaging Initiative, 1BioClinica, Inc., Lyon, France; 2BioClinica, Inc., Newark, CA, USA. Contact e-mail: [email protected] Background: Brain cortical thickness is gaining popularity as an
endpoint in AD clinical trials. While a global approach provides valuable insights, a regional analysis may be more sensitive. The aim of this work is to determine if a regional approach is preferable and potentially which aggregation of regions is the most sensitive for prediction of conversion to AD. Methods: Cortical thickness measurements were performed on the Baseline 3DT1-weighted sequences of 232 MCI subjects (116 who did not convert to AD within 36 months, 116 who did) from the ADNI-1 database (http://adni.loni.ucla.edu). FreeSurfer v5.3 (http://surfer.nmr.mgh. harvard.edu/) was used for cortical parcellation. Cortical thickness was assessed by solving Laplace’s equation, building the set of paths between the inner and outer cortical surfaces and deriving the average thickness from them, for each cortical region provided by FreeSurfer, as well as overall. The Lasso method (Tibshirani, J. Royal. Statist. Soc B. 1996) was used in order to optimally combine regions. Threefold cross-validation, run 100 times, was used to estimate an AUC for this model. Then the model was fitted on the whole database in order to determine which aggregation of regions was best suited for discriminating between converters and non-
Poster Presentations: Wednesday, July 27, 2016
P1097
Table 1 Optimal aggregation of cortical regions for the detection of conversion from MCI to AD
Middelheim and Hoge Beuken, Antwerp, Belgium. Contact e-mail: [email protected]
Left Caudal Anterior Cingulate Left Frontal Pole Left Fusiform Left Inferior Parietal Left Posterior Cingulate Left Precentral Left Rostral Anterior Cingulate Left Temporal Pole Right Banks of the Superior Temporal Sulcus Right Caudal Anterior Cingulate Right Entorhinal Right Inferior Temporal Right Parsopercularis Right Postcentral Right Precuneus
visualized by PET is a key biomarker in Alzheimer’s disease (AD). Static SUV-ratio (SUVr) measures Aß plaque load but is sensitive to heterogeneous blood flow changes between target and reference region1. Therefore, we determined volume of distribution (VT) with full kinetic modeling for more accurate discrimination between diagnostic groups and correlated model based relative [18F]-AV45 delivery (R1) with [15O]-H2O perfusion scans. Methods: Dynamic 60-min [18F]-AV45 (292669 MBq) and 1-min [15O]-H2O (370 MBq) scans were obtained in 6 probable AD dementia patients (ADD; 7265y; MMSE 2263), 18 amnestic mild cognitively impairment patients (aMCI; 7368y; MMSE 2563) and 10 cognitively healthy control subjects (HC; 6966y; MMSE 2961). The [18F]AV45 arterial input function was measured continuously complemented with 7 manual samples for HPLC metabolite analysis. [18F]-AV45 tissue time activity curves were modeled using the two tissue compartmental model to calculate VT and the delivery of [18F]-AV45 to the target tissue relative to the cerebellum (R1¼K1/ K’1). Static SUVr (reference: cerebellar grey) was calculated from 50-60 min p.i.. R1 was correlated with perfusion as quantified by [15O]-H2O SUVr. All data were corrected for partial volume effects
Table 2 Group separation (AUC) AUC Whole cortex Right Inferior Temporal Left Fusiform Left Interior Parietal Left Middle Temporal Right Middle Temporal Left Precuneus Lasso method
0.664 0.698 0.691 0.684 0.674 0.659 0.659 0.700
converters. Results: Average cortical thickness over the whole cortex yielded a 0.664 AUC to distinguish between MCI converters and non-converters. Of the available regions from the FreeSurfer atlas, the Right Inferior Temporal was the most sensitive (AUC ¼ 0.698). Results for the next best other individual regions are shown in Table 2. The Lasso method selected a composite region made of 15 subregions (see Table 1), showing marginally higher sensitivity (AUC ¼ 0.700). Conclusions: While a regional analysis of cortical thickness best predicts conversion to AD, compared to whole cortex analysis, looking for an optimal aggregation did not provide significantly better results. Nevertheless, such approach may be more robust to image quality issues, which is to be further confirmed on other datasets. A similar analysis will be performed on longitudinal data in order to determine which single or composite region would provide the best effect size when measuring change in cortical thickness over time.
P4-194
18
Background: Increased brain uptake of [ F]-AV45 (Florbetapir)
Figure 1. Regional [15O]-H20 SUVr (mean 6 std) values for the three groups. Stars denote significant differences with the HC group (****: p<0.0001).
BLOOD FLOW–INDEPENDENT QUANTIFICATION OF [18F]-AV45 PET USING MODEL-BASED KINETICS WITH A METABOLITE-CORRECTED ARTERIAL INPUT FUNCTION
Julie Ottoy1, Jeroen Verhaeghe1, Ellis Niemantsverdriet2, Leonie Wyffels3, Charisse Somers2, Ellen Elisa De Roeck2, Hanne Struyfs2, Steven Deleye1, Sarah Ceyssens3, Sigrid Stroobants3, Sebastiaan Engelborghs2,4, Steven Staelens1, 1Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium; 2Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium; 3 University Hospital of Antwerp, Antwerp, Belgium; 4Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA)
Figure 2. Average spatially normalized [15O]-H2O SUVr images for the three groups. White arrows indicate regions with reduced blood flow compared to HC (precuneus, parietal and temporal cortex).