- Email: [email protected]
CHARACTERISING THE PROGRESSION OF ALZHEIMER’S DISEASE SUBTYPES USING SUBTYPE AND STAGE INFERENCE (SUSTAIN)
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Poster Presentations: Saturday, July 15, 2017
Table 1 Summary of the 70 subjects from ADNI that were included in this study Group Amyloid b Positive
Amyloid b Negative
Normal Controls Mild Cognitive Impairment Patients with Alzheimer’s disease Normal Controls Mild Cognitive Impairment
Number of Subjects
MMSE
13 15
28.5 6 1.5 27.8 6 2.2
5
22.4 6 5.5
23 14
29.3 6 0.9 28.7 6 2.1
Table 2 ROI analysis results. Spearman correlation was used. Analyses were performed each side separately. Positive correlation indicates higher tau burden correlates with more cortical thinning. * indicates the correlation is statistically significant Measurements Region
Side
Statistics
ERC
Left Right Left Right Left Right Left Right
rho ¼ 0.12, p ¼ 0.511 rho [ 0.53, p < 0.001 * rho [ 0.39, p [ 0.024 * rho [ 0.62, p < 0.001 * rho [ 0.60, p<0.001 * rho [ 0.46, p [ 0.006 * rho ¼ 0.23, p ¼ 0.198 rho ¼ 0.08, p ¼ 0.657
BA35 BA36 Hippocampus
IC-P-154
volumes transition between different z-scores relative to controls (amyloid negative, CSF Ab42>192pg/ml, CN participants). We performed 10-fold cross-validation to assess the reproducibility of the SuStaIn subtypes and to determine the optimal number of subtypes. We tested for associations between patient subtypes and stages assigned by SuStaIn and ADAS-cog sub-scores, conversion between diagnoses, and longitudinal decline in cognitive test scores. Results: SuStaIn revealed three major subtypes of AD (Figure 1A-C), which we describe as typical, cortical, and subcortical. SuStaIn additionally finds a small group of outliers (4%), which we describe as parietal (Figure 1D). The parietal subgroup had worse performance on certain praxic and spatiallydemanding ADAS-cog subtests, but similar performance in memory domains, compared to those assigned to the typical AD subgroup (Table 1), and were 9.5 years younger on average. We find that the subtypes and stages assigned by SuStaIn have added utility for patient stratification and staging, with significant differences in both MCI to AD conversion times (SuStain subtype p¼6.9x10-3, stage p¼1.5x10-5), and in the rate of MMSE score decline (SuStain subtype p¼7.2x10-3, stage p¼1.4x10-5). Conclusions: The SuStaIn subtypes are consistent with neuropathological studies of AD, and provide greater detail than has been seen previously. Additionally SuStaIn identified a small subgroup with impaired parietal function, which may represent outliers with a posterior cortical atrophy phenotype. The SuStaIn model has utility for disease subtyping and staging, with potential applications in clinical trials and healthcare.
CHARACTERISING THE PROGRESSION OF ALZHEIMER’S DISEASE SUBTYPES USING SUBTYPE AND STAGE INFERENCE (SUSTAIN)
Alexandra L. Young1, Razvan Valentin Marinescu2, Keir Yong3, Nicholas C. Firth1, Neil P. Oxtoby1, David M. Cash4, Nick C. Fox3, Sebastian J. Crutch3, Jonathan D. Rohrer3, Jonathan M. Schott5, Daniel C. Alexander1 and Alzheimer’s Disease Neuroimaging Initiative (ADNI), 1University College London, London, United Kingdom; 2 Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom; 3UCL Institute of Neurology, London, United Kingdom; 4Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, United Kingdom; 5Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom. Contact e-mail: [email protected] Background: Characterising the heterogeneity in the evolution of
Alzheimer’s disease (AD) biomarkers in different individuals would provide insights into the underlying disease process as well as a mechanism for patient staging and stratification. Here we use Subtype and Stage Inference (SuStaIn): a novel datadriven model to find the best description of AD as a set of disease subtypes with distinct patterns of regional brain volume loss. Methods: We included 576 ADNI subjects (180 cognitively normal (CN), 274 mild cognitive impairment (MCI), 122 AD) with crosssectional regional volumes from 1.5T MRI scans whose regional measures passed overall quality control. We applied SuStaIn to estimate disease subtypes with distinct atrophy patterns. Each atrophy pattern consists of a sequence in which regional brain
Subtype and Stage Inference (SuStaIn) modelling of ADNI dataset. Subfigures (A)-(D) show the progression pattern of each of the four subtypes estimated by SuStaIn. The cumulative probability each region has reached a particular z-score is shown for different stages along the progression; the cumulative probability of a region going from a z-score of 0-sigma to 1-sigma ranges from 0 in white to 1 in red, the cumulative probability of a region going from a z-score of 1-sigma to 2-sigma ranges from 0 in red to 1 in magenta, and the cumulative probability of a region going from a z-score of 2-sigma to 3-sigma ranges from 0 in magenta to 1 in blue. f is the proportion of subjects assigned to each subtype. CVS is the model cross-validation similarity: the average similarity of the subtype progression patterns across cross-validation folds, measured using the Bhattacharyya coefficient. The CVS ranges from 0 (no similarity) to 1 (maximum similarity).
Poster Presentations: Saturday, July 15, 2017 Table showing the statistical significance (Mann-Whitney U test) of differences in ADAS-cog sub-scores between the typical (Figure 1A) and parietal (Figure ID) subgroups estimated by SuStaln (p<0.05 shaded in blue). Parietal ¼ AD patients with a higher probability of belonging to the parietal subtype (Figure 1D) than any of the other subtypes; Typical ¼ AD patients with a higher probability of belonging to the typical subtype (Figure 1A) than any of the other subtypes; Strong Parietal ¼ AD patients with probability>0.75 of belonging to the parietal subtype (Figure 1D); Strong Typical ¼ AD patients with probability>0.75 of belonging to the typical subtype (Figure 1A).
IC-P-155
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temetamol [18F]florbetapir, [11C]PIB); Tau neurofibrillary tangles ([18F]AV1451, [18F]THK5351, [11C]PBB3) and Neuroinflammation -TSPO ([11C]PK11195 [11C]PBR28 [18F]DPA714). Further to this, it is envisaged that the network will require GMP implementation of up to 8 novel PET radioligands over the next 3 years to support clinical imaging studies in neurodegenerative disorders. Conclusions: Through this radiochemistry network we will more effectively underpin UK multicentre PET imaging trials for dementia research, addressing a common bottleneck of access to the required radiotracers. The first multicentre PET study to use this network will start in 2017 with the MRC funded Deep and Frequent Phenotyping (DFP) study.
ESTABLISHMENT OF A PET RADIOTRACER NETWORK FOR DEMENTIA RESEARCH
Franklin I. Aigbirhio1, Erik Arstad2, Michael Carroll3, Tony D. Gee4, Nick Long5, Christophe Lucatelli6, Adam McMahon7, Chris Marshall8, Phil Miller5, Jan Passchier9, 1 University of Cambridge, Cambridge, United Kingdom; 2 University College London, London, United Kingdom; 3 Newcastle University, Newcastle, United Kingdom; 4King’s College London, London, United Kingdom; 5Imperial College London, London, United Kingdom; 6University of Edinburgh, Edinburgh, United Kingdom; 7University of Manchester, Manchester, United Kingdom; 8Cardiff University, Cardiff, United Kingdom; 9Imanova Ltd, London, United Kingdom. Contact e-mail: [email protected] Background: Embedded within the MRC Dementia Platform UK
(DP-UK) Network are centres with facilities to conduct patient studies with positron emission tomography (PET). Recent UK Medical Research Council (MRC) funding has allowed further enhancement of imaging capabilities through the acquisition of five state-of the-art combined PET-MRI scanners. To capitalise on this unique infrastructure, UK PET radiochemistry groups are working together to ensure each of the centres has access to a broad portfolio of the required short-lived radiotracers for dementia research. Methods: To maximise availability of tracers across participating centres, the network will undertake the following; i) Facilitate knowledge transfer between centres i.e. radiotracer methods, standard operating procedures and IMPDs; ii) guide the informed choice of relevant radiotracers for clinical studies within and outside the DP-UK network; iii) be a portal for industrial interactions in partnership with rest of DP-UK imaging Network iv) engage with UK regulatory agencies to address issues with EU GMP and clinical trials regulations, their interpretations and applications in the use of PET radiotracers and v) establish sustainable training programmes for PET radiochemists and allied professionals. Results: For imaging the following key pathologies of interest for dementia researchers, to date, the group has established the following portfolio of radiotracers for DP-UK users: Beta-amyloid ([18F]flu-
IC-P-156
4D-FLOW IN THE CEREBRAL ARTERIES PROVIDES UNIQUE INFORMATION ABOUT CEREBROVASCULAR HEALTH BEYOND ISCHEMIC LESION BURDEN AND SIGNIFICANTLY PREDICTS COGNITIVE OUTCOMES
Sara Elizabeth Berman1,2,3, Lindsay R. Clark3,4, Leonardo A. Rivera5, Cynthia M. Carlsson3,4,6, Patrick Turski7, Howard A. Rowley7, Sanjay Asthana3,6, Oliver Wieben5,8, Sterling C. Johnson4,7,9, 1 Neuroscience Training Program, University of Wisconsin, Madison, WI, USA; 2University of Wisconsin School of Medicine and Public Health, Medical Scientist Training Program, Madison, WI, USA; 3Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; 4Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; 5University of Wisconsin-Madison, Madison, WI, USA; 6 Geriatric Research Education and Clinical Center, W.S. Middleton Memorial Veterans Hospital, Madison, WI, USA; 7Wisconsin Alzheimer’s
Poster Presentations: Saturday, July 15, 2017
Table 1 Summary of the 70 subjects from ADNI that were included in this study Group Amyloid b Positive
Amyloid b Negative
Normal Controls Mild Cognitive Impairment Patients with Alzheimer’s disease Normal Controls Mild Cognitive Impairment
Number of Subjects
MMSE
13 15
28.5 6 1.5 27.8 6 2.2
5
22.4 6 5.5
23 14
29.3 6 0.9 28.7 6 2.1
Table 2 ROI analysis results. Spearman correlation was used. Analyses were performed each side separately. Positive correlation indicates higher tau burden correlates with more cortical thinning. * indicates the correlation is statistically significant Measurements Region
Side
Statistics
ERC
Left Right Left Right Left Right Left Right
rho ¼ 0.12, p ¼ 0.511 rho [ 0.53, p < 0.001 * rho [ 0.39, p [ 0.024 * rho [ 0.62, p < 0.001 * rho [ 0.60, p<0.001 * rho [ 0.46, p [ 0.006 * rho ¼ 0.23, p ¼ 0.198 rho ¼ 0.08, p ¼ 0.657
BA35 BA36 Hippocampus
IC-P-154
volumes transition between different z-scores relative to controls (amyloid negative, CSF Ab42>192pg/ml, CN participants). We performed 10-fold cross-validation to assess the reproducibility of the SuStaIn subtypes and to determine the optimal number of subtypes. We tested for associations between patient subtypes and stages assigned by SuStaIn and ADAS-cog sub-scores, conversion between diagnoses, and longitudinal decline in cognitive test scores. Results: SuStaIn revealed three major subtypes of AD (Figure 1A-C), which we describe as typical, cortical, and subcortical. SuStaIn additionally finds a small group of outliers (4%), which we describe as parietal (Figure 1D). The parietal subgroup had worse performance on certain praxic and spatiallydemanding ADAS-cog subtests, but similar performance in memory domains, compared to those assigned to the typical AD subgroup (Table 1), and were 9.5 years younger on average. We find that the subtypes and stages assigned by SuStaIn have added utility for patient stratification and staging, with significant differences in both MCI to AD conversion times (SuStain subtype p¼6.9x10-3, stage p¼1.5x10-5), and in the rate of MMSE score decline (SuStain subtype p¼7.2x10-3, stage p¼1.4x10-5). Conclusions: The SuStaIn subtypes are consistent with neuropathological studies of AD, and provide greater detail than has been seen previously. Additionally SuStaIn identified a small subgroup with impaired parietal function, which may represent outliers with a posterior cortical atrophy phenotype. The SuStaIn model has utility for disease subtyping and staging, with potential applications in clinical trials and healthcare.
CHARACTERISING THE PROGRESSION OF ALZHEIMER’S DISEASE SUBTYPES USING SUBTYPE AND STAGE INFERENCE (SUSTAIN)
Alexandra L. Young1, Razvan Valentin Marinescu2, Keir Yong3, Nicholas C. Firth1, Neil P. Oxtoby1, David M. Cash4, Nick C. Fox3, Sebastian J. Crutch3, Jonathan D. Rohrer3, Jonathan M. Schott5, Daniel C. Alexander1 and Alzheimer’s Disease Neuroimaging Initiative (ADNI), 1University College London, London, United Kingdom; 2 Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom; 3UCL Institute of Neurology, London, United Kingdom; 4Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, United Kingdom; 5Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom. Contact e-mail: [email protected] Background: Characterising the heterogeneity in the evolution of
Alzheimer’s disease (AD) biomarkers in different individuals would provide insights into the underlying disease process as well as a mechanism for patient staging and stratification. Here we use Subtype and Stage Inference (SuStaIn): a novel datadriven model to find the best description of AD as a set of disease subtypes with distinct patterns of regional brain volume loss. Methods: We included 576 ADNI subjects (180 cognitively normal (CN), 274 mild cognitive impairment (MCI), 122 AD) with crosssectional regional volumes from 1.5T MRI scans whose regional measures passed overall quality control. We applied SuStaIn to estimate disease subtypes with distinct atrophy patterns. Each atrophy pattern consists of a sequence in which regional brain
Subtype and Stage Inference (SuStaIn) modelling of ADNI dataset. Subfigures (A)-(D) show the progression pattern of each of the four subtypes estimated by SuStaIn. The cumulative probability each region has reached a particular z-score is shown for different stages along the progression; the cumulative probability of a region going from a z-score of 0-sigma to 1-sigma ranges from 0 in white to 1 in red, the cumulative probability of a region going from a z-score of 1-sigma to 2-sigma ranges from 0 in red to 1 in magenta, and the cumulative probability of a region going from a z-score of 2-sigma to 3-sigma ranges from 0 in magenta to 1 in blue. f is the proportion of subjects assigned to each subtype. CVS is the model cross-validation similarity: the average similarity of the subtype progression patterns across cross-validation folds, measured using the Bhattacharyya coefficient. The CVS ranges from 0 (no similarity) to 1 (maximum similarity).
Poster Presentations: Saturday, July 15, 2017 Table showing the statistical significance (Mann-Whitney U test) of differences in ADAS-cog sub-scores between the typical (Figure 1A) and parietal (Figure ID) subgroups estimated by SuStaln (p<0.05 shaded in blue). Parietal ¼ AD patients with a higher probability of belonging to the parietal subtype (Figure 1D) than any of the other subtypes; Typical ¼ AD patients with a higher probability of belonging to the typical subtype (Figure 1A) than any of the other subtypes; Strong Parietal ¼ AD patients with probability>0.75 of belonging to the parietal subtype (Figure 1D); Strong Typical ¼ AD patients with probability>0.75 of belonging to the typical subtype (Figure 1A).
IC-P-155
P117
temetamol [18F]florbetapir, [11C]PIB); Tau neurofibrillary tangles ([18F]AV1451, [18F]THK5351, [11C]PBB3) and Neuroinflammation -TSPO ([11C]PK11195 [11C]PBR28 [18F]DPA714). Further to this, it is envisaged that the network will require GMP implementation of up to 8 novel PET radioligands over the next 3 years to support clinical imaging studies in neurodegenerative disorders. Conclusions: Through this radiochemistry network we will more effectively underpin UK multicentre PET imaging trials for dementia research, addressing a common bottleneck of access to the required radiotracers. The first multicentre PET study to use this network will start in 2017 with the MRC funded Deep and Frequent Phenotyping (DFP) study.
ESTABLISHMENT OF A PET RADIOTRACER NETWORK FOR DEMENTIA RESEARCH
Franklin I. Aigbirhio1, Erik Arstad2, Michael Carroll3, Tony D. Gee4, Nick Long5, Christophe Lucatelli6, Adam McMahon7, Chris Marshall8, Phil Miller5, Jan Passchier9, 1 University of Cambridge, Cambridge, United Kingdom; 2 University College London, London, United Kingdom; 3 Newcastle University, Newcastle, United Kingdom; 4King’s College London, London, United Kingdom; 5Imperial College London, London, United Kingdom; 6University of Edinburgh, Edinburgh, United Kingdom; 7University of Manchester, Manchester, United Kingdom; 8Cardiff University, Cardiff, United Kingdom; 9Imanova Ltd, London, United Kingdom. Contact e-mail: [email protected] Background: Embedded within the MRC Dementia Platform UK
(DP-UK) Network are centres with facilities to conduct patient studies with positron emission tomography (PET). Recent UK Medical Research Council (MRC) funding has allowed further enhancement of imaging capabilities through the acquisition of five state-of the-art combined PET-MRI scanners. To capitalise on this unique infrastructure, UK PET radiochemistry groups are working together to ensure each of the centres has access to a broad portfolio of the required short-lived radiotracers for dementia research. Methods: To maximise availability of tracers across participating centres, the network will undertake the following; i) Facilitate knowledge transfer between centres i.e. radiotracer methods, standard operating procedures and IMPDs; ii) guide the informed choice of relevant radiotracers for clinical studies within and outside the DP-UK network; iii) be a portal for industrial interactions in partnership with rest of DP-UK imaging Network iv) engage with UK regulatory agencies to address issues with EU GMP and clinical trials regulations, their interpretations and applications in the use of PET radiotracers and v) establish sustainable training programmes for PET radiochemists and allied professionals. Results: For imaging the following key pathologies of interest for dementia researchers, to date, the group has established the following portfolio of radiotracers for DP-UK users: Beta-amyloid ([18F]flu-
IC-P-156
4D-FLOW IN THE CEREBRAL ARTERIES PROVIDES UNIQUE INFORMATION ABOUT CEREBROVASCULAR HEALTH BEYOND ISCHEMIC LESION BURDEN AND SIGNIFICANTLY PREDICTS COGNITIVE OUTCOMES
Sara Elizabeth Berman1,2,3, Lindsay R. Clark3,4, Leonardo A. Rivera5, Cynthia M. Carlsson3,4,6, Patrick Turski7, Howard A. Rowley7, Sanjay Asthana3,6, Oliver Wieben5,8, Sterling C. Johnson4,7,9, 1 Neuroscience Training Program, University of Wisconsin, Madison, WI, USA; 2University of Wisconsin School of Medicine and Public Health, Medical Scientist Training Program, Madison, WI, USA; 3Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; 4Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; 5University of Wisconsin-Madison, Madison, WI, USA; 6 Geriatric Research Education and Clinical Center, W.S. Middleton Memorial Veterans Hospital, Madison, WI, USA; 7Wisconsin Alzheimer’s