- Email: [email protected]
MAGNETIC RESONANCE SPECTROSCOPY BASED METABOLITE MEASUREMENT DIFFERENTIATES ALZHEIMER'S FROM HEALTHY BRAIN
P924
Poster Presentations: Tuesday, July 26, 2016
increased T1signal resulting in an apparent increase in CBF. Further studies examining the mechanisms between CBF quantified by ASL and hemoglobin and hematocrit blood levels are warranted.
P3-249
MAGNETIC RESONANCE SPECTROSCOPY BASED METABOLITE MEASUREMENT DIFFERENTIATES ALZHEIMER’S FROM HEALTHY BRAIN
Nasim Sheikh-Bahaei1, Seyed Ahmad Sajjadi2, Roido Manavaki1, Mary McLean1, John T. O’Brien1, Jonathan H. Gillard1, 1University of Cambridge, Cambridge, United Kingdom; 2University of California, Irvine, Irvine, CA, USA. Contact e-mail: [email protected]
Figure 1. Features from eigenimage associated with primary Canonical Variate of Am+/- EFA classifier, consistent with late timeframe regions of amyloid accumulation.
Background: Magnetic resonance spectroscopy (MRS) is a feasible
technique and a potential valuable biomarker for AD. There is, however, a lack of translation from research to clinical practice and previous studies have reported conflicting results. This could, in part, be secondary to variable methods used for selection of regions of interest for MRS analysis. The aim of this study was to compare the levels of various brain metabolites across normal and hypo-metabolic and amyloid positive and negative brain regions identified using fluoro-deoxy-glucose (FDG) and Pittsburgh compound B (PiB) PET scans respectively. Methods: The study comprised five patients with a clinical diagnosis of either mild probable AD (n¼2) or amnestic MCI (n¼3) and five healthy volunteers. The participants underwent MRI and MRS following 90-minute dynamic PiB- and 30-minute static FDG-PET scans as part of a study conducted at the University of Cambridge, UK. After co-registration of PET scans on MRI images, two areas each of maximum amyloid uptake and minimum glucose metabolism (4 in total) were identified in each patient and corresponding regions of interests (ROI) were selected in controls. The levels of metabolites myo-inositol, total N-acetyl (tNA) groups, choline, and glutamine and glutamate (glx) were compared across normal and abnormal regions. Results: Pairwise comparison of metabolite levels showed significantly higher levels of myo-inositol (p<0.0001), and choline (p¼0.005) and lower level of tNA (p¼0.007) in the FDGPET identified hypo-metabolic areas. Glx level was not significantly different. Moreover, compared to corresponding normal areas, there was significant reduction in tNA (p¼0.003) and increase in myo-inositol (p¼0.05) levels in the amyloid positive brain regions. Choline and glx levels were not significantly different. Conclusions: Given the relatively small samples size, these significant findings signal the importance of a uniform and pathophysiologically relevant selection of MRS voxels. To our knowledge, this study is the first to have utilised both PiB- and FDG-PET data for this purpose. Adopting such an approach might lead to resolution of inconsistencies in the MRS literature. P3-250
dynamic modeling. Both approaches require reaching a quasiequilibrium stage involving a tracer-dependent post-injection waiting period of, for example, 50 or as much as 90 minutes followed by additional scan time. We applied multivariate machine learning classifier approaches to florbetapir dynamic frames acquired during the first twenty minutes post-injection with the goal of obtaining a measure of amyloid burden that can be efficiently combined with the functional information available through the initial minutes of the same scan. Methods: Using dynamic florbetapir PET image frames acquired during the first twenty minutes post- injection for 104 ADNI subjects, we applied machine learning using discriminant analysis with iterative resampling to develop and test image classifiers. Training classes for an Am+/- classifier consisted of 10 NL amyloid-negative(-), 19 subjective memory complaints (SMC)-, 11 NL/SMC+, 9 MCI+, and 14 AD+. Training classes for a second, amyloid progression classifier were based upon progressively greater late timeframe SUVR . In both cases, subclasses were formed using discrete timeframes. Independent testing was
MEASUREMENT OF AMYLOID BURDEN USING THE EARLY FRAMES OF AMYLOID PET AND A MULTIVARIATE CLASSIFIER
Dawn C. Matthews1, Ana S. Lukic1, Randolph D. Andrews1, Mark E. Schmidt2, Miles N. Wernick1,3, Stephen C. Strother1,4 and Alzheimer’s Disease Neuroimaging Initiative, 1ADM Diagnostics LLC, Northbrook, IL, USA; 2Janssen Research and Development, Beerse, Belgium; 3Illinois Institute of Technology, Chicago, IL, USA; 4Rotman Research Institute, Baycrest, Toronto, ON, Canada. Contact e-mail: [email protected] Background: Amyloid burden is typically measured using a late
timeframe Standardized Uptake Value Ratio (SUVR) or full
Figure 2. Leave-One-Out results of the Am+/- classifier. Columns are mean for each group; bars are SEM. Classes were grouped by clinical diagnosis and AM+/- rather than a particular amyloid SUVR range.
Poster Presentations: Tuesday, July 26, 2016
increased T1signal resulting in an apparent increase in CBF. Further studies examining the mechanisms between CBF quantified by ASL and hemoglobin and hematocrit blood levels are warranted.
P3-249
MAGNETIC RESONANCE SPECTROSCOPY BASED METABOLITE MEASUREMENT DIFFERENTIATES ALZHEIMER’S FROM HEALTHY BRAIN
Nasim Sheikh-Bahaei1, Seyed Ahmad Sajjadi2, Roido Manavaki1, Mary McLean1, John T. O’Brien1, Jonathan H. Gillard1, 1University of Cambridge, Cambridge, United Kingdom; 2University of California, Irvine, Irvine, CA, USA. Contact e-mail: [email protected]
Figure 1. Features from eigenimage associated with primary Canonical Variate of Am+/- EFA classifier, consistent with late timeframe regions of amyloid accumulation.
Background: Magnetic resonance spectroscopy (MRS) is a feasible
technique and a potential valuable biomarker for AD. There is, however, a lack of translation from research to clinical practice and previous studies have reported conflicting results. This could, in part, be secondary to variable methods used for selection of regions of interest for MRS analysis. The aim of this study was to compare the levels of various brain metabolites across normal and hypo-metabolic and amyloid positive and negative brain regions identified using fluoro-deoxy-glucose (FDG) and Pittsburgh compound B (PiB) PET scans respectively. Methods: The study comprised five patients with a clinical diagnosis of either mild probable AD (n¼2) or amnestic MCI (n¼3) and five healthy volunteers. The participants underwent MRI and MRS following 90-minute dynamic PiB- and 30-minute static FDG-PET scans as part of a study conducted at the University of Cambridge, UK. After co-registration of PET scans on MRI images, two areas each of maximum amyloid uptake and minimum glucose metabolism (4 in total) were identified in each patient and corresponding regions of interests (ROI) were selected in controls. The levels of metabolites myo-inositol, total N-acetyl (tNA) groups, choline, and glutamine and glutamate (glx) were compared across normal and abnormal regions. Results: Pairwise comparison of metabolite levels showed significantly higher levels of myo-inositol (p<0.0001), and choline (p¼0.005) and lower level of tNA (p¼0.007) in the FDGPET identified hypo-metabolic areas. Glx level was not significantly different. Moreover, compared to corresponding normal areas, there was significant reduction in tNA (p¼0.003) and increase in myo-inositol (p¼0.05) levels in the amyloid positive brain regions. Choline and glx levels were not significantly different. Conclusions: Given the relatively small samples size, these significant findings signal the importance of a uniform and pathophysiologically relevant selection of MRS voxels. To our knowledge, this study is the first to have utilised both PiB- and FDG-PET data for this purpose. Adopting such an approach might lead to resolution of inconsistencies in the MRS literature. P3-250
dynamic modeling. Both approaches require reaching a quasiequilibrium stage involving a tracer-dependent post-injection waiting period of, for example, 50 or as much as 90 minutes followed by additional scan time. We applied multivariate machine learning classifier approaches to florbetapir dynamic frames acquired during the first twenty minutes post-injection with the goal of obtaining a measure of amyloid burden that can be efficiently combined with the functional information available through the initial minutes of the same scan. Methods: Using dynamic florbetapir PET image frames acquired during the first twenty minutes post- injection for 104 ADNI subjects, we applied machine learning using discriminant analysis with iterative resampling to develop and test image classifiers. Training classes for an Am+/- classifier consisted of 10 NL amyloid-negative(-), 19 subjective memory complaints (SMC)-, 11 NL/SMC+, 9 MCI+, and 14 AD+. Training classes for a second, amyloid progression classifier were based upon progressively greater late timeframe SUVR . In both cases, subclasses were formed using discrete timeframes. Independent testing was
MEASUREMENT OF AMYLOID BURDEN USING THE EARLY FRAMES OF AMYLOID PET AND A MULTIVARIATE CLASSIFIER
Dawn C. Matthews1, Ana S. Lukic1, Randolph D. Andrews1, Mark E. Schmidt2, Miles N. Wernick1,3, Stephen C. Strother1,4 and Alzheimer’s Disease Neuroimaging Initiative, 1ADM Diagnostics LLC, Northbrook, IL, USA; 2Janssen Research and Development, Beerse, Belgium; 3Illinois Institute of Technology, Chicago, IL, USA; 4Rotman Research Institute, Baycrest, Toronto, ON, Canada. Contact e-mail: [email protected] Background: Amyloid burden is typically measured using a late
timeframe Standardized Uptake Value Ratio (SUVR) or full
Figure 2. Leave-One-Out results of the Am+/- classifier. Columns are mean for each group; bars are SEM. Classes were grouped by clinical diagnosis and AM+/- rather than a particular amyloid SUVR range.