- Email: [email protected]
Regional cortical thinning predicts worsening apathy and hallucinations in mild cognitive impairment and mild Alzheimer's disease dementia
P342
Poster Presentations: P2
Background: Alzheimer’s disease (AD) patients show significant changes in white matter (WM) structural integrity. Diffusion tensor imaging (DTI) is a neuroimaging technique that allows in vivo assessment of WM fiber tract integrity and, thus, could support the diagnosis of AD as an additional biomarker. Current research focuses on machine learning (ML) methods to automatically detect AD specific structural WM changes. Therefore, the algorithms used must be robust and stable to work with data recorded across different scanners. Within the newly created framework of the European DTI study in Dementia (EDSD) we have collected data of more than 330 subjects from ten scanners located at nine sites. Objective: To assess the accuracy of ML classifiers for the detection of AD based on a large multicenter DTI data set using different approaches to reduce inter-site variability. Methods: After strict quality control we pooled the remaining 280 DTI and MRI scans derived from 137 patients with clinically probable AD and 143 healthy elderly controls. For classification we used fractional anisotropy (FA) maps and mean diffusivity (MD) maps and performed a tenfold cross validation. We selected discriminative voxels using the information gain criterion and classified the data with a Support Vector Machine. In a second step, we eliminated variance attributable to center and other covariates including age, education, gender, using principal component analysis (PCA) before repeating the classification procedure. Results: For FA and MD the feature selection identified areas in the medial temporal lobe and corpus callosum that had the strongest contribution to the group separation. We achieved an accuracy of 80% for FA and 83% for MD. For the tissue density maps we obtained 83% for WM and 89% for gray matter. The reduction of variance components arising from center, gender, age and education effects did not significantly change the classification results for FA and MD. Conclusions: Multicenter acquisition of DTI data in combination with multivariate ML approaches show promising results which can be compared to earlier monocenter DTI studies. Variance introduced by different scanners can be detected by PCA, but it seems not to affect the performance of the classifier. P2-235
PHARMACOG: MULTI-SITE MRI CALIBRATION TO STUDY PROGRESSION OF ALZHEIMER’S DISEASE
Jorge Jovicich1, Genoveffa Borsci2, Moira Marizzoni3, Roser Salauria Bargall o4, David Bartres-Faz4, Jens Benninghoff5, Llonch4, N Jens Wiltfang6, Luca Roccatagliata7, Flavio Nobili8, Karl-Titus Hoffmann9, Thomas G€ unther9, Peter Sch€onknecht9, Aurelien Monnet10, Regis Bordet11, Valerie Chanoine12, Alexandra Auffret12, Jean-Philippe Ranjeva12, Oliver Blin12, Helene Gros-Dagnac13, Pierre Payoux14, Giada Zoccatelli15, Franco Alessandrini15, Alberto Beltramello15, Hans-Goran Hardemark16, Giovanni Frisoni17, 1University of Trento, Trento, Italy; 2LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine - IRCCS San Giovanni di Dio-FBF, Brescia, Italy; 3Laboratory of Epidemiology and Neuroimaging, IRCCS Fatebenefratelli, Brescia, Italy, Brescia, Italy; 4 Universitat de Barcelona and IDIBAPS, Barcelona, Spain; 5Universitaet Duisburg-Essen Department of Psychiatry and Nuclear Medicine, Essen, Germany; 6Universitaet Duisburg-Essen Department of Psychiatry and Nuclear Medicine, Essem, Germany; 7Department of Neuroscience, Ophthalmology and Genetics University of Genoa, Genoa, Italy; 8Clinical Neurophysiology, Department of Neurosciences, Ophthalmology and Genetics, University of Genoa, Genoa, Italy; 9University Hospital Leipzig, Department of Psychiatry, Section of Neuroradiology, Leipzig, Germany; 10 Universite Lille 2, Lille, France; 11Universite Lille 2, UL2, Lille, France; 12 CIC-UPCET, CHU La Timone, AP-HM, UMR CNRS-Universite de la Mediterranee, Marseille, France; 13Institut National de la Sante et de la Recherche Medicale, Toulouse, France; 14Institut National de la Sante et de la Recherche Medicale, Toulouse, France; 15General Hospital, Verona, Italy; 16AstraZencea R&D Clinical Neuroscience Therapy Area, S€odert€alje, Sweden; 17IRCCS Fatebenefratelli, Brescia, Italy. Background: Pharmacog is an industry-academic project aimed at finding new biomarkers for Alzheimer’s disease [1]. The main issue concerning multicenter neuroimaging clinical studies is the difficulty in getting comparable analytical responses. Here we present preliminary work aimed at developing and implementing standardized procedures to acquire and
analyze multi-site MRI data. These procedures will be used to evaluate biomarkers specific to the progression of AD in longitudinal patient data. Methods: Eight 3T MRI sites are participating across Italy, Spain, France and Germany. Phantom QA procedure from the function BIRN were adopted and extended to include spike signal detection [2]. After local ethics approval each site is recruiting 5 local healthy volunteers in the age range of the clinical population for two acquisitions a week apart of the full protocol: two structural T1 (MPRAGE), one structural T2 (GE), one structural FLAIR (IR-FSE), one resting state fMRI (EPI), one B0 map for distortion correction, and a DTI (30 directions, b ¼ 700 s/mm2, 5 b0). Where appropriate, parameters were set according to ADNI [3] and BIRN [2] experiences. Data analysis : i) MPRAGE Freesurfer segmentation, ii) ICA resting state network with FSL and iii) fractional anisotropy (FA) maps from the DTI data using FSL. Results: The phantom stability QA has been successfully implemented and gives comparable results across sites. Qualitative assessment of image contrast and artefacts suggested that no protocol changes were needed. Quantitative comparisons of the morphometry (gray matter, white matter and CSF volumes), resting state fMRI (default mode network spatial map) and FA data indicated good consistency with the literature, tested across sites. Preliminary test-retest reproducibility results are also consistent with the literature. Conclusions: A multi-site MRI protocol was implemented and successfully tested for QA and for very basic preliminary reproducibility tests on healthy volunteers. References: [1] http:// www.alzheimer-europe.org/FR/Research/PharmaCog. [2] Friedman et al. Neuroimage (2006). [3] Jack et al. J Magn Reson Imaging (2008). P2-236
REGIONAL CORTICAL THINNING PREDICTS WORSENING APATHY AND HALLUCINATIONS IN MILD COGNITIVE IMPAIRMENT AND MILD ALZHEIMER’S DISEASE DEMENTIA
Nancy Donovan1, Lauren Wadsworth2, Natacha Lorius3, Joseph Locascio4, Dorene Rentz5, Keith Johnson6, Reisa Sperling5, 1Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School and Cambridge Health Alliance, Boston, Massachusetts, United States; 2Massachusetts General Hospital, Charlestown, Massachusetts, United States; 3Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, Massachusetts, United States; 4Massachusetts General Hospital, Boston, Massachusetts, United States; 5Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States; 6 Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States. Background: Apathy and hallucinations are debilitating neuropsychiatric symptoms accompanying cognitive and functional decline in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. Prior cross-sectional studies of apathy in AD dementia have most consistently implicated structural and functional changes in anterior cingulate and medial orbitofrontal cortices. The pathophysiological basis for hallucinations in AD is poorly understood. The objective of this study was to examine magnetic resonance imaging (MRI) cortical thickness and cerebrospinal fluid (CSF) AD biomarkers in relation to apathy and hallucinations, cross-sectionally and longitudinally, in a continuum of individuals with normal cognition (NC), MCI, and mild AD dementia. Methods: Eight hundred and twelve subjects from the Alzheimer’s Disease Neuroimaging Initiative study (229 NC, 395 MCI, 188 AD) underwent structural MRI at baseline and clinical assessments at baseline and longitudinally up to 3 years. CSF abeta, total tau, and phospho-tau were obtained for a subset of 413 subjects at baseline. Backward elimination mixed random/fixed coefficient longitudinal regression models were used to evaluate the relationships between baseline cortical thickness in 6 regions (anterior cingulate, medial orbitofrontal, dorsolateral prefrontal, supramarginal, inferior temporal, occipital) and CSF biomarkers versus change in apathy and hallucinations measured by the Neuropsychiatric Inventory-Questionnaire. Covariates included the baseline dependent variable, diagnosis, gender, age, Apolipoprotein E, premorbid intelligence, memory performance, executive function, antidepressant use, and AD duration. General linear regression models were used to examine analogous cross-sectional
Poster Presentations: P2 associations at baseline. Results: Reduced baseline inferior temporal cortical thickness was predictive of increasing apathy over time (P <0.0001; R 2 ¼ 0.59 for full model with random and fixed terms), while reduced supramarginal cortical thickness was predictive of increasing hallucinations over time (P ¼ 0.04; R 2 ¼ 0.66 for model). There was no association with cortical thickness cross-sectionally. CSF biomarkers were not related to apathy or hallucinations severity in cross-sectional or longitudinal analyses. Conclusions: These results suggest that temporal and parietal cortical thinning is associated with worsening apathy and hallucinations in a large cohort across the AD spectrum. CSFAD biomarkers did not show associations with these neuropsychiatric symptoms. Additional longitudinal studies may further elucidate the expression and time course of these debilitating symptoms in relation to AD biomarkers.
P2-237
NOVEL 18F-LABELED QUINOLINE DERIVATIVES FOR IN VIVO DETECTION OF TAU PATHOLOGY IN ALZHEIMER’S DISEASE
Nobuyuki Okamura1, Shozo Furumoto1, Ryuichi Harada1, Michelle Fodero-Tavoletti2, Victor Villemagne2, Ren Iwata3, Kazuhiko Yanai1, Yukitsuka Kudo4, 1Tohoku University School of Medicine, Sendai, Japan; 2University of Melbourne, Melbourne, Australia; 3CYRIC, Tohoku University, Sendai, Japan; 4Innovation of New Biomedical Engineering Center, Tohoku University, Sendai, Japan. Background: Imaging of amyloid-b (Ab) and tau fibrils in the brain is considered a useful biomarker for the non-invasive diagnosis of Alzheimer’s disease (AD) pathology. Although several PET Ab imaging agents are currently available, a selective tau imaging agent is still unavailable for clinical studies. We developed novel 18 F-labeled quinoline derivatives (THK-5105, THK-5117 and THK-5125), as candidate compounds for PET tau imaging probes. In this study, binding and pharmacokinetic properties of these compounds were assessed as potential tau imaging agents. Methods: 18 F-labeled THK compounds were prepared from the corresponding tosylated precursors. Binding affinity (Kd or Ki) of THK compounds to tau and Ab aggregates was determined and compared with other available PET tracers. Binding to tau pathology was evaluated by autoradiography in AD hippocampal sections. Brain uptake of THK compounds was assessed in biodistribution studies in normal mice. Small animal PET studies in tau and APP/PS1 transgenic mice were performed using [18 F]THK-5105. Results: Three novel compounds bind to tau fibrils with higher affinity (Kd or Ki < 12 nM) than THK-523. Binding affinity of these compounds to Ab fibrils was relatively lower (Kd > 20 nM) than tau fibrils. Autoradiography of hippocampal sections demonstrated THK compounds co-localized with tau pathology in AD brain. Biodistribution studies in mice showed high uptake (> 6 %ID/g at 2 min post injection) and rapid clearance (< 1 %ID/g at 60 min post injection) from normal brain tissue. Animal PET studies revealed higher brain retention of [18 F] THK-5105 in tau transgenic mice compared with their wild-type littermates or APP/PS1 mice. Conclusions: These findings suggest that three THK compounds are potential candidates for imaging of tau pathology in AD.
P2-238
P343
plying data driven techniques to identify spatial patterns of correlated longitudinal brain change over a 2-year period, based on structural magnetic resonance imaging (MRI). Methods: We quantified inter-regional covariance in cortical gray matter changes in 313 Alzheimer’s Disease Neuroimaging Initiative participants who were clinically diagnosed with amnestic mild cognitive impairment at baseline and underwent serial MRI at 6-month intervals over the course of 2 years. A set of 35 bilateral cortical gray matter region volumes were estimated for each MRI using FreeSurfer. Baseline region volumes and rates of change in volume over 2 years were derived from region specific growth curve models. The covariance of the rates of change between regions was analyzed with exploratory structural equation modeling (ESEM). The ESEM model was used to estimate a factor analysis model with pre-specified residual covariance structure to identify factors (i.e. groupings of regions) that exhibited highly correlated rates of change. Results: A four-factor model provided the best account of regional changes: this model exhibited adequate fit (CFI ¼ 0.965, RMSEA ¼ 0.06) and minimized the Bayesian Information Criterion over all models between 1 and 5 factors (see Table and Figure). The four factors approximately corresponded to co-occurring change within the prefrontal cortex; medial temporal lobe; posterior default mode network (i.e., posterior cingulate, precuneus, and inferior parietal regions); and regions largely spared by the early pathological course of AD (i.e., sensorimotor and occipital cortex). Conclusions: The data-driven observation of coordinated “frontal aging” superimposed upon traditional early-AD atrophy and default mode network changes supports the view that in individuals at high risk of eventual clinical AD, multiple co-occurring patterns of distributed neuronal death may be
COEVOLUTION OF BRAIN STRUCTURES IN MILD COGNITIVE IMPAIRMENT
Owen Carmichael1, Donald McLaren2, Douglas Tommet3, Dan Mungas4, Richard Jones3, 1University of California, Davis, Davis, California, United States; 2Harvard Medical School, Bedford, Massachusetts, United States; 3 Institute for Aging Research, Boston, Massachusetts, United States; 4 University of California, Davis, Sacramento, California, United States. Background: Network accounts of Alzheimer’s disease (AD), based on cross-sectional brain imaging observations, postulate that the biological course of the disease is characterized by coordinated spatial patterns of brain change to distributed cognitive networks. We tested this conjecture by ap-
Figure 1. Visual depiction of the four grouping (factors) of regions that showed strong correlation in rates of change according to ESEM modeling. For each such factor, regions with factor loadings greater than .4 are shown with a yellow box.
Poster Presentations: P2
Background: Alzheimer’s disease (AD) patients show significant changes in white matter (WM) structural integrity. Diffusion tensor imaging (DTI) is a neuroimaging technique that allows in vivo assessment of WM fiber tract integrity and, thus, could support the diagnosis of AD as an additional biomarker. Current research focuses on machine learning (ML) methods to automatically detect AD specific structural WM changes. Therefore, the algorithms used must be robust and stable to work with data recorded across different scanners. Within the newly created framework of the European DTI study in Dementia (EDSD) we have collected data of more than 330 subjects from ten scanners located at nine sites. Objective: To assess the accuracy of ML classifiers for the detection of AD based on a large multicenter DTI data set using different approaches to reduce inter-site variability. Methods: After strict quality control we pooled the remaining 280 DTI and MRI scans derived from 137 patients with clinically probable AD and 143 healthy elderly controls. For classification we used fractional anisotropy (FA) maps and mean diffusivity (MD) maps and performed a tenfold cross validation. We selected discriminative voxels using the information gain criterion and classified the data with a Support Vector Machine. In a second step, we eliminated variance attributable to center and other covariates including age, education, gender, using principal component analysis (PCA) before repeating the classification procedure. Results: For FA and MD the feature selection identified areas in the medial temporal lobe and corpus callosum that had the strongest contribution to the group separation. We achieved an accuracy of 80% for FA and 83% for MD. For the tissue density maps we obtained 83% for WM and 89% for gray matter. The reduction of variance components arising from center, gender, age and education effects did not significantly change the classification results for FA and MD. Conclusions: Multicenter acquisition of DTI data in combination with multivariate ML approaches show promising results which can be compared to earlier monocenter DTI studies. Variance introduced by different scanners can be detected by PCA, but it seems not to affect the performance of the classifier. P2-235
PHARMACOG: MULTI-SITE MRI CALIBRATION TO STUDY PROGRESSION OF ALZHEIMER’S DISEASE
Jorge Jovicich1, Genoveffa Borsci2, Moira Marizzoni3, Roser Salauria Bargall o4, David Bartres-Faz4, Jens Benninghoff5, Llonch4, N Jens Wiltfang6, Luca Roccatagliata7, Flavio Nobili8, Karl-Titus Hoffmann9, Thomas G€ unther9, Peter Sch€onknecht9, Aurelien Monnet10, Regis Bordet11, Valerie Chanoine12, Alexandra Auffret12, Jean-Philippe Ranjeva12, Oliver Blin12, Helene Gros-Dagnac13, Pierre Payoux14, Giada Zoccatelli15, Franco Alessandrini15, Alberto Beltramello15, Hans-Goran Hardemark16, Giovanni Frisoni17, 1University of Trento, Trento, Italy; 2LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine - IRCCS San Giovanni di Dio-FBF, Brescia, Italy; 3Laboratory of Epidemiology and Neuroimaging, IRCCS Fatebenefratelli, Brescia, Italy, Brescia, Italy; 4 Universitat de Barcelona and IDIBAPS, Barcelona, Spain; 5Universitaet Duisburg-Essen Department of Psychiatry and Nuclear Medicine, Essen, Germany; 6Universitaet Duisburg-Essen Department of Psychiatry and Nuclear Medicine, Essem, Germany; 7Department of Neuroscience, Ophthalmology and Genetics University of Genoa, Genoa, Italy; 8Clinical Neurophysiology, Department of Neurosciences, Ophthalmology and Genetics, University of Genoa, Genoa, Italy; 9University Hospital Leipzig, Department of Psychiatry, Section of Neuroradiology, Leipzig, Germany; 10 Universite Lille 2, Lille, France; 11Universite Lille 2, UL2, Lille, France; 12 CIC-UPCET, CHU La Timone, AP-HM, UMR CNRS-Universite de la Mediterranee, Marseille, France; 13Institut National de la Sante et de la Recherche Medicale, Toulouse, France; 14Institut National de la Sante et de la Recherche Medicale, Toulouse, France; 15General Hospital, Verona, Italy; 16AstraZencea R&D Clinical Neuroscience Therapy Area, S€odert€alje, Sweden; 17IRCCS Fatebenefratelli, Brescia, Italy. Background: Pharmacog is an industry-academic project aimed at finding new biomarkers for Alzheimer’s disease [1]. The main issue concerning multicenter neuroimaging clinical studies is the difficulty in getting comparable analytical responses. Here we present preliminary work aimed at developing and implementing standardized procedures to acquire and
analyze multi-site MRI data. These procedures will be used to evaluate biomarkers specific to the progression of AD in longitudinal patient data. Methods: Eight 3T MRI sites are participating across Italy, Spain, France and Germany. Phantom QA procedure from the function BIRN were adopted and extended to include spike signal detection [2]. After local ethics approval each site is recruiting 5 local healthy volunteers in the age range of the clinical population for two acquisitions a week apart of the full protocol: two structural T1 (MPRAGE), one structural T2 (GE), one structural FLAIR (IR-FSE), one resting state fMRI (EPI), one B0 map for distortion correction, and a DTI (30 directions, b ¼ 700 s/mm2, 5 b0). Where appropriate, parameters were set according to ADNI [3] and BIRN [2] experiences. Data analysis : i) MPRAGE Freesurfer segmentation, ii) ICA resting state network with FSL and iii) fractional anisotropy (FA) maps from the DTI data using FSL. Results: The phantom stability QA has been successfully implemented and gives comparable results across sites. Qualitative assessment of image contrast and artefacts suggested that no protocol changes were needed. Quantitative comparisons of the morphometry (gray matter, white matter and CSF volumes), resting state fMRI (default mode network spatial map) and FA data indicated good consistency with the literature, tested across sites. Preliminary test-retest reproducibility results are also consistent with the literature. Conclusions: A multi-site MRI protocol was implemented and successfully tested for QA and for very basic preliminary reproducibility tests on healthy volunteers. References: [1] http:// www.alzheimer-europe.org/FR/Research/PharmaCog. [2] Friedman et al. Neuroimage (2006). [3] Jack et al. J Magn Reson Imaging (2008). P2-236
REGIONAL CORTICAL THINNING PREDICTS WORSENING APATHY AND HALLUCINATIONS IN MILD COGNITIVE IMPAIRMENT AND MILD ALZHEIMER’S DISEASE DEMENTIA
Nancy Donovan1, Lauren Wadsworth2, Natacha Lorius3, Joseph Locascio4, Dorene Rentz5, Keith Johnson6, Reisa Sperling5, 1Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School and Cambridge Health Alliance, Boston, Massachusetts, United States; 2Massachusetts General Hospital, Charlestown, Massachusetts, United States; 3Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, Massachusetts, United States; 4Massachusetts General Hospital, Boston, Massachusetts, United States; 5Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States; 6 Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States. Background: Apathy and hallucinations are debilitating neuropsychiatric symptoms accompanying cognitive and functional decline in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. Prior cross-sectional studies of apathy in AD dementia have most consistently implicated structural and functional changes in anterior cingulate and medial orbitofrontal cortices. The pathophysiological basis for hallucinations in AD is poorly understood. The objective of this study was to examine magnetic resonance imaging (MRI) cortical thickness and cerebrospinal fluid (CSF) AD biomarkers in relation to apathy and hallucinations, cross-sectionally and longitudinally, in a continuum of individuals with normal cognition (NC), MCI, and mild AD dementia. Methods: Eight hundred and twelve subjects from the Alzheimer’s Disease Neuroimaging Initiative study (229 NC, 395 MCI, 188 AD) underwent structural MRI at baseline and clinical assessments at baseline and longitudinally up to 3 years. CSF abeta, total tau, and phospho-tau were obtained for a subset of 413 subjects at baseline. Backward elimination mixed random/fixed coefficient longitudinal regression models were used to evaluate the relationships between baseline cortical thickness in 6 regions (anterior cingulate, medial orbitofrontal, dorsolateral prefrontal, supramarginal, inferior temporal, occipital) and CSF biomarkers versus change in apathy and hallucinations measured by the Neuropsychiatric Inventory-Questionnaire. Covariates included the baseline dependent variable, diagnosis, gender, age, Apolipoprotein E, premorbid intelligence, memory performance, executive function, antidepressant use, and AD duration. General linear regression models were used to examine analogous cross-sectional
Poster Presentations: P2 associations at baseline. Results: Reduced baseline inferior temporal cortical thickness was predictive of increasing apathy over time (P <0.0001; R 2 ¼ 0.59 for full model with random and fixed terms), while reduced supramarginal cortical thickness was predictive of increasing hallucinations over time (P ¼ 0.04; R 2 ¼ 0.66 for model). There was no association with cortical thickness cross-sectionally. CSF biomarkers were not related to apathy or hallucinations severity in cross-sectional or longitudinal analyses. Conclusions: These results suggest that temporal and parietal cortical thinning is associated with worsening apathy and hallucinations in a large cohort across the AD spectrum. CSFAD biomarkers did not show associations with these neuropsychiatric symptoms. Additional longitudinal studies may further elucidate the expression and time course of these debilitating symptoms in relation to AD biomarkers.
P2-237
NOVEL 18F-LABELED QUINOLINE DERIVATIVES FOR IN VIVO DETECTION OF TAU PATHOLOGY IN ALZHEIMER’S DISEASE
Nobuyuki Okamura1, Shozo Furumoto1, Ryuichi Harada1, Michelle Fodero-Tavoletti2, Victor Villemagne2, Ren Iwata3, Kazuhiko Yanai1, Yukitsuka Kudo4, 1Tohoku University School of Medicine, Sendai, Japan; 2University of Melbourne, Melbourne, Australia; 3CYRIC, Tohoku University, Sendai, Japan; 4Innovation of New Biomedical Engineering Center, Tohoku University, Sendai, Japan. Background: Imaging of amyloid-b (Ab) and tau fibrils in the brain is considered a useful biomarker for the non-invasive diagnosis of Alzheimer’s disease (AD) pathology. Although several PET Ab imaging agents are currently available, a selective tau imaging agent is still unavailable for clinical studies. We developed novel 18 F-labeled quinoline derivatives (THK-5105, THK-5117 and THK-5125), as candidate compounds for PET tau imaging probes. In this study, binding and pharmacokinetic properties of these compounds were assessed as potential tau imaging agents. Methods: 18 F-labeled THK compounds were prepared from the corresponding tosylated precursors. Binding affinity (Kd or Ki) of THK compounds to tau and Ab aggregates was determined and compared with other available PET tracers. Binding to tau pathology was evaluated by autoradiography in AD hippocampal sections. Brain uptake of THK compounds was assessed in biodistribution studies in normal mice. Small animal PET studies in tau and APP/PS1 transgenic mice were performed using [18 F]THK-5105. Results: Three novel compounds bind to tau fibrils with higher affinity (Kd or Ki < 12 nM) than THK-523. Binding affinity of these compounds to Ab fibrils was relatively lower (Kd > 20 nM) than tau fibrils. Autoradiography of hippocampal sections demonstrated THK compounds co-localized with tau pathology in AD brain. Biodistribution studies in mice showed high uptake (> 6 %ID/g at 2 min post injection) and rapid clearance (< 1 %ID/g at 60 min post injection) from normal brain tissue. Animal PET studies revealed higher brain retention of [18 F] THK-5105 in tau transgenic mice compared with their wild-type littermates or APP/PS1 mice. Conclusions: These findings suggest that three THK compounds are potential candidates for imaging of tau pathology in AD.
P2-238
P343
plying data driven techniques to identify spatial patterns of correlated longitudinal brain change over a 2-year period, based on structural magnetic resonance imaging (MRI). Methods: We quantified inter-regional covariance in cortical gray matter changes in 313 Alzheimer’s Disease Neuroimaging Initiative participants who were clinically diagnosed with amnestic mild cognitive impairment at baseline and underwent serial MRI at 6-month intervals over the course of 2 years. A set of 35 bilateral cortical gray matter region volumes were estimated for each MRI using FreeSurfer. Baseline region volumes and rates of change in volume over 2 years were derived from region specific growth curve models. The covariance of the rates of change between regions was analyzed with exploratory structural equation modeling (ESEM). The ESEM model was used to estimate a factor analysis model with pre-specified residual covariance structure to identify factors (i.e. groupings of regions) that exhibited highly correlated rates of change. Results: A four-factor model provided the best account of regional changes: this model exhibited adequate fit (CFI ¼ 0.965, RMSEA ¼ 0.06) and minimized the Bayesian Information Criterion over all models between 1 and 5 factors (see Table and Figure). The four factors approximately corresponded to co-occurring change within the prefrontal cortex; medial temporal lobe; posterior default mode network (i.e., posterior cingulate, precuneus, and inferior parietal regions); and regions largely spared by the early pathological course of AD (i.e., sensorimotor and occipital cortex). Conclusions: The data-driven observation of coordinated “frontal aging” superimposed upon traditional early-AD atrophy and default mode network changes supports the view that in individuals at high risk of eventual clinical AD, multiple co-occurring patterns of distributed neuronal death may be
COEVOLUTION OF BRAIN STRUCTURES IN MILD COGNITIVE IMPAIRMENT
Owen Carmichael1, Donald McLaren2, Douglas Tommet3, Dan Mungas4, Richard Jones3, 1University of California, Davis, Davis, California, United States; 2Harvard Medical School, Bedford, Massachusetts, United States; 3 Institute for Aging Research, Boston, Massachusetts, United States; 4 University of California, Davis, Sacramento, California, United States. Background: Network accounts of Alzheimer’s disease (AD), based on cross-sectional brain imaging observations, postulate that the biological course of the disease is characterized by coordinated spatial patterns of brain change to distributed cognitive networks. We tested this conjecture by ap-
Figure 1. Visual depiction of the four grouping (factors) of regions that showed strong correlation in rates of change according to ESEM modeling. For each such factor, regions with factor loadings greater than .4 are shown with a yellow box.