- Email: [email protected]
Combining Two Large MRI Data Sets (AddNeuroMed and ADNI) Using Multivariate Data Analysis to Distinguish between Patients with Alzheimer's Disease and Healthy Controls
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IC-P-136
Alzheimer’s Imaging Consotium IC-P: Imaging Posters
THYROID HORMONES AND CSF BIOMARKERS IN RELATION TO COGNITION IN MILD COGNITIVE IMPAIRMENT
Patrick S. Quinlan1, Arto Nordlund1, Deborah R. Gustafson1,2, Anders Wallin1, 1Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 2Departments of Neurology and Medicine, Section for Neuroepidemiology, State University of New York - Downstate Medical Center, New York City, NY, USA. Contact e-mail: [email protected] Background: Overt and subclinical hyper- and hypothyroidism are associated with cognitive impairment and dementia. Higher normal levels of thyroid hormones (TH) have been associated with increased risk for dementia, as well as formation of neurofibrillary tangles and neuritic plaques. This study examines the association of thyroid hormones and cerebrospinal fluid (CSF) biomarkers in relationship to cognitive function in MCI. Methods: In 43 euthyroid MCI patients and 26 healthy controls, serum levels of TSH, total T4, free T4, total T3 (TT3), and CSF levels of beta-amyloid 42 (Ab42), total- tau (T-tau), and phospho-tau (P-tau) were measured. Each participant underwent a comprehensive neuropsychological test battery, consisting of 20 tests covering the cognitive domains speed/attention, memory, visuospatial functions, language and executive functions. A composite cognitive score was calculated. Results: TT3 and T-tau were inversely related to cognitive function within the MCI group. Interaction between TT3 and P-tau levels were found, explaining 54% of the variance of cognitive composite scores compared to 26% of the variance explained by TT3 alone. P-tau levels alone did not predict cognitive function nor were THs and CSFbiomarkers related. Stratification of MCI patients according to TT3 levels revealed decreased episodic memory, executive and visuospatial function with higher normal TT3 levels. Conclusions: Increasing TT3 levels within the normal range were associated with a distinct cognitive profile typical of prodromal Alzheimer’s disease. A more pronounced inverse association of TT3 and cognitive function was observed in MCI cases with high P-tau levels. IC-P-137
COMBINING TWO LARGE MRI DATA SETS (ADDNEUROMED AND ADNI) USING MULTIVARIATE DATA ANALYSIS TO DISTINGUISH BETWEEN PATIENTS WITH ALZHEIMER’S DISEASE AND HEALTHY CONTROLS
Eric Westman1, Andrew Simmons2, J.-Sebastian Muehlboeck1, Femida Gwadry-Sridhar3, Simon Fristed Eskildsen4, Per Julin5, Niclas Sjo¨gren5, D. Louis Collins6, Alan Evans6, Patrizia Mecocci7, Bruno Vellas8, Magda Tsolaki9, Iwona Kłoszewska10, Hilkka Soininen11, Michael Weiner12, S. Lovestone2, Christian Spenger1, Lars-Olof Wahlund1, AddNeuroMed consortium, 1Karolinska Institutet, Stockholm, Sweden; 2 Kings College London, London, United Kingdom; 3Lawson Health Research Institute, London, ON, Canada; 4Aalborg University, Aalborg, Denmark; 5AstraZeneca R&D, So¨derta¨lje, Sweden; 6McGill University, Montreal, QC, Canada; 7University of Perugia, Perugia, Italy; 8University of Toulouse, Toulouse, France; 9Aristotle University of Thessaloniki, Thessaloniki, Greece; 10Medical University of Lodz, Lodz, Poland; 11University and University Hospital of Kuopio, Kuopio, Finland; 12University of California, San Francisco, CA, USA. Contact e-mail: [email protected] Background: The European Union AddNeuroMed project and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-centre initiatives designed to analyse and validate biomarkers for AD. This study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and multivariate data analysis. Methods: A total of 664 subjects were included in this study (AddNeuroMed: 126 AD, 115 CTL, ADNI: 194 AD, 229 CTL) Data acquisition for the AddNeuroMed project was set up to be compatible with the ADNI study and the high resolution sagital 3D T1w MP-RAGE datasets used for image analysis. Regional segmentation of the brain was carried out using the multi-scale ANIMAL image analysis technique (Automated Non-linear Image Matching and Anatomical Labeling). Cortical thickness measurements were performed using CLASP. A total of 24 measures were
pooled together for multivariate analysis using the OPLS method (orthogonal partial least squares). Models were created for the two cohorts and for the combined cohorts to discriminate between AD patients and controls. Finally the ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort. Results: Using cross-validation, we achieved the following values: AddNeuroMed cohort: sensitivity ¼ 79%, specificity ¼ 86%; ADNI cohort: sensitivity ¼ 79%, specificity ¼ 87%; both cohorts combined: sensitivity ¼ 83%, specificity ¼ 83%. Using the AddNeuroMed cohort as a training set and validating the model with the ADNI cohort resulted in a sensitivity of 78% and specificity of 87%. All three models created showed very similar results. Examples of important variables for discriminating between AD and CTL included temporal lobe grey matter volume, total CSF volume and mean cortical thickness. Conclusions: Multivariate data analysis is a powerful tool for distinguishing between different patient groups. The AddNeuroMed, ADNI and combined cohorts showed similar patterns of atrophy and the predictive power was very similar. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned. IC-P-138
ANALYSIS OF PERIPHERAL BLOOD BIOMARKERS: TOWARDS THE EARLY DIAGNOSIS ALZHEIMER’S DISEASE
Simon M. Laws1,2, Alinda Mondal1,2, Chiou-Peng Lam3, David Ames4,5, Ashley I. Bush6,7, Kathryn A. Ellis4,5, James Doecke8, Noel G. Faux6,9, Veer Gupta1,2, James K. Lui1,2, Colin L. Masters6,9, Christopher C. Rowe10, Cassandra Szoeke5,11, Kevin Taddei1,2, Victor L. Villemagne6,10, Ralph N. Martins1,2, AIBL Research Group, 1Centre of Excellence for Alzheimer’s Disease Research & Care, School of Exercise Biomedical and Health Sciences, Edith Cowan University, Joondalup, Australia; 2Sir James McCusker Alzheimer’s Disease Research Unit, Hollywood Private Hospital, Perth, Australia; 3School of Computer & Security Science, Edith Cowan University, Mt. Lawley, Australia; 4Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, St. Vincent’s Aged Psychiatry Service, St George’s Hospital, Melbourne, Australia; 5 National Ageing Research Institute, Parkville, Australia; 6Mental Health Research Institute, The University of Melbourne, Parkville, Australia; 7Department of Pathology, The University of Melbourne, Parkville, Australia; 8 CSIRO, Brisbane, Australia; 9Centre for Neuroscience, The University of Melbourne, Parkville, Australia; 10Department of Nuclear Medicine & Centre for PET, Austin Health, Heidelberg, Australia; 11CSIRO, Parkville, Australia. Contact e-mail: [email protected] Background: Epidemiological studies have estimated that approximately 24 million people suffered from dementia worldwide in 2001, with this figure projected to double by 2020 and increase to 81 million by 2040. The development of an effective and early diagnostic test for AD is essential, as it may allow detection of disease before the onset of symptoms - when damage to the brain can be reasonably expected to be minimal. Methods: A discovery panel of 152 markers (RBM, Austin, Texas) was assessed using Luminex xMAP based technology across the entire AIBL cohort of 1112 individuals, a subset of which had undergone Pittsburgh Compound B imaging via Positron Emission Topography (PiB-PET). Data mining was undertaken on the collated data set independently and in combination with other clinically significant variables. Exploration involving classification, regression, and feature selection utilised the extensive range of algorithms from Waikato Environment for Knowledge Analysis (WEKA, version 3.6) and Tanagra 1.4. Feature selection was performed using techniques associated with linear forward selection and backward elimination. The evaluation of the performance of different algorithms included ‘10 times 10 fold’ cross validation and bootstrapping. Results: Initial cleaning of the discovery panel dataset reduced the number of variables to 116. Dependent upon the algorithm utilised in the analysis the discovery panel of biomarkers was further reduced to provide classification models with different variable numbers (16-22 variables) and sensitivity and specificity of 60-64% and 92-94%, respectively. In addition to the discovery panel of biomarkers, other clinically significant variables (e.g. plasma beta-amyloid levels, clinical pathology data) were added at the later stage of data analysis. Further, re-classification in terms of
IC-P-136
Alzheimer’s Imaging Consotium IC-P: Imaging Posters
THYROID HORMONES AND CSF BIOMARKERS IN RELATION TO COGNITION IN MILD COGNITIVE IMPAIRMENT
Patrick S. Quinlan1, Arto Nordlund1, Deborah R. Gustafson1,2, Anders Wallin1, 1Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 2Departments of Neurology and Medicine, Section for Neuroepidemiology, State University of New York - Downstate Medical Center, New York City, NY, USA. Contact e-mail: [email protected] Background: Overt and subclinical hyper- and hypothyroidism are associated with cognitive impairment and dementia. Higher normal levels of thyroid hormones (TH) have been associated with increased risk for dementia, as well as formation of neurofibrillary tangles and neuritic plaques. This study examines the association of thyroid hormones and cerebrospinal fluid (CSF) biomarkers in relationship to cognitive function in MCI. Methods: In 43 euthyroid MCI patients and 26 healthy controls, serum levels of TSH, total T4, free T4, total T3 (TT3), and CSF levels of beta-amyloid 42 (Ab42), total- tau (T-tau), and phospho-tau (P-tau) were measured. Each participant underwent a comprehensive neuropsychological test battery, consisting of 20 tests covering the cognitive domains speed/attention, memory, visuospatial functions, language and executive functions. A composite cognitive score was calculated. Results: TT3 and T-tau were inversely related to cognitive function within the MCI group. Interaction between TT3 and P-tau levels were found, explaining 54% of the variance of cognitive composite scores compared to 26% of the variance explained by TT3 alone. P-tau levels alone did not predict cognitive function nor were THs and CSFbiomarkers related. Stratification of MCI patients according to TT3 levels revealed decreased episodic memory, executive and visuospatial function with higher normal TT3 levels. Conclusions: Increasing TT3 levels within the normal range were associated with a distinct cognitive profile typical of prodromal Alzheimer’s disease. A more pronounced inverse association of TT3 and cognitive function was observed in MCI cases with high P-tau levels. IC-P-137
COMBINING TWO LARGE MRI DATA SETS (ADDNEUROMED AND ADNI) USING MULTIVARIATE DATA ANALYSIS TO DISTINGUISH BETWEEN PATIENTS WITH ALZHEIMER’S DISEASE AND HEALTHY CONTROLS
Eric Westman1, Andrew Simmons2, J.-Sebastian Muehlboeck1, Femida Gwadry-Sridhar3, Simon Fristed Eskildsen4, Per Julin5, Niclas Sjo¨gren5, D. Louis Collins6, Alan Evans6, Patrizia Mecocci7, Bruno Vellas8, Magda Tsolaki9, Iwona Kłoszewska10, Hilkka Soininen11, Michael Weiner12, S. Lovestone2, Christian Spenger1, Lars-Olof Wahlund1, AddNeuroMed consortium, 1Karolinska Institutet, Stockholm, Sweden; 2 Kings College London, London, United Kingdom; 3Lawson Health Research Institute, London, ON, Canada; 4Aalborg University, Aalborg, Denmark; 5AstraZeneca R&D, So¨derta¨lje, Sweden; 6McGill University, Montreal, QC, Canada; 7University of Perugia, Perugia, Italy; 8University of Toulouse, Toulouse, France; 9Aristotle University of Thessaloniki, Thessaloniki, Greece; 10Medical University of Lodz, Lodz, Poland; 11University and University Hospital of Kuopio, Kuopio, Finland; 12University of California, San Francisco, CA, USA. Contact e-mail: [email protected] Background: The European Union AddNeuroMed project and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-centre initiatives designed to analyse and validate biomarkers for AD. This study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and multivariate data analysis. Methods: A total of 664 subjects were included in this study (AddNeuroMed: 126 AD, 115 CTL, ADNI: 194 AD, 229 CTL) Data acquisition for the AddNeuroMed project was set up to be compatible with the ADNI study and the high resolution sagital 3D T1w MP-RAGE datasets used for image analysis. Regional segmentation of the brain was carried out using the multi-scale ANIMAL image analysis technique (Automated Non-linear Image Matching and Anatomical Labeling). Cortical thickness measurements were performed using CLASP. A total of 24 measures were
pooled together for multivariate analysis using the OPLS method (orthogonal partial least squares). Models were created for the two cohorts and for the combined cohorts to discriminate between AD patients and controls. Finally the ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort. Results: Using cross-validation, we achieved the following values: AddNeuroMed cohort: sensitivity ¼ 79%, specificity ¼ 86%; ADNI cohort: sensitivity ¼ 79%, specificity ¼ 87%; both cohorts combined: sensitivity ¼ 83%, specificity ¼ 83%. Using the AddNeuroMed cohort as a training set and validating the model with the ADNI cohort resulted in a sensitivity of 78% and specificity of 87%. All three models created showed very similar results. Examples of important variables for discriminating between AD and CTL included temporal lobe grey matter volume, total CSF volume and mean cortical thickness. Conclusions: Multivariate data analysis is a powerful tool for distinguishing between different patient groups. The AddNeuroMed, ADNI and combined cohorts showed similar patterns of atrophy and the predictive power was very similar. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned. IC-P-138
ANALYSIS OF PERIPHERAL BLOOD BIOMARKERS: TOWARDS THE EARLY DIAGNOSIS ALZHEIMER’S DISEASE
Simon M. Laws1,2, Alinda Mondal1,2, Chiou-Peng Lam3, David Ames4,5, Ashley I. Bush6,7, Kathryn A. Ellis4,5, James Doecke8, Noel G. Faux6,9, Veer Gupta1,2, James K. Lui1,2, Colin L. Masters6,9, Christopher C. Rowe10, Cassandra Szoeke5,11, Kevin Taddei1,2, Victor L. Villemagne6,10, Ralph N. Martins1,2, AIBL Research Group, 1Centre of Excellence for Alzheimer’s Disease Research & Care, School of Exercise Biomedical and Health Sciences, Edith Cowan University, Joondalup, Australia; 2Sir James McCusker Alzheimer’s Disease Research Unit, Hollywood Private Hospital, Perth, Australia; 3School of Computer & Security Science, Edith Cowan University, Mt. Lawley, Australia; 4Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, St. Vincent’s Aged Psychiatry Service, St George’s Hospital, Melbourne, Australia; 5 National Ageing Research Institute, Parkville, Australia; 6Mental Health Research Institute, The University of Melbourne, Parkville, Australia; 7Department of Pathology, The University of Melbourne, Parkville, Australia; 8 CSIRO, Brisbane, Australia; 9Centre for Neuroscience, The University of Melbourne, Parkville, Australia; 10Department of Nuclear Medicine & Centre for PET, Austin Health, Heidelberg, Australia; 11CSIRO, Parkville, Australia. Contact e-mail: [email protected] Background: Epidemiological studies have estimated that approximately 24 million people suffered from dementia worldwide in 2001, with this figure projected to double by 2020 and increase to 81 million by 2040. The development of an effective and early diagnostic test for AD is essential, as it may allow detection of disease before the onset of symptoms - when damage to the brain can be reasonably expected to be minimal. Methods: A discovery panel of 152 markers (RBM, Austin, Texas) was assessed using Luminex xMAP based technology across the entire AIBL cohort of 1112 individuals, a subset of which had undergone Pittsburgh Compound B imaging via Positron Emission Topography (PiB-PET). Data mining was undertaken on the collated data set independently and in combination with other clinically significant variables. Exploration involving classification, regression, and feature selection utilised the extensive range of algorithms from Waikato Environment for Knowledge Analysis (WEKA, version 3.6) and Tanagra 1.4. Feature selection was performed using techniques associated with linear forward selection and backward elimination. The evaluation of the performance of different algorithms included ‘10 times 10 fold’ cross validation and bootstrapping. Results: Initial cleaning of the discovery panel dataset reduced the number of variables to 116. Dependent upon the algorithm utilised in the analysis the discovery panel of biomarkers was further reduced to provide classification models with different variable numbers (16-22 variables) and sensitivity and specificity of 60-64% and 92-94%, respectively. In addition to the discovery panel of biomarkers, other clinically significant variables (e.g. plasma beta-amyloid levels, clinical pathology data) were added at the later stage of data analysis. Further, re-classification in terms of