Dementia Diagnosis - What Can We Learn from Structural Analysis
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Thursday May 12th
Room 710B  16:00 - 18:00 Moderators: Vincent Magnotta and Pia C. Maly Sundgren

16:00 681.   T2-VBM is more sensitive to Alzheimer's disease pathology than conventional T1-VBM 
Julio Acosta-Cabronero1, Lara Z Diaz-de-Grenu1, Joao MS Pereira1, George Pengas1, Guy B Williams1, and Peter J Nestor1
1Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom

In this study we tested the hypothesis that voxel-based morphometry (VBM) using the recently-developed T2-weighted SPACE acquisition would be more sensitive to grey matter pathology in Alzheimer’s disease (AD) than conventional T1-VBM using MPRAGE images with the same resolution. The distribution of abnormalities identified by T2-VBM, but not with T1-VBM, bore a striking resemblance to the distribution of amyloid plaque deposition in AD. This study demonstrates that T2-VBM is more sensitive to histopathological brain changes than the conventional T1-VBM method.

16:12 682.   HARDI-Based Microstructural Complexity Mapping Reveals Distinct Subcortical and Cortical Grey Matter Changes in Mild Cognitive Impairment and Alzheimer's Disease 
Hamied Ahmad Haroon1,2, Heather Reynolds1, Stephen F Carter2,3, Karl V Embleton2,4, Karl G Herholz2,3, and Geoff J Parker1,2
1Imaging Science & Biomedical Engineering, School of Cancer & Enabling Sciences, The University of Manchester, Manchester, England, United Kingdom, 2Biomedical Imaging Institute, The University of Manchester, Manchester, England, United Kingdom, 3Wolfson Molecular Imaging Centre, School of Cancer & Enabling Sciences, The University of Manchester, Manchester, England, United Kingdom, 4School of Psychological Sciences, The University of Manchester, Manchester, England, United Kingdom

We apply probabilistic characterization of grey matter diffusion complexity to patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), and healthy controls of similar age. We find statistical differences in the regional median probabilities of observing n distinct dominant diffusion orientations between these subject groups providing evidence for the microstructural changes in grey matter associated with these pathologies. We find that the degree of abnormality in grey matter complexity increases in AD relative to MCI, consistent with the concept of MCI being a prodromal state of AD.

16:24 683.   Anatomical connectivity to assess brain tissue modifications in Alzheimer’s disease 
Marco Bozzali1, Geoff Parker2, Laura Serra1, Roberta Perri3, Franco Giubilei4, Camillo Marra5, Carlo Caltagirone3, and Mara Cercignani1
1Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy, 2Imaging Science & Biomedical Engineering, University of Manchester, Manchester, United Kingdom,3Department of Clinical and Behavioural Neurology, Santa Lucia Foundation, Rome, Italy, 4Department of Neurology, II Faculty of Medicine, “Sapienza” University of Rome, Rome, 5Institute of Neurology, Università Cattolica, Rome, Italy

A recent application of anatomical connectivity mapping (ACM) to Alzheimer’s disease (AD) patients, has shown expected reductions as well as unexpected increases of structural brain connectivity. The latter finding was interpreted as a possible consequence of processes of brain plasticity driven by treatment with cholinesterase inhibitors (ChEIs). Here, we confirm and extend all preliminary findings by assessing ACM in a larger group of patients with AD, half of them under ChEIs medication and half drug naïve. This study further supports the hypothesis that ChEIs induce mechanisms of plasticity in AD brains, which may also interact with measures of global cognition.

16:36 684.   Robust high-dimensional morphological metric: application to the ADNI multi-centric dataset 
Nicolas Robitaille1, Abderazzak Mouiha1, and Simon Duchesne1,2
1Centre de recherche Université Laval Robert-Giffard, Québec, QC, Canada, 2Radiology, Université Laval, Quebec, QC, Canada

Structural MRI has been proposed to fulfill the role of quantitative biomarker in Alzheimer’s disease. We proposed a high-dimensional morphological metric extracted from T1-weighted MRI and now wish to demonstrate its robustness in a multi-centric setting. To form our metric we used data from two different studies, totaling 300 subjects. We tested the metric over the 797 subjects of the ADNI dataset, and found an average scan/repeat scan distance of 1.7%. In order to detect a 15% difference between groups, this minimum precision threshold results in an increase from 59 to 75 participants to reach identical power in a trial.

16:48 685.   Automated imaging classification based on volumetric analysis: application on primary progressive aphasia 
Andreia Vasconcellos Faria1,2, Kyrana Tsapkini3, Jennifer Crinion4, Hangyi Jiang1, Xin Li1, Kenichi Oishi1, Peter van Zijl1, Michael Miller5, Argye Hillis3, and Susumu Mori1
1Radiology, Johns Hopkins University, Baltimore, MD, United States, 2Radiology, State University of Campinas, Campinas, SP, Brazil, 3Neurology, Johns Hopkins University, Baltimore, MD, United States, 4Institute of Cognitive Neuroscience, University College London, 5Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States

Based on large-deformation diffeomorphic metric mapping and Atlas-based analysis, we developed a method to capture the anatomical features and classifying primary progressive aphasia (PPA) patients. Principal component analysis, multivariate techniques and predictive modeling were applied to a 32 PPA patients and 27 controls. The variables used to create and test the classification model were selected among the volumes of the 211 regions obtained from the automated 3D segmentation. The percentage of correct classification was 83% after two-level cross-validation. The results from this automated quantitative analysis can be user-friendly displayed and this method can be applied to routine clinical practice

17:00 686.   Magnetization Transfer Imaging of Individual Beta-Amyloid Plaques in Alzheimer's Disease 
Mark David Meadowcroft1,2, Zachary George Herse1, James R Connor3, and Qing X Yang1
1Radiology - Center for NMR Research, Pennsylvania State University - College of Medicine, Hershey, PA, United States, 2DMCP - Neuroimaging, Bristol-Myers Squibb, Wallingford, CT, United States, 3Neurosurgery, Pennsylvania State University - College of Medicine, Hershey, PA, United States

Our previous research illuminated that transverse relaxation and contrast related to Alower case Greek beta plaques seen in T2* weighted images are associated with plaque morphology and iron content. The fibrillar organization and macromolecule architecture of Alower case Greek beta plaques presents an ideal setting for the usage of magnetization transfer (MT) imaging to view tightly bond protons on the surface of component fibrils in the Alower case Greek beta plaques. The data demonstrate the detailed MT associated with individual plaques and the different MT ratios of Alower case Greek beta ROI’s compared to surrounding tissue. To our knowledge, this data represents the first MT imaging of individual Alower case Greek beta plaques.

17:12 687.   Structural differences can be found between MCI converters and non-converters more than 2 years prior to conversion to AD 
Gwenaelle Douaud1, Ricarda Menke1, Achim Gass2, Andreas Monsch3, Marc Sollberger2,3, Anil Rao4, Brandon Whitcher4, Paul Matthews4, and Stephen Smith1
1FMRIB Centre, University of Oxford, Oxford, Oxfordshire, United Kingdom, 2Departments of Neurology and Neuroradiology, University Hospital Basel, Switzerland,3Memory Clinic, Department of Geriatrics, University Hospital Basel, Switzerland, 4GlaxoSmithKline,Clinical Imaging Centre, Hammersmith Hospital London

We investigated for the first time grey and white matter differences at baseline between “stable” amnestic MCI patients and those who later converted to AD. Importantly, we focused on late conversion, as patients converted at least two years after their scan. Using FSL-VBM and TBSS, we found significantly reduced GM mainly in the striatum and the left hippocampus and increased FA where the SLF crosses the CST in the MCI converters. Remarkably, these white matter microstructural alterations detected in crossing-fibre tracts using diffusion imaging proved more sensitive to predict conversion to AD.

17:24 688.   Multi-modal MRI analysis with disease specific spatial filtering: initial testing to predict mild cognitive impairment patients who convert to Alzheimer’s disease 
Kenichi Oishi1, Michelle M Mielke2, Andreia V Faria1, Michael I Miller3, Perer C.M. van Zijl3,4, Marilyn Albert5,6, Constantine G Lyketsos2,6, and Susumu Mori1,4
1Radiology, Johns Hopkins University, Baltimore, MD, United States, 2Psychiatry and Behavioral Sciences, Johns Hopkins University, 3Johns Hopkins University, 4Kennedy Krieger Institute, 5Neurology, Johns Hopkins University, 6The Johns Hopkins Alzheimer’s Disease Research Center

We have developed an image analysis tool in which information extracted from multiple MRI modalities, using disease-specific spatial filters, is combined to generate a disease score. This tool was tested as an automated method to predict the conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer’s disease (AD). We created disease-specific filters for each modality and optimized the combination to separate AD from control participants, using a training dataset, and applied the tool to calculate disease scores of 22 aMCI patients. The disease score predicted the conversion better than a single-modality approach, indicating the potential value for clinical application.

17:36 689.   Joint analysis of structural and quantitative magnetization transfer MRI for classification of Alzheimer’s disease and normal aging 
Giovanni Giulietti1, Marco Bozzali1, Viviana Figura1, Roberta Perri2, Camillo Marra3, Franco Giubilei4, and Mara Cercignani1
1Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy, 2Department of Clinical and Behavioural Neurology, Santa Lucia Foundation, Rome, Italy, 3Institute of Neurology, Cattolica University, Rome, Italy, 4Department of Neurology, Sapienza University, Rome, Italy

Quantitative magnetisation transfer imaging (qMTI) is an extension of MTI which allows the binary spin bath model parameters to be estimated. In this study we have extended the assessment of qMT parameters in patients with Alzheimer’s disease (AD) to the whole brain and determined the joint contribution of gray matter regional atrophy and qMT parameters for the classification of AD using a logistic regression analysis. Our results indicate that a decrese of RM0B (forward exchange rate) in the hipoccampal/parahippocampal areas, in the posterior cingulate, and in the posterior parietal cortex is significantly predictive of AD diagnosis.

17:48 690.   Decreased Brain Stiffness in Alzheimer's Disease Determined by Magnetic Resonance Elastography 
Matthew C Murphy1, John Huston, III1, Clifford R Jack, Jr.1, Kevin J Glaser1, Armando Manduca1, Joel P Felmlee1, and Richard L Ehman1
1Department of Radiology, Mayo Clinic, Rochester, MN, United States

MRE is a technique for noninvasively measuring tissue stiffness. The purpose of this work was to assess reproducibility of a 3D MRE exam of the brain in 10 healthy volunteers, and to use the 3D brain MRE exam to study the effects of Alzheimer’s disease (AD) in 7 subjects with probable AD, 14 age- and gender-matched PIB- controls and 7 age- and gender-matched PIB+ controls. MRE detected a significant decrease in brain stiffness in subjects with AD compared to both control groups. This decrease in stiffness likely reflects a loss of normal cytoarchitecture of the brain parenchyma due to AD.