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Thursday May 12th
Room 710A  16:00 - 18:00 Moderators: Jonathan Clayden and Derek Jones

16:00 671.   Tensor Based Morphometry of White Matter Tracts using Fibre Orientation Distributions  -permission withheld
David Raffelt1,2, Olivier Salvado1, Stephen Rose3, Robert Henderson4, Alan Connelly5,6, Stuart Crozier2, and J-Donald Tournier5,6
1The Australian E-Health Research Centre, CSIRO, Brisbane, QLD, Australia, 2Biomedical Engineering, School of ITEE, University of Queensland, Brisbane, QLD, Australia,3Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia, 4Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia, 5Brain Research Institute, Florey Neuroscience Institutes (Austin), Melbourne, VIC, Australia, 6Department of Medicine, University of Melbourne, Melbourne, VIC, Australia

Tensor based morphometry (TBM) exploits information obtained during spatial normalisation to investigate differences in brain anatomical structure across populations and time. Using a cohort of Motor Neurone Disease and healthy subjects, we demonstrate a novel method for investigating morphological changes to white matter. We used a Fibre Orientation Distribution (FOD) registration method to normalise data towards a group average template, followed by group average fibre tractography to identify voxels and orientations of interest. Voxel-based analysis was then performed using the inferred fibre orientations to compute differences in perpendicular cross sectional area (and therefore differences in the number of axons).

16:12 672.   The fiber pathways of the brain organized as a highly curved woven grid 
Van Wedeen1, Douglas Rosene2, Guangping Dai1, Ruopeng Wang1, Jon Kaas3, and Isaac Tseng4
1Radiology, Martinos Center/ MGH, Charlestown, MA, United States, 2Anatomy and Neurobiology, Boston University Medical, Boston, MA, United States, 3Cell and Developmental Biology, Vanderbilt University, Nashville, TN, United States, 4Center for Optoelectronic Biomedicine, National Taiwan University College of Medicine, Taipei, Taiwan

To investigate the 3D structure of the fiber pathways of the brain, we obtained diffusion spectrum MRI in fixed whole-brain specimens of 11 mammalian species including 4 primates and analyzed their tractography. Defining the neighborhood of a pathway to be the set of all pathways that approach within 1 voxel, we find such neighborhoods astonishingly well-organized, as parallel sheets of orthogonal pathways forming a 3D grid. This grid structure encompasses continuously the cerebral white matter in all species. Thus, the cerebral pathways form a single 3D curved coordinate grid continuous with the 3 axes of the bilaterian body plan.

16:24 673.   A novel paradigm for automated segmentation of very large whole-brain probabilistic tractography data sets 
Robert Elton Smith1,2, Jacques-Donald Tournier1,2, Fernando Calamante1,2, and Alan Connelly1,2
1Brain Research Institute, Florey Neuroscience Institutes, Heidelberg West, Victoria, Australia, 2Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia

Conventional clustering based upon pair wise similarities has proven inadequate for the task of meaningful segmentation of whole brain probabilistic tractography. A new fully-automated algorithm has been developed based upon the identification of bound coherent bundles of tracks; fibers are segmented based upon their traversal through a common structure, rather than similarity along their entire lengths. It identifies anatomically-meaningful structures at a wide range of physical scales, and intrinsically captures the structural connectivity of each region. We demonstrate the technique on a 10,000,000 probabilistic streamlines data set.

16:36 674.   A Study of Effect of Compiling Method on Interregional Connectivity Maps of Brain Networks via Diffusion Tractography 
Longchuan Li1, James Rilling2, Todd Preuss3, Frederick Damen4, and Xiaoping Hu4
1School of Medicine, Emory University/Georiga Institute of Technology, Atlanta, GA, United States, 2Division of Psychobiology, Yerkes National Primate Research Center, Atlanta, GA, United States, 3Division of Neuroscience, Yerkes National Primate Research Center, Atlanta, GA, United States, 4Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States

Estimating interregional structural connections of the brain via diffusion tractography can be a complex procedure and chosen parameters may affect the outcomes of the connectivity matrix. Here, we investigated the influence of reconstruction method on connectivity maps of brain networks. Specifically, we applied three reconstruction methods, i.e., initiating tracking from deep white matter (method #1, M1), from gray matter/white matter interface (M2), and from gray matter /white matter interface with thresholded tract volume (M3) as the connectivity index, on the same set of diffusion MR data. Hub identification was then calculated and compared across methods. Despite moderate to high correlations in the graph theoretic measures across different methods, significant variability was observed in the identified hubs, highlighting the importance of including reconstruction method as a variable influencing network parameters across studies. Consistent with the prior reports, left precuneus was unanimously identified as a hub region in all three methods, suggesting its prominent structural role in brain networks.

16:48 675.   Inter-subject variability of structural network: a DTI study 
Hu Cheng1, Jinhua Sheng2, Yang Wang2, Olaf Sporns1, Andrew Saykin2, William Kronenberger2, Vincent Mathews2, and Thomas Hummer2
1Indiana University, Bloomington, IN, United States, 2Indiana University, Indianapolis, IN, United States

Structural network was constructed based DTI tractography and FreeSurfer parcellation on fifty six young normal male subjects. Various network analyses were applied to examine the feature of the backbone network as well as inter-subject variations. The result shows that the backbone network can be clustered into four modules. Although some global measures of the network are less fluctuated, the pattern of local connectivity may vary dramatically from subject to subject.

17:00 676.   Mapping hubs in the neocortical structural network of the human brain shows lateralization 
Emil Harald Jeroen Nijhuis1,2, Anne-Marie van Cappellen van Walsum2,3, and David G Norris1,4
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, 2MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Netherlands, 3Department of Anatomy, Radboud University Nijmegen Medical Center, Netherlands, 4Erwin L Hahn Institute for MRI, Universität Duisburg-Essen, Germany

Lateralization is a known phenomena in the human brain and has been described and investigated through various MR imaging techniques. This study provides to the best of our knowledge the first evidence through graph theoretical measures that neocortical hubs are lateralized. The presented research uses high angular resolution diffusion imaging (HARDI) data to reconstruct detailed neocortical networks with 1000 nodes/ROIs for a cohort of 46 young adults. Using graph theory and surface based analysis we identify hubs in the neocortical network. Our results show that critical hubs in the neocortex coincide with the default mode network and language processing areas.

17:12 677.   Track density imaging (TDI): validation of super-resolution property 
Fernando Calamante1,2, Jacques-Donald Tournier1,2, Robin M Heidemann3, Alfred Anwander3, Graeme D Jackson1,2, and Alan Connelly1,2
1Brain Research Institute, Florey Neuroscience Institutes, Heidelberg West, Victoria, Australia, 2Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia, 3Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Super-resolution track-density imaging (TDI) has been recently introduced as a means to achieve high-quality images, with very high spatial-resolution and anatomical contrast; the long-range information contained in the diffusion MRI fibre-tracks provides intra-voxel information to generate an image with higher resolution than that of the acquired source diffusion data. As with any new technique offering super-resolution, the question arises as to the validity of the extra information generated. We validate here the super-resolution property of the TDI method by using in vivo human 7T diffusion data, and in silico diffusion data from a well-characterised numerical phantom.

17:24 678.   “Tractometry” – Comprehensive Multi-modal Quantitative Assessment of White Matter Along Specific Tracts 
Sonya Bells1, Mara Cercignani2, Sean Deoni3,4, Yaniv Assaf5, Ofer Pasternak6, C John Evans1, A Leemans7, and Derek K Jones1
1CUBRIC, School of Psychology, Cardiff, United Kingdom, 2Santa Lucia Foundation, Neuroimaging Laboratory, Rome, Italy, 3School of Engineering, Brown University, Providence, Rhode Island, United States, 4Centre of Neuroimaging Sciences-Institute of Psychiatry, King's College, London, United Kingdom, 5Department of Neurobiology, Tel Aviv University, Tel Aviv, Israel, 6Brigham and Women's Hospital, Harvard Medical School, Bostan, MA, United States, 7Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands

A new technique called tractometry is introduced. Tractometry is a comprehensive assessment of tract-specific microstructural measurements is introduced. This method combines macromolecular measurements from optimized magnetization transfer imaging, multicomponent T2 species from relaxometry and ‘axon density’ measurements from CHARMED along specific white matter pathways reconstructed from diffusion MRI proving us with a comprehensive assessment of multiple microstructure metrics in a unique way. Importantly, we find little correlation between proxy indices of myelination and axonal morphology, suggesting that additional complementary WM microstructural information is obtained with our approach.

17:36 679.   Microstructure Tracking (MicroTrack): An Algorithm for Estimating a Multiscale Hierarchical White Matter Model from Diffusion-Weighted MRI 
Anthony Jacob Sherbondy1, Tim B Dyrby2, Matthew C Rowe3, Maurice Ptito2,4, Brian A Wandell1, and Daniel C Alexander3
1Psychology Department, Stanford University, Stanford, CA, United States, 2Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark, 3Centre for Medical Image Computing, University College London, London, United Kingdom, 4School of Optometry, University of Montreal, Montreal, Canada

MicroTrack combines whole-brain global tractography and local tissue microstructure estimation. The algorithm simultaneously estimates macrostructure (tract cross-section and connectivity) and microstructure (average axon radii and axon volume fraction) parameters for a white matter connectome using a mutliscale forward model. To date, tractography algorithms and microstructure parameter estimation operate entirely independently. However, connectivity and microstructure estimates have great potential to inform one another. We use MicroTrack to demonstrate this hypothesis for the first time on synthetic data and post-mortem monkey-brain data.

17:48 680.   Reliability of tract-specific q-space imaging metrics in healthy spinal cord 
Torben Schneider1, Olga Ciccarelli2, Carolina Kachramanoglou2, David L Thomas2, and Claudia AM Wheeler-Kingshott1
1Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom, 2Department of Brain Repair & Rehabilitation, UCL Institute of Neurology, London, United Kingdom

For the first time we report reproducibility of q-space metrics acquired on a standard 3T clinical MRI scanner parallel and perpendicular to the major fibre tracts. We compare q-space imaging derived parameters in different ascending and descending tracts of the cervical spinal cord and investigate associations between q-space parameters and apparent diffusion coefficient (ADC). We demonstrate good reproducibility of q-space imaging metrics, superior to simple ADC analysis. We conclude that q-space parameters provides complementary metrics that allow discrimination of white matter tracts in healthy controls that cannot be distinguished with ADC alone.