David Raffelt^1,2, Olivier Salvado¹, Stephen Rose³, Robert Henderson⁴, Alan Connelly^5,6, Stuart Crozier², and J-Donald Tournier^5,6
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).

Van Wedeen¹, Douglas Rosene², Guangping Dai¹, Ruopeng Wang¹, Jon Kaas³, and Isaac Tseng^4
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.

Robert Elton Smith^1,2, Jacques-Donald Tournier^1,2, Fernando Calamante^1,2, and Alan Connelly^1,2
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.

Longchuan Li¹, James Rilling², Todd Preuss³, Frederick Damen⁴, and Xiaoping Hu^4
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.

Hu Cheng¹, Jinhua Sheng², Yang Wang², Olaf Sporns¹, Andrew Saykin², William Kronenberger², Vincent Mathews², and Thomas Hummer^2
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.

Emil Harald Jeroen Nijhuis^1,2, Anne-Marie van Cappellen van Walsum^2,3, and David G Norris^1,4
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.

Fernando Calamante^1,2, Jacques-Donald Tournier^1,2, Robin M Heidemann³, Alfred Anwander³, Graeme D Jackson^1,2, and Alan Connelly^1,2
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.

Sonya Bells¹, Mara Cercignani², Sean Deoni^3,4, Yaniv Assaf⁵, Ofer Pasternak⁶, C John Evans¹, A Leemans⁷, and Derek K Jones^1
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.

Anthony Jacob Sherbondy¹, Tim B Dyrby², Matthew C Rowe³, Maurice Ptito^2,4, Brian A Wandell¹, and Daniel C Alexander^3
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.

Torben Schneider¹, Olga Ciccarelli², Carolina Kachramanoglou², David L Thomas², and Claudia AM Wheeler-Kingshott^1
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.