ISMRM 24th Annual Meeting & Exhibition • 07-13 May 2016 • Singapore

Scientific Session: Diffusion Tractography

Monday, May 9, 2016
Room 324-326
14:15 - 16:15
Moderators: Qiuyun Fan, J-Donald Tournier

U-fiber Quantification in Non-Lesional Epilepsy
Rafael O'Halloran1, Rebecca Feldman1, Madeline Fields1, Laura Marcuse1, and Priti Balchandani1
1Icahn School of Medicine at Mount Sinai, New York, NY, United States
A method for the quantification of cortical-to-cortical U-fiber fraction based on 7T MRI is presented and used to demonstrate group differences in the the U-fiber fractions in non-lesional and lesional epilepsy patients compared to healthy controls. Non-lesional epilepsy patients had the lowest u-fiber fractions followed by healthy control subjects, and then by lesional epilepsy subjects with the highest u-fiber fractions.

The influence of node assignment strategies and track termination criteria on diffusion MRI-based structural connectomics
Chun-Hung Yeh1, Robert Elton Smith1, Thijs Dhollander1, Fernando Calamante1, and Alan Connelly1
1The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
This study highlights the issue of using the common strategy for assigning individual streamlines to an atlas-based brain parcellation. This process is non-trivial and can introduce ambiguity into connectome quantification. In many fibre-tracking algorithms, track termination criteria can cause premature termination of streamlines within WM or CSF, which can result in up to ~50–80% of streamlines failing in identifying pairwise connections between nodes from streamline endpoints. Our results demonstrate that such issue can be largely ameliorated through the combination of biologically meaningful track terminations and an appropriate node assignment mechanism. This could therefore be advantageous to structural connectome construction.

Behavioral response time as explained by a fiber-based analysis of generalized fractional anisotropy measured using diffusion spectrum imaging
Kayako Matsuo1, Yung-Chin Hsu2, Yasuo Takehara3, Wen-Yih Isaac Tseng2, and Norio Mori1
1Dept. Psychiatry, Hamamatsu University School of Medicine, Hamamatsu, Japan, 2Institute of Medical Devices and Imaging System, National Taiwan University College of Medicine, Taipei, Taiwan, 3Dept. Radiology, Hamamatsu University School of Medicine, Hamamatsu, Japan
DSI on a GE 3T was conducted for 22 normal controls to examine the neural basis of the response time (RT). RT was measured outside the scanner using button pressing by left or right hand in response to visual or auditory stimulation. Faster RT was associated with greater GFA of portions near the cortical hand area in the corticospinal tract (CST). Left and right hand specializations were found in the deeper CST. Greater GFA in portions near the cortex in the left auditory radiation was associated with faster RT by visual stimulations, suggesting an influence of language processing speed.

Image quality transfer benefits tractography of low-resolution data
Daniel C. Alexander1, Aurobrata Ghosh1, Samuel A. Hurley2, and Stamatios N. Sotiropoulos2
1Computer Science, UCL, London, United Kingdom, 2FMRIB, Oxford University, Oxford, United Kingdom
We show benefits of image quality transfer to tractography. Diffusion MRI super-resolution through image quality transfer enables recovery of thin tracts in a dataset with low spatial resolution (2.5mm isotropic). Specifically, we reconstruct four pathways arising from the motor area that have been distinguished before when using high (1.25mm) resolution HCP data. Quantitative results confirm that image quality transfer enhances tractography more than standard interpolation. The results highlight the major potential of image quality transfer in learning information from bespoke high quality data sets to enhance the specificity of information derived from more modest but readily available data.

QuickBundlesX: Sequential clustering of millions of streamlines in multiple levels of detail at record execution time
Eleftherios Garyfallidis1, Marc-Alexandre Côté1, François Rheault1, and Maxime Descoteaux1
1Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
QuickBundlesX shows a remarkable 20+X speedup over it’s predecessor who was until today the fastest clustering algorithm for streamlines. In addition, it returns a useful tree of clusters at different resolutions which allows to query streamlines and easily process millions of streamlines by comparing only with their neighbours.

Structural Fingerprinting of the Human Brain: How unique is tract shape to the individual?
Greg D Parker1, George J.A. Evans2, and Derek K Jones1,3
1CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom, 2School of Medicine, Newcastle University, Newcastle, United Kingdom, 3Neuroscience and Mental Health Research Institute (NMHRI), School of Medicine, Cardiff University, Cardiff, United Kingdom
Even amongst healthy subjects, brain function and structure is known to be highly variable across individuals1,2. Recently3 it was shown that inter-subject variation in functional connectivity is sufficient to allow robust and reliable identification of individuals across different sessions and tasks. Here we demonstrate for the first time that the same is true of white matter structure; using the shape of an individual's white matter tracts we generate fingerprints that uniquely identify individuals across different scan sessions.

Fibers crossing the white/gray matter boundary: a semi-global, histology-informed dMRI model
Michiel Cottaar1, Matteo Bastiani1, Charles Chen2, Krikor Dikranian2, David C. Van Essen2, Timothy E. Behrens1, Stamatios N. Sotiropoulos1, and Saad Jbabdi1
1FMRIB, Oxford University, Oxford, United Kingdom, 2Washington University School of Medicine, Saint Louis, MO, United States
Close to the cortical white/gray matter boundary surface fiber orientations sharply transition from being nearly tangential to the surface in the white matter to mostly radial in the gray matter. We propose a geometric model that describes this transition at sub-voxel resolution based on high-resolution histology data and fit this model to lower resolution diffusion MRI data. We assess its performance using qualitative comparisons with histology and test the reproducibility of the estimated parameters across multiple diffusion MRI resolutions. This model allows the in-vivo estimation of fiber orientations across the white/gray matter boundary, which may improve tracking to the cortex.

Microscopic DTI for quantitative tractography of MAP6-KO mice: validation by fluorescent microscopy on cleared brains
Ulysse Gimenez1, Franck Mauconduit1, Benoit Boulan2, Eric Denarier2, Jacques Brocard2, Sylvie Gory-Fauré2, Annie Andrieux2, Jean Christophe Deloulme2, and Hana Lahrech1
1Clinatec Lab U1205, INSERM, Grenoble, France, 2Grenoble Institute of Neurosciences, INSERM, La Tronche, France
High spatial resolution 3D DTI was developed and used for white matter tractography to quantify neuronal tract alterations on the MAP6-KO mouse. In this model, the microtubule-associated protein 6 (MAP6) which is involved in the neuromorphogenesis is deleted leading to a model characterized by severe behavior impairments, similar to the clinical features of schizophrenia. As 3D DTI tractography and fluorescent microscopy on cleared brains both show a deficiency of the post-commissural fornix, in accordance with our previous 2D DTI results, the 3D DTI tractography imaging is validated. Using 3D DTI tractography, new major alterations in different neuronal tracts are detected. 

Network integration and segregation differentiate between Alzheimer Disease and Vascular Dementia
Fulvia Palesi1,2, Andrea De Rinaldis2,3, Letizia Casiraghi2,4, Gloria Castellazzi2,3, Paolo Vitali5, Nicoletta Anzalone6, Federica Denaro7, Elena Sinforiani8, Giuseppe Micieli7, Egidio D'Angelo2,4, and Claudia Angela Michela Gandini Wheeler-Kingshott2,9
1Department of Physics, University of Pavia, Pavia, Italy, 2Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy, 3Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy, 4Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, 5Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy,6Scientific Institute H. S. Raffaele, Milan, Italy, 7Department of Emergency Neurology, C. Mondino National Neurological Institute, Pavia, Italy, 8Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, C. Mondino National Neurological Institute, Pavia, Italy, 9NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
Dementia is the most common disorder in elderly people and comprises Alzheimer’s disease (AD) and vascular disease (VaD). In this work graph theoretical approach was applied to a cohort of AD, VaD and healthy controls (HC) aimed at investigating the presence of a disease-specific pattern of alterations. Brain structural networks were built using the Cohen functional atlas (nodes) and advanced probabilistic tractography (edges). Our main finding was that VaD patients showed severe impairment in the large-scale brain networks while AD patients mainly showed inefficiency of short-range connections emphasizing the fact that alterations are restricted to specific brain regions.

Estimating Network Topology in Weighted and Dense Connectomes
Luis Manuel Colon-Perez1, Michelle Couret2, William Triplett3, Catherine Price3, and Thomas H Mareci3
1Psychiatry, University of Florida, Gainesville, FL, United States, 2Medicine, Columbia University, New York, NY, United States, 3University of Florida, Gainesville, FL, United States
Brain networks are organized in a heterogeneous range of white-matter tract sizes suggesting that the brain is organized in broad range of white matter connection strengths. Studies of brain structure with a binary connection model have shown a small-world network topological organization of the brain. We developed a generalized framework to estimate the topological properties of brain networks using weighted connections, which offers a more realistic model of the brain compared to the binary connection model. In addition, this model reduces the need for thresholding to obtain topological properties in dense and weighted connectomes.

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