Mark D Olchanyi^1,2,3, Brian L Edlow^1,4, Emery N Brown^2,3,5,6, and Juan E Iglesias ^4,7,8
1Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States, ²Neurostatistics Research Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States, ⁵Picower Institute, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁶Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, ⁷Centre for Medical Image Computing, University College London, London, United Kingdom, ⁸Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States

Traumatic brain injury that causes sheering of white matter pathways that connect ascending arousal network (AAN) nuclei in the rostral brainstem to cortical and subcortical targets leads to disorders of consciousness. Connectivity studies involving these nuclei are limited due to their small size, vague boundaries, and lack of reliable annotations. We present a method to automatically segment AAN nuclei from diffusion MR volumes using image registration constrained by brainstem structures with definable contrast boundaries. We test AAN segmentation robustness with probabilistic tractography and provide new insights into connectivity between AAN nuclei and the thalamus.

Kurt G Schilling¹, Derek Archer², Fang-Cheng Yeh³, Francois Rheault², Leon Y Cai², Timothy Hohman², Andrea T Shafer⁴, Susan M Resnick⁴, Angela Jefferson², Adam W Anderson², Hakmook Kang², and Bennett A Landman^2
1Vanderbilt University Medical Center, Nashville, TN, United States, ²Vanderbilt University, Nashville, TN, United States, ³University of Pittsburgh Medical Center, Pittsburg, PA, United States, ⁴National Institute on Aging, Baltimore, MD, United States

Superficial U-fiber systems are understudied using diffusion tractography, despite making up a majority of the white matter connections. Here, we used a large, longitudinal dataset from the Baltimore Longitudinal Study of Aging, in combination with innovations in U-fiber tractography dissection, to study U-fiber systems in an aging population. We characterize microstructural features, and for the first time, macrostructural features of length and volume, and find significant associations with age. Characterizing what changes occur, and where they occur, in U-fiber systems will complement traditional long-range tractography research to better understand the biological mechanisms of normal aging.

Shaun of Warrington¹, Elinor Thompson¹, Jessica Dubois², Luke Baxter³, Rebeccah Slater³, Rogier B Mars^4,5, Saad Jbabdi⁴, Matteo Bastiani¹, and Stamatios N Sotiropoulos^1,4,6
1Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ²University of Paris, Inserm, NeuroDiderot Unit; University Paris-Saclay, CEA, NeuroSpin center, Paris, France, ³Department of Paediatrics, University of Oxford, Oxford, United Kingdom, ⁴Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ⁵Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Nijmegen, Netherlands, ⁶National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham, United Kingdom

The neonatal brain undergoes rapid development in the months after birth. Diffusion tractography is a unique method for probing developing white matter connections. We present a novel and comprehensive library of tractography protocols for the neonatal brain, whilst ensuring correspondence with previously developed protocols for the adult and macaque brain. We demonstrate protocol robustness across data quality and show that the resultant tracts capture a-priori known trends in white matter microstructure. We show that these protocols open avenues for quantitative comparisons across the lifespan, but also species, which we exemplify by revealing developmental trends in connectivity patterns.

 

Yu-Chien Wu¹, Qiuting Wen², Jessica Gill³, Rhea Thukral⁴, Sujuan Gao⁵, Kathleen Lane⁵, Timothy Meier⁶, Larry Riggen⁵, Jaroslaw Harezlak⁷, Christopher Giza⁸, Joshua Goldman⁹, Kevin Guskiewicz¹⁰, Jason Mihalik ¹⁰, Stephen LaConte¹¹, Stefan Duma¹², Steven Broglio¹³, Andrew Saykin², Thomas McAllister¹⁴, and Michael McCrea^6
1Department of Radiology and Imaging Sciences, Department of RadioIndiana University School of Medicine, Indianapolis, IN, United States, ²Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States, ³National Institute of Health, Bethesda, MD, United States, ⁴Department of Radiology, Indiana School of Medicine, Indianapolis, IN, United States, ⁵Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, United States, ⁶Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States, ⁷Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, United States, ⁸Department of Neurosurgery, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, United States, ⁹Family Medicine, Ronald Reagan UCLA Medical Center, UCLA Health - Santa Monica Medical Center, Los Angeles, CA, United States, ¹⁰Department of Exercise and Sport Science, Matthew Gfeller Sport-Related Traumatic Brain Injury Research Center, Chapel Hill, NC, United States, ¹¹School of Biomedical Engineering and Sciences, Wake-Forest and Virginia Tech University, Roanoke, VA, United States, ¹²School of Biomedical Engineering and Sciences, Wake-Forest and Virginia Tech University, Blacksburg, VA, United States, ¹³NeuroTrauma Research Laboratory, Michigan Concussion Center, University of Michigan, Ann Arbor, MI, United States, ¹⁴Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States

White matter structural changes occur when an athlete experiences a concussion. To determine if serum biomarkers are a good concussion diagnosis, it seems prudent to associate white matter structural changes to longitudinal biomarker samples. Longitudinal blood biomarkers and DTI scanning from 77 collegiate athletes were collected across 3 timepoints and tested for associations. Tau was found to have the most significant associations. It was concluded that tau is the most sensitive for white-matter micro-structures. 

Ricardo Coronado-Leija¹, Ali Abdollahzadeh², Hong-Hsi Lee³, Santiago Coelho¹, Raimo A Salo², Jussi Tohka², Alejandra Sierra², Dmitry S Novikov¹, and Els Fieremans^1
1Radiology, New York University School of Medicine, New York, NY, United States, ²University of Eastern Finland, Kuopio, Finland, ³Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

To validate the standard model of diffusion in white matter, we characterized injured rat brain microstructure using 3D EM and ex-vivo MRI. Using histology, we demonstrated that the rotational invariants $$$p_l$$$ of the fiber orientation distribution follow a power-law and that the axon diameter histogram is described well by a generalized extreme value distribution. Comparing 3D EM and dMRI, we found that the SM parameters correlate specifically with their histology counterpart, and revealed the specificity of the intra-axonal diffusivity to axonal beading in traumatic brain injury.

Lisa Novello¹, Rafael Neto Henriques², Andrada Ianuş², Thorsten Feiweier³, Noam Shemesh², and Jorge Jovicich^1,4
1Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto (Trento), Italy, ²Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, ³Siemens Healthcare GmbH, Erlangen, Germany, ⁴Center for Medical Sciences - CISMeD, University of Trento, Rovereto (Trento), Italy

The Correlation Tensor MRI (CTI) framework has been recently formulated to disentangle anisotropic, isotropic, and microscopic kurtosis sources without a priori assumptions from Double-Diffusion-Encoding (DDE) data. In this work, group average maps (templates) are presented for the first time, thereby mapping the contrasts for anisotropic and isotropic kurtosis, and non-vanishing positive microscopic kurtosis in grey and white matter. The relative weight of the microscopic kurtosis within the total kurtosis, and the bias associated with neglecting this component under the Multiple Gaussian Component assumption are investigated and suggest that this component significantly contributes to the total diffusional kurtosis.

Stefanie A Tremblay^1,2, Amir Pirhadi³, Zaki Alasmar⁴, Felix Carbonell⁵, Yasser Iturria-Medina^6,7,8, Claudine J Gauthier^1,2, and Christopher J Steele^4,9
1Physics, Concordia University, Montreal, QC, Canada, ²Montreal Heart Institute, Montreal, QC, Canada, ³Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada, ⁴Psychology, Concordia University, Montreal, QC, Canada, ⁵Biospective Inc., Montreal, QC, Canada, ⁶Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, Montreal, QC, Canada, ⁷McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada, ⁸Ludmer Centre for NeuroInformatics and Mental Health, Montreal, QC, Canada, ⁹Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, approaches commonly used are biased by the covariance between variables. Here, we propose computing the voxel-wise Mahalanobis distance (MhD), as a measure of deviation from normality that accounts for covariance between metrics. We show that this measure can be linked to behavior and to potential physiological underpinnings by extracting metrics contributing most to the MhD. Integrative multivariate models are crucial to expand our understanding of the multiple factors underlying disease development and progression.

Vladimir Grouza^1,2, Hooman Bagheri³, Marius Tuznik^1,2, Hanwen Liu^1,2, Alan C Peterson^2,3,4, and David A Rudko^1,2,5
1McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada, ²Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada, ³Department of Human Genetics, McGill University, Montreal, QC, Canada, ⁴Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada, ⁵Department of Biomedical Engineering, McGill University, Montreal, QC, Canada

We investigated the application of blind source separation, robust principal component analysis (BSS-rPCA) for myelin water imaging in a panel of mice exhibiting varied myelination profiles. BSS-rPCA exhibited sensitivity to myelin as evidenced by region of interest (ROI) analysis of several white matter tracts as a function of hypomyelinating genotype. By combining BSS-rPCA with estimates of neurite orientation dispersion and density imaging (NODDI)-derived axon water fraction (AWF) maps, we conclude that BSS-rPCA is sensitive to variations in myelin content in the presence of stable axon density.