M. Okan Irfanoglu¹, Amritha Nayak^1,2, and Carlo Pierpaoli^1
1QMI, NIBIB/NIH, Bethesda, MD, United States, ²The Henry Jackson Foundation for the Advancement of Military Medicine, Rockville, MD, United States

In this work, we have created diffusion MRI (dMRI) atlases from the young adult Human Connectome Project (HCP) data. In order to achieve increased anatomical detail and to enable subsequent morphological analysis, we have reprocessed the entire HCP1200 dataset. The DTI atlases, derived scalar maps and JHU atlas inspired white matter ROIs have been made publicly available. The reprocessed DWIs will be made available for HARDI analysis in the near future.

Chiara Maffei¹, Gabriel Girard^2,3, Kurt G. Schilling⁴, Nagesh Adluru⁵, Dogu Baran Aydogan⁶, Andac Hamamci⁷, Fang-Cheng Yeh⁸, Matteo Mancini^9,10, Ye Wu¹¹, Alessia Sarica¹², Achille Teillac^13,14,15, Steven H. Baete^16,17, Davood Karimi¹⁸, Ying-Chia Lin^16,17, Fernando Boada^16,17, Nathalie Richard¹³, Bassem Hiba¹³, Aldo Quattrone¹², Yoonmi Hong¹¹, Dinggang Shen¹¹, Pew-Thian Yap¹¹, Tommy Boshkovski¹⁰, Jennifer S. W. Campbell¹⁹, Nikola Stikov¹⁰, G. Bruce Pike²⁰, Barbara B. Bendlin⁵, Andrew L. Alexander⁵, Vivek Prabhakaran⁵, Adam Anderson²¹, Bennett A. Landman^4,21, Erick J.Z. Canales-Rodrígue^3,22,23, Muhamed Barakovic^3,24, Jonathan Rafael-Patino³, Thomas Yu³, Gaëtan Rensonnet^3,25, Simona Schiavi^3,26, Alessandro Daducci²⁶, Marco Pizzolato³, Elda Fischi-Gomez^3,24, Jean-Philippe Thiran^2,3, George Dai²⁷, Giorgia Grisot²⁸, Nikola Lazovski²⁹, Albert Puente²⁹, Matt Rowe²⁹, Irina Sanchez²⁹, Vesna Prchkovska²⁹, Robert Jones¹, Julia Lehman³⁰, Suzanne Haber³⁰, and Anastasia Yendiki^1
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States, ²Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland, ³Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁴Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, ⁵University of Wisconsin, Madison, WI, United States, ⁶Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland, ⁷Department of Biomedical Engineering, Faculty of Engineering, Yeditepe University, Instanbul, Turkey, ⁸Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States, ⁹Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom, ¹⁰NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada, ¹¹Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States, ¹²Neuroscience Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy, ¹³CNRS/ISC, Bron, France, ¹⁴Université de Bordeaux, Bordeaux, France, ¹⁵CNRS/INCIA, Bordeaux, France, ¹⁶Center for Advanced Imaging Innovation and Research (CAI2 R), NYU School of Medicine, New York, NY, United States, ¹⁷Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, United States, ¹⁸Boston Children's Hospital, Boston, MA, United States, ¹⁹Montreal Neurological Institute, McGill University, Montreal, QC, Canada, ²⁰Hotchkiss Brain Institute and Department of Radiology, University of Calgary, Calgary, AB, Canada, ²¹Department of Electrical Engineering, Vanderbilt University, Nashville, TN, United States, ²²FIDMAG Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain, ²³Mental Health Research Networking Center (CIBERSAM), Madrid, Spain, ²⁴Translational Imaging in Neurology (ThINK), Department of Medicine and Biomedical Engineering, University Hospital and University of Basel, Basel, Switzerland, ²⁵ICTEAM Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium, ²⁶Computer Science Department, University of Verona, Verona, Italy, ²⁷Wellesley College, Wellesley, Wellesley, MA, United States, ²⁸DeepHealth, Inc., Cambridge, MA, United States, ²⁹QMENTA, Inc., Barcelona, Spain, ³⁰Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, NY, United States

We present results from IronTract, the first challenge to evaluate tractography on the two-shell diffusion scheme of the Human Connectome Project (HCP). Accuracy was evaluated by comparison to tracer injections in the same macaque brains as the diffusion data. Training and validation datasets involved different injection sites. We observed that optimizing data analysis with respect to one injection site does not guarantee optimality for another; encouragingly, two teams could achieve consistently high performance in both datasets. We also found that, when analysis methods are optimized, the HCP scheme may achieve similar accuracy as a more demanding diffusion spectrum imaging acquisition.

Shenjun Zhong¹, Zhaolin Chen¹, and Gary Egan^1,2
1Monash Biomedical Imaging, Monash University, Australia, Melbourne, Australia, ²School of Psychological Sciences, Monash University, Australia, Melbourne, Australia

Clustering white matter streamlines is still a challenging task. The existing methods based on spatial coordinates rely on manually engineered features, and/or labeled dataset. This work introduced a novel method that solves the problem of streamline clustering without needing labeled data. This is achieved by training a deep LSTM-based autoencoder to learn and embed any lengths of streamlines into a fixed-length vector, i.e. latent representation, then perform clustering in an unsupervised learning manner.

Ye Wu¹, Yoonmi Hong¹, Weili Lin¹, Pew-Thian Yap¹, and the UNC/UMN Baby Connectome Project Consortium^1
1Department of Radiology and BRIC, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States

Fiber bundle parcellation is key to bundle-specific analysis of white matter pathways.In this abstract, we propose an efficient framework for parcellation of white matter tractograms using discriminative dictionary learning. The key to our framework is to learn a compact dictionary for each fiber bundle so that the streamlines within the bundle can be succinctly represented. Experiments on a bundle-labeled HCP dataset and an infant dataset highlight the ability of our framework in grouping streamlines into anatomically plausible bundles.

Arnaud Attyé^1,2, Felix Renard³, Monica Baciu², Elise Roger², Laurent Lamalle⁴, Patrick Dehail⁵, Hélène Cassoudesalle⁵, and Fernando Calamante^6,7
1School of Biomedical Engineering, University of Sydney, Sydney, Australia, ²CNRS LPNC UMR 5105, University of Grenoble Alpes, Grenoble, France, ³Laboratoire d'informatique de Grenoble, Grenoble, France, ⁴University of Grenoble Alpes, Grenoble, France, ⁵Bordeaux Universitary Hospital, Bordeaux, France, ⁶Sydney Imaging Lab, University of Sydney, Sydney, Australia, ⁷School of Aerospace, Mechanical and Mechatronic Engineering, Sydney, Australia

Here we present a unified framework for brain fascicles quantitative analyses by geodesic learning (TractLearn) —  as a data-driven unsupervised learning task. TractLearn allows a mapping between the image high-dimensional domain and the reduced latent space of brain fascicles. Besides providing a framework to test the reliability of various brain metrics with a global overview, it allows to identify subtle quantitative alteration in disease model with small subset of patients and/or data sparsity. With this regard, TractLearn is a ready-to-use algorithm for precision medicine.

Simona Schiavi^1,2, Po-Jui Lu^3,4, Matthias Weigel^3,4,5, Derek K. Jones^6,7,8, Ludwig Kappos^3,4, Cristina Granziera^3,4, and Alessandro Daducci^1
1Department of Computer Science, University of Verona, Verona, Italy, ²DINOGMI, University of Genoa, Genoa, Italy, ³Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ⁴Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ⁵Radiological Physics, Department of Radiology, University Hospital Basel and University of Basel, Basel, Switzerland, ⁶Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom, ⁷Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom, ⁸Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia

Tractometry is a widely used tool to investigate microstructural properties that reflect differences in white matter tracts. It consists in averaging tissue features (obtained from any voxel-wise map) along streamlines recovered with diffusion tractography. To overcome the bias introduced by the average, we propose a new method to deconvolve on each individual tract the actual value measured by the microstructural map. For the first time we were able to assess the bundle-specific myelin content by combining tractography with two myelin-sensitive sequences and we obtained good agreement with previously reported values on the cortex.

Scott Trinkle¹, Sean Foxley¹, Narayanan Kasthuri², and Patrick La Rivière^1
1Department of Radiology, The University of Chicago, Chicago, IL, United States, ²Department of Neurobiology, The University of Chicago, Chicago, IL, United States

In this study, we generated tractography-derived, mesoscale mouse “connectomes” from diffusion MRI and synchrotron microCT data on the same mouse brain and evaluated their accuracy using anterograde tracer data from the Allen Mouse Brain Connectivity Atlas. We are developing whole-brain synchrotron microCT, which provides micron-level isotropic resolution across whole mouse brains with no physical sectioning. Even when binning the microCT orientation information to the MRI resolution, microCT tractography outperformed MRI. However, both datasets performed similarly to a tractography connectome constructed entirely with random orientation data, highlighting the importance of understanding geometric biases in tractography connectome construction.

Andrey Zhylka¹, Alexander Leemans², Josien Pluim¹, and Alberto De Luca^2
1Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ²Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands

Neurosurgery planning is an important application of fiber tractography which requires the results to be consistent and accurate. Deterministic tractography methods are generally characterized by high specificity and limited sensitivity, whereas the opposite typically holds for probabilistic methods. Here, we propose a multi-level fiber tractography strategy that takes fiber branching into account and incorporates an anatomical prior to provide a balance between true and false positive reconstructions. We evaluated our approach on the MASSIVE dataset and compared its performance to the existing state of art.

Anastasia Yendiki¹, Robert Jones², Adrian Dalca^1,3, Hui Wang¹, and Bruce Fischl^1
1Radiology, Harvard Medical School & Massachusetts General Hospital, Charlestown, MA, United States, ²Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ³Massachusetts Institute of Technology, Cambridge, MA, United States

Orientation distributions estimated from diffusion MRI have multiple peaks per voxel. Tractography algorithms must choose among them arbitrarily, leading to errors. We propose a novel approach to making this choice in a manner informed by the data. We use post mortem optical and MRI data to train a convolutional neural network that can recognize voxel-wise connection patterns directly from diffusion data, circumventing the conventional paradigm of an orientation distribution. We introduce TRACARIS (TRACt Architectures Recovered from Imaging Signals), a tractography algorithm that uses these network-predicted, local connection patterns. We present preliminary validation results from a post mortem human brain sample.

Chenying Zhao^1,2, Minhui Ouyang¹, Qinlin Yu¹, and Hao Huang^1,3
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, ²Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States, ³Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

Short-range association fibers (SAFs), linking adjacent cortical regions, are dominant in structural connectome and associated with autism and schizophrenia. However, SAFs are not well characterized due to challenges in high-throughput tracing of SAF with diffusion MRI and challenges of identifying and labeling reproducible SAFs. The vast amount of SAFs also make it difficult to delineate them. To meet these challenges, we established a protocol “STTAR” including high-throughput streamline tracing with a regularized FDT probabilistic tractography and semi-automatic identification of reproducible SAFs with novel HDBSCAN clustering. Newly identified reproducible SAFs and those consistently reported in the literature are also demonstrated.