Carlo Pierpaoli

In this talk we will provide an “historical” overview of various modelling approaches that have been proposed for inferring anatomy and connectivity of white matter fibers from diffusion MRI data. We will describe the foundations of methods for deterministic tractography, and probabilistic tractography that will be addressed in detail by the subsequent lecturers. We will also provide background information to introduce the lecture on “validation” of Diffusion MRI tractography. Proper data preprocessing, however, can effectively minimize potential sources of artifacts. We will examine various steps that are desirable in a diffusion MRI preprocessing pipeline.

Flavio Dell'Acqua

J. Donald Tournier

Probabilistic tractography approaches account for the inherent, multiple sources of uncertainty that impact on the fibre tracking process. They aim to provide a more representative depiction of the range of connections that are consistent with the data. These approaches typically depend on the availability of the distribution of fibre orientations, from which statistical samples can be obtained and used in the streamline propagation process. It is important to note that probabilistic approaches do not in general provide estimates of the probability of a connection; they aim to depict the full range of likely connections that are consistent with the data.

Kurt Schilling

Amy FD Howard¹, Istvan N Huszar¹, Michiel Cottaar¹, Greg Daubney², Alexandre A Khrapitchev³, Rogier B Mars^1,4, Jeroen Mollink¹, Connor Scott⁵, Nicola Sibson³, Adele Smart^1,5, Jerome Sallet², Saad Jbabdi¹, and Karla L Miller^1
1FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, ²Wellcome Centre for Integrative Neuroimaging, Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom, ³MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom, ⁴Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands, ⁵Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

The BigMac dataset is a unique resource that includes extensive MRI and densely sampled microscopy data acquired in a single, whole macaque brain. However, the high-resolution microscopy currently only informs on the fibre orientations in the 2D plane of sampled slides, precluding 3D reconstruction of the microscopy connectome. Here we use precise co-registration and joint modelling of diffusion MRI and polarised light images to reconstruct the microscopy fibre orientations in 3D. This will facilitate future determination of the whole brain, microscopy-inspired connectome, which we expect will provide neuroanatomical insight, and play a vital role in validating and advancing invivo tractography.

Chiara Maffei¹, Gabriel Girard^2,3,4, Kurt G. Schilling⁵, Dogu Baran Aydogan⁶, Nagesh Adluru⁷, Andrey Zhylka⁸, Ye Wu⁹, Matteo Mancini^10,11,12, Andac Hamamci¹³, Alessia Sarica¹⁴, Davood Karimi¹⁵, Fang-Cheng Yeh¹⁶, Mert E. Yildiz¹³, Ali Gholipour¹⁵, Andrea Quattrone¹⁷, Aldo Quattrone¹⁴, Pew-Thian Yap⁹, Alberto de Luca^18,19, Josien Pluim⁸, Alexander Lemans¹⁸, Vivek Prabhakaran⁷, Barbara B. Bendlin⁷, Andrew L. Alexander⁷, Bennett A. Landman⁵, Erick J. Canales-Rodríguez⁴, Muhamed Barakovic²⁰, Jonathan Rafael-Patino⁴, Thomas Yu⁴, Gaëtan Rensonnet⁴, Simona Schiavi²¹, Alessandro Daducci²¹, Marco Pizzolato^4,22, Elda Fischi-Gomez⁴, Jean-Philippe Thiran^2,3,4, George Dai²³, Giorgia Grisot²⁴, Santi Puch²⁵, Marc Ramos²⁵, Nikola Lazovski²⁵, Paulo Rodrigues²⁵, Vesna Prchkovska²⁵, Robert Jones¹, Julia Lehman²⁶, Suzanne Haber²⁶, and Anastasia Yendiki^1
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States, ²University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ³CIBM Center for BioMedical Imaging, Lausanne, Switzerland, ⁴École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁵Vanderbilt University, Nashville, TN, United States, ⁶Aalto University School of Science, Espoo, Finland, ⁷University of Wisconsin, Madison, WI, United States, ⁸Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ⁹Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapell Hill, NC, United States, ¹⁰Department of Neuroscience, Brighton and Sussex Medical School University of Sussex, Brighton, United Kingdom, ¹¹CUBRIC, Cardiff University, Cardiff, United Kingdom, ¹²NeuroPoly, Polytechnique Montreal, Montreal, QC, Canada, ¹³Department of Biomedical Engineering, Faculty of Engineering, Yeditepe University, Instanbul, Turkey, ¹⁴Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy, ¹⁵Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States, ¹⁶Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States, ¹⁷Institute of Neurology, University “Magna Graecia”, Catanzaro, Italy, ¹⁸Imaging Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, ¹⁹Neurology Department, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands, ²⁰University of Basel, Basel, Switzerland, ²¹University of Verona, Verona, Italy, ²²Technical University of Denmark, Kongens Lyngby, Denmark, ²³Wellesley College, Wellesley, MA, United States, ²⁴DeepHealth, Inc., Cambridge, MA, United States, ²⁵QMENTA, Inc., Barcelona, Spain, ²⁶Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, NY, United States

We present results from round 2 of IronTract, the first challenge to evaluate the accuracy of tractography using i) tracer injections and diffusion MRI from the same macaque brains, and ii) DSI and HCP two-shell diffusion acquisition schemes. In round 1, only two teams achieved similarly high performance between the two different injection sites that we used for training and validation. Here we investigate the extent to which this was due to the pre- and post-processing used by those teams. We show that, when other teams use the same pre- and post-processing, their accuracy and robustness can improve as well.

Charles Poirier¹, Étienne St-Onge¹, and Maxime Descoteaux^1
1Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada

In general, fiber orientation distribution functions are defined as symmetric spherical functions. The present work shows how local averaging can be used to compute averaged asymmetric fODF (ava-fODF) from input symmetric white matter (WM) fODF. The resulting ava-fODF shows the presence of asymmetric patterns in voxels containing multiple crossings as well as along the white matter/gray matter interface. Moreover, the proportion of WM voxels displaying asymmetric features after filtering accounts for at least 30% of all WM voxels. Ava-fODF reveals new possibilities for classification of fiber configurations, with unidirectional fODF, bending fODF, Y-branching fODF and other complex fiber configurations.

Kurt G Schilling¹, Francois Rheault², Laurent Petit³, Chantal Tax⁴, Maxime Descoteaux², Adam W Anderson⁵, and Bennett A Landman^5
1Vanderbilt University Medical Center, Nashville, TN, United States, ²Universite de Sherbrooke, Sherbrooke, QC, Canada, ³CEA University of Bordeaux, Bordeaux, France, ⁴Cardiff University, Cardiff, United Kingdom, ⁵Vanderbilt University, Nashville, TN, United States

Bottleneck regions of the brain, or areas where multiple white matter pathways of the brain converge and subsequently diverge, present a challenge to anatomically accurate fiber tractography of the brain. In this work, we investigated the prevalence and locations of bottleneck regions. Our results indicate that most white matter contains multiple overlapping and crossing bundles. Moreover, individual orientations withina voxel are associated with multiple bundles, which represent bottlenecks. These findings have profound implications for tractography algorithms which aim to map unknown connections across the brain, and strengthen the awareness of limitations or challenges facing these image processing techniques. 

Matteo Battocchio¹, Simona Schiavi¹, Maxime Descoteaux², and Alessandro Daducci^1
1University of Verona, Verona, Italy, ²University of Sherbrooke, Sherbrooke, QC, Canada

Here we introduce the concept of bundle-o-graphy to tackle tractography reconstruction from a different point of view: instead of indirectly reconstructing fiber bundles streamline-by-streamline, we consider them as the objects to be reconstructed and we directly seek for them using global optimization. We show the potential of bundle-based tractography on synthetic and real data, possibly opening avenues to a new way of thinking about tractography. 

Chenying Zhao^1,2, Minhui Ouyang¹, 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 cortices, are dominant in connectome and altered in psychiatric disorders such as autism and schizophrenia. However, few atlases are dedicated to SAFs. Higher inter-subject variation and large amount of SAFs impede their identification with traditional methods for long-range fibers. To meet these challenges, we built the SAF atlas using STTAR (Short-range Tractography with high Throughput And Reproducibility), a novel and advanced protocol designed for SAFs. Here, we demonstrated the SAF atlas in parietal lobe, featured with reproducibly and comprehensively traced SAFs readily usable in 3D volume format in the ICBM152 space.