Maia J Baumbach¹, Jason W. Allen², Daniel L. Barrow², David A. Reiter², and John N. Oshinski^2
1Georgia Institute of Technology, Atlanta, GA, United States, ²Emory University, Atlanta, GA, United States

Here we use quantitative T2 mapping of the spinal cord in Chiari malformation patients to evaluate the ability to discriminate between spinal cord tissue in Chiari patients with syrinx and spinal cord tissue without. Spinal cord tissue adjacent to syrinx lesions show significantly elevated T2 values suggesting potential utility of T2 mapping for clinical management of Chiari patients. 

Clara F Weber¹, Stefan P Haider¹, Pratik Mukherjee², Evelyn MR Lake¹, Dustin Scheinost¹, Nigel S Bamford³, Laura Ment³, Todd Constable¹, and Sam Payabvash^1
1Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States, ²University of California, San Francisco, San Francisco, CA, United States, ³Pediatrics, Neurology, Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT, United States

Analyzing four study cohorts spanning from infancy to adulthood, we compared DTI-derived diffusion metrics as well as connectome Edge Density between subjects with Autism Spectrum Disorders (ASD) and neurotypical controls. Additionally, we explored the performance of several machine learning algorithms applied to tract-based values for prediction of ASD. We found age- and ASD-related alterations in white matter microstructure and connectome in both voxel-wise and tract-based analyses that show how ASD-associated abnormalities emerge and change over time. Our machine learning analysis evaluated several different approaches and identified a model that achieved 0.75 AUC in the prediction of an ASD diagnosis.

Clio Gonzalez Zacarias^1,2, Soyoung Choi^1,2, Richard M. Leahy^2,3, and John C. Wood^4,5
1Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States, ²Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States, ³Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States, ⁴Department of Pediatrics and Radiology, Children's Hospital Los Angeles, Los Angeles, CA, United States, ⁵Biomedical Engineering, University of Southern California, Los Angeles, CA, United States

Chronic anemia is commonly observed in patients with hemoglobinopathies, mainly represented by disorders in hemoglobin structure (e.g., sickle cell anemia, SCA) and hemoglobin synthesis (e.g., thalassemia syndrome). These hemoglobin disorders have been associated with white matter alterations. In this study, changes in white matter connectivity on chronic anemic patients were characterized by quantifying the volumetric mean of fractional anisotropy along the pathway of tracks connecting two ROIs (defined by BrainSuite’s BCI-DNI atlas) and comparing it with healthy individuals. SCA patients showed interhemispheric FA derangements but not to the same extent as changes observed in the watershed areas of non-SCA patients.

Maëlig Chauvel¹, Ivy Uszynski¹, Clara Fischer¹, Fabrice Poupon¹, Christophe Destrieux², Igor Maldonado², and Cyril Poupon^1
1BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France, ²Unité Imagerie & Cerveau (iBrain), UMRS INSERM 1253, Université de Tours, Tours, France

In this study, we investigated the controversial presence of the inferior fronto-occipital fasciculus (IFOF) in the macaque species (Macaca fascicularis) using ultra-high field MRI (11.7 T) diffusion data. Thanks to a fiber clustering approach, we were able to reconstruct the IFOF in both hemispheres. We observed thin frontal connections and a more developed fasciculus on the right hemisphere compared to the left. The presence of this special fasciculus, known in Humans as being part of the ventral pathways for multi-modal language processing, is a new step forward concerning the comprehension of the macaque brain, and the primate brain in general.

Gabriel Girard^1,2,3, Jonathan Rafael-Patino^2,3, Raphaël Truffet⁴, Dogu Baran Aydogan^5,6,7, Nagesh Adluru^8,9, Veena A. Nair⁹, Vivek Prabhakaran⁹, Barbara B. Bendlin¹⁰, Andrew L. Alexander^8,11,12, Sara Bosticardo^13,14, Ilaria Gabusi^13,15, Mario Ocampo-Pineda¹³, Matteo Battocchio^13,16, Zuzana Piskorova^13,17, Pietro Bontempil^13,18, Simona Schiavi¹⁹, Alessandro Daducci¹³, Aleksandra Stafiej²⁰, Dominika Ciupek²⁰, Fabian Bogusz²⁰, Tomasz Pieciak²¹, Matteo Frigo²², Sara Sedlar²², Samuel Deslauriers-Gauthier²², Ivana Kojcic²², Mauro Zucchelli²², Hiba Laghrissi²², Yang Ji²², Rachid Deriche²², Kurt G Schilling²³, Bennett A Landman^23,24, Alberto Cacciola²⁵, Gianpaolo Antonio Basile²⁵, Salvatore Bertino²⁵, Nancy Newlin²⁴, Praitayini Kanakaraj²⁴, Francois Rheault²⁴, Patryk Filipiak²⁶, Timothy Shepherd²⁶, Ying-Chia Lin²⁶, Dimitris G Placantonakis²⁷, Fernando E Boada²⁶, Steven H Baete²⁶, Erick Hernández-Gutiérrez¹⁶, Alonso Ramírez-Manzanares²⁸, Ricardo Coronado-Leija²⁹, Pablo Stack-Sánchez²⁸, Luis Concha³⁰, Maxime Descoteaux¹⁶, Sina Mansour L^31,32, Caio Seguin^32,33, Andrew Zalesky^31,32, Kenji Marshall^3,34, Erick J Canales-Rodríguez³, Ye Wu³⁵, Sahar Ahmad³⁵, Pew-Thian Yap³⁵, Antoine Théberge¹⁶, Florence Gagnon¹⁶, Frédéric Massi¹⁶, Juan Luis Villarreal Haro³, Marco Pizzolato^3,36, Emmanuel Caruyer⁴, and Jean-Philippe Thiran^1,2,3
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, ²Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland, ³Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁴Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France, ⁵A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland, ⁶Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland, ⁷Department of Psychiatry, Helsinki University Hospital, Helsinki, Finland, ⁸Waisman Center, University of Wisconsin-Madison, Madison, WI, United States, ⁹Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, ¹⁰Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, ¹¹Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ¹²Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States, ¹³Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy, ¹⁴Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ¹⁵Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy, ¹⁶Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada, ¹⁷Brno Faculty of Electrical Engineering and Communication, Department of mathematics, University of Technology, Brno, Czech Republic, ¹⁸Department of neurosciences, biomedicine and movement sciences, University of Verona, Verona, Italy, ¹⁹Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy, ²⁰AGH University of Science and Technology, Kraków, Poland, ²¹Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain, ²²Athena Project Team, Centre Inria d'Université Côte d'Azur, Sophia Antipolis, France, ²³Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ²⁴Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States, ²⁵Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy, ²⁶Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States, ²⁷Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, NY, United States, ²⁸Computer Science Department. Centro de Investigación en Matemáticas A.C., Guanajuato, Mexico, ²⁹Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States, ³⁰Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Mexico, ³¹Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia, ³²Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Australia, ³³The University of Sydney, School of Biomedical Engineering, Sydney, Australia, ³⁴McGill University, Montréal, QC, Canada, ³⁵Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ³⁶Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

The estimation of structural connectivity from DW-MRI is a challenging task, in part due to false-positive connections. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was organized with the aim of evaluating connectivity methods using large-scale synthetic datasets obtained from DW-MRI Monte-Carlo simulations. The outcome of the challenge suggests that methods selected by the 14 teams participating in the challenge provide both high correlations between estimated and ground-truth connectivity weights and high accuracy in binary connectivity identification. Furthermore, the challenge provided unique data with realistic connectivity and microstructure properties to foster the development of connectivity estimation methods.

Dogu Baran Aydogan^1
1A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

Tractography is a powerful tool to study structural connectivity of the brain. However, its accuracy is known to be limited and tractograms are contaminated with large numbers of false positive streamlines. In this work, we propose a novel tractogram filtering approach. Our method leverages the topographic regularity of connections with which nearby streamline tend to follow similar trajectories. With this observation, we introduce an anisotropic smoothing approach for track orientation density images. These images are back projected onto the streamlines, which provides information about fiber-to-bundle coupling (FICO). Streamlines are then filtered by thresholding their FICO value.

Benjamin Ades-Aron¹, Santiago Coelho¹, Gregory Lemberskiy¹, Alon Mogilner², Dmitry S. Novikov¹, Timothy M. Shepherd¹, and Els Fieremans^1
1Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University, school of medicine, New York, NY, United States, ²Neurosurgery, New York University, school of medicine, New York, NY, United States

Direct visualization of anisotropic structures in the brainstem, basal ganglia and thalamus could improve functional neurosurgery planning and characterize changes associated with pathology, treatment response or side effects. Postmortem MRI microscopy demonstrates the potential for high resolution diffusion MRI to detect such functional organization. However, in vivo signal-to-noise limitations have precluded accurate quantitative diffusion measurements at sufficient spatial resolution for these structures. We demonstrate that MP-PCA denoising of complex dMRI at 1-mm isotropic resolution and b-values up to 2000 s/mm² enables direct visualization of the dentatorubrothalamic tract, the key white matter pathway for functional neurosurgical treatment of tremor.

Tommy Boshkovski¹, Guillermo Gallardo¹, Oscar Peña-Nogales¹, Marc Ramos¹, Kire Trivodaliev¹, Paulo Rodrigues¹, and Vesna Prchkovska^1
1QMENTA Inc., Barcelona, Spain

Despite recent advances in tractography, automatically reconstructing brain bundles in clinical settings remains a challenge. Here we present Q-FiberMapper (QFM), a framework that automatically preprocesses clinical data and reconstructs 33 major brain bundles with minimal user intervention. Furthermore, it derives insightful biomarkers and summarizes the findings in a human readable report. We validate QFM using a large cohort of 600 subjects, and show that it correctly reconstructs the shape and lateralization of major white-matter bundles. By simplifying and speeding the process of clinical reconstruction, QFM could help clinicians in the diagnosis and monitoring of brain pathologies.

Maëlig Chauvel¹, Ivy Uszynski¹, Alexandros Popov¹, William Hopkins², Jean-François Mangin¹, and Cyril Poupon^1
1BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France, ²Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Bastrop, TX, United States

A way to better appreciate the complex human brain evolution relies on comparative investigations with homologous species. We present here the superficial connectivity organization of the Chimpanzee brain using a combination of image processing and DBSCAN clustering analyzes obtained from 39 chimpanzees DTI scans. The results revealed the presence of U-fiber, V-fiber and Curved-fiber shapes already identified in Human superficial fiber connectivity studies. This non-negligeable short fibers organisation resemblance between Chimpanzees and Humans, brings out new perspectives on hominid brain singularity knowledge.

Andrey Zhylka¹, Josien Pluim¹, Florian Kofler^2,3,4, Ahmed Radwan^5,6, Alberto De Luca^7,8, Axel Schroeder⁹, Benedikt Wiestler^2,4, Jan S. Kirschke^2,10, Bjoern Menze^3,11, Stefan Sunaert^5,6,12, Alexander Leemans⁷, Sandro M. Krieg^9,13, and Nico Sollmann^2,13,14
1Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ²Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, ³Department of Informatics, Technical University of Munich, Munich, Germany, ⁴TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany, ⁵Department of Imaging and Pathology, Translational MRI, KU Leuven, Leuven, Belgium, ⁶Department of Neuroscience, Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium, ⁷Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, ⁸Neurology Department, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands, ⁹Department of Neurosurgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, ¹⁰TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munic, Munich, Germany, ¹¹Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland, ¹²Department of Radiology, UZ Leuven, Leuven, Belgium, ¹³TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, ¹⁴Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany

Navigated transcranial magnetic stimulation (nTMS) motor mapping allows locating function in the individual patient pre-operatively and, to a certain extent, decreases the false-positive rate for fiber tractography. However, the false-negative rate is still high. In this work we evaluate novel multi-level fiber tractography (MLFT) along with conventional deterministic algorithms in patients with motor-eloquent high-grade glioma, combined with nTMS mapping-based region of interest (ROI) placement. The results were compared based on the coverage of nTMS motor maps, revealing reconstruction of the corticospinal tract (CST) with significantly higher volume and better coverage of the eloquent motor cortex for MLFT.

Guangyao Liu¹, Hong Liu¹, Pengfei Zhang¹, Jing Zhang¹, and Zhe Zhang^2
1Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China, ²Department of Biomedical Engineering, Zhejiang University, Zhejiang, China

Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in the rich club organization. Our study described the alterations of anatomical rich club organization and abnormalities of dynamic SC-FC coupling in treatment-naïve newly diagnosed JME. The results showed that the anatomical rich club organization was disrupted in the patient group, along with decreased connectivity strength among rich club hub nodes. The aberrant dynamic SC-FC coupling of rich club organization suggests a selective influence of interconnected network core in JME at the early phase of the disease.

Huaguang Yang¹, Weiyin Vivian Liu², Liang Li¹, Zhi Wen¹, and Yunfei Zha^1
1Renmin Hospital of Wuhan University, Wuhan, China, ²GE Healthcare, Beijing, China

Cerebellum is responsible for posture and gait control. 67% of patients with multiple system atrophy experienced FOG. Patients with the damaged cerebellar locomotor region had gait-freezing-like symptoms, suggesting that the cerebellum may be involved in the occurrence and development of FOG symptoms. This study suggested that the cerebellum volume atrophy may be involved in FOG development in MSA patients and subsequently induce functional abnormality in the cerebellum-cerebral circuit. This study provided neuroimaging evidence for clinical understanding of cerebellum role in MSA patients with FOG injury.