JOURNÉE SCIENTIFIQUE ANNUELLE - ANNUAL SCIENTIFIC DAY 7 FÉVRIER 2020 - RBIQ

JOURNÉE SCIENTIFIQUE ANNUELLE
 7 FÉVRIER 2020

 ANNUAL SCIENTIFIC DAY
 FEBRUARY 7, 2020

 Images de Luankang Lu, Christopher Steele, Daniel Almeida, et Michael Bernier

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

PROGRAMME / SCHEDULE
 8:50 -
 Bienvenue / Welcome - Martin Lepage and Christine Tardif
 9:00

 Thème 2 : Bioimagerie fondamentale et modèle animal Thème 1 séance d’affiches : Avancées
 9:00 - 3 présentations de 20 min et table ronde méthodologiques
 10:30 Theme 2: Basic bio-imaging and animal models Theme 1 poster session: Methodological
 3 20-min talks and round table advances

 10:30 -
 Pause café / Coffee break
 10:45

 10:45 -
 Nouvelles et annonces du réseau / News and announcements from the network
 11:15

 Conférence « Étoile montante en bio-imagerie au Québec » / Rising star in bio-imaging in
 11:15 - Quebec lecture
 12:00
 Dr. Maxime Descoteaux, Université de Sherbrooke (Introduction by Christine Tardif)
 Title TBD

 12:00 -
 Lunch
 13:00

 Thème 1 : Avancées méthodologiques Thème 3 séance d’affiches : Bioimagerie clinique et
 13:00 - 3 présentations de 20 min et table ronde études humaines
 14:30 Theme 1: Methodological advances Theme 3 poster session: Clinical bio-imaging and
 3 20-min talks and round table human studies

 14:30 -
 Pause café / Coffee break
 14:45

 Thème 3 : Bioimagerie clinique et études humaines Thème 2 séance d’affiches : Bioimagerie fondamentale
 14:45 - 3 présentations de 20 min et table ronde et modèle animal
 16:15 Theme 3: Clinical bio-imaging and human studies Theme 2 poster session: Basic bio-imaging and animal
 3 20-min talks and round table models

 Conférence William Feindel / William Feindel lecture
 16:15 -
 17:15 Dr. Hedvig Hricak, Memorial Sloan Kettering Cancer Center, New York, NY (Introduction by Martin Lepage)
 Oncologic Imaging 2020 and Beyond

 17:15 -
 Cocktail et remise de prix / Cocktail and awards
 18:00

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THÈMES / THEMES

 1 Avancées méthodologiques / Methodological advances

 Moderators: Ives Lévesqe, McGill University, & Michèle Desjardins, Université Laval

 Speakers: Flavie Lavoie-Cardinal, Université Laval
 Imaging the brain at the nanoscale

 Jean Provost, Polytechnique Montréal
 Imaging function with ultrafast ultrasound

 Jamie Near, McGill University
 Fun with MRS: developing methods to probe tissue chemistry and metabolism in the brain

 2 Bioimagerie fondamentale et modèle animal / Basic bio-imaging and animal models

 Moderators: Brigitte Guérin, Université de Sherbrooke, & Mallar Chakravarty, McGill University

 Speakers: Jean Da Silva, Université de Montreal
 Development of F-18 fluoropyridine analogs of losartan and candesartan for PET imaging of AT1 receptors

 Brian Nieman, SickKids
 Brain development after childhood cancer: costs and causes

 Stephanie Tullo, McGill University
 Title TBD

 3 Bioimagerie clinique et études humaines / Clinical bio-imaging and human studies

 Moderators: Claudine Gauthier, Concordia University, & Philippe Albouy, Université Laval

 Speakers: Linda Booij, Concordia University
 The relevance of peripheral DNA methylation for human brain development and risk for psychopathology

 Benjamin Morillon, Aix-Marseille University
 Asymmetric sampling in human auditory cortex reveals spectral processing hierarchy

 Mathieu Roy, McGill University
 Neural signatures of pain: can we have objective markers of pain?

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CONFÉRENCIERS INVITÉS / INVITED SPEAKERS

Étoile montante en Bio-Imagerie au Québec / Rising Star in Bio-Imaging in Quebec

Maxime Descoteaux, PhD, Université de Sherbrooke

Title of talk TBD

Bio of Dr. Maxime Descoteaux

Maxime Descoteaux, PhD, is Professor in Computer Science since 2009 at the Science Faculty of Sherbrooke University. He
is the founder and director of the Sherbrooke Connectivity Imaging Laboratory (SCIL) (http://scil.usherbrooke.ca/). His
research focuses on brain connectivity from state-of-the-art diffusion MRI acquisition, reconstruction, tractography,
processing and visualization. The aim of the SCIL is to better understand structural connectivity, develop novel tractography
algorithms, validate them and use them for human brain mapping and connectomics applications. Maxime Descoteaux did a
post-doctorat fellow at NeuroSpin under the supervision of Cyril Poupon and Denis Le Bihan. He also obtained a PhD in
Computer Science at INRIA Sophia Antipolis - Mediterranée, supervised by R. Deriche after he obtained a M.Sc under the
supervision of K. Siddiqi in Computer Science at Center for Intelligent Machines, McGill University, where he also obtained a
B.Sc, graduating from the joint honors Mathematics and Computer Science program. Pr Descoteaux holds the USherbrooke
Institutional Research Chair in NeuroInformatics. He has been cited more than 6700+ times and has 95+ journal publications,
according to google scholar.

Conférence William Feindel / William Feindel Lecture:

Hedvig Hricak, MD, PhD, Dr.h.cm, Memorial Sloan Kettering Cancer Center, New York, NY

Oncologic Imaging 2020 and Beyond

Cancer care—including imaging—is undergoing profound change. Over the last quarter century, researchers have been
assembling the increasingly precise biological syntax and lexicon that are now shaping modern oncology. Shifting public
expectations and technological break-throughs are furthermore intensifying progress toward precision oncology.

Imaging already plays essential roles throughout the cancer care pathway, from diagnosis to treatment planning and follow-
up. Now, due to advances in physical sciences, computation, and life sciences, imaging is poised to play an increasingly
critical role in molecularly-based precision oncology. This lecture will focus on emerging opportunities in oncologic imaging
and the need to embrace and implement them.

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JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

Cross-sectional imaging modalities such as CT, MRI and PET/CT have been a mainstay of oncologic imaging for over 25
years. They are incorporated in NCCN guidelines and continue to play critical role in daily cancer care. Meanwhile, advances
in molecular imaging are accelerating. An assortment of new tracers for molecular imaging with PET have either been
introduced, are in clinical trials, and many more are in development. This growing armamentarium of tracers will aid in
treatment selection and treatment follow-up; create new avenues for the assessment of pharmacodynamics during drug
development; and facilitate biologically-targeted biopsies by allowing non-invasive assessment of biological heterogeneity
within and across all tumor sites. In addition, new molecular imaging technologies such as hyperpolarized (HP) MRI and PET/
MRI are being employed in clinical trials. The expanding array of molecular imaging tracers and technologies, especially HP
MRI, will also offer new biomarkers of early treatment response in the absence of tumor size changes, thereby assisting in
drug development and the patient-specific optimization of therapy. In addition, new theranostic approaches, which combine
targeted imaging with targeted radionuclide therapies, are being developed.

Interventional radiology which is also critical to cancer care is advancing as well. Advanced IR suites now feature
combinations of angiography with cross-sectional imaging modalities such as CT, MRI and in some cases even PET/CT,
which offer better anatomic imaging as well as improved tissue characterization. Performance of targeted biopsies by IR
physicians is becoming essential to diagnosis and treatment selection for precision oncology. In addition, advances in IR-
guided targeted therapies are proliferating, and new combination therapies are being introduced.

In the midst of all the changes described above, cognitive computing is set to transform the entire field of radiology. Cognitive
computing will not only improve the efficiency of radiology workflow, it will also introduce quantitative measurements into daily
clinical practice, as post-processing tools will increasingly enable cross-sectional imaging studies to be converted into
hundreds or even thousands of quantitative, “radiomics” features. These quantitative features, when correlated with other
sources of “big data,” will provide valuable prognostic and predictive imaging biomarkers.

The clinical dissemination of these many new tools and approaches will alter and further strengthen the roles of imaging in
cancer care. It will further enrich our knowledge of disease-relevant molecular data and make the goal of integrated
diagnostics achievable—allowing us to combine information on morphology, function and metabolism in increasingly powerful
diagnostic and clinical decision-making algorithms.

Bio of Dr. Hedvig Hricak

Hedvig Hricak is Chair, Department of Radiology, Memorial Sloan Kettering Cancer Center, a member of the Molecular
Pharmacology and Chemistry Program, Sloan Kettering Institute, and Professor, Gerstner Sloan Kettering Graduate School of
Biomedical Sciences, New York, NY, and Professor at Weill Medical College of Cornell University. She is renowned for
translating and pioneering applications of novel imaging technologies that address pressing clinical needs, particularly in
oncology. She is a member of the National Academy of Medicine and has also been elected a “foreign” member of both the
Russian Academy of Sciences and the Croatian Academy of Sciences and Arts. She received honorary doctorates from both
Ludwig Maximilian University, Munich, Germany and University of Toulouse III, Paul Sabatier in Toulouse, France. She has
served/serves on many academic/government national and international advisory boards. She served on the Board of
Scientific Counselors of the National Institutes of Health, the Scientific Advisory Board of the National Cancer Institute, the

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JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

Advisory Council of the National Institute of Biomedical Imaging and Bioengineering, and the Nuclear and Radiation Studies
Board of the National Academy of Sciences. She is a member of the National Cancer Policy Forum of the NAS and presently
serves on the External Advisory Board, University of Michigan, Ann Arbor, MI, External Advisory Board of Sidney Kimmel
Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, Advisory Board, University of Vienna, Austria and is a
Member, Scientific Committee of the DKFZ, Germany. Dr Hricak has been president of numerous professional societies,
including the Radiological Society of North America (RSNA), the California Academy of Medicine the International Society for
Strategic Studies in Radiology (ISSSR) and the Academy for Radiology and Biomedical Imaging.

For her many contributions to professional societies and academic , national and government agencies, Dr Hricak received
numerous awards including the Marie Curie Award from the Society of Women in Radiology; the gold medals of the
International Society for Magnetic Resonance in Medicine, the Association of University Radiologists, the Asian Oceanian
Society of Radiology, the European Society of Radiology, and the RSNA; the Béclère medal of the International Society of
Radiology; the Schinz Medal of the Swiss Society of Radiology; the Morocco Medal of Merit; the Jean A. Vezina French
Canadian Award of Innovation; and the Order of Croatian Morning Star of Katarina Zrinska Presidential Award of Croatia. In
2018, Dr Hricak received the David Rall Medal for Distinguished Leadership from the National Academy of Medicine.

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RÉSUMÉS / ABSTRACTS

 T1-01 Quatifying white matter disconnection in patient populations

 Abdelrahman Zayed1, Yasser Iturria-Medina2, Bernhard Sehm3 and Christopher J. Steele4.

 1 Department of Electrical and Computer Engineering and PERFORM Centre, Concordia University, Montreal, Quebec, Canada.
 2 Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
 3 Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences and Clinic for Cognitive Neurology,
 University of Leipzig, Leipzig, Germany.
 4 Department of Psychology, Concordia University, Montreal, Quebec, Canada.

 With an estimated five million new stroke survivors every year and a rapidly aging population suffering from
 hyperintensities and diseases of presumed vascular origin that affect white matter and contribute to cognitive decline, it
 is critical that we understand the impact of white matter damage on brain structure and behavior. Current techniques for
 assessing the impact of lesions/damage consider only location, type, and extent, while ignoring how the affected region
 was connected to the rest of the brain. Regional brain function is a product of both local structure and its connectivity.
 Therefore, obtaining a map of white matter disconnection is a crucial step that could help us predict the behavioral
 deficits that patients exhibit. In the present work, we introduce a new practical method for computing lesion/damage-
 based white matter disconnection maps that require only moderate computational resources. We achieve this by
 creating diffusion tractography models of the brains of healthy adults and assessing the connectivity between small
 regions (nodes). We then interrupt these connectivity models by projecting patients' lesions into them to compute the
 predicted white matter disconnection. Importantly, the resulting maps of disconnection are patient-specific, quantitative,
 and therefore enable direct comparison between patients suffering from a variety of lesions in different locations and/or
 with different behavioral deficits. Our method leverages pre-computed connectivity matrices to reduce storage space
 and decrease computation time. Each model is reduced from ~6GB (raw streamlines file) to

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 A comparison of locally and publicly implemented QSM pre-processing
 T1-02 techniques

 Alex Ensworth1, Véronique Fortier1,2, Ives R. Levesque1,2,3

 1 Medical Physics Unit, McGill University
 2 Biomedical Engineering, McGill University
 3 Research Institute of the McGill University Health Centre

 Introduction: Annually 2,000,000 cases of Traumatic brain injury (TBI) are reported in North America. Mild TBI is the
 most common type and currently there is no reliable way of characterizing it. Magnetic Resonance Imaging (MRI) is a
 promising way of observing brain changes after mild injury, potentially with quantitative susceptibility mapping (QSM).
 This is complicated due to the common location of the injury, often adjacent to skull bone, which by its magnetic
 properties degrades the QSM. Our goal is to improve QSM to obtain useful information in the brain cortex adjacent to
 the skull.

 Currently, there are many different methods to generate QSMs, but no ground truth. This work demonstrates the
 comparison of local and public implementation of the various techniques used in the QSM process, in a group of
 volunteers. The results will inform the pipeline that will be used to quantify mild TBI.

 Methods: One public and one local implementation of the Laplacian‐based and region growing unwrapping techniques
 were compared. The highest quality unwrapping technique was then used as the basis for a background removal
 comparison. One public and one local implementation of the projection onto dipole fields (PDF) and Laplacian Boundary
 Value (LBV) background removal techniques were compared. The highest quality background removal technique was
 then used as the basis for the dipole inversion technique. Three different dipole inversion techniques were compared:
 iterative Susceptibility Weighted Imaging and Mapping (iSWIM), Morphology Enabled Dipole Inversion (MEDI) and
 Truncated K-space Division (TKD). The unwrapped phase differences and background removed images were compared
 qualitatively, while the QSMs were compared directly by region of interest extraction and comparing the average values
 to previously documented susceptibilities in specific brain tissue.

 Results: The public and local implementations of Laplacian unwrapping disagreed. It is not yet clear which technique
 agrees with the Quality Guided Region Growing unwrapping. There was no significant difference between
 implementations for background removal, however the PDF technique proved superior to LBV. The dipole inversion
 techniques varied in different aspects.

 Conclusion: While publicly available techniques are available, one should not assume that they function optimally. This
 work determined that locally implemented techniques were necessary to provide an accurate local field map prior to the
 dipole inversion step in the QSM pipeline.

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 Analytical B1 Inhomogeneity Correction for MTsat Values for use in
 T1-03 Cortical ihMT Mapping

 Christopher D. Rowley1,2, Zhe Wu1,2,3, Ilana R. Leppert1, Jennifer S.W. Campbell1, David A. Rudko11,2,4, G. Bruce Pike5,
 Christine L. Tardif1,2,4

 1 McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
 2 Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
 3 Techna Institute, University Health Network, Toronto, ON, Canada,
 4 Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
 5 Hotchkiss Brain Institute and Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Canada

 Introduction: Inhomogeneous magnetization transfer (ihMT) contrast in the brain has been reported to be a more
 specific myelin biomarker than conventional MT, but can be impacted by B1 inhomogeneities, reducing its accuracy. The
 ihMT contrast is primarily driven by the size of the semi-solid dipolar coupled pool, where it is produced during single
 frequency offset (-Δf or +Δf) MT saturation but not in dual frequency MT saturation (-Δf and + Δf). Thus, the difference
 between dual and single frequency MT-weighted images can provide insight into the dipolar coupled pool.
 Current methods for calculating ihMT with B1 correction assume a single excitation and readout, either a k-space line or
 plane, per saturation module. Here we derive equations for use of an arbitrary number of readout segments collected
 after an arbitrary number of MT pulses in a saturation preparation module. The resulting implementation produces B1
 corrected MTsat maps for dual and single frequency MT saturation pulses, which can be combined to generate an ihMT
 map.

 Methods: One healthy volunteer was scanned on a 3T Siemens Prisma MRI system. T1 mapping was performed using
 the variable flip angle method with the following parameters: α1 = 5°, α2 = 20°, TR = 30ms. MT-weighted images were
 subsequently collected using the following parameters: Δf=7kHz, B1=9.8μT, 12 MT pulses in each preparation module
 (each RF with 0.96 ms duration, 1.76 ms spacing), α = 5°, 11 readouts per 175ms TR, and 5.3ms echo-spacing for the
 GRE kernel. The central frequency for the MT pulses was shifted by -100Hz to better align with the center of the lipid
 pool. All images were collected with 1.5mm isotropic resolution. B1 and B0 maps were also collected.
 The steady-state signal was solved using Bloch equations to solve for the MTsat values at each voxel using T1, M0, and
 B1 values to correct the excitation flip angle α, and the MT saturation. ihMTsat was calculated as the difference between
 the saturation in the dual vs single offset frequency cases. ihMTR was calculated for comparison by using the 5° flip
 angle image with no MT preparation from the VFA experiment to normalize the signal change. The results were projected
 onto the mid-cortical surface for analysis.

 Results and discussion: ihMTR in the cortex displays a strong correlation with B1 (r = 0.46), whereas ihMTsat displays
 no correlation with B1 (r = 0.07). Both ihMTR (r = 0.74) and ihMTsat (r = 0.75) show correlations with the myelin-sensitive
 metric R1. Our results support that ihMTsat is a sensitive marker of cortical myelination with minimal bias from B1
 inhomogeneity.

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 Pattern Recognition Algorithms to study Tumour Perfusion using
 T1-04 Dynamic Contrast-Enhanced MRI

 Dipal Patel, Zaki Ahmed, Ives Levesque

 Medical Physics Unit, Faculty of Medicine, McGill University
 Research Institute of the McGill University Health Centre

 Introduction: The efficacy of radiotherapy treatments of cancer tumours is related to tumour vasculature, as well-
 oxygenated tumours are more susceptible to radiation treatment. The irregular vasculature within the tumour results in
 hypoxic regions with a heterogeneous perfusion distribution. In dynamic contrast enhanced magnetic resonance
 imaging (DCE-MRI), tumour and tissue perfusion can be described using temporally resolved data to understand
 contrast agent uptake within the tumour region. This study seeks to identify and adapt a pattern recognition algorithm
 known as non-negative matrix factorization (NMF) for DCE-MRI to produce quantitative spatial maps of the
 heterogenous distribution of perfusion in cancer tumors to longitudinally assess treatment response.

 Methods: The study included patients (n=18) with high-grade soft tissue sarcoma with 3 exams over the course of their
 neoadjuvant radiotherapy treatments. Imaging was performed serially on a 1.5 T MRI scanner (Signa, GE Healthcare).
 T1-weighted time-series images were acquired using a 3D Fast Spoiled Gradient Echo sequence: TE=4.2 ms,
 TR=6.036 ms, flip angle=25 degrees, bandwidth=42 kHz, matrix size=256×128, FOV of 24-28 cm. Each sarcoma was
 manually contoured by a radiation oncologist and images were masked to exclude healthy tissue from the analysis.
 Alternating Negative Least Squares using Block Pivot Principle (ANLS-BPP) and Hierarchical Alternating Least Squares
 (HALS) NMF algorithms were used to identify two (k=2) time-course patterns in the image data and produce the
 corresponding weight maps. These perfusion curves were normalized and interpreted as high perfusion and low
 perfusion according to their shape. Perfusion curves and weight maps were compared across timepoints per patient.

 Results: The NMF algorithms identified source curves that resemble signal enhancement curves for k=2. The source
 curves for high perfusion and low perfusion patterns show very strong similarities across patients and timepoints. The
 weight maps that correspond to high and low perfusion curves were superimposed in colour images, revealing the
 heterogeneity in the perfusion distribution for all tumours, which changes across timepoints.

 Conclusion: Pattern recognition algorithms can identify perfusion curves in DCE-MRI data and produce the respective
 weight maps that reflect the heterogenous distribution of perfusion in high grade soft-tissue sarcoma. Pattern
 recognition could be a more practical and robust data-driven approach to characterizing blood supply in tumours and
 could be used to study tumour progression through the phases of radiotherapy treatment.

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 T1-05 Improving the Speed of Surface Registrations

 Francis Carter1, Pierre-Louis Bazin2, Christopher Steele1,2

 1 Concordia University, Montreal, Quebec,
 2 Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

 Averaging or comparing brain Magnetic Resonance Imaging (MRI) data between subjects requires image registration.
 While standard volumetric registration methods typically perform well on subcortical structures, the cortex is much more
 difficult to co-register due to large amounts of individual variability. Treating the cortex as a 2D manifold to be aligned
 according to its shape and curvature can result in significantly more accurate registration, but this approach often uses
 3D meshes and suffers from greater computational complexity. Previous work by Tardif et al. adapts the computational
 advantages of volumetric registration and provides equivalent cortical registration accuracy to mesh-based approaches.
 The goal of the current work was to adapt and implement the main ideas proposed by Tardif et al. to develop a rapid
 surface-based volumetric registration pipeline in python.

 The main idea of the method proposed by Tardif et al. was to represent the cortical surface volumetrically as a level set
 (signed distance function, where values denote the distance to a surface), and then perform registration on the level sets
 rather than the raw images. This was done using the nighres package and ANTs to develop a python-based pipeline for
 rapid and efficient volumetric surface-based registration.

 First, brain segmentation was performed with MGDM in nighres. Second, the CRUISE cortex extraction tool was used to
 extract a representation of the cortical surface. Third, the extracted contrical boundary level sets were averaged to
 provide a level set of the middle of the cortex. Lastly, the level sets were defined only within a maximum distance of 10
 mm from the cortex, and were used as input to ANTs multivariate template construction to generate the common-space
 template (3 iterative steps, with default linear and nonlinear registration parameters). Since level sets vary smoothly
 across the volume, the more computationally efficient Demons image difference metric was used to assess image
 similarity without affecting accuracy.

 For standard T1-weighted structural images (1mm3), image segmentation, cortex extraction, and level set fusions take

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 Effects of anatomy in a motor and a non-motor region using MRI and
 T1-06 TMS

 Francis Houde, Russell Butler, Étienne St-Onge, Marylie Martel, Véronique Thivierge, Maxime Descoteaux, Kevin
 Whittingstall, Guillaume Léonard.

 Université de Sherbrooke

 Introduction: Transcranial magnetic stimulation (TMS) protocols are often performed in non-motor cortical areas. When
 stimulating non-motor areas with TMS, the most accepted guideline to determine stimulation intensity is the resting
 motor threshold (rMT; lowest TMS intensity required to elicit a motor evoked potential). This method assumes that all
 brain regions have the same excitability and homogenous anatomical characteristics so that TMS effects on a region
 can be directly transposed to another. The aim of this study was to retrospectively assess, from a previous pain memory
 study, how anatomy influenced the effects of TMS in a motor and in a non-motor brain area.

 Methods: The effects of TMS on healthy participants (n = 20) over the primary motor cortex (M1; motor region) and
 over the superior temporal gyrus (STG; non-motor region) were evaluated using the rMT, and pain unpleasantness
 mnemonic bias, respectively. Pain was evaluated using a 0-10 visual analog scale (VAS) immediately after a painful event
 and at recall, 2 months later. Pain memory accuracy (VAS recall - VAS initial) was calculated. The attenuation of pain
 memories was the desired effect (negative scores). Two anatomical variables, both at M1 and STG, were calculated
 based on TMS stimulations coordinates and anatomical images of the head/brain of the participants (t1-weighted
 magnetic resonance images): scalp-cortex distance (SCD; mm) and gray matter thickness (GMT; mm). Spearman
 correlations and Mann Whitney analysis were performed to assess how SCD and GMT affected the rMT and the pain
 unpleasantness memory bias, in both regions (M1 and STG).

 Results: SCD at M1, but not STG, correlates with rMT (r = 0.71, p < 0.001). GMT was significantly smaller at M1 when
 compared to STG (mean M1 = 2.96 ± 0.18, mean STG = 3.27 ± 0.11, p < 0.001) and tended to correlate with rMT at
 M1 (r = -0.422, p = 0.06), but not STG (p > 0.10). The effect of TMS on STG were more variable (p = 0.048) for
 participants with STG > M1 SCD (pain mnemonic bias = 0.00 ± 2.67, n = 10) compared to those with M1 > STG SCD
 (mean pain mnemonic bias = -1.04 ± 1.29, n = 10). GMT at STG tends to correlate with the pain unpleasantness
 mnemonic bias (r = -0.383, p = 0.096).

 Conclusion: These observations provide evidences that factors such as SCD and GMT most likely vary across brain
 regions, as it is the case with M1 and STG in our study. Therefore, we should not assume homogenous anatomical
 characteristics across regions, and we should strive to develop new approaches, using modeling as example, to
 account for these anatomical differences when determining which TMS intensity to use in non-motor TMS studies.

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 T1-07 The bias of veins on resting state measures of centrality

 Julia Huck1*, Anna-Thekla Jäger2*, Audrey P. Fan3, Sophia Grahl2, Uta Schneider2, Arno Villringer2,4,5,6, Christine L.
 Tardif7,8, Pierre-Louis Bazin2,9, Claudine J. Gauthier1,10, Christopher J. Steele2,11

 ¹ Concordia University, Department of Physics / PERFORM center, Montreal, Canada,
 ² Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany,
 ³ Stanford University, Stanford, United States,
 ⁴ Clinic for Cognitive Neurology, University of Leipzig, Leipzig, Germany,
 ⁵ Leipzig University Medical Centre, IFB Adiposity Diseases, Leipzig, Germany,
 ⁶ Leipzig University Medical Centre, Collaborative Research Centre 1052-A5, Leipzig, Germany,
 ⁷ McGill University, Department of Biomedical Engineering, Montreal, Canada,
 ⁸ Montreal Neurological Institute, Montreal, Canada,
 ⁹ University of Amsterdam, Faculty of Social and Behavioural Sciences, Amsterdam, Netherlands,
 ¹⁰ Montreal Heart Institute, Montreal, Canada,
 ¹¹ Concordia University, Department of Psychology, Montreal, Canada

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 Introduction: Resting-state functional MRI (rs-fMRI) is used to detect low frequency fluctuations using the blood
 oxygen-level dependent (BOLD) signal. Synchronous fluctuations has been shown to be greater when regions are
 functionally connected. However, since BOLD is a venous contrast, veins may also introduce a bias in the amplitude and
 measured location of brain activation¹. These biases are known to affect local BOLD signal and may influence measures
 of centrality; potentially biasing subsequent inferences regarding connectivity. However, these biases are not well
 understood. Here, we investigated venous biases on rs-fMRI centrality measures to understand the impact of vein
 density, diameter and distance on degree centrality (DC) and eigenvector centrality (EC) values².

 Methods: DC and EC maps were created from the rs-fMRI data of 38 participants with 5 scanning session each using
 a multiband EPI sequence (TR/TE=1130/22ms; 1.2mm isotropic) on a 7T MRI. Preprocessing included motion
 correction, fieldmap unwarping, and nuisance regression. DC and EC gray matter maps were computed in native
 space².

 Flow-compensated 3D multi-echo gradient images (TR/TE1/TE2=29/8.16/18.35ms; 0.6mm isotropic) were used to
 reconstruct quantitative susceptibility maps (QSM)³. Veins were segmented from the QSM images⁴. The resulting partial
 volume (PV) and diameter maps, and the 2.4mm smoothed DC and EC maps were registered to MNI152 space. PV
 maps were converted to distance maps and the diameter maps were propagated to neighboring regions.

 Linear regressions were used to identify the relationship between centrality scores and distance in the whole brain and in
 7 functional connectivity parcellations⁵. Resulting slopes were tested against the null hypothesis with a two-sided t-test.
 Vascular density was calculated by dividing the sum of PV voxels within each network by its volume.

 Results: We found low but significant inverse associations between DC and EC values and distance from all vessel
 sizes (all p’s

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 Design and simulation of a positron detector to measure the AIF for
 T1-08 dynamic PET

 Liam Carroll, Shirin A. Enger

 McGill University

 Introduction: Dynamic positron emission tomography (dPET) can be used for a more accurate assessment of radiotracer
 uptake and metabolism than possible with static PET. One promising use for dPET is with gallium-68 (68¬Ga) prostate
 specific membrane antigen (PSMA) PET for prostate cancer patients where dPET outperforms static PET in identifying
 cancerous lesions. One factor holding back further research and clinical adoption of techniques, such as the one
 outlined above, is the need to acquire the arterial input function (AIF) when performing dPET imaging. The purpose of
 this study was to refine the design and then simulate a previously validated non-invasive positron detector, hereinafter
 called NID, developed to determine the AIF for dPET. Methods: The NID was simulated using the Geant4 Monte Carlo
 toolkit. Particle transport was performed by using the Penelope low-energy electromagnetic physics and optical photon
 processes. The NID consisted of 64 plastic scintillating fibers, 0.97 mm in diameter and 10 cm long. Each end of the
 fibers was coupled to a photomultiplier tube (PMT). The fibers were arranged around a 64.13 mm diameter polyethylene
 cylinder that represents a patient’s wrist. Two 2.30 mm diameter cylinders were placed inside the wrist to simulate the
 radial artery and vein of a patient. These cylinders were 6 mm apart and 2 mm below the surface of the wrist.
 Simulations were performed by simulating decay events of either oxygen-15 (15O) or fluorine-18 (18F) where the
 decaying particles were randomly distributed in the artery and in the vein. Interactions were recorded when energy was
 deposited in the scintillators. Total deposited energy in the scintillator was calculated and the location of interaction for
 each event was scored. The number of optical photons that accumulated in each PMT were also calculated. The data
 was analyzed using a python algorithm to separate the arterial signal from the venous signal. The location of interaction
 was determined by taking the PMT that had the highest signal per event. Results: With 15O, the arterial signal produced
 a full width half max (FWHM) of 5.28 mm in the cross-section between the artery and the vein. The arterial signal was
 104% of the true arterial signal. With 18F, the arterial signal produced a FWHM of 10.32 mm. The arterial signal was
 112% of the true arterial signal. The algorithm was able to correctly determine the location of interaction in the scintillator
 with an accuracy of 98%. Conclusion: The results show that the NID has sufficient spatial resolution to distinguish
 between the radial artery and radial vein in a patient and is thus suitable to determine the AIF during dPET scans.

 7 Février 2020 / February 7, 2020 !15

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 Shear wave elastography potential to characterize tissues in breast
 T1-09 cancer during radiotherapy

 Marie-Claude Lehoux1,2, Ivan Dimov1,2,3, Maria Bichay1,2,4, François Vincent1,2, Frédéric Chapuis1,2, François Berthod5,6,
 Guy Cloutier7,8,9,10, Antony Bertrand-Grenier1,2,11

 1 Centre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-Québec (CIUSSS MCQ), Trois-Rivières,
 Québec, Canada.
 2 Centre hospitalier affilié universitaire régional, CIUSSS MCQ, Trois-Rivières, Québec, Canada.
 3 Faculté de médecine, Université de Montréal, Montréal, Québec, Canada.
 4 Département de génie mécanique, Université du Québec à Trois-Rivières, Trois-Rivières, Québec, Canada.
 5 Laboratoire en organogénèse expérimentale (LOEX), Centre de recherche du Centre hospitalier universitaire de Québec (CHUQ),
 Université Laval, Québec, Québec, Canada.
 6 Département de chirurgie, faculté de médecine, Université Laval, Québec, Québec, Canada.
 7 Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, Québec, Canada.
 8 Laboratoire de biorhéologie et d’ultrasonographie médicale (LBUM), CRCHUM, Montréal, Québec, Canada.
 9 Département de radiologie, radio-oncologie et médecine nucléaire, Université de Montréal, Montréal, Québec, Canada.
 10 Institut de génie biomédical, Université de Montréal, Montréal, Québec, Canada.
 11 Département de chimie, biochimie et physique, Université du Québec à Trois-Rivières, Trois-Rivières, Québec, Canada.

 Introduction : La lutte contre le cancer est souvent entravée par les résistances physiques des tumeurs et les
 dommages collatéraux que causent les traitements. En plus d'être un marqueur de malignité, la rigidification tumorale
 peut promouvoir la prolifération des cellules cancéreuses et la migration des métastases. La radiothérapie est un
 traitement utilisant la radiation pour détruire les cellules cancéreuses. La difficulté de la radiothérapie réside dans la
 destruction de ces cellules tumorales tout en épargnant les tissus sains environnants. Évaluer la réponse au traitement
 de la tumeur est un enjeu très important afin d’éviter le décès du patient et diminuer les risques de récidive.
 L’élastographie dynamique ultrasonore (SWE) permet de mesurer des propriétés mécaniques des tissus. Cette
 technique pourrait permettre d’observer l’évolution des propriétés mécaniques de la zone tumorale et des tissus tout au
 long du traitement de radiothérapie.

 Méthodes : 10 patientes ont été recrutées afin d’avoir 5 examens de SWE, soient avant (1), pendant (3) et après (1) les
 traitements. Des corrélations des mesures de SWE ont été effectuées notamment avec la paroi tumorale, l’épaisseur de
 peau, la dose et le temps.

 Résultats : Le diamètre et l’aire de la cavité et la paroi tumorale ont diminué de façon significative (P < 0,05). Les
 valeurs de SWE (vitesse d’ondes de cisaillement) fut statistiquement significative pour la quasi-totalité des différentes
 régions entre-elles (lesion, sein affecté, côté symétrique, sein non affecté) (P < 0,05). Trois patientes ont eu un cancer
 plusieurs années auparavant et des différentes statistiques significatives ont été observés en comparant celles-ci aux
 autres patientes n’ayant pas eu de cancer auparavant (P < 0,001). Des différences significatives en ce qui a trait au
 temps a été observés pour les valeurs de SWE de la lésion (p = 0,026; (rs) = -0,693) et du sein affecté (p = 0,036; (rs) =
 -0,841), alors qu’aucune différence significative a été observé pour le sein « contrôle ». L’épaisseur de peau du sein
 affecté (1,99±0,38 mm) a été significativement différente du sein contrôle (1,48±0,29 mm).

 Conclusion : Ce projet présente l’application du SWE au suivi de traitement du cancer du sein par radiothérapie.
 L’étude des propriétés mécaniques des tissus permettrait d’obtenir de nouvelles informations sur la radiorésistance des
 tumeurs et la réponse au traitement. Les données obtenues grâce au SWE pourraient être utilisées afin d’adapter les
 planifications de radiothérapie pour chaque patiente dans le but d’optimiser les traitements, diminuer le taux de
 mortalité, diminuer les risques de récidive et épargner les tissus sains.

 7 Février 2020 / February 7, 2020 !16

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 Building a cortical brain MRI atlas following a phylogenetic approach:
 T1-10 first quantitative results

 Maryna Zhernovaia1, Mahsa Dadar2,3, Yashar Zeighami3, Josefina Maranzano1,2,3

 1 Université du Québec à Trois-Rivières
 2 Université Laval
 3 McGill University

 Background: Several cortical atlases have been developed to perform a consistent division of the human cortex in
 areas that have common structural as well as meaningful and distinctive functional characteristics. Our study proposes
 the division of cortical areas following a phylogenetic approach.

 Objective: to construct an MRI atlas that divides the cortex into five main regions of interest (ROIs): 1-archicortex (AC),
 2-paleocortex (PC), 3-peri-archicortex (PAC) 4- proisocortex (PIC), 5-temporopolar-isocortex (TPIC).

 Methods: MRI scans: 1) T1weighted (T1w) average MNI ICBM 152 non-linear 6th generation symmetric average brain
 MRI model; 2) T1w and magnetization transfer ratio (MTR) images of 10 healthy participants (HP) from the Cambridge
 Centre for Ageing and Neuroscience data repository.

 Image analysis: Manual segmentation on the MNI ICBM model of the five ROIs using the interactive software package
 ‘Display’, which allows the creation of cortical masks for each ROI. The native T1w of each HP was registered to the
 model using a linear transformation. A hierarchical multiscale non-linear fitting algorithm technique was used to obtain
 the deformation vector field that maps the HP T1w to the template, and the inverse of the non-linear HP-to-template
 transformation was used to obtain the native T1w cortical masks of the ROIs. The masks obtained were manually
 corrected. Dice-Kappa were calculated between the automated and corrected masks to quantify the manual corrections
 per ROI. Volumes and MTR values of each ROI were computed and compared.

 Results: Volumes of left AC, PC, PAC, PIC were not significantly different than the right volumes. The right TPIC was
 significantly larger than the left one: 3.9cc vs 3.3cc (t=3.4, p

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 TRAMPOLINO: the Swiss Army Knife for exploratory tractography and
 T1-11 reproducible workflows

 Matteo Mancini1,2,3, Tommy Boshkovski2, Agah Karakuzu2, Elizabeth Dupre4, Jean-Baptiste Poline4, Bruce Pike5,
 Jennifer Campbell4, Mara Cercignani1, Nikola Stikov2

 1 Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
 2 NeuroPoly Lab, Polytechnique Montreal, Montreal, Canada
 3 CUBRIC, Cardiff University, Cardiff, UK
 4 Montreal Neurological Institute, McGill University, Montreal, Canada
 5 Hotchkiss Brain Institute and Department of Radiology, University of Calgary, Calgary, Canada

 Introduction: Across the neuroimaging field, several tools have been developed to process diffusion MRI data, in
 particular for tractography. Despite all of the promising efforts, the unavoidable presence of false positives and negatives
 still makes comparison different methods and understanding of parameter sensitivity necessary to improve tractography.
 Here we introduce TRAMPOLINO (TRActography Meta-Pipeline cOmmand LINe tOol), a tool based on Nipype that aims
 to provide an immediate command-line interface to the most popular software packages available. The goal of this tool
 is to offer a framework for reproducible workflows with a common interface to the already implemented tractography
 approaches.

 Methods: TRAMPOLINO defines and organizes workflows in a modular fashion, focusing on the three main steps:
 reconstruction, where the diffusion data are used to estimate either a tensor or a fiber orientation distribution (FOD) for
 each voxel; tracking, where the streamlines are reconstructed on the basis of the FOD/tensor; and filtering, where using
 selected constraints or assumptions are used to identify and remove spurious. As a consequence of its modular
 architecture, it is possible to run all the three steps or any subset of them.

 Each workflow is specific to one of the processing steps and consists of a set of nodes interconnected with each other.
 Each node represents a Nipype interface to a specific command-line tool and has specific input and output fields. The
 workflows are implemented using a common input/output structure for each processing step: in this way, it is possible
 to easily compare different workflows when using common parameters.

 The tracking step offers tailored features to make exploratory tractography easy and immediate. Regardless of the
 specific workflow, it facilitates automatic generation of multiple outcomes, providing a comma-separated list of available
 algorithms, angular threshold and minimal length constraints. For the angular threshold in particular it is possible to
 generate several results for a range of thresholds defined by the lower and upper limits.

 Conclusions: Tractography is an ever-growing field and the availability of different methods and implementations has
 made clear its tremendous potential but also its current limits. For the sake of both research and clinical applications,
 reproducibility and variability across methods become fundamental topics to explore, and it is therefore necessary to
 have a clear and shared interface with the software that has been developed so far. With TRAMPOLINO, we want to
 provide such a tool.

 7 Février 2020 / February 7, 2020 !18

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 The Effect of Noise Correction Techniques on Resting-State Functional
 T1-12 Connectivity

 Michalis Kassinopoulos1, Georgios D. Mitsis2

 1 Graduate Program in Biological and Biomedical Engineering, McGill University
 2 Department of Bioengineering, McGill University

 It is well established that confounding factors related to head motion and physiological processes (e.g. cardiac and
 breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However,
 even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no
 consensus on the optimal strategy. This may be partly because the quality control (QC) metrics used to evaluate
 differences in performance across pipelines often yielded contradictory results. Importantly, noise correction techniques
 based on physiological recordings or expansions of tissue-based techniques such as aCompCor have not received
 enough attention.

 Here, to address the aforementioned issues, we evaluate the performance of a large range of pipelines by using
 previously proposed and novel quality control (QC) metrics. Specifically, we examine the effect of three commonly used
 practices: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e.,
 scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. To this
 end, we propose a framework that summarizes the scores from eight QC metrics to a reduced set of two QC metrics
 that reflect the signal-to-noise ratio (SNR) and the reduction in motion artifacts and biases in the preprocessed fMRI
 data. Using resting-state fMRI data from the Human Connectome Project, we show that the best data quality, is
 achieved when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from
 the data. In addition, while scrubbing does not yield any further improvement, low-pass filtering at 0.20 Hz leads to a
 small improvement.

 7 Février 2020 / February 7, 2020 !19

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 Quantitative imaging performance of MARS spectral photon-counting
 T1-13 CT for radiotherapy

 Mikaël Simard, Raj Panta, Stephen Bell, Anthony Butler, Hugo Bouchard

 1 Département de Physique, Université de Montréal
 2 School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand

 Purpose: To evaluate the quantitative imaging performance of novel CT technology, a spectral photon-counting CT
 (SPCCT), for radiotherapy applications. Specifically, an experimental comparison of the quantitative performance of a
 clinical Siemens dual-energy CT (DECT) and a Medipix All Resolution System (MARS) SPCCT is performed to estimate
 physical properties relevant to radiotherapy of contrast agent solutions and human substitute materials. In human
 substitute materials, the accuracy of quantities relevant to photon therapy, proton therapy and Monte-Carlo simulations,
 such as the electron density, proton stopping power and elemental mass fractions is evaluated. For contrast agent
 solutions, the accuracy of the contrast agent concentrations as well as the virtual non-contrast (VNC) electron density is
 evaluated.

 Methods: Human tissue substitute phantoms (Gammex 467 and 472) as well as diluted solutions of contrast agents
 (iodine and gadolinium based) are scanned with commercial systems : a Siemens SOMATOM Definition Flash dual-
 source CT and a MARS spectral photon-counting micro-CT (MARS V5.2, MARS Bioimaging Ltd., Christchurch, New
 Zealand). Material decomposition is performed in a maximum a posteriori framework with an optimized material basis
 tailored to characterize human substitute materials and contrast agents in the context of experimental multi-energy CT
 data.

 Results: The root-mean-square error (RMSE) of the electron density calculated over all Gammex plugs is reduced from
 1.09 to 0.89% when going from DECT to SPCCT. For the proton stopping power, the RMSE is reduced from 1.92 to
 0.89%. Elemental mass fractions of hydrogen, carbon, nitrogen, oxygen and calcium are more accurately estimated with
 the MARS. The RMSE on the iodine-based contrast agents concentration is reduced from 0.27 to 0.12 mg/mL with
 SPCCT, and the VNC electron density from 0.40 to 0.22%. A reduction of beam hardening artifacts is also observed on
 parametric maps of iodine concentration with the MARS.

 Conclusion: In the present phantom study, a MARS photon-counting scanner provides superior accuracy than a
 Siemens SOMATOM Definition Flash DECT scanner to quantify physical parameters relevant to radiotherapy. This work
 experimentally demonstrates the benefits of using more energies in computed tomography to characterize human tissue
 equivalent materials. This highlights the potential of SPCCT for particle therapy, where more accurate tissue
 characterization is needed, as well as for Monte-Carlo based planning, which requires accurate elemental mass
 fractions.

 7 Février 2020 / February 7, 2020 !20

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 T1-14 The Modular Organization of Heritability Across the Cortex

 Nadia Blostein1,7, Sejal Patel1,4,5, Gabriel A. Devenyi1,6, Raihaan Patel1,2, M. Mallar Chakravarty1,2,3,6

 1 Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Canada
 2 Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
 3 Integrated Program in Neuroscience, McGill University, Montreal, Canada
 4 Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
 5 Institute of Medical Science, University of Toronto, Toronto, Canada
 6 Department of Psychiatry, McGill University, Montreal, Canada
 7 Faculty of Arts & Science, McGill University, Montreal, Canada

 Introduction: Heritability estimates are a straightforward gauge of the specificity of additive genetic effects and can be
 extended to a bivariate model in order to examine the genetic relationship between two phenotypes. It has been shown
 that cortical thickness (CT) and surface area (SA) across regions of interest (ROIs) are moderately to highly heritable.
 Given the connectomic architecture of the brain, it is unlikely that traits such as CT and SA are inherited without any
 dependence on more distal brain regions. The current project extends previous work from our group to examine the
 shared heritability and genetic cross-correlation of CT and SA across the human cortex, using a twin and non-twin
 sibling heritability design.

 Methods: We obtained 3 Tesla T1-weighted structural magnetic resonance images of 875 healthy adult twins and non-
 twin siblings (Human Connectome Project), using the CIVET/2.1.0 pipeline. CIVET averaged vertex-wise CT and
 summed vertex-wise SA within 78 cortical ROIs, predefined by the Automated Anatomical Labeling atlas. Manual quality
 control for image segmentation and removal of subjects without siblings reduced the sample size to 757. The OpenMx
 package (2.12.2) in R (3.5.1) was used to compute the shared heritability and genetic cross-correlation (degree of
 genetic overlap between two traits) of the CT and SA between each possible pair of cortical ROIs. A structural
 correlation matrix of the CT and SA between each pairwise combination of ROIs was computed. Each of these three
 matrices were hierarchically clustered using the Python 3.6.1 sklearn 0.18.1 package. This segregated the cortical
 regions into modules that were either mediated by the same genetic factors or highly structurally correlated.

 Results: The heritability model did not output significant results for CT. The shared heritability of the SA between
 pairwise combinations of cortical ROIs is significant (p < 0.05) and does not form clusters. The genetic cross-correlation
 of the SA between pairwise combinations of cortical ROIs is significant (p < 0.05) and forms four modules. The structural
 correlation matrices of ROI-wise CT and SA cluster into four modules that do not overlap with the genetically correlated
 clusters.

 Conclusion: These results show that there are four cortical SA modules that are mediated by the same genetic factors
 and that they are not driven by structural correlation. Given the spatially heterogeneous laminar structure of the cortex,
 gene expression plays a significant in corticogenesis. These cortical modules could therefore inform future studies
 aiming to parse the relationship between neurodevelopment and human-specific cortical expansion.

 7 Février 2020 / February 7, 2020 !21

JOURNÉE SCIENTIFIQUE ANNUELLE DU RBIQ / QBIN ANNUAL SCIENTIFIC DAY

 Baseline hippocampal grading predicts cognitive decline in subjects
 T1-15 with mild Alzheimer’s disease

 Neda Shafiee, Mahsa Dadar, D. Louis Collins

 McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada

 Early detection of Alzheimer’s disease (AD) enables early therapeutic intervention which may increase the effectiveness
 of treatment. The hippocampus (HC) has shown early involvement in the pathological process of AD and can be
 measured in vivo with MRI. Consequently, abnormal patterns of atrophy in the HC could be used as an early biomarker
 in the progression of AD [Frisoni et al., 2010]. In order to quantify AD-related atrophy patterns, we used the Nonlocal
 Image Patch Estimator (SNIPE) method [Coupé et al., 2012]. We then used the SNIPE score to predict cognitive decline
 from a single visit in individuals with mild AD. Data were obtained from The Alzheimer’s Disease Neuroimaging Initiative
 (ADNI) database including ADNI1, ADNI2, and ADNIGO. The mild AD cohort (N=100) was chosen based on the Clinical
 Dementia Rating (CDR=0.5) and the Mini-Mental State Exam (MMSE, range 24-30) scores. This cohort was then divided
 into two subgroups based on the changes in CDR score in the two years follow up clinical visits. Subjects with more
 than one point increase in CDR were considered having cognitive decline (N=41). The HC was first segmented using a
 non-local patch-based method [Coupé et al., 2011]. We then computed SNIPE scores for left and right HC using a
 template library image drawn from the ADNI1 dataset. SNIPE aims to learn the non-local similarity between the target
 subject and a training set including both healthy controls and AD cohort data and assigns a similarity score to each
 voxel in the target structure. We trained a decision tree classifier to predict cognitive decline in the next two years using
 a feature set including HC SNIPE scores as the MRI biomarkers, sex, and age. The classifier was then tested using
 leave one out cross-validation. Results showed that there is a significant difference between the two groups in
 hippocampus SNIPE scores (