Séminaire Mehraveh Salehi

SÉMINAIRES DE L’UNF Présentateur: Mehraveh Salehi, Candidate au Ph.D. Titre: Individualized and state-specific human brain parcellation in multiple scales. Endroit: CRIUGM – Local M6804 (http://www.criugm.qc.ca/en/contact.html) Date: Jeudi 29 novembre 2018, 13h-14h *La conférence sera présentée en anglais Mehraveh Salehi is a Ph.D. candidate in Electrical Engineering department at Yale University. She is currently working as a research intern at Google DeepMind in Montreal. She earned her Bachelor degree in Electrical Engineering from Sharif University of Technology, Tehran, Iran. Her research lies at the intersection of statistical machine learning and computational neuroscience. She is interested in developing models that relate human behavior to individual brain connectivity patterns using optimization and machine learning techniques. She has received a number of awards including the Young Scientist Award from the International Conference on Medical Image Computing and Computer Assisted Intervention 2017 (MICCAI), and the best poster award from the BioImaging Sciences Retreat 2018. She is also the recipient of Tananbaum Fellowship, Advanced Graduate Leadership Program (AGLP) Fellowship, and CRA-Women Graduate Fellowship.   Abstract: The goal of human brain mapping has long been to delineate functionally coherent regions in the brain and elucidate the functional role of these regions. Previous work has shown great success on defining functionally coherent regions at multiple scales, by grouping voxels into nodes and further grouping those nodes to form communities or networks in the brain. While majority of previous work has assumed fixed functional units across individuals and states, we show that the parcellation of human brain is both individual and state dependent. In this talk, I will first present a recently developed individualized and state-specific parcellation technique that...

Séminaire – Désirée Lussier

SÉMINAIRES DE L’UNF Présentateur: Désirée Lussier, Candidate au Ph.D. Titre: Brain structure in Pain and Aging Endroit: CRIUGM – Local E1910 (http://www.criugm.qc.ca/en/contact.html) Date: Jeudi 25 octobre 2018, 13h-14h *La conférence sera présentée en anglais Désirée Lussier is a postdoctoral candidate at the University of Florida dual specializing in Developmental Psychology and Behavioral and Cognitive Neuroscience with an anticipated graduation date of May, 2019. Her research interests lie in structural volumetric and connectivity between brain regions in aging, and associated clinical outcomes, using magnetic resonance (MRI) and diffusion tensor imaging (DTI). Her master’s thesis, completed at the University of Florida in the Department of Psychology, investigated age-differential effects of intranasal oxytocin resting-state functional connectivity in women. She is currently investigating how interindividual variation in brain morphology and the oxytocin system is impacted by and contribute to the experience of pain, pain perception, and cognition in older adults. Abstract: This talk will focus on changes in brain structure as a result of aging and chronic pain and the associations with the endogenous oxytocin system. Based on these associations, the potential for intranasal oxytocin as a treatment for chronic pain in older adults will be evaluated and discussed. The contribution of interindividual variation in brain structure on clinical outcome will be...

Séminaire – Anders Eklund

SÉMINAIRES DE L’UNF Présentateur: Anders Eklund, Ph.D. Titre: Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates. Endroit: CRIUGM – Local E1910 (http://www.criugm.qc.ca/en/contact.html) Date: Lundi 6 août 2018, 13h-14h *La conférence sera présentée en anglais Dr Anders Eklund is a research fellow at Linköping University (Sweden) in the Department of Biomedical Engineering (IMT). His main research interests are within analysis of functional magnetic resonance imaging (fMRI) data, mainly regarding development and testing of statistical algorithms. Other interests include high performance computing using graphics cards, image registration and machine learning. Abstract: Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomisation of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all datasets, meaning that data cleaning is not only important for resting state fMRI, but also for task...

Séminaire – Benjamin De Leener

SÉMINAIRES DE L’UNF Présentateur: Benjamin De Leener, Ph.D. Titre: The MRI anatomy of the spinal cord, Part I Endroit: CRIUGM – Local E1910 (http://www.criugm.qc.ca/en/contact.html) Date: Jeudi 7 juin, 13h-14h *La conférence sera présentée en anglais Dr. Benjamin De Leener (PhD) is a HBHL Postdoctoral Fellow at Doyon Lab). He has a strong passion for medical imaging technologies and computer vision in general. Being able to understand and utilize the content of an image has the potential to revolutionize the way we interact with the world. His main contribution is the development of the Spinal Cord Toolbox (SCT), a comprehensive and open-source software for analyzing MRI images of the spinal cord. SCT includes tools for automatically detecting and segmenting spinal cord structures and extracting multi-parametric MRI data from white matter pathways and gray matter sub regions. His research interests are the development of new analysis and processing methods for medical data, with a particular interest in MRI and neurosciences. Abstract: Over the last decade, the neuroimaging community has developed various tools for processing and analyzing MRI data of the spinal cord. Particularly, recent advances in MRI templates of the spinal cord allows unbiased multicentric studies of large groups of patients, by providing a common referential space. However, the coordinate systems used to build these templates and atlases are based on anatomical structure (a.k.a. the vertebral bodies) and do not appropriately represent the functions of the spinal cord, therefore leading to potential errors when analyzing functional MRI data or the spinal cord internal structure (gray/white matter). This study presents a novel approach for approximating the position of the spinal roots along...

Séminaire – Anisha Keshavan

SÉMINAIRES DE L’UNF Présentateur: Dr Anisha Keshavan Titre: Leveraging Web Technology to address challenges with Big Data in Neuroscience. Endroit: CRIUGM – Local E1910 (http://www.criugm.qc.ca/en/contact.html) Date: Jeudi 31 mai, 13h-14h *La conférence sera présentée en anglais Dr Anisha Keshavan works with Jason Yeatman in Speech and Hearing Sciences and Ariel Rokem at the eScience Institute. Her research focuses on big data methods in neuroimaging. Advances in MRI technology and image segmentation algorithms have enabled researchers to begin to understand the mechanisms of healthy brain development, psychiatric and neurological disorders. However, accurately measuring the brain at a scale large enough to accommodate genetic association and precision medicine studies is challenging; expert neuroanatomist tracings can take a long time, while automated algorithms are not accurate enough. Dr Keshavan aims to develop methods to combine the accuracy of an experts with the speed of computers by incorporating crowdsourced image segmentation with deep learning algorithms. She received a doctoral degree in Bioengineering from the UC Berkeley – UCSF Joint Graduate Program, and a Bachelor’s degree in Aerospace Engineering and Applied Mathematics from the University of California, Los Angeles. Abstract: Advances in technology have enabled neuroscientists to collect massive amounts of data to answer important scientific questions. But the drawback is that we are experiencing a “data deluge”, which has brought about new challenges that we must overcome in order to truly reap the benefits that Big Data promises. In this talk, I propose that web technology can help us overcome big data challenges, and present examples of how this is done in the field of neuroimaging. First, how web-based data visualization can address...