deCODE genetics contributions in imaging genetics
Magnús Örn Úlfarsson
deCODE genetics, a global leader in human genetics, has an extensive collection of DNA, multi-omic, imaging, and phenotypic data. Since 2003, it has identified numerous genetic variants linked to diverse diseases and traits. Noteworthy contributions include unraveling inherited risks for Alzheimer's disease, schizophrenia, some common cancer types, cardiovascular diseases, and traits and phenotypes such as hair and eye color and cognition. This presentation spotlights deCODE's achievements in imaging genetics, focusing on breakthroughs like decoding the genetic basis of intracranial volume, effects of sequence variants on neurodevelopment, brain structure and function, and cognition and brain age.
Magnús Örn Úlfarsson received his Bachelor of Science (BSc) and Master of Science (MSc) degrees in Electrical and Computer Engineering from the University of Iceland. He earned a Ph.D. degree in Electrical and Computer Engineering from the University of Michigan, Ann Arbor in 2007. His main research interest is in signal/image processing, and the development and application of machine learning. Magnús is the head of Artificial Intelligence at deCODE genetics. He is also a Professor in the Faculty of Electrical and Computer Engineering at the University of Iceland
Neuronal activity modulates myelin plasticity and regeneration
Ragnhildur Thóra Káradóttir
The CNS is responsive to an ever-changing environment. Until recently, studies of neural plasticity focused almost exclusively on functional and structural changes of neuronal synapses. In recent years, myelin plasticity has emerged as a potential modulator of neural networks. Myelination of previously unmyelinated axons, and changes in the structure on already-myelinated axons, can have large effects on network function. The heterogeneity of the extent of how axons in the CNS are myelinated offers diverse scope for dynamic myelin changes to fine-tune neural circuits. The traditionally held view of myelin as a passive insulator of axons is now changing to one of lifelong changes in myelin, modulated by neuronal activity and experience.
Myelin, produced by oligodendrocytes (OLs), is essential for normal brain function, as it provides fast signal transmission, promotes synchronization of neuronal signals and helps to maintain neuronal function. OLs differentiate from oligodendrocyte precursor cells (OPCs), which are distributed throughout the adult brain, and myelination continues into late adulthood. OPCs can sense neuronal activity as they receive synaptic inputs from neurons and express voltage-gated ion channels and neurotransmitter receptors, and differentiate into myelinating OLs in response to changes in neuronal activity.
This lecture will review myelin plasticity in adult animal, whether myelin changes occur in non-motor learning tasks, and questions whether myelin plasticity and myelin regeneration are two sides of the same coin.
Ragnhildur Thóra Káradóttir is currently the director of the MS Society Cambridge Centre for Myelin Repair, a professor of cellular Neuroscience at the Department of veterinary Medicine, and a group leader at the Wellcome -MRC Cambridge Stem Cell Institute. Her research interests are to determine the changes in myelin and myelin regeneration throughout the lifespan and to understand how neuronal activity can regulate oligodendrocyte precursor cells (OPCs) differentiation and myelin plasticity in health and disease.
Since establishing her lab she has been awarded a number of awards, including the Lister Institute Research Prize, the Allen Distinguished Investigator Award and an ERC consolidator award. In 2015 she was elected to the FENS-Kavli Network of Excellence (and in 2017 awarded the Fabiane Carvalho Miranda International Prize for the best paper published in the years 2015-2017 in myelin biology and MS related research.
Identifying Brain Imaging Biomarkers for Early Diagnosis of Rare Dementia through MRI Post-Processing
Lotta María Ellingsen
Diagnosing dementia in its early stages is a significant challenge due to the phenotypic overlap of various types of dementia. Some diseases can go misdiagnosed for years before the correct diagnosis is reached. On the other hand, certain causes of dementia present with distinct structural changes in the brain; however, these changes can be extremely subtle in the early stages, making them hard to detect through visual inspection. This presentation introduces deep learning-based image processing strategies for accurate segmentation and labeling of anatomical structures linked to rare forms of dementia. Our ultimate goal is to facilitate fast and automated computation of novel imaging biomarkers that have the potential to help characterize the structural changes in the brain at earlier disease stages than what is currently possible.
Lotta María Ellingsen PhD, is an Associate Professor and the Head of the Faculty of Electrical and Computer Engineering at the University of Iceland. She received her M.S.E. (2004) and Ph.D. (2008) degrees in Electrical and Computer Engineering from the Johns Hopkins University, Baltimore, USA. Her research interests are in the field of medical image processing and analysis with emphasis on 3D medical image registration and segmentation, with application to brains, bones, and statistical atlases. Her current research includes development of pipelines for automatic segmentation and labeling of brain anatomy for systematic analysis of brain dysmorphology, to better characterize neurodegenerative diseases for personalized medicine.
