25 декабря 2019, 16:00
DECEMBER 25th, 2019. 4.00 p.m.
Skolkovo Institute of Science and Technology, New Campus, E-B4-3007

СКОЛКОВСКИЙ ИНСТИТУТ НАУКИ И ТЕХНОЛОГИЙ


Seminar by:
Dr. Anatoly Buchin and
Dr. Boris Gutman
ALZHEIMER'S DISEASE AND EPILEPSY
CNBR_Open Events
Boris Gutman
Disease Progression Models and Brain Imaging
Dr. Boris Gutman is Assistant Professor of Biomedical Engineering at the Illinois Institute of Technology in Chicago, a member of Illinois Tech's Medical Imaging Research Center, and director of the Laboratory for Modeling and Computation in Imaging and Genetics (LAMCIG).

He received his BS in Applied Mathematics and PhD in Biomedical Engineering from the University of California Los Angeles.

In his research, Dr. Gutman develops computational methods to jointly analyze brain MR images arising from multiple modalities, as well as discover the genetic underpinning of healthy brain variation and brain-related disorders.

Specific modalities of interest include anatomical, diffusion, and elasto-mechanical MRI (MRE) of the brain.

Combining mathematical image analysis and recent developments in predictive modeling, Dr. Gutman seeks to fuse knowledge from genetics, brain anatomy and connectivity, microscopic tissue properties, and cellular dynamics.
16:00 – 16:40
Anatoly Buchin
Temporal Lobe Epilepsy

Anatoly Buchin has worked in the field of Computational Neuroscience for the last 10 years.

He has held the position of Scientist at the Allen Institute since 2017 and is in the Modeling, Analysis and Theory (MAT) group.

Before joining the Allen Institute, Anatoly was a postdoctoral researcher in the Department of Biophysics at the University of Washington.

He received his PhD in Computational Neuroscience from École Normale Supérieure in Paris, as well as an MPhil in Physics from St. Petersburg Polytechnic University and an MPhil in Interdisciplinary Research from Paris Descartes University.

In his spare time, Anatoly enjoys playing the saxophone and flute in various bands.
16:50 – 17:30
As humans, we like to predict all manner of things, not least of them being our physical health.
And while there is much excitement in the world of artificial intelligence about the ever-improving accuracy with which we can predict things, we are often less concerned with domain-specific relevance and utility of the prediction.

Particularly in regards to health, direct AI-based diagnosis prediction has found only limited use among medical practitioners and clinical researchers. Simple binary questions such as "does this patient have disease X?" or even "will the patient acquire disease X in Y years?" have proven less interesting than "how quickly will the patient's health deteriorate?", "when and in what order will future symptoms appear?" and "what are the connections among the observable biomarkers and between biomarkers and symptom onset?"

In the first part of this talk, I will introduce a broad class of mathematical models, generally termed "disease progression models" (DPMs), which attempt to address the more interesting questions. We will go over intuitive examples of empirical and mechanistic models in several domains, e.g. neurological diseases, cancer and HIV.

The second half of the talk will focus on applications of DPMs to brain imaging, showing how one can exploit the structure and biology underlying imaging data in a disease modeling context.

Dr. Boris Gutman

Dr. Anatoly Buchin
Temporal lobe epilepsy is the fourth most common neurological disorder with about 40% of patients not responding to pharmacological treatment. Increased cellular loss in the hippocampus is linked to disease severity and pathological phenotypes such as heightened seizure propensity.

While the hippocampus is the target of therapeutic interventions such as temporal lobe resection, the impact of the disease at the cellular level remains unclear in humans.

Here we show that properties of hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from epilepsy patients. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume, surface area and spine density.

Single-cell RNA sequencing combined with simulations ascribe the observed 30 electrophysiological changes to gradual modification in three key ion channel conductances: BK, Cav2.2 and Kir2.1. In a bio-realistic computational network model, we show that the changes related to disease progression bring the circuit into a more excitable state.

In turn, we observe that by reversing these changes in the three key conductances produces a less excitable, "early disease-like" state.

These results provide mechanistic understanding of epilepsy in humans and will inform future therapies such as viral gene delivery to reverse the course of the disorder.
Organiser
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25 th of December, 16.00 | SKOLTECH. BIG CAMPUS. E-B4-3007
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