Towards personalized neuroimaging in neurosurgery: linking brain networks and cognitive function 

Abstract: The importance of quality of life of patients following neurosurgery for brain tumors has been increasingly recognized in recent years. Emphasizing the balance between oncological and functional outcome, an emerging discipline at the forefront of research and patient care focuses on cognitive function. In current surgical standard practice, focal electrical stimulation on the exposed brain while patients are awake is used for mapping areas critical for motor function as well as language to prevent irreversible damage as a result of tissue removal. However, some cognitive functions are harder to map with standard stimulation alone. In the talk, I will present my work aimed at developing techniques and tools for mapping cognitive function in neurosurgery. I will focus on a particularly challenging aspect of cognition – executive functions – how we set and achieve goals, make plans, and prioritize tasks, which are essential to all aspects of our everyday life. Because of the complex nature of these functions and the distributed neural systems that support them, there are currently no established techniques for their functional mapping in neurosurgery. I will introduce a novel method that I developed for mapping executive function during awake neurosurgery using electrocorticography (ECOG) – recording directly from the surface of the brain – while patients perform cognitive tasks. I will show evidence for the feasibility and utility of this method as a first step towards establishing its foundations. Critical to bridging the translational gap and bringing neuroimaging into use in neurosurgery is our understanding of the functional role of the neural networks associated with cognitive functions and our ability to identify them in individuals. I will therefore present supporting findings for these using functional MRI (fMRI) data in healthy human volunteers. Finally, I will discuss future research directions towards developing multi-modality neuroimaging with advanced data analysis techniques for personalized medicine in neurosurgery. 
 

"ZOOM" SEMINAR

School of Mechanical Engineering Seminar
Monday, November 23, 2020 at 14:00

Multi-criteria Search of Robot Control Strategies for Applications in an Environment with an Adversary

by

Meir Harel

Ph.D. student under to supervision of Dr. Amiram Moshaiov

Multi-Objective Games (MOGs) are games in which each player has more than one objective to accomplish. Given the multiple objectives, each player may experience a self-conflict about its objective preferences. In recent studies, a novel solution approach to non-cooperative MOGs has been suggested which is termed the rationalizability solution concept. This approach assumes that the players of the MOG are undecided about their objective preferences. Employing the rationalizability approach provides each of the players with a Set of Rationalizable Strategies (SRS). This set exposes the performance tradeoffs among the various rationalizable strategies.

Following the rationalizable solution concept, the current work suggests a modification to the rationalizability solution concept and introduces an approach to reduce the SRS. The suggested modification, which incorporates subjective preferences of the objectives by the players, is based on some Multi-Criteria Decision-Analysis (MCDA) ideas. An evolutionary algorithm, which is based on the introduced modification, is developed. This algorithm results in a reduced SRS which is termed the Set of Preferred Strategies (SPS). The proposed modification is realized by the introduction of auxiliary criteria into the evolutionary search, which could reduce the computational efforts and supports the decision-making.

In addition to modifying the rationalizability approach and the development of the associated algorithms, this work investigates the implementation of the proposed ideas to real-life robotic/dynamic MOGs. For this purpose, a real-life aerial MOG is taken, which involves a navigator and a coalition between the navigation target and a missile that pursuits the navigator. To support solving the aforementioned MOG a novel analytical solution of the problem is introduced. The resulted sets of all possible strategies are analysed using full sorting according to the rationalizability concept. Next, the analytical solution undergoes a numerical modification using neural-networks. The proposed modification undergoes a search using the proposed evolutionary algorithm to produce rationalizable strategies to the original MOG problem. Finally, the resulting control strategies were analysed and examined for their performance characteristics under some changes to the initial conditions of the game.

https://zoom.us/j/96584758181?pwd=WC9PMXdsYzJ3NFdEN2Q5ZUtOZEVjdz09 The meeting will be recorded and made available on the School’s site.