Bratislav Misic, PhD - Assistant Professor, Neurology and Neurosurgery, McGill University, Montreal, CA
When: November 15th, 9:45 - 11:00 am
NYC location: Belfer Building (413 E69 St), 302-D
Ithaca Location: Weill Hall, 224
Title: Signaling and transport in brain networks

Abstract: The complex network spanned by millions of axons and synaptic contacts acts as a conduit for both healthy brain function and for dysfunction. Collective signaling and communication among populations of neurons supports flexible behaviour and cognitive operations. Perturbations, such as stimulation-induced dynamic activity or the accumulation of pathogenic proteins, often spread from their source location via axonal projections. Here I will focus on how two fundamental types of dynamics - electrical signaling and molecular transport - can be modeled in brain networks.

Biography: Dr. Bratislav Misic leads the Network Neuroscience Lab. We investigate how cognitive operations and complex behaviour emerge from the connections and interactions among brain areas. The goal of this research is to quantify the effects of disease on brain structure and function. Our research program emphasizes representations and models that not only embody the topological organization of the brain, but also capture the complex multi-scale relationships that link brain network topology to dynamic biological processes, such as neural signalling and disease spread. Our research lies at the intersection of network science, dynamical systems and multivariate statistics, with a focus on complex data sets involving multiple neuroimaging modalities, including fMRI, DWI, MEG/EEG and PET.

Joaquin Goni, PhD - Assistant Professor, School of Industrial Engineering & Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN
When: December 6th, 9:45 - 11:00 am
NYC location: Belfer Building, 204-A
Ithaca Location: Phillips Hall 233
Title: Brain connectomics: from maximizing subjects identifiability to disentangling heritable and environment traits

Abstract: In the 17th century, physician Marcello Malpighi observed the existence of patterns of ridges and sweat glands on fingertips. This was a major breakthrough and originated a long and continuing quest for ways to uniquely identify individuals based on fingerprints. In the modern era, the concept of fingerprinting has expanded to other sources of data, such as voice recognition and retinal scans. It is only in the last few years that technologies and methodologies have achieved high-quality data for individual human brain imaging, and the subsequent estimation of structural and functional connectivity. In this context, the next challenge for human identifiability is posed on brain data, particularly on brain networks, both structural and functional.

Here I present how the individual fingerprint of a human structural or functional connectome (as represented by a network) can be maximized from a reconstruction procedure based on group-wise decomposition in a finite number of orthogonal brain connectivity modes. By using data from the Human Connectome Project and from a local cohort, I also introduce different extensions of this work, including an extended version of the framework for inter-scanner identifiability, evaluating identifiability on graph theoretical measurements and an ongoing extended version of the framework towards disentangling heritability and environmental brain network traits.

Biography: I am a Computational Neuroscientist who works in the emergent research area of Brain Connectomics. I am the head of the CONNplexity Lab, which focuses on the application of Complex Systems approaches in Neuroscience and Cognitive Science, including frameworks such as graph theory, information theory or fractal theory. Projects include relating structural and functional connectivity within the human brain. My interest includes healthy and disease conditions, including neurodegenerative diseases. I also make contributions to theoretical foundations of Complex Systems.

I earned my degree in Computer Engineering in 2003 (University of the Basque Country) and my Ph.D in 2008 from the Department of Physics and Applied Mathematics (University of Navarra). After a first postdoc at a Functional Neuroimaging Lab at University of Navarra, from 2011 to 2014 I was a postdoctoral researcher at the group of Dr. Olaf Sporns at Indiana University. In 2015, I joined Purdue University as an Assistant Professor.

Konrad Kording, PhD - Professor, Department of Bioengineering and Neuroscience, University of Pennsylvania, Philadelphia, PA
When: January 23rd, 3:15 - 4:30 pm
NYC location: ST8A-05 (Starr building, floor 8A)
Ithaca Location: Weill Hall 224
Title: Is most of medical machine learning wrong or misleading?

Abstract: The promise to convert large datasets into medical insights is driving the transition of medicine towards a data rich discipline. Consequently, many scientists focus on machine learning from such datasets. Countless papers are exciting, but very little has clinical impact. Here I argue that this is due to the way we do machine learning, and how common practices lead to non-replication or misleading interpretations of machine learning results. I will discuss ways of minimizing such problems.

Biography: Dr. Kording's (He/Him) is trying to understand how the world and in particular the brain works using data. Early research in the Kording lab focused on computational neuroscience and in particular movement. But as the approaches matured, the focus has more been on discovering ways in which new data sources as well as emerging data analysis can enable awesome possibilities. The current focus is on Causality in Data science applications - how do we know how things work if we can not randomize? But we are also very much excited about understanding how the brain does credit assignment. The kording lab style of working is transdisciplinary, we collaborate on virtually every project.

Ben Glocker, PhD - Reader in Machine Learning for Imaging, Faculty of Engineering, Department of Computing, Imperial College London, London UK
When: February 14th, 9:45 - 11:00 am
NYC location: Belfer Building (413 E69), BB 204-C
Ithaca Location: Phillips Hall, 233
Title: Causality Matters in Medical Imaging

Abstract: We use causal reasoning to shed new light on key challenges in medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice. We argue that causal relationships between images, annotations, and data-collection processes can not only have profound effects on the performance of predictive models, but may even dictate which learning strategies should be considered in the first place. Semi-supervision, for example, may be unsuitable for image segmentation - one of the possibly surprising insights from our causal considerations in medical image analysis. We also discuss two approaches for tackling the problem of domain (or acquisition) shift. We conclude that it is important for the success of machine-learning-based image analysis that researchers are aware of and account for the causal relationships underlying their data.

Biography: Dr. Ben Glocker is Reader (eq. Associate Professor) in Machine Learning for Imaging at Imperial College London. He holds a PhD from TU Munich and was a post-doc at Microsoft and a Research Fellow at the University of Cambridge. His research is at the intersection of medical image analysis and artificial intelligence aiming to build computational tools for improving image-based detection and diagnosis of disease.

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