Analysis Methods
analysis methods
Do we measure what we think we are measuring?
Tests used in the empirical sciences are often (implicitly) assumed to be representative of a target mechanism in the sense that similar tests should lead to similar results. In this talk, using resting-state electroencephalogram (EEG) as an example, I will argue that this assumption does not necessarily hold true. Typically EEG studies are conducted selecting one analysis method thought to be representative of the research question asked. Using multiple methods, we extracted a variety of features from a single resting-state EEG dataset and conducted correlational and case-control analyses. We found that many EEG features revealed a significant effect in the case-control analyses. Similarly, EEG features correlated significantly with cognitive tasks. However, when we compared these features pairwise, we did not find strong correlations. A number of explanations to these results will be discussed.
The pervasive role of visuospatial coding
Historically, retinotopic organisation (the spatial mapping of the retina across the cortical surface) was considered the purview of early regions of visual cortex (V1-V4) only and that anterior, more cognitively involved regions abstracted this information away. The contemporary view is quite different. Here, with Advancing technologies and analysis methods, we see that retinotopic information is not simply thrown away by these regions but rather is maintained to the potential benefit of our broader cognition. This maintenance of visuospatial coding extends not only through visual cortex, but is present in parietal, frontal, medial and subcortical structures involved with coordinating-movements, mind-wandering and even memory. In this talk, I will outline some of the key empirical findings from my own work and the work of others that shaped this contemporary perspective.
GED: A flexible family of versatile methods for hypothesis-driven multivariate decompositions
Does that title put you to sleep or pique your interest? The goal of my presentation is to introduce a powerful yet under-utilized mathematical equation that is surprisingly effective at uncovering spatiotemporal patterns that are embedded in data -- but that might be inaccessible in traditional analysis methods due to low SNR or sparse spatial distribution. If you flunked calculus, then don't worry: the math is really easy, and I'll spend most of the time discussing intuition, simulations, and applications in real data. I will also spend some time in the beginning of the talk providing a bird's-eye-view of the empirical research in my lab, which focuses on mesoscale brain dynamics associated with error monitoring and response competition.
An open-source experimental framework for automation of cell biology experiments
Modern biological methods often require a large number of experiments to be conducted. For example, dissecting molecular pathways involved in a variety of biological processes in neurons and non-excitable cells requires high-throughput compound library or RNAi screens. Another example requiring large datasets - modern data analysis methods such as deep learning. These have been successfully applied to a number of biological and medical questions. In this talk we will describe an open-source platform allowing such experiments to be automated. The platform consists of an XY stage, perfusion system and an epifluorescent microscope with autofocusing. It is extremely easy to build and can be used for different experimental paradigms, ranging from immunolabeling and routine characterisation of large numbers of cell lines to high-throughput imaging of fluorescent reporters.