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Project 1: Functional connectivity research: can we find a common ground?

Author: Natalia Bielczyk, MSc

Senior supervisor: Michał Bola, PhD 

Abstract: Functional connectivity (FC) research has become one of the leading concepts used for characterising network dynamics across multiple disciplines, from neuroimaging, through gene expression networks, to social networks. It is also a basis for graph theoretical biomarkers of psychiatric disorders and as such, it become an important subfield of cognitive neuroimaging. FC is usually operationalised by means of Pearson’s and partial correlation, however the implementation of FC can vary between different fields, and different applications. Then, there is a question: does an optimal method to quantify functional connectivity exist? Or is the choice dependent on the data properties? How to choose the right method? In this project, we will use open-access data from functional Magnetic Resonance Imaging, EEG, gene expression data, stock exchange data and a few other open-access datasets, and we will compare the leading methods for computing FC when applied to these datasets. We will attempt to answer the questions: what are the pros and cons of different methods for quantifying FC? What are the differences and the similarities between different datasets, and how to choose the right method for the given dataset? 

A list of 1-5 key papers/materials summarising the subject: 

[a] http://www.scholarpedia.org/article/Brain_connectivity 

[b] http://journal.frontiersin.org/article/10.3389/fnsys.2015.00175/full 

[c] A. K. Enge, C. Gerloff,C. C. Hilgetag and G. Nolte (2013). Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity. Neuron 80 (4): 867–86 

[d] M. Bola and V. Borchardt (2016). Cognitive Processing Involves Dynamic Reorganization of the Whole-Brain Network's Functional Community Structure. Journal of Neuroscience 36 (13): 3633–5 

A list of requirements for taking part in the project (education level / English level / programming language required): 

[a] BSc program, or higher 

[b] English: good, not necessarily proficient 

[c] programming languages / other competences: basics of Matlab, Python, LaTeX, basic statistics 

A maximal number of participants: 8 

Skills and competences you can learn during the project: 

[a] looking for parallels in the datasets from different disciplines, representing the datasets with a model 

[b] group project planning (we will discuss and divide tasks on the site) 

[c] programming in a team, solving problems in parallel 

[d] scientific writing (at least one paragraph per participant) 

is there a plan for extending this work to a paper in case the results are promising? yes  
 


Fig: different types of networks. A: a social network (Facebook); B: correlations on the stock exchange (106 companies listed at NASDAQ-100); C: a gene co-expression network (image adapted from http://wikipedia.org on CC BY-SA 3.0 license); D: large scale resting state networks in the brain (image adapted from Smith et al, 2009)