UH Biocomputation Group - Event-related designhttp://biocomputation.herts.ac.uk/2021-05-20T09:12:19+01:00Feasibility of Topological Data Analysis for Event-Related fMRI2021-05-20T09:12:19+01:002021-05-20T09:12:19+01:00Shabnam Kadirtag:biocomputation.herts.ac.uk,2021-05-20:/2021/05/20/feasibility-of-topological-data-analysis-for-event-related-fmri.html<p class="first last">Shabnam Kadir's Journal Club session where she will talk about a paper "Feasibility of Topological Data Analysis for Event-Related fMRI"</p>
<p>This week on Journal Club session Shabnam Kadir will talk about a paper "Feasibility of Topological Data Analysis for Event-Related fMRI".</p>
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<p>Recent fMRI research shows that perceptual and cognitive representations are
instantiated in high-dimensional multivoxel patterns in the brain. However, the
methods for detecting these representations are limited. Topological data
analysis (TDA) is a new approach, based on the mathematical field of topology,
that can detect unique types of geometric features in patterns of data. Several
recent studies have successfully applied TDA to study various forms of neural
data; however, to our knowledge, TDA has not been successfully applied to data
from event-related fMRI designs. Event-related fMRI is very common but limited
in terms of the number of events that can be run within a practical time frame
and the effect size that can be expected. Here, we investigate whether
persistent homology- a popular TDA tool that identifies topological
features in data and quantifies their robustness- can identify known
signals given these constraints. We use fmrisim, a Python-based simulator of
realistic fMRI data, to assess the plausibility of recovering a simple
topological representation under a variety of conditions. Our results suggest
that persistent homology can be used under certain circumstances to recover
topological structure embedded in realistic fMRI data simulations.How do we
represent the world? In cognitive neuroscience it is typical to think
representations are points in high-dimensional space. In order to study these
kinds of spaces it is necessary to have tools that capture the organization of
high- dimensional data. Topological data analysis (TDA) holds promise for
detecting unique types of geometric features in patterns of data. Although
potentially useful, TDA has not been applied to event-related fMRI data. Here
we utilized a popular tool from TDA, persistent homology, to recover
topological signals from event-related fMRI data. We simulated realistic fMRI
data and explored the parameters under which persistent homology can
successfully extract signal. We also provided extensive code and
recommendations for how to make the most out of TDA for fMRI analysis.</p>
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<p>Papers:</p>
<ul class="simple">
<li>C. Ellis, M. Lesnick, G. {Henselman-Petrusek}, B. Keller, J. Cohen, <a class="reference external" href="https://doi.org/10.1162/netn_a_00095">"Feasibility of Topological Data Analysis for Event-Related {{fMRI}}"</a>, 2019, Network Neuroscience, 3, 695--706</li>
<li>C. Giusti, E. Pastalkova, C. Curto, V. Itskov, <a class="reference external" href="https://doi.org/10.1073/pnas.1506407112">"Clique Topology Reveals Intrinsic Geometric Structure in Neural Correlations"</a>, 2015, National Academy of Sciences, 13455--13460</li>
<li>B. Stolz, H. Harrington, M. Porter, <a class="reference external" href="https://doi.org/10.1063/1.4978997">"Persistent Homology of Time-Dependent Functional Networks Constructed from Coupled Time Series"</a>, 2017, Chaos: An Interdisciplinary Journal of Nonlinear Science, 27, 047410</li>
<li>A. Zomorodian, <a class="reference external" href="https://doi.org/10.1007/s00454-004-1146-y">"Computing Persistent Homology"</a>, 2005, Discrete & Computational Geometry, 33, 249--274</li>
</ul>
<p><strong>Date:</strong> 2021/05/21 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>