UH Biocomputation Group - Topological data analysishttp://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>
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<p><strong>Date:</strong> 2021/05/21 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
Organization of Cell Assemblies in the Hippocampus2021-04-14T15:00:00+01:002021-04-14T15:00:00+01:00Emil Dmitruktag:biocomputation.herts.ac.uk,2021-04-14:/2021/04/14/organization-of-cell-assemblies-in-the-hippocampus.html<p class="first last">Emil Dmitruk's Journal Club session where he will talk about a paper "Organization of Cell Assemblies in the Hippocampus"</p>
<p>This week on Journal Club session Emil Dmitruk will talk about a paper "Organization of Cell Assemblies in the Hippocampus" and will briefly
presnt how this subject can be approached with topological data analysis.</p>
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<p>Neurons can produce action potentials with high temporal precision. A
fundamental issue is whether, and how, this capability is used in
information processing. According to the "cell assembly" hypothesis,
transient synchrony of anatomically distributed groups of neurons
underlies processing of both external sensory input and internal
cognitive mechanisms. Accordingly, neuron populations should be
arranged into groups whose synchrony exceeds that predicted by common
modulation by sensory input. Here we find that the spike times of
hippocampal pyramidal cells can be predicted more accurately by using
the spike times of simultaneously recorded neurons in addition to the
animals location in space. This improvement remained when the spatial
prediction was refined with a spatially dependent theta phase
modulation. The time window in which spike times are best predicted
from simultaneous peer activity is 10-30,ms, suggesting
that cell assemblies are synchronized at this timescale. Because this
temporal window matches the membrane time constant of pyramidal
neurons, the period of the hippocampal gamma oscillation and the time
window for synaptic plasticity, we propose that cooperative activity
at this timescale is optimal for information transmission and storage
in cortical circuits.</p>
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<p>Papers:</p>
<ul class="simple">
<li>K. Harris, J. Csicsvari, H. Hirase, G. Dragoi, G. Buzsaki, <a class="reference external" href="https://doi.org/10.1038/nature01834">"Organization of Cell Assemblies in the Hippocampus"</a>, 2003, Nature, 424, 552--556</li>
<li>C. Giusti, R. Ghrist, D. Bassett, <a class="reference external" href="https://doi.org/10.1007/s10827-016-0608-6">"Two's Company, Three (or More) Is a Simplex: Algebraic-Topological Tools for Understanding Higher-Order Structure in Neural Data"</a>, 2016, Journal of Computational Neuroscience, 41, 1--14</li>
</ul>
<p><strong>Date:</strong> 2021/04/16 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
Towards a new approach to reveal dynamical organization of the brain using topological data analysis2020-07-15T18:35:55+01:002020-07-15T18:35:55+01:00Emil Dmitruktag:biocomputation.herts.ac.uk,2020-07-15:/2020/07/15/towards-a-new-approach-to-reveal-dynamical-organization-of-the-brain-using-topological-data-analysis.html<p class="first last">Emil Dmitruk's journal club session where he will talk about the paper "Towards a new approach to reveal dynamical organization of the brain using topological data analysis".</p>
<p>This week on Journal Club session Emil Dmitruk will talk about the paper "Towards a new approach to reveal dynamical organization of the brain using topological data analysis".</p>
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<p>Little is known about how our brains dynamically adapt for efficient functioning. Most
previous work has focused on analyzing changes in co-fluctuations between a set of
brain regions over several temporal segments of the data. We argue that by
collapsing data in space or time, we stand to lose useful information about the brain’s
dynamical organization. Here we use Topological Data Analysis to reveal the overall
organization of whole-brain activity maps at a single-participant level—as an
interactive representation—without arbitrarily collapsing data in space or time. Using
existing multitask fMRI datasets, with the known ground truth about the timing of
transitions from one task-block to next, our approach tracks both within- and
between-task transitions at a much faster time scale (~4–9 s) than before. The
individual differences in the revealed dynamical organization predict task
performance. In summary, our approach distills complex brain dynamics into
interactive and behaviorally relevant representations.</p>
<p>Papers:</p>
<ul class="simple">
<li>Saggar, M., Sporns, O., Gonzalez-Castillo, J. et al. <a class="reference external" href="https://www.nature.com/articles/s41467-018-03664-4">"Towards a new approach to reveal dynamical organization of the brain using topological data analysis"</a> ,Nat Commun 9, 1399 (2018). doi.org/10.1038/s41467-018-03664-4</li>
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<p><strong>Date:</strong> 17/07/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: online</p>