Feasibility of Topological Data Analysis for Event-Related fMRI

2021-05-20T09:12:19+01:00

This week on Journal Club session Shabnam Kadir will talk about a paper "Feasibility of Topological Data Analysis for Event-Related fMRI".

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.

Papers:

C. Ellis, M. Lesnick, G. {Henselman-Petrusek}, B. Keller, J. Cohen, "Feasibility of Topological Data Analysis for Event-Related {{fMRI}}", 2019, Network Neuroscience, 3, 695--706
C. Giusti, E. Pastalkova, C. Curto, V. Itskov, "Clique Topology Reveals Intrinsic Geometric Structure in Neural Correlations", 2015, National Academy of Sciences, 13455--13460
B. Stolz, H. Harrington, M. Porter, "Persistent Homology of Time-Dependent Functional Networks Constructed from Coupled Time Series", 2017, Chaos: An Interdisciplinary Journal of Nonlinear Science, 27, 047410
A. Zomorodian, "Computing Persistent Homology", 2005, Discrete & Computational Geometry, 33, 249--274

Date: 2021/05/21
Time: 14:00
Location: online

Bursting Neurons Signal Input Slope

2021-04-21T11:37:00+01:00

This week on Journal Club session Volker Steuber will talk about a paper "Bursting Neurons Signal Input Slope".

Brief bursts of high-frequency action potentials represent a common firing mode of pyramidal neurons, and there are indications that they represent a special neural code. It is therefore of interest to determine whether there are particular spatial and temporal features of neuronal inputs that trigger bursts. Recent work on pyramidal cells indicates that bursts can be initiated by a specific spatial arrangement of inputs in which there is coincident proximal and distal dendritic excitation (Larkum et al., 1999). Here we have used a computational model of an important class of bursting neurons to investigate whether there are special temporal features of inputs that trigger bursts. We find that when a model pyramidal neuron receives sinusoidally or randomly varying inputs, bursts occur preferentially on the positive slope of the input signal. We further find that the number of spikes per burst can signal the magnitude of the slope in a graded manner. We show how these computations can be understood in terms of the biophysical mechanism of burst generation. There are several examples in the literature suggesting that bursts indeed occur preferentially on positive slopes (Guido et al., 1992; Gabbiani et al., 1996). Our results suggest that this selectivity could be a simple consequence of the biophysics of burst generation. Our observations also raise the possibility that neurons use a burst duration code useful for rapid information transmission. This possibility could be further examined experimentally by looking for correlations between burst duration and stimulus variables.

Papers:

A. Kepecs, X. Wang, J. Lisman, "Bursting Neurons Signal Input Slope", 2002, The Journal of Neuroscience, 22, 9053--9062

Date: 2021/04/23
Time: 14:00
Location: online

UH Biocomputation Group - Simulation

Feasibility of Topological Data Analysis for Event-Related fMRI

Bursting Neurons Signal Input Slope