Dataset Information

Feasibility of topological data analysis for event-related fMRI.

ABSTRACT: 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.

SUBMITTER: Ellis CT

PROVIDER: S-EPMC6663178 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Feasibility of topological data analysis for event-related fMRI.

Ellis Cameron T CT Lesnick Michael M Henselman-Petrusek Gregory G Keller Bryn B Cohen Jonathan D JD

Network neuroscience (Cambridge, Mass.) 20190701 3

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, ...[more]

PMID: 31410374

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Project description:fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. "Structure" within fMRI data is determined by dynamic fluctuations in spatially distributed signals over time, and TDA is well positioned to help researchers better characterize mass dynamics of the signal by rigorously capturing shape within it. To accurately motivate this idea, we a) survey an established method in TDA ("persistent homology") to reveal and describe how complex structures can be extracted from data sets generally, and b) describe how persistent homology can be applied specifically to fMRI data. We provide explanations for some of the mathematical underpinnings of TDA (with expository figures), building ideas in the following sequence: a) fMRI researchers can and should use TDA to extract structure from their data; b) this extraction serves an important role in the endeavor of functional discovery, and c) TDA approaches can complement other established approaches toward fMRI analyses (for which we provide examples). We also provide detailed applications of TDA to fMRI data collected using established paradigms, and offer our software pipeline for readers interested in emulating our methods. This working overview is both an inter-disciplinary synthesis of ideas (to draw researchers in TDA and fMRI toward each other) and a detailed description of methods that can motivate collaborative research.

Project description:The investigation of abstract cognitive tasks, e.g., semantic processing of speech, requires the simultaneous use of a carefully selected stimulus design and sensitive tools for the analysis of corresponding neural activity that are comparable across different studies investigating similar research questions. Multi-voxel pattern analysis (MVPA) methods are commonly used in neuroimaging to investigate BOLD responses corresponding to neural activation associated with specific cognitive tasks. Regions of significant activation are identified by a thresholding operation during multivariate pattern analysis, the results of which are susceptible to the applied threshold value. Investigation of analysis approaches that are robust to a large extent with respect to thresholding, is thus an important goal pursued here. The present paper contributes a novel statistical analysis method for fMRI experiments, searchlight classification informative region mixture model (SCIM), that is based on the assumption that the whole brain volume can be subdivided into two groups of voxels: spatial voxel positions around which recorded BOLD activity does convey information about the present stimulus condition and those that do not. A generative statistical model is proposed that assigns a probability of being informative to each position in the brain, based on a combination of a support vector machine searchlight analysis and Gaussian mixture models. Results from an auditory fMRI study investigating cortical regions that are engaged in the semantic processing of speech indicate that the SCIM method identifies physiologically plausible brain regions as informative, similar to those from two standard methods as reference that we compare to, with two important differences. SCIM-identified regions are very robust to the choice of the threshold for significance, i.e., less "noisy," in contrast to, e.g., the binomial test whose results in the present experiment are highly dependent on the chosen significance threshold or random permutation tests that are additionally bound to very high computational costs. In group analyses, the SCIM method identifies a physiologically plausible pre-frontal region, anterior cingulate sulcus, to be involved in semantic processing that other methods succeed to identify only in single subject analyses.

Dataset Information

Feasibility of topological data analysis for event-related fMRI.

Publications

Feasibility of topological data analysis for event-related fMRI.

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