Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses

By Jeanette A. Mumford, Benjamin O. Turner, F. Gregory Ashby, Russell A. Poldrack. NeuroImage, 2011

740 citations recorded by the source corpus.

Use of multivoxel pattern analysis (MVPA) to predict the cognitive state of a subject during task performance has become a popular focus of fMRI studies. The input to these analyses consists of activation patterns corresponding to different tasks or stimulus types. These activation patterns are fairly straightforward to calculate for blocked trials or slow event-related designs, but for rapid event-related designs the evoked BOLD signal for adjacent trials will overlap in time, complicating the identification of signal unique to specific trials. Rapid event-related designs are often preferred because they allow for more stimuli to be presented and subjects tend to be more focused on the task, and thus it would be beneficial to be able to use these types of designs in MVPA analyses. The present work compares 8 different models for estimating trial-by-trial activation patterns for a range of rapid event-related designs varying by interstimulus interval and signal-to-noise ratio. The most effective approach obtains each trial’s estimate through a general linear model including a regressor for that trial as well as another regressor for all other trials. Through the analysis of both simulated and real data we have found that this model shows some improvement over the standard approaches for obtaining activation patterns. The resulting trial-by-trial estimates are more representative of the true activation magnitudes, leading to a boost in classification accuracy in fast event-related designs with higher signal-to-noise. This provides the potential for fMRI studies that allow simultaneous optimization of both univariate and MVPA approaches.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/614f65c4f479731ce2e3bfe5f91ef54304efe3f0

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In Cognitive Neuroscience

Citations in the corpus

Strong Evidence for Pattern Separation in Human Dentate Gyrus (2016, methodology): “We estimated trial-by-trial activation patterns using an effective approach for rapid event-related designs (Mumford et al., 2012).”
Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise (2017, methodology): “[51], was necessary to avoid confounds related to hemodynamic lag when applying machine learning models to trials in an fMRI timeseries.”
Deconstructing multivariate decoding for the study of brain function (2017, background): “Beta estimates are often based on individual trials or the entire time-series, and different approaches have been suggested for their estimation in the context of multivariate decoding (Mumford et al., 2012).”
Neural reactivation in parietal cortex enhances memory for episodically linked information (2018, methodology): “To examine reactivation in these networks in the brain, we used RSA of fMRI data, a method that allows one to examine the degree of similarity between patterns of neural activity across different trials (23).”
The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data (2015, background): “As mentioned above, rather than running a decoding analysis on individual images, it has become quite common to use single trial estimates of data (Mumford et al., 2012) or even estimates that combine multiple trials within one run (Haynes et al., 2007).”
Revealing the co-existence of written and spoken language coding neural populations in the visual word form area (2025, unknown): “In order to conduct MVPA, trial-wise estimates (i.e., β coefficients) were extracted for each condition in the lexical decision task by using the Least Squares — Separate (LSS) method ( Mumford et al., 2012) which ran a GLM for each trial (3dLSS in AFNI).”
Neural Correlates of Reactivation and Vividness Reveal Separable Contributions to Objective and Subjective Measures of Episodic Memory (2021, methodology): “To extract task related signal, a GLM was run for each subject over functional runs using a “least squares separate” beta estimation method (Mumford et al., 2012).”