Task-based fMRI is a critical innovation in cognitive neuroscience that provides a window into the neural activation within an individual while they perform a cognitive task. One focus of my research involves improving the methodology for extracting neural activation both within-individuals and across large groups to investigate the neural basis of executive function, including response inhibition and working memory (Molloy et al., 2019; Molloy et al., 2018; Lin et al., 2025).
References
2025
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Population-weighted Image-on-scalar Regression Analyses of Large Scale Neuroimaging Data
Zikai Lin, M. Fiona Molloy, Chandra Sripada, Jian Kang, and Yajuan Si
Nov 2025
ISSN: 3067-2007 Pages: 2025.04.21.25326171
Recent advances in neuroimaging modeling highlight the importance of accounting for subgroup heterogeneity in population-based neuroscience research through various investigations in large scale neuroimaging data collection. To integrate survey methodology with neuroscience research, we present an imaging data analysis aiming to achieve population generalizability with screened subsets of data. The Adolescent Brain Cognitive Development (ABCD) Study has enrolled a large cohort of participants to reflect the individual variation of the U.S. population in adolescent development. To ensure population representation, the ABCD Study has released the base weights. We estimated the associations between brain activities and cognitive performance using the functional Magnetic Resonance Imaging (fMRI) data from the ABCD Study’s n-back working memory task. Notably, the imaging subsample exhibits differences from the baseline cohort in key child characteristics, and such discrepancies cannot be addressed simply by applying the ABCD base weights. We developed new population weights specific to the subsample and included the adjusted weights in the image-on-scalar regression model. We validated the approach through synthetic simulations and applications to fMRI data from the ABCD Study. Our findings indicate that population weighting adjustments influence association estimates between brain activities and cognition, emphasizing the importance of evaluating validity and generalizability in population neuroscience research.
2019
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Individual Differences in the Neural Dynamics of Response Inhibition
M. Fiona Molloy, Giwon Bahg, Zhong-Lin Lu, and Brandon M. Turner
Journal of Cognitive Neuroscience, Dec 2019
Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group’s behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.
2018
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Hierarchical Bayesian Analyses for Modeling BOLD Time Series Data
M. Fiona Molloy, Giwon Bahg, Xiangrui Li, Mark Steyvers, Zhong-Lin Lu, and Brandon M. Turner
Computational Brain & Behavior, Jun 2018
Hierarchical Bayesian analyses have become a popular technique for analyzing complex interactions of important experimental variables. One application where these analyses have great potential is in analyzing neural data. However, estimating parameters for these models can be complicated. Although many software programs facilitate the estimation of parameters within hierarchical Bayesian models, due to some restrictions, complicated workarounds are sometimes necessary to implement a model within the software. One such restriction is convolution, a technique often used in neuroimaging analyses to relate experimental variables to models describing neural activation. Here, we show how to perform convolution within the R programming environment. The strategy here is to pass the convolved neural signal to existing software package for fitting hierarchical Bayesian models to data such as JAGS (Plummer 2003) or Stan (Carpenter et al. 2017). We use the convolution technique as a basis for describing neural time series data and develop five models to describe how subject-, condition-, and brain-area-specific effects interact. To provide a concrete example, we apply these models to fMRI data from a stop-signal task. The models are assessed in terms of model fit, parameter constraint, and generalizability. For these data, our results suggest that while subject and condition constraints are important for both fit and generalization, region of interest constraints did not substantially improve performance.