publications
publications by categories in reversed chronological order. generated by jekyll-scholar.
2025
- Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapesOmid Kardan, Natasha Jones, Muriah D. Wheelock, Mike Angstadt, Cleanthis Michael, M. Fiona Molloy, Jiaxin Cindy Tu, Lora M. Cope, Meghan E. Martz, Katherine L. McCurry, Jillian E. Hardee, Monica D. Rosenberg, Alexander S. Weigard, Luke W. Hyde, Chandra S. Sripada, and Mary M. HeitzegDevelopmental Cognitive Neuroscience, Jun 2025
Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individual’s resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youth’s connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9–10 years; n = 6469) and 2-year-follow-up (Y2: 11–12 years; n = 5060). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.
- Pilot study comparing effects of infrared neuromodulation and transcranial magnetic stimulation using magnetic resonance imagingSophia A. Bibb, Emily J. Yu, M. Fiona Molloy, John LaRocco, Patricia Resnick, Kevin Reeves, K. Luan Phan, Sanjay Krishna, and Zeynep M. SayginFrontiers in Human Neuroscience, Mar 2025Publisher: Frontiers
- Somatomotor disconnection links sleep duration with socioeconomic context, screen time, cognition, and psychopathologyCleanthis Michael, Aman Taxali, Mike Angstadt, Katherine L. McCurry, Alexander Weigard, Omid Kardan, M. Fiona Molloy, Katherine Toda-Thorne, Lily Burchell, Maria Dziubinski, Jason Choi, Melanie Vandersluis, Luke W. Hyde, Mary M. Heitzeg, and Chandra SripadaBiological Psychiatry Global Open Science, Apr 2025
Background Sleep is critical for healthy brain development and emotional wellbeing, especially during adolescence when sleep, behavior, and neurobiology are rapidly evolving. Theoretical reviews and empirical research have historically focused on how sleep influences mental health through its impact on higher-order brain systems. No studies have leveraged data-driven network neuroscience methods to uncover interpretable, brain-wide signatures of sleep duration in adolescence, their socio-environmental origins, and their consequences for cognition and psychopathology. Methods We implement graph theory and component-based predictive modeling to examine how a multimodal index of sleep duration (parent-report, youth-report, Fitbit) is associated with intrinsic brain architecture in 3,037 youth (11-12 years) from the Adolescent Brain Cognitive DevelopmentSM Study. Results We demonstrate that network integration/segregation exhibit a strong, generalizable multivariate association with sleep duration (r=.23, p\textless.001). The multivariate signature of shorter sleep predominantly involved increasing disconnection of a lower-order system, the somatomotor network, from other systems. We next identify a single component of brain architecture as the dominant contributor of this relationship (r=.15), which again exhibited this somatomotor disconnection motif. Finally, greater somatomotor disconnection is associated with lower socioeconomic resources, longer screen times, reduced cognitive/academic performance, and elevated externalizing problems (β’s\textgreater0.03, p’s≤.007). Conclusions These findings reveal a novel neural signature of shorter sleep in adolescence that is intertwined with environmental risk, cognition, and psychopathology. By robustly elucidating the key involvement of an understudied brain system in sleep, this study can inform theoretical and translational research directions on sleep to promote neurobehavioral development and mental health during the adolescent transition.
- Regional, but not brain-wide, graph theoretic measures are robustly and reproducibly linked to general cognitive abilityM Fiona Molloy, Aman Taxali, Mike Angstadt, Tristan Greathouse, Katherine Toda-Thorne, Katherine L McCurry, Alexander Weigard, Omid Kardan, Lily Burchell, Maria Dziubinski, Jason Choi, Melanie Vandersluis, Cleanthis Michael, Mary M Heitzeg, and Chandra SripadaCerebral Cortex, Apr 2025
General cognitive ability (GCA), also called “general intelligence,” is thought to depend on network properties of the brain, which can be quantified through graph theoretic measures such as small worldness and module degree. An extensive set of studies examined links between GCA and graphical properties of resting state connectomes. However, these studies often involved small samples, applied just a few graph theory measures in each study, and yielded inconsistent results, making it challenging to identify the architectural underpinnings of GCA. Here, we address these limitations by systematically investigating univariate and multivariate relationships between GCA and 17 whole-brain and node-level graph theory measures in individuals from the Adolescent Brain Cognitive Development Study (n = 5937). We demonstrate that whole-brain graph theory measures, including small worldness and global efficiency, fail to exhibit meaningful relationships with GCA. In contrast, multiple node-level graphical measures, especially module degree (within-network connectivity), exhibit strong associations with GCA. We establish the robustness of these results by replicating them in a second large sample, the Human Connectome Project (n = 847), and across a variety of modeling choices. This study provides the most comprehensive and definitive account to date of complex interrelationships between GCA and graphical properties of the brain’s intrinsic functional architecture.
- Joint Cognitive Models Reveal Sources of Robust Individual Differences in Conflict ProcessingMary Molloy, Taraz Lee, John Jonides, Han Zhang, Jacob Sellers, Andrew Heathcote, Chandra Sripada, and Alexander WeigardJun 2025
Experimental manipulations in conflict tasks, e.g., the Stroop, Flanker, and Simon tasks, lead to systematically poorer performance in “incongruent” conditions that feature stimuli that contradict task goals. However, substantial recent debate surrounds whether individual differences in conflict task behavior reflect reliable, trait-like mechanistic processes. Much prior work uses difference scores, contrasting performance between incongruent and congruent trials to index conflict suppression ability, but recent work demonstrates these scores exhibit poor psychometric properties. Formal cognitive process models suggest that individual differences in conflict suppression are driven by task-general processes, as opposed to processes specialized for conflict. However, this prior work separately models cognitive process parameters and their covariation, which fails to adequately account for measurement error. Here, we model distinct mechanisms of conflict task performance and their covariance simultaneously using hierarchical Bayesian joint modeling methods for the first time which improves individual estimation and accounts for error. We fit the conflict linear ballistic accumulator model (LBA) to two large datasets containing multiple conflict tasks and test-retest sessions, and an additional large dataset containing a conflict task and simple perceptual decision-making task. First, within conflict tasks, we found moderate test-retest reliability for both conflict-specific processing mechanisms, and, to a larger degree, task-general mechanisms. Second, task-general, but not conflict-specific, mechanisms were correlated across different conflict tasks. Third, these task-general mechanisms were correlated between conflict tasks and a simple decision-making task without conflict suppression demands. Overall, we found robust individual differences in computational mechanisms underlying general decision-making, but not mechanisms specific to conflict processing.
2024
- Socioeconomic resources in youth are linked to divergent patterns of network integration/segregation across the brain’s transmodal axisCleanthis Michael, Aman Taxali, Mike Angstadt, Omid Kardan, Alexander Weigard, M Fiona Molloy, Katherine L McCurry, Luke W Hyde, Mary M Heitzeg, and Chandra SripadaPNAS Nexus, Sep 2024
Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully relate to intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neuroscientific meaning of these widespread neural differences remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage graph theory to precisely characterize multivariate and univariate associations between SER across household and neighborhood contexts and the intrinsic functional architecture of brain regions in 5,821 youth (9–10 years) from the Adolescent Brain Cognitive Development Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are associated with greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that topological associations with SER are spatially patterned along the unimodal–transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread associations between SER and brain organization. This study highlights both higher-order and somatomotor networks that are differentially implicated in environmental stress, disadvantage, and opportunity in youth.
- Predicting high-level visual areas in the absence of task fMRIM. Fiona Molloy, Zeynep M. Saygin, and David E. OsherScientific Reports, May 2024
The ventral visual stream is organized into units, or functional regions of interest (fROIs), specialized for processing high-level visual categories. Task-based fMRI scans ("localizers") are typically used to identify each individual’s nuanced set of fROIs. The unique landscape of an individual’s functional activation may rely in large part on their specialized connectivity patterns; recent studies corroborate this by showing that connectivity can predict individual differences in neural responses. We focus on the ventral visual stream and ask: how well can an individual’s resting state functional connectivity localize their fROIs for face, body, scene, and object perception? And are the neural processors for any particular visual category better predicted by connectivity than others, suggesting a tighter mechanistic relationship between connectivity and function? We found, among 18 fROIs predicted from connectivity for each subject, all but one were selective for their preferred visual category. Defining an individual’s fROIs based on their connectivity patterns yielded regions that were more selective than regions identified from previous studies or atlases in nearly all cases. Overall, we found that in the absence of a domain-specific localizer task, a 10-min resting state scan can be reliably used for defining these fROIs.
2023
- Effect of Extremely Preterm Birth on Adolescent Brain Network OrganizationM. Fiona Molloy, Emily J. Yu, Whitney I. Mattson, Kristen R. Hoskinson, H. Gerry Taylor, David E. Osher, Eric E. Nelson, and Zeynep M. SayginBrain Connectivity, Sep 2023Publisher: Mary Ann Liebert, Inc., publishers
Introduction: Extremely preterm (EPT) birth, defined as birth at a gestational age (GA) \textless28 weeks, can have a lasting impact on cognition throughout the life span. Previous investigations reveal differences in brain structure and connectivity between infants born preterm and full-term (FT), but how does preterm birth impact the adolescent connectome? Methods: In this study, we investigate how EPT birth can alter broadscale network organization later in life by comparing resting-state functional magnetic resonance imaging connectome-based parcellations of the entire cortex in adolescents born EPT (N = 22) to age-matched adolescents born FT (GA ≥37 weeks, N = 28). We compare these parcellations to adult parcellations from previous studies and explore the relationship between an individual’s network organization and behavior. Results: Primary (occipital and sensorimotor) and frontoparietal networks were observed in both groups. However, there existed notable differences in the limbic and insular networks. Surprisingly, the connectivity profile of the limbic network of EPT adolescents was more adultlike than the same network in FT adolescents. Finally, we found a relationship between adolescents’ overall cognition score and their limbic network maturity. Discussion: Overall, preterm birth may contribute to the atypical development of broadscale network organization in adolescence and may partially explain the observed cognitive deficits.
2022
- Individual variability in functional organization of the neonatal brainM. Fiona Molloy and Zeynep M. SayginNeuroImage, Jun 2022
The adult brain is organized into distinct functional networks, forming the basis of information processing and determining individual differences in behavior. Is this network organization genetically determined and present at birth? And what is the individual variability in this organization in neonates? Here, we use unsupervised learning to uncover intrinsic functional brain organization using resting-state connectivity from a large cohort of neonates (Developing Human Connectome Project). We identified a set of symmetric, hierarchical, and replicable networks: sensorimotor, visual, default mode, ventral attention, and high-level vision. We quantified individual variability across neonates, and found the most individual variability in the ventral attention networks. Crucially, the variability of these networks was not driven by SNR differences or differences from adult networks (Yeo et al., 2011). Finally, differential gene expression provided a potential explanation for the emergence of these distinct networks and identified potential genes of interest for future developmental and individual variability research. Overall, we found neonatal connectomes (even at the voxel-level) can reveal broad individual-specific information processing units. The presence of individual differences in neonates and the framework for personalized parcellations demonstrated here has the potential to improve prediction of behavior and future outcomes from neonatal and infant brain data.
2020
- Hierarchies improve individual assessment of temporal discounting behavior.M. Fiona Molloy, Ricardo J. Romeu, Peter D. Kvam, Peter R. Finn, Jerome Busemeyer, and Brandon M. TurnerDecision, Jun 2020Publisher: US: Educational Publishing Foundation
2019
- Individual Differences in the Neural Dynamics of Response InhibitionM. Fiona Molloy, Giwon Bahg, Zhong-Lin Lu, and Brandon M. TurnerJournal 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.
- On the Neural and Mechanistic Bases of Self-ControlBrandon M Turner, Christian A Rodriguez, Qingfang Liu, M Fiona Molloy, Marjolein Hoogendijk, and Samuel M McClureCerebral Cortex, Feb 2019
Intertemporal choice requires a dynamic interaction between valuation and deliberation processes. While evidence identifying candidate brain areas for each of these processes is well established, the precise mechanistic role carried out by each brain region is still debated. In this article, we present a computational model that clarifies the unique contribution of frontoparietal cortex regions to intertemporal decision making. The model we develop samples reward and delay information stochastically on a moment-by-moment basis. As preference for the choice alternatives evolves, dynamic inhibitory processes are executed by way of asymmetric lateral inhibition. We find that it is these lateral inhibition processes that best explain the contribution of frontoparietal regions to intertemporal decision making exhibited in our data.
2018
- What’s in a response time?: On the importance of response time measures in constraining models of context effects.M. Fiona Molloy, Matthew Galdo, Giwon Bahg, Qingfang Liu, and Brandon M. TurnerDecision, Feb 2018Publisher: US: Educational Publishing Foundation
- Hierarchical Bayesian Analyses for Modeling BOLD Time Series DataM. Fiona Molloy, Giwon Bahg, Xiangrui Li, Mark Steyvers, Zhong-Lin Lu, and Brandon M. TurnerComputational 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.