Biology subjects

Winters, D. E.

Publications and source records attributed to Winters, D. E..

6 recordsLinked to original sources

bioRxiv2025

Probabilistic Cognitive State Modeling (PCSM): Decoding Latent Spatiotemporal Dynamics to Reveal Serial-Parallel Processing, Cognitive Demand, and Serial Bottleneck in Task-Based fMRI

Studying flexible, adaptive transitions between cognitive tasks and serial-parallel processing under changing task demands has been central to understanding human cognition. Advances in neuroimaging analysis have improved the ability to link cognition with brain function, motivating methods that characterize dynamic brain activity to quantify emergent cognitive properties during task-based fMRI. Probabilistic Cognitive State Modeling (PCSM) combines Finite Impulse Response modeling of BOLD activity with a Gaussian Mixture Model-Hidden Markov Model to infer recurring multivariate patterns of task-evoked BOLD responses across spatially distributed regions over time ("brain states"). From the resulting posterior structure, PCSM deterministically derives interpretable processing metrics, including serial-parallel deviation, cognitive demand, and serial bottleneck. Data-informed generative simulations evaluated PCSM across systematically varied noise levels and transition regimes. Results show that PCSM reliably recovers latent structure ([~]98% state-alignment accuracy under known generative conditions) and produces stable parameter estimates across simulation regimes. Threshold analyses identify reliable boundaries between parallel, mixed, and serial processing modes and recover expected relationships among demand, and bottleneck. Together, these results demonstrate that PCSM provides a principled framework for characterizing dynamic task-evoked processing architectures and estimating individual-level cognitive dynamics from task-based fMRI, supporting future investigation of cognitive processing constraints across tasks and populations.

neuroscience↗

bioRxiv2023

Executive function and underlying brain network distinctions for callous-unemotional traits and conduct problems in adolescents

The complexity of executive function (EF) impairments in youth antisocial phenotypes of callous-unemotional (CU) traits and conduct problems (CP) challenge identifying phenotypic specific EF deficits. We can redress these challenges by (1) accounting for EF measurement error and (2) testing distinct functional brain properties accounting for differences in EF. Thus, we employed a latent modeling approach for EFs (inhibition, shifting, fluency, common EF) and extracted connection density from matching contemporary EF brain models with a sample of 112 adolescents (ages 13-17, 42% female). Path analysis indicated CU traits associated with lower inhibition. Inhibition network density positively associated with inhibition, but this association was strengthened by CU and attenuated by CP. Common EF associated with three-way interactions between density*CP by CU for the inhibition and shifting networks. This suggests those higher in CU require their brain to work harder for lower inhibition, whereas those higher in CP have difficulty engaging inhibitory brain responses. Additionally, those with CP interacting with CU show distinct brain patterns for a more general EF capacity. Importantly, modeling cross-network connection density in contemporary EF models to test EF involvement in core impairments in CU and CP may accelerate our understanding of EF in these phenotypes.

neuroscience↗

bioRxiv2022

Connectome-based predictive modeling of empathy in adolescents with and without the low-prosocial emotion specifier

Empathy impairments are an important part of a broader affective impairments defining the youth antisocial phenotype callous-unemotional (CU) traits and the DSM-5 low prosocial emotion (LPE) specifier. While functional connectivity underlying empathy and CU traits have been well studied, less is known about what functional connections underly differences in empathy amongst adolescents qualifying for the LPE specifier. Such information can provide mechanistic distinctions for this clinically relevant specifier. The present study uses connectome-based predictive modeling that uses whole-brain resting-state functional connectivity data to predict cognitive and affective empathy for those meeting the LPE specifier (n= 29) and those that do not (n= 57). Additionally, we tested if models of empathy generalized between groups as well as density differences for each model of empathy between groups. Results indicate the LPE group had lower cognitive and affective empathy as well as higher CU traits and conduct problems. Negative and positive models were identified for affective empathy for both groups, but only the negative model for the LPE and positive model for the normative group reliably predicted cognitive empathy. Models predicting empathy did not generalize between groups. Density differences within the default mode, salience, executive control, limbic, and cerebellar networks were found as well as between the executive control, salience, and default mode networks. And, importantly, connections between the executive control and default mode networks characterized empathy differences the LPE group such that more positive connections characterized cognitive differences and less negative connections characterized affective differences. These findings indicate neural differences in empathy for those meeting LPE criteria that may explain decrements in empathy amongst these youth. These findings support theoretical accounts of empathy decrements in the LPE clinical specifier and extend them to identify specific circuits accounting for variation in empathy impairments. The identified negative models help understand what connections inhibit empathy whereas the positive models reveal what brain patterns are being used to support empathy in those with the LPE specifier. LPE differences from the normative group and could be an appropriate biomarker for predicting CU trait severity. Replication and validation using other large datasets are important next steps.

neuroscience↗

bioRxiv2022

Efficiency of heterogenous functional connectomes explains variance in callous-unemotional traits after computational lesioning of cortical midline and salience regions

Callous-unemotional (CU) traits are a youth antisocial phenotype hypothesized to be a result of differences in the integration of multiple brain systems. However, mechanistic insights into these brain systems are a continued challenge. Where prior work describes activation and connectivity of the connectome in relation to these systems, new mechanistic insights can be derived by removing nodes and characterizing changes in network properties (hereafter referred to as computational lesioning) to characterize the resilience and vulnerability of the brains functional connectome. Here, we study the resilience of connectome integration in CU traits by estimating changes in efficiency after computationally lesioning individual-level connectomes. From resting-state data of 86 participants (48% female, age 14.52{+/-}1.31) drawn from the Nathan Kline institutes Rockland study, individual-level connectomes were estimated using graphical lasso. Computational lesioning was conducted both sequentially and by targeting global and local hubs. We calculated changes in network efficiency after each lesion. Then, elastic net regression was applied to determine how these changes explained variance in CU traits. Follow-up analyses characterized modeled node hubs, examined moderation, determined impact of targeting, and decoded the brain mask by comparing regions to meta-analytic maps. Elastic net regression revealed that computational lesioning of 23 nodes, network modularity, and Tanner stage explained variance in CU traits. Hub assignment of selected hubs differed at higher CU traits. No evidence for moderation between simulated lesioning and CU traits was found. Targeting global hubs increased efficiency and targeting local hubs had no effect at higher CU traits. Identified brain mask meta-analytically associated with more emotion and cognitive terms. Although reliable patterns were found across participants, adolescent brains were heterogeneous even for those with a similar CU traits score. Adolescent brain response to simulated lesioning revealed a pattern of connectome resiliency and vulnerability that explains variance in CU traits, which can aid prediction of youth at greater risk for higher CU traits.

neuroscience↗

bioRxiv2022

Resting-state connectivity underlying cognitive controls association with perspective taking in callous-unemotional traits

Callous-Unemotional (CU) traits are often associated with impairments in perspective taking and cognitive control (regulating goal directed behavior); and adolescents with CU traits demonstrate aberrant brain activation/connectivity in areas underlying these processes. Together cognitive control and perspective taking are thought to link mechanistically to explain CU traits. Because increased cognitive control demands modulate perspective taking ability among both typically developing samples and individuals with elevated CU traits, understanding the neurophysiological substrates of these constructs could inform efforts to alleviate societal costs of antisocial behavior. The present study uses GIMME to examine the heterogenous functional brain properties (i.e., connection density, node centrality) underlying cognitive controls influence on perspective taking among adolescents on a CU trait continuum. Results reveal that cognitive control had a negative indirect association with CU traits via perspective taking; and brain connectivity indirectly associated with lower CU traits - specifically the social network via perspective taking and conflict network via cognitive control. Additionally, less negative connection density between the social and conflict networks was directly associated with higher CU traits. Our results support the growing literature on cognitive controls influence on socio-cognitive functioning in CU traits and extends that work by identifying underlying functional brain properties.

neuroscience↗

bioRxiv2021

Resting-state network topology characterizing callous-unemotional traits in adolescence

BackgroundCallous-unemotional (CU) traits, a youth antisocial phenotype, are hypothesized to associate with aberrant connectivity (dis-integration) across the salience (SAL), default mode (DMN), and frontoparietal (FPN) networks. However, CU traits have a heterogeneous presentation and previous research has not modeled individual heterogeneity in resting-state connectivity amongst adolescents with CU traits. The present study models individual-specific network maps and examines topological features of individual and subgroup maps in relation to CU traits. MethodsParticipants aged 13-17 completed resting-state functional connectivity and the inventory of callous-unemotional traits as part of the Nathan Klein Rockland study. A sparse network approach (GIMME) was used to derive individual-level and subgroup maps of all participants. We then examined heterogeneous network features associated with CU traits. ResultsHigher rates of CU traits increased probability of inclusion in one subgroup, which had the highest mean level of CU traits. Analysis of network features reveals less density within the FPN and greater density between DMN-FPN associated with CU traits. DiscussionFindings indicate heterogeneous person-specific connections and some subgroup connections amongst adolescents associate with CU traits. Higher CU traits associate with lower density in the FPN, which has been associated with attention and inhibition, and higher density between the DMN-FPN, which have been linked with cognitive control, social working memory, and empathy. Our findings suggest less efficiency in FPN function which, when considered mechanistically, could result in difficulty suppressing DMN when task positive networks are engaged. This is an area for further exploration but could explain cognitive and socio-affective impairments in CU traits.

neuroscience↗