Cortical and subcortical brain networks predict prevailing heart rate.

Resting heart rate may confer risk for cardiovascular disease (CVD) and other adverse cardiovascular events. While the brainstem's autonomic control over heart rate is well established, less is known about the regulatory role of higher level cortical and subcortical brain regions, especially in humans. This study sought to characterize the brain networks that predict variation in prevailing heart rate in otherwise healthy adults. We used machine learning approaches designed for complex, high-dimensional data sets, to predict variation in instantaneous heart period (the inter-heartbeat-interval) from whole-brain hemodynamic signals measured by fMRI. Task-based and resting-state fMRI, as well as peripheral physiological recordings, were taken from two data sets that included extensive repeated measurements within individuals. Our models reliably predicted instantaneous heart period from whole-brain fMRI data both within and across individuals, with prediction accuracies being highest when measured within-participants. We found that a network of cortical and subcortical brain regions, many linked to visceral motor and visceral sensory processes, were reliable predictors of variation in heart period. This adds to evidence on brain-heart interactions and constitutes an incremental step toward developing clinically applicable biomarkers of brain contributions to CVD risk.

D2 dopamine receptor expression, reactivity to rewards, and reinforcement learning in a complex value-based decision-making task.

Different dopamine subtypes have opposing dynamics at post-synaptic receptors, with the ratio of D1 to D2 receptors determining the relative sensitivity to gains and losses, respectively, during value-based learning. This effective sensitivity to different reward feedback interacts with phasic dopamine levels to determine the effectiveness of learning, particularly in dynamic feedback situations where frequency and magnitude of rewards need to be integrated over time to make optimal decisions. We modeled this effect in simulations of the underlying basal ganglia pathways and then tested the predictions in individuals with a variant of the human dopamine receptor D2 (DRD2; -141C Ins/Del and Del/Del) gene that associates with lower levels of D2 receptor expression (N=119) and compared their performance in the Iowa Gambling Task (IGT) to non-carrier controls (N=319). Ventral striatal (VS) reactivity to rewards was measured in the Cards task with fMRI. DRD2 variant carriers made less effective decisions than non-carriers, but this effect was not moderated by VS reward reactivity as is hypothesized by our model. These results suggest that the interaction between dopamine receptor subtypes and reactivity to rewards during learning may be more complex than originally thought.

How cortico-basal ganglia-thalamic subnetworks can shift decision policies to maximize reward rate.

All mammals exhibit flexible decision policies that depend, at least in part, on the cortico-basal ganglia-thalamic (CBGT) pathways. Yet understanding how the complex connectivity, dynamics, and plasticity of CBGT circuits translates into experience-dependent shifts of decision policies represents a longstanding challenge in neuroscience. Here we used a computational approach to address this problem. Specifically, we simulated decisions driven by CBGT circuits under baseline, unrewarded conditions using a spiking neural network, and fit the resulting behavior to an evidence accumulation model. Using canonical correlation analysis, we then replicated the existence of three recently identified control ensembles (responsiveness, pliancy and choice) within CBGT circuits, with each ensemble mapping to a specific configuration of the evidence accumulation process. We subsequently simulated learning in a simple two-choice task with one optimal (i.e., rewarded) target. We find that value-based learning, via dopaminergic signals acting on cortico-striatal synapses, effectively manages the speed-accuracy tradeoff so as to increase reward rate over time. Within this process, learning-related changes in decision policy can be decomposed in terms of the contributions of each control ensemble, and these changes are driven by sequential reward prediction errors on individual trials. Our results provide a clear and simple mechanism for how dopaminergic plasticity shifts specific subnetworks within CBGT circuits so as to strategically modulate decision policies in order to maximize effective reward rate.

Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum.

Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here we investigate this suggestion by harnessing a biologically-constrained spiking model of the corticobasal ganglia-thalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect path-way spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits.

Similarity in evoked responses does not imply similarity in macroscopic network states.

It is commonplace in neuroscience to assume that if two tasks activate the same brain areas in the same way, then they are recruiting the same underlying networks. Yet computational theory has shown that the same pattern of activity can emerge from many different underlying network representations. Here we evaluated whether similarity in activation necessarily implies similarity in network architecture by comparing region-wise activation patterns and functional correlation profiles from a large sample of healthy subjects (N = 242). Participants performed two executive control tasks known to recruit nearly identical brain areas, the color-word Stroop task and the Multi-Source Interference Task (MSIT). Using a measure of instantaneous functional correlations, based on edge time series, we estimated the task-related networks that differed between incongruent and congruent conditions. We found that the two tasks were much more different in their network profiles than in their evoked activity patterns at different analytical levels, as well as for a wide range of methodological pipelines. Our results reject the notion that having the same activation patterns means two tasks engage the same underlying representations, suggesting that task representations should be independently evaluated at both node and edge (connectivity) levels.

As a dynamical system, the brain can encode information at the module (e.g., brain regions) or the network level (e.g., connections between brain regions). This means that two tasks can produce the same pattern of activation, but differ in their network profile. Here we tested this using two tasks with largely similar cognitive requirements. Despite producing nearly identical macroscopic activation patterns, the two tasks produced different functional network profiles. These findings confirm prior theoretical work that similarity in task activation does not imply the same similarity in underlying network states.

Rethinking the external globus pallidus and information flow in cortico-basal ganglia-thalamic circuits.

For decades, the external globus pallidus (GPe) has been viewed as a passive way-station in the indirect pathway of the cortico-basal ganglia-thalamic (CBGT) circuit, sandwiched between striatal inputs and basal ganglia outputs. According to this model, one-way descending striatal signals in the indirect pathway amplify the suppression of downstream thalamic nuclei by inhibiting GPe activity. Here, we revisit this assumption, in light of new and emerging work on the cellular complexity, connectivity and functional role of the GPe in behaviour. We show how, according to this new circuit-level logic, the GPe is ideally positioned for relaying ascending and descending control signals within the basal ganglia. Focusing on the problem of inhibitory control, we illustrate how this bidirectional flow of information allows for the integration of reactive and proactive control mechanisms during action selection. Taken together, this new evidence points to the GPe as being a central hub in the CBGT circuit, participating in bidirectional information flow and linking multifaceted control signals to regulate behaviour.

Stressor-evoked brain activity, cardiovascular reactivity, and subclinical atherosclerosis in midlife adults.

Background: Cardiovascular responses to psychological stressors have been separately associated with preclinical atherosclerosis and hemodynamic brain activity patterns across different studies and cohorts; however, what has not been established is whether cardiovascular stress responses reliably link indicators of stressor-evoked brain activity and preclinical atherosclerosis that have been measured in the same individuals. Accordingly, the present study used cross-validation and predictive modeling to test for the first time whether stressor-evoked systolic blood pressure (SBP) responses statistically mediated the association between concurrently measured brain activity and a vascular marker of preclinical atherosclerosis in the carotid arteries. Methods: 624 midlife adults (aged 28-56 years, 54.97% female) from two different cohorts underwent two information-conflict fMRI tasks, with concurrent SBP measures collected. Carotid artery intima-media thickness (CA-IMT) was measured by ultrasonography. A mediation framework that included harmonization, cross-validation, and penalized principal component regression was then employed, while significant areas in possible direct and indirect effects were identified through bootstrapping. Sensitivity analysis further tested the robustness of findings after accounting for prevailing levels of cardiovascular disease risk and brain imaging data quality control. Results: Task-averaged patterns of hemodynamic brain responses exhibited a generalizable association with CA-IMT, which was mediated by an area-under-the-curve measure of aggregate SBP reactivity. Importantly, this effect held in sensitivity analyses. Implicated brain areas in this mediation included the ventromedial prefrontal cortex, anterior cingulate cortex, insula and amygdala. Conclusions: These novel findings support a link between stressor-evoked brain activity and preclinical atherosclerosis accounted for by individual differences in corresponding levels of stressor-evoked cardiovascular reactivity.

Cortical and subcortical brain networks predict prevailing heart rate.

Resting heart rate may confer risk for cardiovascular disease (CVD) and other adverse cardiovascular events. While the brainstem's autonomic control over heart rate is well established, less is known about the regulatory role of higher-level cortical and subcortical brain regions, especially in humans. The present study sought to characterize the brain networks that predict variation in prevailing heart rate in otherwise healthy adults. We used machine learning approaches designed for complex, high-dimensional datasets, to predict variation in instantaneous heart period (the inter-heartbeat-interval) from whole brain hemodynamic signals measured by fMRI. Task-based and resting-state fMRI, as well as peripheral physiological recordings, were taken from two datasets that included extensive repeated measurements within individuals. Our models reliably predicted instantaneous heart period from whole brain fMRI data both within and across individuals, with prediction accuracies being highest when measured within-participants. We found that a network of cortical and subcortical brain regions, many linked to psychological stress, were reliable predictors of variation in heart period. This adds to evidence on brain-heart interactions and constitutes an incremental step towards developing clinically-applicable biomarkers of brain contributions to CVD risk.

Effects of a laboratory-based aerobic exercise intervention on brain volume and cardiovascular health markers: protocol for a randomised clinical trial.

INTRODUCTION: Physical activity (PA) has beneficial effects on brain health and cardiovascular disease (CVD) risk. Yet, we know little about whether PA-induced changes to physiological mediators of CVD risk influence brain health and whether benefits to brain health may also explain PA-induced improvements to CVD risk. This study combines neurobiological and peripheral physiological methods in the context of a randomised clinical trial to better understand the links between exercise, brain health and CVD risk. METHODS AND ANALYSIS: In this 12-month trial, 130 healthy individuals between the ages of 26 and 58 will be randomly assigned to either: (1) moderate-intensity aerobic PA for 150 min/week or (2) a health information control group. Cardiovascular, neuroimaging and PA measurements will occur for both groups before and after the intervention. Primary outcomes include changes in (1) brain structural areas (ie, hippocampal volume); (2) systolic blood pressure (SBP) responses to functional MRI cognitive stressor tasks and (3) heart rate variability. The main secondary outcomes include changes in (1) brain activity, resting state connectivity, cortical thickness and cortical volume; (2) daily life SBP stress reactivity; (3) negative and positive affect; (4) baroreflex sensitivity; (5) pulse wave velocity; (6) endothelial function and (7) daily life positive and negative affect. Our results are expected to have both mechanistic and public health implications regarding brain-body interactions in the context of cardiovascular health. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the University of Pittsburgh Institutional Review Board (IRB ID: 19020218). This study will comply with the NIH Data Sharing Policy and Policy on the Dissemination of NIH-Funded Clinical Trial Information and the Clinical Trials Registration and Results Information Submission rule. TRIAL REGISTRATION NUMBER: NCT03841669.

Competing neural representations of choice shape evidence accumulation in humans.

Making adaptive choices in dynamic environments requires flexible decision policies. Previously, we showed how shifts in outcome contingency change the evidence accumulation process that determines decision policies. Using in silico experiments to generate predictions, here we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in decision policies. When action contingencies change, dopaminergic plasticity redirects the balance of power, both within and between action representations, to divert the flow of evidence from one option to another. When competition between action representations is highest, the rate of evidence accumulation is the lowest. This prediction was validated in in vivo experiments on human participants, using fMRI, which showed that (1) evoked hemodynamic responses can reliably predict trial-wise choices and (2) competition between action representations, measured using a classifier model, tracked with changes in the rate of evidence accumulation. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals.

CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making.

Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled off of the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a wide range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort.

Rethinking the external globus pallidus and information flow in cortico-basal ganglia-thalamic circuits.

For decades the external globus pallidus (GPe) has been viewed as a passive way-station in the indirect pathway of the cortico-basal ganglia-thalamic (CBGT) circuit, sandwiched between striatal inputs and basal ganglia outputs. According to this model, one-way descending striatal signals in the indirect pathway amplify the suppression of downstream thalamic nuclei by inhibiting GPe activity. Here we revisit this assumption, in light of new and emerging work on the cellular complexity, connectivity, and functional role of the GPe in behavior. We show how, according to this new circuit-level logic, the GPe is ideally positioned for relaying ascending and descending control signals within the basal ganglia. Focusing on the problem of inhibitory control, we illustrate how this bidirectional flow of information allows for the integration of reactive and proactive control mechanisms during action selection. Taken together, this new evidence points to the GPe as being a central hub in the CBGT circuit, participating in bidirectional information flow and linking multifaceted control signals to regulate behavior.

Multivariate Brain Activity while Viewing and Reappraising Affective Scenes Does Not Predict the Multiyear Progression of Preclinical Atherosclerosis in Otherwise Healthy Midlife Adults.

Cognitive reappraisal is an emotion regulation strategy that is postulated to reduce risk for atherosclerotic cardiovascular disease (CVD), particularly the risk due to negative affect. At present, however, the brain systems and vascular pathways that may link reappraisal to CVD risk remain unclear. This study thus tested whether brain activity evoked by using reappraisal to reduce negative affect would predict the multiyear progression of a vascular marker of preclinical atherosclerosis and CVD risk: carotid artery intima-media thickness (CA-IMT). Participants were 176 otherwise healthy adults (50.6% women; aged 30-51 years) who completed a functional magnetic resonance imaging task involving the reappraisal of unpleasant scenes from the International Affective Picture System. Ultrasonography was used to compute CA-IMT at baseline and a median of 2.78 (interquartile range, 2.67 to 2.98) years later among 146 participants. As expected, reappraisal engaged brain systems implicated in emotion regulation. Reappraisal also reduced self-reported negative affect. On average, CA-IMT progressed over the follow-up period. However, multivariate and cross-validated machine-learning models demonstrated that brain activity during reappraisal failed to predict CA-IMT progression. Contrary to hypotheses, brain activity during cognitive reappraisal to reduce negative affect does not appear to forecast the progression of a vascular marker of CVD risk. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-021-00098-y.

Integrating multiple brain imaging modalities does not boost prediction of subclinical atherosclerosis in midlife adults.

BACKGROUND: Human neuroimaging evidence suggests that cardiovascular disease (CVD) risk may relate to functional and structural features of the brain. The present study tested whether combining functional and structural (multimodal) brain measures, derived from magnetic resonance imaging (MRI), would yield a multivariate brain biomarker that reliably predicts a subclinical marker of CVD risk, carotid-artery intima-media thickness (CA-IMT). METHODS: Neuroimaging, cardiovascular, and demographic data were assessed in 324 midlife and otherwise healthy adults who were free of (a) clinical CVD and (b) use of medications for chronic illnesses (aged 30-51 years, 49% female). We implemented a prediction stacking algorithm that combined multimodal brain imaging measures and Framingham Risk Scores (FRS) to predict CA-IMT. We included imaging measures that could be easily obtained in clinical settings: resting state functional connectivity and structural morphology measures from T1-weighted images. RESULTS: Our models reliably predicted CA-IMT using FRS, as well as for several individual MRI measures; however, none of the individual MRI measures outperformed FRS. Moreover, stacking functional and structural brain measures with FRS did not boost prediction accuracy above that of FRS alone. CONCLUSIONS: Combining multimodal functional and structural brain measures through a stacking algorithm does not appear to yield a reliable brain biomarker of subclinical CVD, as reflected by CA-IMT.

Identifying control ensembles for information processing within the cortico-basal ganglia-thalamic circuit.

In situations featuring uncertainty about action-reward contingencies, mammals can flexibly adopt strategies for decision-making that are tuned in response to environmental changes. Although the cortico-basal ganglia thalamic (CBGT) network has been identified as contributing to the decision-making process, it features a complex synaptic architecture, comprised of multiple feed-forward, reciprocal, and feedback pathways, that complicate efforts to elucidate the roles of specific CBGT populations in the process by which evidence is accumulated and influences behavior. In this paper we apply a strategic sampling approach, based on Latin hypercube sampling, to explore how variations in CBGT network properties, including subpopulation firing rates and synaptic weights, map to variability of parameters in a normative drift diffusion model (DDM), representing algorithmic aspects of information processing during decision-making. Through the application of canonical correlation analysis, we find that this relationship can be characterized in terms of three low-dimensional control ensembles within the CBGT network that impact specific qualities of the emergent decision policy: responsiveness (a measure of how quickly evidence evaluation gets underway, associated with overall activity in corticothalamic and direct pathways), pliancy (a measure of the standard of evidence needed to commit to a decision, associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (a measure of commitment toward one available option, associated with differences in direct and indirect pathways across action channels). These analyses provide mechanistic predictions about the roles of specific CBGT network elements in tuning the way that information is accumulated and translated into decision-related behavior.

Dissociable use-dependent processes for volitional goal-directed reaching.

Repetition of specific movement biases subsequent actions towards the practiced movement, a phenomenon known as use-dependent learning (UDL). Recent experiments that impose strict constraints on planning time have revealed two sources of use-dependent biases, one arising from dynamic changes occurring during motor planning and another reflecting a stable shift in motor execution. Here, we used a distributional analysis to examine the contribution of these biases in reaching. To create the conditions for UDL, the target appeared at a designated 'frequent' location on most trials, and at one of six 'rare' locations on other trials. Strikingly, the heading angles were bimodally distributed, with peaks at both frequent and rare target locations. Despite having no constraints on planning time, participants exhibited a robust bias towards the frequent target when movements were self-initiated quickly, the signature of a planning bias; notably, the peak near the rare target was shifted in the frequently practiced direction, the signature of an execution bias. Furthermore, these execution biases were not only replicated in a delayed-response task but were also insensitive to reward. Taken together, these results extend our understanding of how volitional movements are influenced by recent experience.

Dynamic decision policy reconfiguration under outcome uncertainty.

In uncertain or unstable environments, sometimes the best decision is to change your mind. To shed light on this flexibility, we evaluated how the underlying decision policy adapts when the most rewarding action changes. Human participants performed a dynamic two-armed bandit task that manipulated the certainty in relative reward (conflict) and the reliability of action-outcomes (volatility). Continuous estimates of conflict and volatility contributed to shifts in exploratory states by changing both the rate of evidence accumulation (drift rate) and the amount of evidence needed to make a decision (boundary height), respectively. At the trialwise level, following a switch in the optimal choice, the drift rate plummets and the boundary height weakly spikes, leading to a slow exploratory state. We find that the drift rate drives most of this response, with an unreliable contribution of boundary height across experiments. Surprisingly, we find no evidence that pupillary responses associated with decision policy changes. We conclude that humans show a stereotypical shift in their decision policies in response to environmental changes.

Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability.

Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.

Opposing relationships of childhood threat and deprivation with stria terminalis white matter.

While stress may be a potential mechanism by which childhood threat and deprivation influence mental health, few studies have considered specific stress-related white matter pathways, such as the stria terminalis (ST) and medial forebrain bundle (MFB). Our goal was to examine the relationships between childhood adversity and ST and MFB structural integrity and whether these pathways may provide a link between childhood adversity and affective symptoms and disorders. Participants were young adults (n = 100) with a full distribution of maltreatment history and affective symptom severity. Threat was determined by measures of childhood abuse and repeated traumatic events. Socioeconomic deprivation (SED) was determined by a measure of childhood socioeconomic status (parental education). Participants underwent diffusion spectrum imaging. Human Connectome Project data was used to perform ST and MFB tractography; these tracts were used as ROIs to extract generalized fractional anisotropy (gFA) from each participant. Childhood threat was associated with ST gFA, such that greater threat was associated with less ST gFA. SED was also associated with ST gFA, however, conversely to threat, greater SED was associated with greater ST gFA. Additionally, threat was negatively associated with MFB gFA, and MFB gFA was negatively associated with post-traumatic stress symptoms. Our results suggest that childhood threat and deprivation have opposing influences on ST structural integrity, providing new evidence that the context of childhood adversity may have an important influence on its neurobiological effects, even on the same structure. Further, the MFB may provide a novel link between childhood threat and affective symptoms.