Human Choice Strategy Varies with Anatomical Projections from Ventromedial Prefrontal Cortex to Medial Striatum

Cortico-striatal white-matter connectivity underlies the ability to exert goal-directed control

The balance between goal-directed and habitual control has been proposed to determine the flexibility of instrumental behaviour, in both humans and animals. This view is supported by neuroscientific studies that have implicated dissociable neural pathways in the ability to flexibly adjust behaviour when outcome values change. A previous Diffusion Tensor Imaging study provided preliminary evidence that flexible instrumental performance depends on the strength of parallel cortico-striatal white-matter pathways previously implicated in goal-directed and habitual control. Specifically, estimated white-matter strength between caudate and ventromedial prefrontal cortex correlated positively with behavioural flexibility, and posterior putamen-premotor cortex connectivity correlated negatively, in line with the notion that these pathways compete for control. However, the sample size of the original study was limited, and so far, there have been no attempts to replicate these findings. In the present study, we aimed to conceptually replicate these findings by testing a large sample of 205 young adults to relate cortico-striatal connectivity to performance on the slips-of-action task. In short, we found only positive neural correlates of goal-directed performance, including striatal connectivity (caudate and anterior putamen) with the dorsolateral prefrontal cortex. However, we failed to provide converging evidence for the existence of a neural habit system that puts limits on the capacity for flexible, goal-directed action. We discuss the implications of our findings for dual-process theories of instrumental action.

Goal commitment is supported by vmPFC through selective attention

When striking a balance between commitment to a goal and flexibility in the face of better options, people often demonstrate strong goal perseveration. Here, using functional MRI (n = 30) and lesion patient (n = 26) studies, we argue that the ventromedial prefrontal cortex (vmPFC) drives goal commitment linked to changes in goal-directed selective attention. Participants performed an incremental goal pursuit task involving sequential decisions between persisting with a goal versus abandoning progress for better alternative options. Individuals with stronger goal perseveration showed higher goal-directed attention in an interleaved attention task. Increasing goal-directed attention also affected abandonment decisions: while pursuing a goal, people lost their sensitivity to valuable alternative goals while remaining more sensitive to changes in the current goal. In a healthy population, individual differences in both commitment biases and goal-oriented attention were predicted by baseline goal-related activity in the vmPFC. Among lesion patients, vmPFC damage reduced goal commitment, leading to a performance benefit.

Prefrontal signals precede striatal signals for biased credit assignment in motivational learning biases

Actions are biased by the outcomes they can produce: Humans are more likely to show action under reward prospect, but hold back under punishment prospect. Such motivational biases derive not only from biased response selection, but also from biased learning: humans tend to attribute rewards to their own actions, but are reluctant to attribute punishments to having held back. The neural origin of these biases is unclear. Specifically, it remains open whether motivational biases arise primarily from the architecture of subcortical regions or also reflect cortical influences, the latter being typically associated with increased behavioral flexibility and control beyond stereotyped behaviors. Simultaneous EEG-fMRI allowed us to track which regions encoded biased prediction errors in which order. Biased prediction errors occurred in cortical regions (dorsal anterior and posterior cingulate cortices) before subcortical regions (striatum). These results highlight that biased learning is not a mere feature of the basal ganglia, but arises through prefrontal cortical contributions, revealing motivational biases to be a potentially flexible, sophisticated mechanism.

Action slips in food choices: A measure of habits and goal-directed control

We report a new, simple instrumental action-slip task, which sets goal-directed action against putative S-R associations. On each training trial, participants were presented with one of two stimuli (blue or green coloured screen). One stimulus (S1) signalled that one joystick response (R1-left or right push) would earn one of two rewards (O1-jellybeans or Pringles points). A second stimulus (S2) signalled a different instrumental relationship (S2:R2-O2). On each test trial, participants were told which outcome could be earnt (O1/O2) on that trial. They were required to withhold responding until the screen changed colour to S1 or S2. On congruent test trials, the stimulus presented (e.g., S1) was associated with the same response (R1) as the outcome available on that trial (O1). On incongruent test trials, in contrast, the outcome (e.g., O1) preceded a stimulus that was associated with a different response (e.g., S2). Hence, in order to obtain the outcome (O1) on incongruent trials, participants were required to suppress any tendency they might have to make the response associated with the stimulus (R2 in response to S2). In two experiments, participants made more errors on incongruent than congruent trials. This result suggests that, on incongruent trials, the stimulus drove responding (e.g., S2 increased R2 responding) in a manner that was inconsistent with goal-directed action (e.g., R1 responding to obtain O1)-an action slip. The results are discussed in terms of popular dual-process theories of instrumental action and a single-process alternative.

Cognitive and neural bases of visual-context-guided decision-making

Humans adjust their behavioral strategies based on feedback, a process that may depend on intrinsic preferences and contextual factors such as visual salience. In this study, we hypothesized that decision-making based on visual salience is influenced by habitual and goal-directed processes, which can be evidenced by changes in attention and subjective valuation systems. To test this hypothesis, we conducted a series of studies to investigate the behavioral and neural mechanisms underlying visual salience-driven decision-making. We first established the baseline behavioral strategy without salience in Experiment 1 (n = 21). We then highlighted the utility or performance dimension of the chosen outcome using colors in Experiment 2 (n = 30). We demonstrated that the difference in staying frequency increased along the salient dimension, confirming a salience effect. Furthermore, the salience effect was abolished when directional information was removed in Experiment 3 (n = 28), suggesting that the salience effect is feedback-specific. To generalize our findings, we replicated the feedback-specific salience effects using eye-tracking and text emphasis. The fixation differences between the chosen and unchosen values were enhanced along the feedback-specific salient dimension in Experiment 4 (n = 48) but unchanged after removing feedback-specific information in Experiment 5 (n = 32). Moreover, the staying frequency was correlated with fixation properties, confirming that salience guides attention deployment. Lastly, our neuroimaging study (Experiment 6, n = 25) showed that the striatum subregions encoded salience-based outcome evaluation, while the vmPFC encoded salience-based behavioral adjustments. The connectivity of the vmPFC-ventral striatum accounted for individual differences in utility-driven, whereas the vmPFC-dmPFC for performance-driven behavioral adjustments. Together, our results provide a neurocognitive account of how task-irrelevant visual salience drives decision-making by involving attention and the frontal-striatal valuation systems. PUBLIC SIGNIFICANCE STATEMENT: Humans may use the current outcome to make behavior adjustments. How this occurs may depend on stable individual preferences and contextual factors, such as visual salience. Under the hypothesis that visual salience determines attention and subsequently modulates subjective valuation, we investigated the underlying behavioral and neural bases of visual-context-guided outcome evaluation and behavioral adjustments. Our findings suggest that the reward system is orchestrated by visual context and highlight the critical role of attention and the frontal-striatal neural circuit in visual-context-guided decision-making that may involve habitual and goal-directed processes.

Goal-directed and habitual decision making under stress in gambling disorder: An fMRI study

The development of addictive behaviors has been suggested to be related to a transition from goal-directed to habitual decision making. Stress is a factor known to prompt habitual behavior and to increase the risk for addiction and relapse. In the current study, we therefore used functional MRI to investigate the balance between goal-directed 'model-based' and habitual 'model-free' control systems and whether acute stress would differentially shift this balance in gambling disorder (GD) patients compared to healthy controls (HCs). Using a within-subject design, 22 patients with GD and 20 HCs underwent stress induction or a control condition before performing a multistep decision-making task during fMRI. Salivary cortisol levels showed that the stress induction was successful. Contrary to our hypothesis, GD patients did not show impaired goal-directed 'model-based' decision making, which remained similar to HCs after stress induction. Bayes factors provided three times more evidence against a difference between the groups or a group-by-stress interaction on the balance between model-based and model-free decision making. Similarly, no differences were found between groups and conditions on the neural estimates of model-based or model-free decision making. These results challenge the notion that GD is related to an increased reliance on habitual (or decreased goal-directed) control, even during stress.

Individual Differences in Corticostriatal White-matter Tracts Predict Successful Daily-life Routine Formation

Despite good intentions, people often fail to cross the "intention-behavior gap," especially when goal achievement requires repeated action. To bridge this gap, the formation of automatized routines may be crucial. However, people may differ in the tendency to switch from goal-directed toward habitual control. To shed light on why some people succeed in forming routines while others struggle, the present study related the automatization of a novel, daily routine to individual differences in white-matter connectivity in corticostriatal networks that have been implicated in goal-directed and habitual control. Seventy-seven participants underwent diffusion-weighted imaging and formed the daily routine of taking a (placebo) pill for 3 weeks. Pill intake was measured by electronic pill boxes, and participants filled out a daily online questionnaire on the subjective automaticity of this behavior. Automatization of pill intake was negatively related to striatal (mainly caudate) connectivity with frontal goal-directed and cognitive control regions, namely, ventromedial pFC and anterior cingulate gyrus. Furthermore, daily pill intake was positively related to individual differences in striatal (mainly caudate) connectivity with cognitive control regions, including dorsolateral and anterior pFC. Therefore, strong control networks may be relevant for implementing a new routine but may not benefit its automatization. We also show that habit tendency (assessed with an outcome-devaluation task), conscientiousness, and daily life regularity were positively related to routine automatization. This translational study moves the field of habit research forward by relating self-reported routine automatization to individual differences in performance on an experimental habit measure and to brain connectivity.

Advanced Reinforcement Learning and Its Connections with Brain Neuroscience

In recent years, brain science and neuroscience have greatly propelled the innovation of computer science. In particular, knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning (RL) by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making. Triggered by this, there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience. In this work, to further strengthen the bidirectional link between the 2 communities and especially promote the research on modern RL technology, we provide a comprehensive survey of recent advances in the area of brain-inspired/related RL algorithms. We start with basis theories of RL, and present a concise introduction to brain neuroscience related to RL. Then, we classify these advanced RL methodologies into 3 categories according to different connections of the brain, i.e., micro-neural activity, macro-brain structure, and cognitive function. Each category is further surveyed by presenting several modern RL algorithms along with their mathematical models, correlations with the brain, and open issues. Finally, we introduce several important applications of RL algorithms, followed by the discussions of challenges and opportunities for future research.

Stress-sensitive inference of task controllability

Estimating the controllability of the environment enables agents to better predict upcoming events and decide when to engage controlled action selection. How does the human brain estimate controllability? Trial-by-trial analysis of choices, decision times and neural activity in an explore-and-predict task demonstrate that humans solve this problem by comparing the predictions of an 'actor' model with those of a reduced 'spectator' model of their environment. Neural blood oxygen level-dependent responses within striatal and medial prefrontal areas tracked the instantaneous difference in the prediction errors generated by these two statistical learning models. Blood oxygen level-dependent activity in the posterior cingulate, temporoparietal and prefrontal cortices covaried with changes in estimated controllability. Exposure to inescapable stressors biased controllability estimates downward and increased reliance on the spectator model in an anxiety-dependent fashion. Taken together, these findings provide a mechanistic account of controllability inference and its distortion by stress exposure.

Whole-Brain Functional Connectivity Dynamics Associated With Electroconvulsive Therapy Treatment Response

BACKGROUND

Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy.

METHODS

In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis.

RESULTS

Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT.

CONCLUSIONS

These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.

Model-based learning retrospectively updates model-free values

Reinforcement learning (RL) is widely regarded as divisible into two distinct computational strategies. Model-free learning is a simple RL process in which a value is associated with actions, whereas model-based learning relies on the formation of internal models of the environment to maximise reward. Recently, theoretical and animal work has suggested that such models might be used to train model-free behaviour, reducing the burden of costly forward planning. Here we devised a way to probe this possibility in human behaviour. We adapted a two-stage decision task and found evidence that model-based processes at the time of learning can alter model-free valuation in healthy individuals. We asked people to rate subjective value of an irrelevant feature that was seen at the time a model-based decision would have been made. These irrelevant feature value ratings were updated by rewards, but in a way that accounted for whether the selected action retrospectively ought to have been taken. This model-based influence on model-free value ratings was best accounted for by a reward prediction error that was calculated relative to the decision path that would most likely have led to the reward. This effect occurred independently of attention and was not present when participants were not explicitly told about the structure of the environment. These findings suggest that current conceptions of model-based and model-free learning require updating in favour of a more integrated approach. Our task provides an empirical handle for further study of the dialogue between these two learning systems in the future.

Direct Approach or Detour: A Comparative Model of Inhibition and Neural Ensemble Size in Behavior Selection

Organisms must cope with different risk/reward landscapes in their ecological niche. Hence, species have evolved behavior and cognitive processes to optimally balance approach and avoidance. Navigation through space, including taking detours, appears also to be an essential element of consciousness. Such processes allow organisms to negotiate predation risk and natural geometry that obstruct foraging. One aspect of this is the ability to inhibit a direct approach toward a reward. Using an adaptation of the well-known detour paradigm in comparative psychology, but in a virtual world, we simulate how different neural configurations of inhibitive processes can yield behavior that approximates characteristics of different species. Results from simulations may help elucidate how evolutionary adaptation can shape inhibitive processing in particular and behavioral selection in general. More specifically, results indicate that both the level of inhibition that an organism can exert and the size of neural populations dedicated to inhibition contribute to successful detour navigation. According to our results, both factors help to facilitate detour behavior, but the latter (i.e., larger neural populations) appears to specifically reduce behavioral variation.

Linear reinforcement learning in planning, grid fields, and cognitive control

It is thought that the brain’s judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we introduce a model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of specific, quantifiable biases. It replaces the classic nonlinear, model-based optimization with a linear approximation that softly maximizes around (and is weakly biased toward) a default policy. This solution demonstrates connections between seemingly disparate phenomena across behavioral neuroscience, notably flexible replanning with biases and cognitive control. It also provides insight into how the brain can represent maps of long-distance contingencies stably and componentially, as in entorhinal response fields, and exploit them to guide choice even under changing goals.

Models of decision making have so far been unable to account for how humans’ choices can be flexible yet efficient. Here the authors present a linear reinforcement learning model which explains both flexibility, and rare limitations such as habits, as arising from efficient approximate computation

Neural markers of procrastination in white matter microstructures and networks

More than 15% of adults suffer from pathological procrastination, which leads to substantial harm to their mental and psychiatric health. Our previous work demonstrated the role of three neuroanatomical networks as neural substrates of procrastination, but their potential interaction remains unknown. Three large-scale independent samples (total n = 901) were recruited. In sample A, tract-based spatial statistics (TBSS) and connectome-based graph-theoretical analysis was conducted to probe association between topological properties of white matter (WM) network and procrastination. In sample B, the above analysis was reproduced to demonstrate replicability. In sample C, machine learning models were built to predict individual procrastination. TBSS results showed a negative association between procrastination and WM integrity of limbic-prefrontal connection, and a positive relationship between intra-connection within the limbic system and procrastination. Also, both the efficiency and integrity of limbic WM network were found to be linked to procrastination. The above findings were all confirmed to replicate in an independent sample; prediction models demonstrated that these WM features can predict procrastination accurately in sample C. In conclusion, this study moves forward our understanding of procrastination by clarifying the role of interplay of self-control and emotional regulation with it.

Dynamic Neural Interactions Supporting the Cognitive Reappraisal of Emotion

The cognitive reappraisal of emotion is hypothesized to involve frontal regions modulating the activity of subcortical regions such as the amygdala. However, the pathways by which structurally disparate frontal regions interact with the amygdala remains unclear. In this study, 104 healthy young people completed a cognitive reappraisal task. Dynamic causal modeling (DCM) was used to map functional interactions within a frontoamygdalar network engaged during emotion regulation. Five regions were identified to form the network: the amygdala, the presupplementary motor area (preSMA), the ventrolateral prefrontal cortex (vlPFC), dorsolateral prefrontal cortex (dlPFC), and ventromedial prefrontal cortex (vmPFC). Bayesian Model Selection was used to compare 256 candidate models, with our winning model featuring modulations of vmPFC-to-amygdala and amygdala-to-preSMA pathways during reappraisal. Moreover, the strength of amygdala-to-preSMA modulation was associated with the habitual use of cognitive reappraisal. Our findings support the vmPFC serving as the primary conduit through which prefrontal regions directly modulate amygdala activity, with amygdala-to-preSMA connectivity potentially acting to shape ongoing affective motor responses. We propose that these two frontoamygdalar pathways constitute a recursive feedback loop, which computes the effectiveness of emotion-regulatory actions and drives model-based behavior.

Hemispheric Asymmetry of Globus Pallidus Relates to Alpha Modulation in Reward-Related Attentional Tasks

Whereas subcortical structures such as the basal ganglia have been widely explored in relation to motor control, recent evidence suggests that their mechanisms extend to the domain of attentional switching. We here investigated the subcortical involvement in reward related top-down control of visual alpha-band oscillations (8-13 Hz), which have been consistently linked to mechanisms supporting the allocation of visuospatial attention. Given that items associated with contextual saliency (e.g., monetary reward or loss) attract attention, it is not surprising that the acquired salience of visual items further modulates. The executive networks controlling such reward-dependent modulations of oscillatory brain activity have yet to be fully elucidated. Although such networks have been explored in terms of corticocortical interactions, subcortical regions are likely to be involved. To uncover this, we combined MRI and MEG data from 17 male and 11 female participants, investigating whether derived measures of subcortical structural asymmetries predict interhemispheric modulation of alpha power during a spatial attention task. We show that volumetric hemispheric lateralization of globus pallidus (GP) and thalamus (Th) explains individual hemispheric biases in the ability to modulate posterior alpha power. Importantly, for the GP, this effect became stronger when the value saliency parings in the task increased. Our findings suggest that the GP and Th in humans are part of a subcortical executive control network, differentially involved in modulating posterior alpha activity in the presence of saliency. Further investigation aimed at uncovering the interaction between subcortical and neocortical attentional networks would provide useful insight in future studies.SIGNIFICANCE STATEMENT Whereas the involvement of subcortical regions into higher level cognitive processing, such as attention and reward attribution, has been already indicated in previous studies, little is known about its relationship with the functional oscillatory underpinnings of said processes. In particular, interhemispheric modulation of alpha band (8-13 Hz) oscillations, as recorded with magnetoencephalography, has been previously shown to vary as a function of salience (i.e., monetary reward/loss) in a spatial attention task. We here provide novel insights into the link between subcortical and cortical control of visual attention. Using the same reward-related spatial attention paradigm, we show that the volumetric lateralization of subcortical structures (specifically globus pallidus and thalamus) explains individual biases in the modulation of visual alpha activity.

A connectional hub in the rostral anterior cingulate cortex links areas of emotion and cognitive control

We investigated afferent inputs from all areas in the frontal cortex (FC) to different subregions in the rostral anterior cingulate cortex (rACC). Using retrograde tracing in macaque monkeys, we quantified projection strength by counting retrogradely labeled cells in each FC area. The projection from different FC regions varied across injection sites in strength, following different spatial patterns. Importantly, a site at the rostral end of the cingulate sulcus stood out as having strong inputs from many areas in diverse FC regions. Moreover, it was at the integrative conjunction of three projection trends across sites. This site marks a connectional hub inside the rACC that integrates FC inputs across functional modalities. Tractography with monkey diffusion magnetic resonance imaging (dMRI) located a similar hub region comparable to the tracing result. Applying the same tractography method to human dMRI data, we demonstrated that a similar hub can be located in the human rACC.

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of our theory enables researchers to make inference on group-level parameters by performing HBI t-test.

Computational modeling of brain and behavior plays an important role in modern neuroscience research. By deconstructing mechanisms of behavior and quantifying parameters of interest, computational modeling helps researchers to study brain-behavior mechanisms. In neuroscience studies, a dataset includes a number of samples, and often the question of interest is to characterize parameters of interest in a population: Do patients with attention-deficit hyperactive disorders exhibit lower learning rate than the general population? Do cognitive enhancers, such as Ritalin, enhance parameters influencing decision making? The success of these efforts heavily depends on statistical methods making inference about validity and robustness of estimated parameters, as well as generalizability of computational models. In this work, we present a novel method, hierarchical Bayesian inference, for concurrent model comparison, parameter estimation and inference at the population level. We show, both theoretically and experimentally, that our approach has important advantages over previous methods. The proposed method has implications for computational modeling research in group studies across many areas of psychology, neuroscience, and psychiatry.

Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

Emotionally Aversive Cues Suppress Neural Systems Underlying Optimal Learning in Socially Anxious Individuals

Learning and decision-making are modulated by socio-emotional processing and such modulation is implicated in clinically relevant personality traits of social anxiety. The present study elucidates the computational and neural mechanisms by which emotionally aversive cues disrupt learning in socially anxious human individuals. Healthy volunteers with low or high trait social anxiety performed a reversal learning task requiring learning actions in response to angry or happy face cues. Choice data were best captured by a computational model in which learning rate was adjusted according to the history of surprises. High trait socially anxious individuals used a less-dynamic strategy for adjusting their learning rate in trials started with angry face cues and unlike the low social anxiety group, their dorsal anterior cingulate cortex (dACC) activity did not covary with the learning rate. Our results demonstrate that trait social anxiety is accompanied by disruption of optimal learning and dACC activity in threatening situations.SIGNIFICANCE STATEMENT Social anxiety is known to influence a broad range of cognitive functions. This study tests whether and how social anxiety affects human value-based learning as a function of uncertainty in the learning environment. The findings indicate that, in a threatening context evoked by an angry face, socially anxious individuals fail to benefit from a stable learning environment with highly predictable stimulus-response-outcome associations. Under those circumstances, socially anxious individuals failed to use their dorsal anterior cingulate cortex, a region known to adjust learning rate to environmental uncertainty. These findings open the way to modify neurobiological mechanisms of maladaptive learning in anxiety and depressive disorders.

Regional structural hypo- and hyperconnectivity of frontal-striatal and frontal-thalamic pathways in behavioral variant frontotemporal dementia

Behavioral variant frontotemporal dementia (bvFTD) has been predominantly considered as a frontotemporal cortical disease, with limited direct investigation of frontal-subcortical connections. We aim to characterize the grey and white matter components of frontal-thalamic and frontal-striatal circuits in bvFTD. Twenty-four patients with bvFTD and 24 healthy controls underwent morphological and diffusion imaging. Subcortical structures were manually segmented according to published protocols. Probabilistic pathways were reconstructed separately from the dorsolateral, orbitofrontal and medial prefrontal cortex to the striatum and thalamus. Patients with bvFTD had smaller cortical and subcortical volumes, lower fractional anisotropy, and higher mean diffusivity metrics, which is consistent with disruptions in frontal-striatal-thalamic pathways. Unexpectedly, regional volumes of the striatum and thalamus connected to the medial prefrontal cortex were significantly larger in bvFTD (by 135% in the striatum, p = .032, and 217% in the thalamus, p = .004), despite smaller dorsolateral prefrontal cortex connected regional volumes (by 67% in the striatum, p = .002, and 65% in the thalamus, p = .020), and inconsistent changes in orbitofrontal cortex connected regions. These unanticipated findings may represent compensatory or maladaptive remodeling in bvFTD networks. Comparisons are made to other neuropsychiatric disorders suggesting a common mechanism of changes in frontal-subcortical networks; however, longitudinal studies are necessary to test this hypothesis.

White-Matter Pathways for Statistical Learning of Temporal Structures

Extracting the statistics of event streams in natural environments is critical for interpreting current events and predicting future ones. The brain is known to rapidly find structure and meaning in unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the brain pathways that support this type of statistical learning. Here, we test whether changes in white-matter (WM) connectivity due to training relate to our ability to extract temporal regularities. By combining behavioral training and diffusion tensor imaging (DTI), we demonstrate that humans adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. In particular, we show that learning relates to the decision strategy that individuals adopt when extracting temporal statistics. We next test for learning-dependent changes in WM connectivity and ask whether they relate to individual variability in decision strategy. Our DTI results provide evidence for dissociable WM pathways that relate to individual strategy: extracting the exact sequence statistics (i.e., matching) relates to connectivity changes between caudate and hippocampus, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for distinct cortico-striatal circuits that show learning-dependent changes of WM connectivity and support individual ability to learn behaviorally-relevant statistics.

Temporal transitions of spontaneous brain activity

Spontaneous brain activity, typically investigated using resting-state fMRI (rsfMRI), provides a measure of inter-areal resting-state functional connectivity (RSFC). Although it has been established that RSFC is non-stationary, previous dynamic rsfMRI studies mainly focused on revealing the spatial characteristics of dynamic RSFC patterns, but the temporal relationship between these RSFC patterns remains elusive. Here we investigated the temporal organization of characteristic RSFC patterns in awake rats and humans. We found that transitions between RSFC patterns were not random but followed specific sequential orders. The organization of RSFC pattern transitions was further analyzed using graph theory, and pivotal RSFC patterns in transitions were identified. This study has demonstrated that spontaneous brain activity is not only nonrandom spatially, but also nonrandom temporally, and this feature is well conserved between rodents and humans. These results offer new insights into understanding the spatiotemporal dynamics of spontaneous activity in the mammalian brain.

Short-term reward experience biases inference despite dissociable neural correlates

Optimal decision-making employs short-term rewards and abstract long-term information based on which of these is deemed relevant. Employing short- vs. long-term information is associated with different learning mechanisms, yet neural evidence showing that these two are dissociable is lacking. Here we demonstrate that long-term, inference-based beliefs are biased by short-term reward experiences and that dissociable brain regions facilitate both types of learning. Long-term inferences are associated with dorsal striatal and frontopolar cortex activity, while short-term rewards engage the ventral striatum. Stronger concurrent representation of reward signals by mediodorsal striatum and frontopolar cortex correlates with less biased, more optimal individual long-term inference. Moreover, dynamic modulation of activity in a cortical cognitive control network and the medial striatum is associated with trial-by-trial control of biases in belief updating. This suggests that counteracting the processing of optimally to-be-ignored short-term rewards and cortical suppression of associated reward-signals, determines long-term learning success and failure.

Making a good decision often requires the weighing of relative short-term rewards against long-term benefits, yet how the brain does this is not understood. Here, authors show that long-term beliefs are biased by reward experience and that dissociable brain regions facilitate both types of learning.

The Intersection between Ocular and Manual Motor Control: Eye-Hand Coordination in Acquired Brain Injury

Acute and chronic disease processes that lead to cerebral injury can often be clinically challenging diagnostically, prognostically, and therapeutically. Neurodegenerative processes are one such elusive diagnostic group, given their often diffuse and indolent nature, creating difficulties in pinpointing specific structural abnormalities that relate to functional limitations. A number of studies in recent years have focused on eye-hand coordination (EHC) in the setting of acquired brain injury (ABI), highlighting the important set of interconnected functions of the eye and hand and their relevance in neurological conditions. These experiments, which have concentrated on focal lesion-based models, have significantly improved our understanding of neurophysiology and underscored the sensitivity of biomarkers in acute and chronic neurological disease processes, especially when such biomarkers are combined synergistically. To better understand EHC and its connection with ABI, there is a need to clarify its definition and to delineate its neuroanatomical and computational underpinnings. Successful EHC relies on the complex feedback- and prediction-mediated relationship between the visual, ocular motor, and manual motor systems and takes advantage of finely orchestrated synergies between these systems in both the spatial and temporal domains. Interactions of this type are representative of functional sensorimotor control, and their disruption constitutes one of the most frequent deficits secondary to brain injury. The present review describes the visually mediated planning and control of eye movements, hand movements, and their coordination, with a particular focus on deficits that occur following neurovascular, neurotraumatic, and neurodegenerative conditions. Following this review, we also discuss potential future research directions, highlighting objective EHC as a sensitive biomarker complement within acute and chronic neurological disease processes.