Reward Reinforcement Creates Enduring Facilitation of Goal-directed Behavior.

Stimulus-response habits benefit behavior by automatizing the selection of rewarding actions. However, this automaticity can come at the cost of reduced flexibility to adapt behavior when circumstances change. The goal-directed system is thought to counteract the habit system by providing the flexibility to pursue context-appropriate behaviors. The dichotomy between habitual action selection and flexible goal-directed behavior has recently been challenged by findings showing that rewards bias both action and goal selection. Here, we test whether reward reinforcement can give rise to habitual goal selection much as it gives rise to habitual action selection. We designed a rewarded, context-based perceptual discrimination task in which performance on one rule was reinforced. Using drift-diffusion models and psychometric analyses, we found that reward facilitates the initiation and execution of rules. Strikingly, we found that these biases persisted in a test phase in which rewards were no longer available. Although this facilitation is consistent with the habitual goal selection hypothesis, we did not find evidence that reward reinforcement reduced cognitive flexibility to implement alternative rules. Together, the findings suggest that reward creates a lasting impact on the selection and execution of goals but may not lead to the inflexibility characteristic of habits. Our findings demonstrate the role of the reward learning system in influencing how the goal-directed system selects and implements goals.

Alpha phase-coding supports feature binding during working memory maintenance.

The ability to successfully retain and manipulate information in working memory (WM) requires that objects' individual features are bound into cohesive representations; yet, the mechanisms supporting feature binding remain unclear. Binding (or swap) errors, where memorized features are erroneously associated with the wrong object, can provide a window into the intrinsic limits in capacity of WM that represent a key bottleneck in our cognitive ability. We tested the hypothesis that binding in WM is accomplished via neural phase synchrony and that swap errors result from perturbations in this synchrony. Using magnetoencephalography data collected from human subjects in a task designed to induce swap errors, we showed that swaps are characterized by reduced phase-locked oscillatory activity during memory retention, as predicted by an attractor model of spiking neural networks. Further, we found that this reduction arises from increased phase-coding variability in the alpha-band over a distributed network of sensorimotor areas. Our findings demonstrate that feature binding in WM is accomplished through phase-coding dynamics that emerge from the competition between different memories.

Multiplexed Levels of Cognitive Control through Delta and Theta Neural Oscillations.

Cognitive control allows behavior to be guided according to environmental contexts and internal goals. During cognitive control tasks, fMRI analyses typically reveal increased activation in frontal and parietal networks, and EEG analyses reveal increased amplitude of neural oscillations in the delta/theta band (2-3, 4-7 Hz) in frontal electrodes. Previous studies proposed that theta-band activity reflects the maintenance of rules associating stimuli to appropriate actions (i.e., the rule set), whereas delta synchrony is specifically associated with the control over the context for when to apply a set of rules (i.e., the rule abstraction). We tested these predictions using EEG and fMRI data collected during the performance of a hierarchical cognitive control task that manipulated the level of abstraction of task rules and their set-size. Our results show a clear separation of delta and theta oscillations in the control of rule abstraction and of stimulus-action associations, respectively, in distinct frontoparietal association networks. These findings support a model by which frontoparietal networks operate through dynamic, multiplexed neural processes.

Cortical timescales and the modular organization of structural and functional brain networks.

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Dynamic Network Connectivity: from monkeys to humans.

Human brain imaging research using functional MRI (fMRI) has uncovered flexible variations in the functional connectivity between brain regions. While some of this variability likely arises from the pattern of information flow through circuits, it may also be influenced by rapid changes in effective synaptic strength at the molecular level, a phenomenon called Dynamic Network Connectivity (DNC) discovered in non-human primate circuits. These neuromodulatory molecular mechanisms are found in layer III of the macaque dorsolateral prefrontal cortex (dlPFC), the site of the microcircuits shown by Goldman-Rakic to be critical for working memory. This research has shown that the neuromodulators acetylcholine, norepinephrine, and dopamine can rapidly change the strength of synaptic connections in layer III dlPFC by (1) modifying the depolarization state of the post-synaptic density needed for NMDA receptor neurotransmission and (2) altering the open state of nearby potassium channels to rapidly weaken or strengthen synaptic efficacy and the strength of persistent neuronal firing. Many of these actions involve increased cAMP-calcium signaling in dendritic spines, where varying levels can coordinate the arousal state with the cognitive state. The current review examines the hypothesis that some of the dynamic changes in correlative strength between cortical regions observed in human fMRI studies may arise from these molecular underpinnings, as has been seen when pharmacological agents or genetic alterations alter the functional connectivity of the dlPFC consistent with the macaque physiology. These DNC mechanisms provide essential flexibility but may also confer vulnerability to malfunction when dysregulated in cognitive disorders.

Cerebral perfusion in post-stroke aphasia and its relationship to residual language abilities.

Stroke alters blood flow to the brain resulting in damaged tissue and cell death. Moreover, the disruption of cerebral blood flow (perfusion) can be observed in areas surrounding and distal to the lesion. These structurally preserved but suboptimally perfused regions may also affect recovery. Thus, to better understand aphasia recovery, the relationship between cerebral perfusion and language needs to be systematically examined. In the current study, we aimed to evaluate (i) how stroke affects perfusion outside of lesioned areas in chronic aphasia and (ii) how perfusion in specific cortical areas and perilesional tissue relates to language outcomes in aphasia. We analysed perfusion data from a large sample of participants with chronic aphasia due to left hemisphere stroke (n = 43) and age-matched healthy controls (n = 25). We used anatomically defined regions of interest that covered the frontal, parietal, and temporal areas of the perisylvian cortex in both hemispheres, areas typically known to support language, along with several control regions not implicated in language processing. For the aphasia group, we also looked at three regions of interest in the perilesional tissue. We compared perfusion levels between the two groups and investigated the relationship between perfusion levels and language subtest scores while controlling for demographic and lesion variables. First, we observed that perfusion levels outside the lesioned areas were significantly reduced in frontal and parietal regions in the left hemisphere in people with aphasia compared to the control group, while no differences were observed for the right hemisphere regions. Second, we found that perfusion in the left temporal lobe (and most strongly in the posterior part of both superior and middle temporal gyri) and inferior parietal areas (supramarginal gyrus) was significantly related to residual expressive and receptive language abilities. In contrast, perfusion in the frontal regions did not show such a relationship; no relationship with language was also observed for perfusion levels in control areas and all right hemisphere regions. Third, perilesional perfusion was only marginally related to language production abilities. Cumulatively, the current findings demonstrate that blood flow is reduced beyond the lesion site in chronic aphasia and that hypoperfused neural tissue in critical temporoparietal language areas has a negative impact on behavioural outcomes. These results, using perfusion imaging, underscore the critical and general role that left hemisphere posterior temporal regions play in various expressive and receptive language abilities. Overall, the study highlights the importance of exploring perfusion measures in stroke.

Task learning is subserved by a domain-general brain network.

One of the most important human faculties is the ability to acquire not just new memories but the capacity to perform entirely new tasks. However, little is known about the brain mechanisms underlying the learning of novel tasks. Specifically, it is unclear to what extent learning of different tasks depends on domain-general and/or domain-specific brain mechanisms. Here human subjects (n = 45) learned to perform 6 new tasks while undergoing functional MRI. The different tasks required the engagement of perceptual, motor, and various cognitive processes related to attention, expectation, speed-accuracy tradeoff, and metacognition. We found that a bilateral frontoparietal network was more active during the initial compared with the later stages of task learning, and that this effect was stronger for task variants requiring more new learning. Critically, the same frontoparietal network was engaged by all 6 tasks, demonstrating its domain generality. Finally, although task learning decreased the overall activity in the frontoparietal network, it increased the connectivity strength between the different nodes of that network. These results demonstrate the existence of a domain-general brain network whose activity and connectivity reflect learning for a variety of new tasks, and thus may underlie the human capacity for acquiring new abilities.

Toward precision brain health: accurate prediction of a cognitive index trajectory using neuroimaging metrics.

The goal of precision brain health is to accurately predict individuals' longitudinal patterns of brain change. We trained a machine learning model to predict changes in a cognitive index of brain health from neurophysiologic metrics. A total of 48 participants (ages 21-65) completed a sensorimotor task during 2 functional magnetic resonance imaging sessions 6 mo apart. Hemodynamic response functions (HRFs) were parameterized using traditional (amplitude, dispersion, latency) and novel (curvature, canonicality) metrics, serving as inputs to a neural network model that predicted gain on indices of brain health (cognitive factor scores) for each participant. The optimal neural network model successfully predicted substantial gain on the cognitive index of brain health with 90% accuracy (determined by 5-fold cross-validation) from 3 HRF parameters: amplitude change, dispersion change, and similarity to a canonical HRF shape at baseline. For individuals with canonical baseline HRFs, substantial gain in the index is overwhelmingly predicted by decreases in HRF amplitude. For individuals with non-canonical baseline HRFs, substantial gain in the index is predicted by congruent changes in both HRF amplitude and dispersion. Our results illustrate that neuroimaging measures can track cognitive indices in healthy states, and that machine learning approaches using novel metrics take important steps toward precision brain health.

Dopamine Modulates Effective Connectivity in Frontal Cortex.

There is increasing evidence that the left lateral frontal cortex is hierarchically organized such that higher-order regions have an asymmetric top-down influence over lower order regions. However, questions remain about the underlying neuroarchitecture of this hierarchical control organization. Within the frontal cortex, dopamine plays an important role in cognitive control functions, and we hypothesized that dopamine may preferentially influence top-down connections within the lateral frontal hierarchy. Using a randomized, double-blind, within-subject design, we analyzed resting-state fMRI data of 66 healthy young participants who were scanned once each after administration of bromocriptine (a dopamine agonist with preferential affinity for D2 receptor), tolcapone (an inhibitor of catechol-O-methyltransferase), and placebo, to determine whether dopaminergic stimulation modulated effective functional connectivity between hierarchically organized frontal regions in the left hemisphere. We found that dopaminergic drugs modulated connections from the caudal middle frontal gyrus and the inferior frontal sulcus to both rostral and caudal frontal areas. In dorsal frontal regions, effectivity connectivity strength was increased, whereas in ventral frontal regions, effective connectivity strength was decreased. These findings suggest that connections within frontal cortex are differentially modulated by dopamine, which may bias the influence that frontal regions exert over each other.

Stimulation along the anterior-posterior axis of lateral frontal cortex reduces visual serial dependence.

Serial dependence is an attractive pull that recent perceptual history exerts on current judgments. Theory suggests that this bias is due to a form of short-term plasticity prevalent specifically in the frontal lobe. We sought to test the importance of the frontal lobe to serial dependence by disrupting neural activity along its lateral surface during two tasks with distinct perceptual and motor demands. In our first experiment, stimulation of the lateral prefrontal cortex (LPFC) during an oculomotor delayed response task decreased serial dependence only in the first saccade to the target, whereas stimulation posterior to the LPFC decreased serial dependence only in adjustments to eye position after the first saccade. In our second experiment, which used an orientation discrimination task, stimulation anterior to, in, and posterior to the LPFC all caused equivalent decreases in serial dependence. In this experiment, serial dependence occurred only between stimuli at the same location; an alternation bias was observed across hemifields. Frontal stimulation had no effect on the alternation bias. Transcranial magnetic stimulation to parietal cortex had no effect on serial dependence in either experiment. In summary, our experiments provide evidence for both functional differentiation (Experiment 1) and redundancy (Experiment 2) in frontal cortex with respect to serial dependence.

Cortical timescales and the modular organization of structural and functional brain networks.

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Poorer aging trajectories are associated with elevated serotonin synthesis capacity.

The dorsal raphe nucleus (DRN) is one of the earliest targets of Alzheimer's disease-related tau pathology and is a major source of brain serotonin. We used [18F]Fluoro-m-tyrosine ([18F]FMT) PET imaging to measure serotonin synthesis capacity in the DRN in 111 healthy adults (18-85 years-old). Similar to reports in catecholamine systems, we found elevated serotonin synthesis capacity in older adults relative to young. To establish the structural and functional context within which serotonin synthesis capacity is elevated in aging, we examined relationships among DRN [18F]FMT net tracer influx (Ki) and longitudinal changes in cortical thickness using magnetic resonance imaging, longitudinal changes in self-reported depression symptoms, and AD-related tau and ß-amyloid (Aß) pathology using cross-sectional [18F]Flortaucipir and [11C]Pittsburgh compound-B PET respectively. Together, our findings point to elevated DRN [18F]FMT Ki as a marker of poorer aging trajectories. Older adults with highest serotonin synthesis capacity showed greatest temporal lobe cortical atrophy. Cortical atrophy was associated with increasing depression symptoms over time, and these effects appeared to be strongest in individuals with highest serotonin synthesis capacity. We did not find direct relationships between serotonin synthesis capacity and AD-related pathology. Exploratory analyses revealed nuanced effects of sex within the older adult group. Older adult females showed the highest DRN synthesis capacity and exhibited the strongest relationships between entorhinal cortex tau pathology and increasing depression symptoms. Together these findings reveal PET measurement of the serotonin system to be a promising marker of aging trajectories relevant to both AD and affective changes in older age.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain.

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

Estradiol and the Catechol-o-methyltransferase Gene Interact to Predict Working Memory Performance: A Replication and Extension.

Decades of evidence across taxa have established the importance of dopamine (DA) signaling in the pFC for successful working memory performance. Genetic and hormonal factors can shape individual differences in prefrontal DA tone. The catechol-o-methyltransferase (COMT) gene regulates basal prefrontal DA, and the sex hormone 17ß-estradiol potentiates DA release. E. Jacobs and M. D'Esposito [Estrogen shapes dopamine-dependent cognitive processes: Implications for women's health. Journal of Neuroscience, 31, 5286-5293, 2011] investigated the moderating role of estradiol on cognition using the COMT gene and COMT enzymatic activity as a proxy for pFC DA tone. They found that increases in 17ß-estradiol within women at two time points during the menstrual cycle influenced working memory performance in a COMT-dependent manner. Here, we aimed to replicate and extend the behavioral findings of Jacobs and D'Esposito by employing an intensive repeated-measures design across a full menstrual cycle. Our results replicated the original investigation. Within-person increases in estradiol were associated with improved performance on 2-back lure trials for participants with low basal levels of DA (Val/Val carriers). The association was in the opposite direction for participants with higher basal levels of DA (Met/Met carriers). Our findings support the role of estrogen in DA-related cognitive functions and further highlight the need to consider gonadal hormones in cognitive science research.

Striatal dopamine synthesis and cognitive flexibility differ between hormonal contraceptive users and nonusers.

In rodents and nonhuman primates, sex hormones are powerful modulators of dopamine (DA) neurotransmission. Yet less is known about hormonal regulation of the DA system in the human brain. Using positron emission tomography (PET), we address this gap by comparing hormonal contraceptive users and nonusers across multiple aspects of DA function: DA synthesis capacity via the PET radioligand 6-[18F]fluoro-m-tyrosine ([18F]FMT), baseline D2/3 receptor binding potential using [11C]raclopride, and DA release using methylphenidate-paired [11C]raclopride. Participants consisted of 36 healthy women (n = 15 hormonal contraceptive users; n = 21 naturally cycling/non users of hormonal contraception), and men (n = 20) as a comparison group. A behavioral index of cognitive flexibility was assessed prior to PET imaging. Hormonal contraceptive users exhibited greater DA synthesis capacity than NC participants, particularly in dorsal caudate, and greater cognitive flexibility. Furthermore, across individuals, the magnitude of striatal DA synthesis capacity was associated with cognitive flexibility. No group differences were observed in D2/3 receptor binding or DA release. Analyses by sex alone may obscure underlying differences in DA synthesis tied to women's hormone status. Hormonal contraception (in the form of pill, shot, implant, ring, or intrauterine device) is used by ~400 million women worldwide, yet few studies have examined whether chronic hormonal manipulations impact basic properties of the DA system. Findings from this study begin to address this critical gap in women's health.

Influence of goals on modular brain network organization during working memory.

Introduction: Top-down control underlies our ability to attend relevant stimuli while ignoring irrelevant, distracting stimuli and is a critical process for prioritizing information in working memory (WM). Prior work has demonstrated that top-down biasing signals modulate sensory-selective cortical areas during WM, and that the large-scale organization of the brain reconfigures due to WM demands alone; however, it is not yet understood how brain networks reconfigure between the processing of relevant versus irrelevant information in the service of WM. Methods: Here, we investigated the effects of task goals on brain network organization while participants performed a WM task that required participants to detect repetitions (e.g., 0-back or 1-back) and had varying levels of visual interference (e.g., distracting, irrelevant stimuli). We quantified changes in network modularity-a measure of brain sub-network segregation-that occurred depending on overall WM task difficulty as well as trial-level task goals for each stimulus during the task conditions (e.g., relevant or irrelevant). Results: First, we replicated prior work and found that whole-brain modularity was lower during the more demanding WM task conditions compared to a baseline condition. Further, during the WM conditions with varying task goals, brain modularity was selectively lower during goal-directed processing of task-relevant stimuli to be remembered for WM performance compared to processing of distracting, irrelevant stimuli. Follow-up analyses indicated that this effect of task goals was most pronounced in default mode and visual sub-networks. Finally, we examined the behavioral relevance of these changes in modularity and found that individuals with lower modularity for relevant trials had faster WM task performance. Discussion: These results suggest that brain networks can dynamically reconfigure to adopt a more integrated organization with greater communication between sub-networks that supports the goal-directed processing of relevant information and guides WM.

A rapid theta network mechanism for flexible information encoding.

Flexible behavior requires gating mechanisms that encode only task-relevant information in working memory. Extant literature supports a theoretical division of labor whereby lateral frontoparietal interactions underlie information maintenance and the striatum enacts the gate. Here, we reveal neocortical gating mechanisms in intracranial EEG patients by identifying rapid, within-trial changes in regional and inter-regional activities that predict subsequent behavioral outputs. Results first demonstrate information accumulation mechanisms that extend prior fMRI (i.e., regional high-frequency activity) and EEG evidence (inter-regional theta synchrony) of distributed neocortical networks in working memory. Second, results demonstrate that rapid changes in theta synchrony, reflected in changing patterns of default mode network connectivity, support filtering. Graph theoretical analyses further linked filtering in task-relevant information and filtering out irrelevant information to dorsal and ventral attention networks, respectively. Results establish a rapid neocortical theta network mechanism for flexible information encoding, a role previously attributed to the striatum.

Structured memory representations develop at multiple time scales in hippocampal-cortical networks.

Influential views of systems memory consolidation posit that the hippocampus rapidly forms representations of specific events, while neocortical networks extract regularities across events, forming the basis of schemas and semantic knowledge. Neocortical extraction of schematic memory representations is thought to occur on a protracted timescale of months, especially for information that is unrelated to prior knowledge. However, this theorized evolution of memory representations across extended timescales, and differences in the temporal dynamics of consolidation across brain regions, lack reliable empirical support. To examine the temporal dynamics of memory representations, we repeatedly exposed human participants to structured information via sequences of fractals, while undergoing longitudinal fMRI for three months. Sequence-specific activation patterns emerged in the hippocampus during the first 1-2 weeks of learning, followed one week later by high-level visual cortex, and subsequently the medial prefrontal and parietal cortices. Schematic, sequence-general representations emerged in the prefrontal cortex after 3 weeks of learning, followed by the medial temporal lobe and anterior temporal cortex. Moreover, hippocampal and most neocortical representations showed sustained rather than time-limited dynamics, suggesting that representations tend to persist across learning. These results show that specific hippocampal representations emerge early, followed by both specific and schematic representations at a gradient of timescales across hippocampal-cortical networks as learning unfolds. Thus, memory representations do not exist only in specific brain regions at a given point in time, but are simultaneously present at multiple levels of abstraction across hippocampal-cortical networks.

Evaluating the reliability, validity, and utility of overlapping networks: Implications for network theories of cognition.

Brain network definitions typically assume nonoverlap or minimal overlap, ignoring regions' connections to multiple networks. However, new methods are emerging that emphasize network overlap. Here, we investigated the reliability and validity of one assignment method, the mixed membership algorithm, and explored its potential utility for identifying gaps in existing network models of cognition. We first assessed between-sample reliability of overlapping assignments with a split-half design; a bootstrapped Dice similarity analysis demonstrated good agreement between the networks from the two subgroups. Next, we assessed whether overlapping networks captured expected nonoverlapping topographies; overlapping networks captured portions of one to three nonoverlapping topographies, which aligned with canonical network definitions. Following this, a relative entropy analysis showed that a majority of regions participated in more than one network, as is seen biologically, and many regions did not show preferential connection to any one network. Finally, we explored overlapping network membership in regions of the dual-networks model of cognitive control, showing that almost every region was a member of multiple networks. Thus, the mixed membership algorithm produces consistent and biologically plausible networks, which presumably will allow for the development of more complete network models of cognition.