Frontal Cortex and the Hierarchical Control of Behavior

Disrupted flexible use of context-dependent relational memory in adults following moderate-severe traumatic brain injury

Learning associative information and extracting regularities from that remembered information to adaptively meet goals is a hallmark of navigating life. Adaptive goal-directive behavior has been historically attributed to prefrontal functions, and more recently to hippocampal relational memory. Disruptions in either of these systems, both frequently seen in Traumatic Brain Injury (TBI), have far reaching consequences in everyday life. In the current study, we investigate the impact of chronic, moderate-to-severe TBI on both relational memory processes as well as the ability to use regularities or rules extracted from that remembered information to guide behavior via both overt responses and eye-tracking. Individuals with and without TBI completed a context-dependent relational memory task designed to assess both 1) the formation and organization of overlapping relational associations (hippocampal-dependent); and 2) the acquisition and flexible use of learned, context-dependent rules (ventromedial prefrontal-dependent). Behavioral measures revealed that relative to neurotypical matched comparison participants, participants with TBI were significantly impaired on context-dependent relational memory measures, but showed spared memory guided rule-use. Eye-tracking data indicated largely intact information gathering at study for participants with TBI, but impaired flexibility at test leading to poor behavioral outcomes. Critically, these data suggest that relational memory impairment is a significant source of behavioral dysfunction in TBI, which likely contributes to poor outcomes in both laboratory testing and real-life, long-term trajectories following injury. Furthermore, this study highlights the feasibility and strength of incorporating eye-tracking into studies of TBI to gain novel insights into information gathering and use across time.

The neural mechanisms subserving the adaptiveness of emotion regulation flexibility and its link to depression

Emotion Regulation Flexibility (ERF) is defined as an individual's ability to adaptively respond to changing situations and goals. Deficits in the adaptiveness of ERF have been linked to depression, suggesting a critical relationship between emotional processing and mental health. The objective of the present study was to investigate how variations in situational and goal-related contexts influence the association between ERF adaptiveness and depression. We employed functional magnetic resonance imaging (fMRI) and behavioral tasks to explore this relationship. Participants completed tasks designed to provoke changing situations and changing goals, while fMRI captured neural activity. Our findings revealed a significant negative correlation between depression scores and ERF adaptiveness. Specifically, during changing-situations, activation was observed in temporal and limbic regions, while changing-goals engaged prefrontal and parietal regions. Correlation analyses indicated that the adaptiveness of ERF was supported by distinct neural contributions: the temporoparietal junction (TPJ) in the changing-situations condition and the dorsolateral prefrontal cortex (dlPFC) in the changing-goals condition. Furthermore, the functional coupling between the dlPFC and the ventromedial prefrontal cortex (vmPFC) mediated the relationship between ERF adaptiveness and depression during changing-goals, but not during changing-situations. These findings elucidate the neural mechanisms of ERF adaptiveness and its implications for understanding and addressing depression.

Increased frontoparietal activity related to lower performance in neuropsychological assessment of working memory

Executive functions, including working memory, are typically assessed clinically with neuropsychological instruments. In contrast, computerized tasks are used to test these cognitive functions in laboratory human and animal studies. Little is known of how neural activity captured by laboratory tasks relates to ability measured by clinical instruments and, by extension, clinical diagnoses of pathological conditions. We therefore sought to determine what aspects of neural activity elicited in laboratory tasks are predictive of performance in neuropsychological instruments. We recorded neural activity from intracranial electrodes implanted in human epilepsy patients as they performed laboratory working memory tasks. These patients had completed neuropsychological instruments preoperatively, including the Weschler Adult Intelligent Scale and the Wisconsin Card Sorting test. Our results revealed that increased high-gamma (70-150 Hz) power in the prefrontal and parietal cortex after presentation of visual stimuli to be remembered was indicative of lower performance in the neuropsychological tasks. On the other hand, we observed a positive correlation between high-frequency power amplitude in the delay period of the laboratory tasks and neuropsychological performance. Our results demonstrate how neural activity around task events relates to executive function and may be associated with clinical diagnosis of specific cognitive deficits.

Repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex in binge eating disorder: a double-blind randomized controlled trial

BACKGROUND

Binge-eating disorder (BED) is characterized by highly distressing episodes of loss-of-control over-eating. We have examined the use of repetitive transcranial magnetic stimulation (rTMS) for the treatment of people with BED and associated obesity. Such non-invasive brain stimulation (NIBS) techniques are used therapeutically in several psychiatric conditions and there is an associated scientific rationale.

METHODS

Sixty participants were randomly allocated to receive 20 sessions of neuronavigated 10 Hz rTMS administered to the left dorsolateral prefrontal cortex (dlPFC) or sham treatment. Primary outcomes were the frequency of binge eating episodes (BEE) and the 'urge to eat' (craving) evaluated at baseline and end-of-treatment (8 weeks post-randomization). Secondary outcomes included body mass index (BMI), hunger, general and specific eating disorder psychopathology. Follow-up analyses were conducted for most outcomes at 16 weeks post-randomization. Multilevel models were used to evaluate group, time, and group-by-time interactions for the association between rTMS exposure and outcomes.

RESULTS

The real rTMS group (compared with sham treatment), showed a significantly greater decrease in the number of BEE at the end of treatment (Estimated Mean [EM]: 2.41 95% CI: 1.84-3.15 versus EM: 1.45 95% CI: 1.05-1.99, p = 0.02), and at follow-up (EM: 3.79 95% CI: 3-4.78 versus EM: 2.45 95% CI: 1.88-3.17, p = 0.02; group × time interaction analysis p = 0.02). No group differences were found for other comparisons.

CONCLUSION

rTMS was associated with reduced BEE during and after treatment: it suggests rTMS is a promising intervention for BED.

The inferior fronto-occipital fasciculus: bridging phylogeny, ontogeny and functional anatomy

The inferior-fronto-occipital fasciculus (IFOF) is a long-range white matter tract that connects the prefrontal cortex with parietal, posterior temporal and occipital cortices. First identified in the 19th century through the pioneering studies of Mayo and Meynert using blunt dissection, its anatomy and function remain contentious topics. Structurally, its projections are well documented in human blunt dissection and tractography literature, yet its existence has been questioned by tract-tracing studies in macaques. Functionally, while traditional results from direct white matter stimulation during awake surgery suggested a contribution to language, recent evidence from stimulation and lesion data may indicate a broader role in executive control, extending to attention, motor cognition, memory, reading, emotion recognition and theory of mind. This review begins by examining anatomical evidence suggesting that the IFOF evolved in non-human primates to connect temporal and occipital cortices to prefrontal regions involved in context-dependent selection of visual features for action. We then integrate developmental, electrophysiological, functional and anatomical evidence for the human IFOF to propose it has a similar role in manipulation of visual features in our species-particularly when inhibition of overriding but task-irrelevant stimuli is required to prioritize a second, task-relevant stimulus. Next, we introduce a graded model in which dorsal (orbitofrontal, superior and middle frontal to precuneal, angular and supero-occipital projections) and ventral (inferior frontal to posterotemporal, basal temporal and infero-occipital) projections of the IFOF support perceptual or conceptual control of visual representations for action, respectively. Leveraging this model, we address controversies in the current literature regarding language, motor cognition, attention and emotion under the unifying view of cognitive control. Finally, we discuss surgical implications for this model and its impact on predicting and preventing neurological deficits in neurosurgery.

Active information sampling in health and disease

Active information gathering is a fundamental cognitive process that enables organisms to navigate uncertainty and make adaptive decisions. Here we synthesise current knowledge on the behavioural, neural, and computational mechanisms underlying information sampling in healthy people and across several brain disorders. The role of cortical and subcortical regions spanning limbic, insular, fronto-parietal, and striatal systems is considered, along with the contributions of key neurotransmitters involving norepinephrine, dopamine, and serotonin. We also examine how various clinical conditions, including schizophrenia, obsessive-compulsive disorder, and Parkinson's disease have an impact on information gathering behaviours. To account for the findings, we outline a neuroeconomic perspective on how the brain may evaluate the costs and benefits of acquiring information to resolve uncertainty. This work highlights how active information gathering is a crucial brain process for adaptive behaviour in healthy individuals and how its breakdown is relevant to several psychiatric and neurological conditions. The findings have important implications for developing novel computational assays as well as targeted interventions in brain disorders.

Distributed Representations for Cognitive Control in Frontal Medial Cortex

In natural and artificial neural networks, modularity and distributed structure afford complementary but competing benefits. The former allows for hierarchical representations that can flexibly recombine modules to address novel problems, whereas the latter can benefit from less constrained training, potentially uncovering fruitful statistical regularities. Here, we investigate these competing demands in the context of human sequential behavior. First, we explore this setting by comparing the properties of several recurrent neural network models. We find that explicit hierarchical structure by itself fails to provide a critical performance advantage when compared with a "flat" model that does not incorporate hierarchical structure. However, hierarchy appears to facilitate cognitive control processes that support nonroutine behaviors and behaviors that are carried out under computational stress. Second, we compare these models against fMRI data using representational similarity analysis. We find that a model that incorporates so-called wiring costs in the cost function, which produces a hierarchically organized gradient of representational structure across the hidden layer of the neural network, best accounts for fMRI data collected from human participants in a previous study [Holroyd, C. B., Ribas-Fernandes, J. J. F., Shahnazian, D., Silvetti, M., & Verguts, T., Human midcingulate cortex encodes distributed representations of task progress. Proceedings of the National Academy of Sciences, U.S.A., 115, 6398-6403, 2018]. The results reveal that the ACC encodes distributed representations of sequential task context along a rostro-caudal gradient of abstraction: Rostral ACC encodes relatively abstract and temporally extended patterns of activity compared with those encoded by caudal ACC. These results provide insight into the role of ACC in motivation and cognitive control.

Beyond impulse control - toward a comprehensive neural account of future-oriented decision making

The dominant focus of current neural models of future-oriented decision making is on the interplay between the brain's reward system and a frontoparietal network thought to implement impulse control. Here, we propose a re-interpretation of the contribution of frontoparietal activation to future-oriented behavior and argue that future-oriented decisions are influenced by a variety of psychological mechanisms implemented by dissociable brain mechanisms. We review the literature on the neural mechanisms underlying the influence of prospection, retrospection, framing, metacognition, and automatization on future-oriented decisions. We propose that the prefrontal cortex contributes to future-oriented decisions not by exerting impulse control but by constructing and updating the value of abstract future rewards. These prefrontal value representations interact with regions involved in reward processing (neural reward system), prospection (hippocampus, temporal cortex), metacognition (frontopolar cortex), and habitual behavior (dorsal striatum). The proposed account of the brain mechanisms underlying future-oriented decisions has several implications for both basic and clinical research: First, by reconciling the idea of frontoparietal control processes with construal accounts of intertemporal choice, we offer an alternative interpretation of the canonical prefrontal activation during future-oriented decisions. Second, we highlight the need for obtaining a better understanding of the neural mechanisms underlying future-oriented decisions beyond impulse control and of their contribution to myopic decisions in clinical disorders. Such a widened focus may, third, stimulate the development of novel neural interventions for the treatment of pathological impulsive decision making.

Cortical Gradients Support Mental Time Travel into the Past and Future: Evidence from Activation Likelihood Estimation Meta-analysis

A longstanding issue concerns the extent to which episodic autobiographical memory (EAM) and episodic future thinking (EFT) are the expression of the same cognitive ability and may be dissociated at the neural level. Here, we provided an updated picture of overlaps and dissociations between brain networks supporting EAM and EFT, using Activation Likelihood Estimation. Moreover, we tested the hypothesis that spatial gradients characterize the transition between activations associated with the two domains, in line with accounts positing a transition in the relative predominance of their features and process components. We showed the involvement of a core network across EAM and EFT, including midline structures, the bilateral hippocampus/parahippocampus, angular gyrus and anterior middle temporal gyrus (aMTG) and the left superior frontal gyrus (SFG). Contrast analyses highlighted a cluster in the right aMTG significantly more activated during EFT compared with EAM. Finally, gradiental transitions were found in the ventromedial prefrontal cortex, left SFG, and bilateral aMTG. Results show that differences between EAM and EFT may arise at least partially through the organization of specific regions of common activation along functional gradients, and help to advocate between different theoretical accounts.

Novel Verbal Instructions Recruit Abstract Neural Patterns of Time-Variable Information Dimensionality

Human performance is endowed by neural representations of information that is relevant for behavior, some of which are also activated in a preparatory fashion to optimize later execution. Most studies to date have focused on highly practiced actions, leaving largely unaddressed the novel reconfiguration of information to generate unique whole task sets. Using electroencephalography, this study investigated the dynamics of the content and geometry reflected on the neural patterns of control representations during reconfiguration of information. We designed a verbal instruction paradigm where each trial involved novel combinations of multicomponent task information. By manipulating three task-relevant factors in a sample of 40 participants (26 females, 14 males), we observed complex coding schemes throughout the trial, during both preparation and implementation stages. The temporal profiles were consistent with a hierarchical structure: whereas task information was active in a sustained manner, the coding of more concrete stimulus features was more transient. Data showed both high dimensionality and abstraction, particularly during instruction encoding and target processing. Our results suggest that whenever task content could be recovered from neural patterns of activity, there was evidence of abstract coding, with an underlying geometry that favored generalization. During target processing, where potential interference across stimulus and response factors increased, orthogonal configurations also appeared. Overall, our findings uncover the dynamic manner with which control representations operate during novel recombination unique scenarios, with changes in dimensionality and abstraction adjusting along processing stages.

Degree centrality values in the left calcarine as a potential imaging biomarker for anxious major depressive disorder

BACKGROUND

Major depressive disorder (MDD) with comorbid anxiety is an intricate psychiatric condition, but limited research is available on the degree centrality (DC) between anxious MDD and nonanxious MDD patients.

AIM

To examine changes in DC values and their use as neuroimaging biomarkers in anxious and non-anxious MDD patients.

METHODS

We examined 23 anxious MDD patients, 30 nonanxious MDD patients, and 28 healthy controls (HCs) using the DC for data analysis.

RESULTS

Compared with HCs, the anxious MDD group reported markedly reduced DC values in the right fusiform gyrus (FFG) and inferior occipital gyrus, whereas elevated DC values in the left middle frontal gyrus and left inferior parietal angular gyrus. The nonanxious MDD group exhibited surged DC values in the bilateral cerebellum IX, right precuneus, and opercular part of the inferior frontal gyrus. Unlike the nonanxious MDD group, the anxious MDD group exhibited declined DC values in the right FFG and bilateral calcarine (CAL). Besides, declined DC values in the right FFG and bilateral CAL negatively correlated with anxiety scores in the MDD group.

CONCLUSION

This study shows that abnormal DC patterns in MDD, especially in the left CAL, can distinguish MDD from its anxiety subtype, indicating a potential neuroimaging biomarker.

Frontoparietal and temporal brain alterations post-cardiopulmonary bypass

Patients undergoing cardiopulmonary bypass (CPB) often experience neurological complications, but the neurobiological mechanisms remain unclear. This study combined resting-state fMRI, structural MRI, and cognitive testing to examine brain changes in 124 CPB patients and matched controls. Reduced amplitude of low-frequency fluctuation (ALFF) in the bilateral frontoparietal lobes indicated diminished neural activity among patients, and these ALFF values were positively correlated with the degree of executive dysfunction measured by the attention network test. Functional connectivity within the frontoparietal executive control network was weakened. Brain structural analysis revealed cortical thinning in frontoparietal and temporal regions, increased sulcal depth in medial orbitofrontal areas, and reduced gyrification in the insula suggesting long-term morphological impacts. These findings demonstrate CPB-associated functional and structural alterations in brain regions critical for cognition, providing neuroimaging evidence for postoperative dysfunction and potential neuroprotective strategies.

Cyclosporine A Accelerates Neurorecovery Transcriptional Trajectory in a Swine Model of Diffuse Traumatic Brain Injury

Mild traumatic brain injury (mTBI) is a leading cause of morbidity in children with both short- and long-term neurological, cognitive, cerebrovascular, and emotional deficits. These deficits have been attributed to ongoing pathophysiological cascades that occur acutely and persist post-injury. Given our limited understanding of the transcriptional changes associated with these pathophysiological cascades, we studied formalin-fixed paraffin-embedded (FFPE) tissues from the frontal cortex (FC) and the hippocampus + amygdala (H&A) regions of swine (N = 40) after a sagittal rapid non-impact head rotation (RNR). We then sequenced RNA to define transcriptional changes at 1 day and 1 week after injury and investigated the protective influence of cyclosporine A (CsA) treatment. Differentially expressed genes (DEGs) were classified into five temporal patterns (Early, Transient, Persistent, Intensified, Delayed, or Late). DEGs were more abundant at 1 week than 1 day. Shared significant gene ontology annotations in both regions included terms associated with neuronal distress at 1 day and neurorecovery at 1 week. CsA (20 mg/kg/day) infused for 1 day (beginning at 6 h after injury) accelerated 466 DEGs in the FC and 2794 DEGs in the H&A, such that the CsA-treated transcriptional profile was associated with neurorecovery. Overall, our data reveal the effects of anatomic region and elapsed time on gene expression post-mTBI and motivate future studies of CsA treatment.

Primate thalamic nuclei select abstract rules and shape prefrontal dynamics

Flexible behavior depends on abstract rules to generalize beyond specific instances and outcome monitoring to adjust actions. Cortical circuits are posited to read out rules from high-dimensional representations of task-relevant variables in prefrontal cortex (PFC). We instead hypothesized that converging inputs from PFC, directly or via basal ganglia (BGs), enable the thalamus to select rules. We measured activity across PFC and connected thalamic nuclei of monkeys applying rules. Abstract rule information first appeared in ventroanterior thalamus (VA)-the main thalamic hub between BG and PFC. Mediodorsal thalamus (MD) also represented rule information before PFC, persisting to help maintain activation of relevant PFC cell ensembles. MD, a major recipient of midbrain dopamine input, was the first to represent information about behavioral outcomes. A PFC-BG-thalamus model reproduced key findings, and thalamic-lesion modeling disrupted PFC rule representations. This suggests that the thalamus selects high-level cognitive information from PFC and monitors behavioral outcomes of these selections.

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex

How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.

How attention and working memory work together in the pursuit of goals: The development of the sampling-remembering trade-off

Most work in the last 50 years on visual working memory and attention has used a classic psychophysical setup: participants are instructed to attend to, or remember, a set of items. This setup sidesteps the role of cognitive control; effort is maximal, tasks are simple, and strategies are limited. While this approach has yielded important insights, it provides no clear path toward an integrative theory (Kristjánsson & Draschkow, 2021) and, like studying a town's walkability by having its college students run the 50-yard dash, it runs the danger of focusing on edge cases. Here, in this theoretical opinion article, we argue for an approach where dynamic relationships between the agent and the environment are understood functionally, in light of an agent's goals. This means a shift in emphasis from the performance of the mechanisms underlying a narrow task ("remember these items!") to their control in pursuit of a naturalistic goal ("make a sandwich!", Land & Hayhoe, 2001). Here, we highlight the sampling-remembering trade-off between exploiting goal-relevant information in the environment versus maintaining it in working memory. We present a dynamic feedback model of this trade-off - where the individual weighs the subjective costs of accessing external information versus those of maintaining it in memory - using insights from existing cognitive control models based on economic principles (Kool & Botvinick, 2018). This trade-off is particularly interesting in children, as the optimal use of internal resources is even more crucial when limited. Our model makes some specific predictions for future research: 1) an individual child strikes a preferred balance between the effort to attend to goal-relevant information in the environment versus the effort to maintain it in working memory, 2) in order to maintain this balance as underlying memory and cognitive control mechanisms improve with age, the child will have to increasingly shift toward remembering, and 3) older children will show greater adaptability to changing task demands.

Latent circuit inference from heterogeneous neural responses during cognitive tasks

Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.

What Is Cognitive Control?

The last two decades have seen major advances in cognitive control research. In this paper, I provide an overview of this research. I next make a case that it might benefit from more reflection on its theoretical foundation. I end by suggesting that action theory might be of use with this.

Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features

Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive biomarkers is essential for personalized treatment. However, most prior research studies have used either electroencephalography (EEG) or clinical features, lack interpretability, or have small sample sizes. This study included 74 patients with MDD who responded (responders) and 43 patients with MDD who did not respond (nonresponders) to rTMS. Eight baseline EEG metrics and clinical features were sent to 7 machine learning models to classify responders and nonresponders. Shapley additive explanations (SHAP) was used to interpret feature contributions. Combining phase locking value and clinical features with support vector machine achieved optimal classification performance (accuracy = 97.33%). SHAP revealed that delta and beta band functional connectivity (F3-P7, F3-P4, P3-P8, T7-Cz) significantly influenced predictions and differed between groups. This study developed an explainable predictive framework to predict rTMS response in MDD, enhancing the accuracy of rTMS response prediction and supporting personalized treatment in MDD.

Associations of cachexia and frailty with amyotrophic lateral sclerosis

In the present study, we investigated the associations of cachexia (loss of muscle, weight and fat) and frailty (loss of weight and muscle) status with the risk of developing amyotrophic lateral sclerosis, because these specific terms are rarely used in this research area. In this prospective study, we extracted cachexia and frailty status from the UK Biobank cohort to study the associations of these conditions (as determined via international classification of disease-10 codes) with amyotrophic lateral sclerosis. There was a greater risk of developing amyotrophic lateral sclerosis among individuals with cachexia and frailty status after adjusting for age, sex, income (pounds), body mass index, UK Biobank centers and smoking status. Among individuals with frailty status: a grip strength of < 21 kg, a slow walking speed, and exhaustion (more than half the days or nearly every day) increase the risk of developing amyotrophic lateral sclerosis. We believe that studying cachexia and frailty status can be used to help define and treat amyotrophic lateral sclerosis.

The Dimensionality of Neural Coding for Cognitive Control Is Gradually Transformed within the Lateral Prefrontal Cortex

Cognitive control relies on neural representations that are inherently high-dimensional and distributed across multiple subregions in the prefrontal cortex (PFC). Traditional approaches tackle prefrontal representation by reducing it into a unidimensional measure (univariate amplitude) or using it to distinguish a limited number of alternatives (pattern classification). In contrast, representational similarity analysis (RSA) enables flexibly formulating various hypotheses about informational contents underlying the neural codes, explicitly comparing hypotheses, and examining the representational alignment between brain regions. Here, we used a multifaceted paradigm wherein the difficulty of cognitive control was manipulated separately for five cognitive tasks. We used RSA to unveil representational contents, measure the representational alignment between regions, and quantify representational generality versus specificity. We found a graded transition in the lateral PFC: The dorsocaudal PFC was tuned to task difficulty (indexed by reaction times), preferentially connected with the parietal cortex, and representationally generalizable across domains. The ventrorostral PFC was tuned to the abstract structure of tasks, preferentially connected with the temporal cortex, and representationally specific. The middle PFC (interposed between the dorsocaudal and ventrorostral PFC) was tuned to individual task sets and ranked in the middle in terms of connectivity and generalizability. Furthermore, whether a region was dimensionally rich or sparse covaried with its functional profile: Low dimensionality (only gist) in the dorsocaudal PFC dovetailed with better generality, whereas high dimensionality (gist plus details) in the ventrorostral PFC corresponded with better ability to encode subtleties. Our findings, collectively, demonstrate how cognitive control is decomposed into distinct facets that transition steadily along prefrontal subregions.

The unity/diversity framework of executive functions: behavioral and neural evidence in older adults

Executive functions (EFs), encompassing inhibition, shifting, and updating as three fundamental subdomains, are typically characterized by a unity/diversity construct. However, given the dedifferentiation trend observed in aging, it remains controversial whether the construct of EFs in older adults becomes unidimensional or maintains unity/diversity. This study aims to explore and validate the construct of EFs in older adults. At the behavioral level, we conducted confirmatory factor analysis on data from 222 older adults who completed six tasks specifically targeting inhibition, shifting, and updating. One unidimensional model and six unity/diversity models of EFs were evaluated. Our results indicated that the EFs of older adults demonstrated greater congruence with the unity/diversity construct. At neural level, thirty older adults completed three thematically consistent fMRI tasks, targeting three subdomains of EFs respectively. Multivariate pattern analysis showed that rostromedial prefrontal cortex robustly showed similar neural representation across different tasks (unity). Meanwhile, the three EF domains were encoded by distinct global neural representation and the lateral prefrontal cortex play a crucial role in classification (diversity). These findings underscore the unity/diversity framework of EFs in older adults and offer important insights for designing interventions aimed at improving EFs in this population.

Genome-wide and phenome-wide studies provided insights into brain glymphatic system function and its clinical associations

We applied an MRI technique diffusion tensor imaging along the perivascular space (DTI-ALPS) for assessing glymphatic system (GS) in a genome-wide association study (GWAS) and phenome-wide association study (PheWAS) of 40,486 European individuals. Exploratory analysis revealed 17 genetic loci significantly associating with the regional DTI-ALPS index. We found 58 genes, including SPPL2C and EFCAB5, which prioritized in the DTI-ALPS index subtypes and associated with neurodegenerative diseases. PheWAS of 241 traits suggested that body mass index and blood pressure phenotypes closely related to GS function. Moreover, we detected disrupted GS function in 44 of 625 predefined disease conditions. Notably, Mendelian randomization and mediation analysis indicated that lower DTI-ALPS index was a risk factor for ischemic stroke (odds ratio = 1.56, P = 0.028) by partly mediating the risk factor of obesity. Results provide insights into the genetic architecture and mechanism for the DIT-ALPS index and highlight its great clinical value, especially in cerebral stroke.

Frontoparietal activity related to neuropsychological assessment of working memory

Executive functions, including working memory, are typically assessed clinically with neuropsychological instruments. In contrast, computerized tasks are used to test these cognitive functions in laboratory human and animal studies. Little is known of how neural activity captured by laboratory tasks relates to ability measured by clinical instruments and, by extension, clinical diagnoses of pathological conditions. We therefore sought to determine what aspects of neural activity elicited in laboratory tasks are predictive of performance in neuropsychological instruments. We recorded neural activity from intracranial electrodes implanted in human epilepsy patients as they performed laboratory working memory tasks. These patients had completed neuropsychological instruments preoperatively, including the Weschler Adult Intelligent Scale and the Wisconsin Card Sorting test. Our results revealed that increased high-gamma (70-150 Hz) power in the prefrontal and parietal cortex after presentation of visual stimuli to be remembered was indicative of lower performance in the neuropsychological tasks. On the other hand, we observed a positive correlation between high-frequency power amplitude in the delay period of the laboratory tasks and neuropsychological performance. Our results demonstrate how neural activity around task events relates to executive function and may be associated with clinical diagnosis of specific cognitive deficits.

A language model of problem solving in humans and macaque monkeys

Human intelligence is characterized by the remarkable ability to solve complex problems by planning a sequence of actions that takes us from an initial state to a desired goal state. Quantifying and comparing problem-solving capabilities across species and finding their evolutionary roots are critical for understanding how the brain carries out this intricate process. We introduce the Language of Problem Solving (LoPS) model as a novel quantitative framework that investigates the structure of problem-solving behavior through a language model. We applied the model to an adapted classic Pac-Man game as a cross-species behavioral paradigm to test both humans and macaque monkeys. The LoPS model extracted the latent structure, or grammar, embedded in the agents' gameplay, revealing the non-Markovian temporal dependency structure of their problem-solving behavior and the hierarchical structures of problem solving in both species. The complexity of LoPS grammar correlated with individuals' game performance and reflected the difference in problem-solving capacity between humans and monkeys. Both species evolved their LoPS grammars during learning, progressing from simpler to more complex ones, suggesting that the structure of problem solving is not fixed but evolves to support more sophisticated and efficient problem solving. Our study provides insights into how humans and monkeys break down problem solving into compositional units and navigate complex tasks, deepening our understanding of human intelligence and its evolution and establishing a foundation for future investigations of the neural mechanisms of problem solving.

Cognitive Control

Humans and other primates have a remarkable ability to perform a wide range of tasks and behaviors, even novel ones, in order to achieve their goals. Further, they are able to shift flexibly among these behaviors as the contexts demand. Cognitive control is the function at the base of this remarkable behavioral generativity and flexibility. The present review provides a survey of current research on cognitive control focusing on two of its primary features within a control systems framework: (a) the ability to select new behaviors based on context and (b) the ability to monitor ongoing behavior and adjust accordingly. Throughout, the review places an emphasis on how differences in the content and structure of task representations affect these core features of cognitive control.

The Extended Mind in Young Children: Cost-Dependent Trade-Off Between External and Internal Memory

Most work on working memory development has children remember a set of items as well as they can. However, this approach sidesteps the extended mind, the integration of external information with memory. Indeed, adults prefer to use external resources (e.g., lists, models) but will remember more as the cost to access them increases. Here, in our shopping game, we investigated this trade-off in 5- to 8-year-olds. Using a touchscreen, children shopped in a virtual store. Their shopping list and the store were not visible simultaneously but could be toggled. We manipulated access cost by varying a delay (0-4 s) before the list's reappearance. Across three preregistered experiments at two sites (the United States and China, N = 141), a pattern emerged: When it was costlier to do so, children revisited the list less often, studied it longer, and selected more correct items. Also, children recognized the costs, identifying the no-delay condition as easier. Young children showed a cost-dependent trade-off of external-resource use versus working memory.

Effects of Ketamine on Frontoparietal Interactions in a Rule-Based Antisaccade Task in Macaque Monkeys

Cognitive control is engaged by working memory processes and high-demand situations like antisaccade, where one must suppress a prepotent response. While it is known to be supported by the frontoparietal control network, how intra- and interareal dynamics contribute to cognitive control processes remains unclear. N-Methyl-d-aspartate glutamate receptors (NMDARs) play a key role in prefrontal dynamics that support cognitive control. NMDAR antagonists, such as ketamine, are known to alter task-related prefrontal activities and impair cognitive performance. However, the role of NMDAR in cognitive control-related frontoparietal dynamics remains underexplored. Here, we simultaneously recorded local field potentials and single-unit activities from the lateral prefrontal (lPFC) and posterior parietal cortices (PPC) in two male macaque monkeys during a rule-based antisaccade task, with both rule-visible (RV) and rule-memorized (RM) conditions. In addition to altering the E/I balance in both areas, ketamine had a negative impact on rule coding in true oscillatory activities. It also reduced frontoparietal coherence in a frequency- and rule-dependent manner. Granger prediction analysis revealed that ketamine induced an overall reduction in bidirectional connectivity. Among antisaccade trials, a greater reduction in lPFC-PPC connectivity during the delay period preceded a greater delay in saccadic onset under the RM condition and a greater deficit in performance under the RV condition. Lastly, ketamine compromised rule coding in lPFC neurons in both RV and RM conditions and in PPC neurons only in the RV condition. Our findings demonstrate the utility of acute NMDAR antagonists in understanding the mechanisms through which frontoparietal dynamics support cognitive control processes.

Partially dissociable roles of the orbitofrontal cortex and dorsal hippocampus in context-dependent hierarchical associations

Reward cues are often ambiguous; what is good in one context is not necessarily good in another. To solve this ambiguity, animals form hierarchical associations in which the context gates the retrieval of appropriate cue-evoked memories. These hierarchical associations regulate cue-elicited behavior and influence subsequent learning, promoting the inference of context-dependency. The orbitofrontal cortex (OFC) and dorsal hippocampus (DH) are both proposed to encode a "cognitive map" encompassing hierarchical, context-dependent associations. However, OFC- and DH-specific contributions to the different functional properties of hierarchical associations remain controversial. Using chemogenetic inactivation in rats, we show that the OFC is essential to both properties of hierarchical associations (performance regulation and learning bias). In contrast, DH's role appears limited to the contextual learning bias conferred by hierarchical associations. This work establishes the OFC as a critical orchestrator of hierarchical associations and provides insights into the extended circuits mediating the functional properties of these associations.

Flight training and the anterior cingulate cortex

Pilots are considered the final line of defense for aviation safety. Before becoming a pilot, an ab initio pilot must undergo systematic flight training. This study included 25 male flying cadets. Kendall's coefficient of concordance was used to measure the regional homogeneity of the time series of a given voxel with its 26 nearest neighboring voxels. This operation was performed for all voxels to generate a regional homogeneity map for each participant based on Kendall's coefficient of concordance. A partial correlation analysis was performed to examine the relationship between regional homogeneity maps and flight training hours. We found that the anterior cingulate cortex in the ab initio group was significantly positively correlated with flight hours. These results suggest a potential relationship between flight training experience and the functional properties of the anterior cingulate cortex.

Neurobiological correlates of comorbidity in disorders across the affective disorders-psychosis spectrum

Disorders across the affective disorders-psychosis spectrum such as major depressive disorder (MDD), bipolar disorder (BD), schizoaffective disorder (SCA), and schizophrenia (SCZ), have overlapping symptomatology and high comorbidity rates with other mental disorders. So far, however, it is largely unclear why some of the patients develop comorbidities. In particular, the specific genetic architecture of comorbidity and its relationship with brain structure remain poorly understood. Therefore, we performed systematic analyses of clinical, genetics and brain structural measures to gain further insights into the neurobiological correlates of mental disorder's comorbidity. We investigated a sub-sample of the Marburg/Münster Cohort Study (MACS), comprising DSM-IV-TR diagnosed patients with a single disorder in the affective disorders-psychosis spectrum (SD, n = 470, MDD; BD; SCA; SCZ), with additional mental disorder's comorbidities (COM, n = 310), and healthy controls (HC, n = 649). We investigated group differences regarding a) the global severity index (based on SCL90-R), b) a cross-disorder polygenic risk score (PRS) calculated with PRS-continuous shrinkage (PRS-CS) using the summary statistics of a large genome-wide association study across mental disorders, and c) whole brain grey matter volume (GMV). The SCL90-R score significantly differed between groups (COM > SD > HC). While SD and COM did not differ in cross-disorder PRS and GMV, SD and COM versus HC displayed increased cross-disorder PRS and decreased GMV in the bilateral insula, the left middle temporal, the left inferior parietal, and several frontal gyri. Our results thus suggest that disorders in the affective disorders-psychosis spectrum with or without additional comorbidities differ in self-reported clinical data, but not on genetic or brain structural levels.

The thalamus encodes and updates context representations during hierarchical cognitive control

Cognitive flexibility relies on hierarchically structured task representations that organize task contexts, relevant environmental features, and subordinate decisions. Despite ongoing interest in the human thalamus, its role in cognitive control has been understudied. This study explored thalamic representation and thalamocortical interactions that contribute to hierarchical cognitive control in humans. We found that several thalamic nuclei, including the anterior, mediodorsal, ventrolateral, and pulvinar nuclei, exhibited stronger evoked responses when subjects switch between task contexts. Decoding analysis revealed that thalamic activity encodes task contexts within the hierarchical task representations. To determine how thalamocortical interactions contribute to task representations, we developed a thalamocortical functional interaction model to predict task-related cortical representation. This data-driven model outperformed comparison models, particularly in predicting activity patterns in cortical regions that encode context representations. Collectively, our findings highlight the significant contribution of thalamic activity and thalamocortical interactions for contextually guided hierarchical cognitive control.

Attenuated processing of task-irrelevant speech and other auditory stimuli: fMRI evidence from arithmetic tasks

When performing cognitive tasks in noisy conditions, the brain needs to maintain task performance while additionally controlling the processing of task-irrelevant and potentially distracting auditory stimuli. Previous research indicates that a fundamental mechanism by which this control is achieved is the attenuation of task-irrelevant processing, especially in conditions with high task demands. However, it remains unclear whether the processing of complex naturalistic sounds can be modulated as easily as that of simpler ones. To address this issue, the present fMRI study examined whether activity related to task-irrelevant meaningful speech is attenuated similarly as that related to meaningless control sounds (nonsense speech and noise-vocoded, unintelligible sounds). The sounds were presented concurrently with three numerical tasks varying in difficulty: an easy control task requiring no calculation, a 'routine' arithmetic calculation task and a more demanding 'creative' arithmetic task, where solutions are generated to reach a given answer. Consistent with their differing difficulty, the tasks activated fronto-parieto-temporal regions parametrically (creative > routine > control). In bilateral auditory regions, activity related to the speech stimuli decreased as task demands increased. Importantly, however, the attenuation was more pronounced for meaningful than nonsense speech, demonstrating that distractor type can strongly modulate the extent of the attenuation. This also suggests that semantic processing may be especially susceptible to attenuation under conditions with increased task demands. Finally, as this is the first study to utilize the 'creative' arithmetic task, we conducted exploratory analyses to examine its potential in assessing neural processes involved in mathematical problem-solving beyond routine arithmetic.

Anatomical Connectivity Constrains Dynamic Functional Connectivity among Neural Systems: Implications for Cognition and Behavior

Leslie Ungerleider had a tremendous impact across many different areas of cognitive neuroscience. Her ideas and her approach, as well as her findings, will continue to impact the field for generations to come. One of the most impactful aspects of her approach was her focus on the ways that anatomical connections constrain functional communications among brain regions. Furthermore, she emphasized that changes in these functional communications, whether from lesions to the anatomical connections or temporary modulations of the efficacy of information transmission resulting from selective attention, have consequences for cognition and behavior. By necessity, this short review cannot cover the vast amount of research that contributed to or benefited from Leslie's work. Rather, we focus on one line of research that grew directly from some of Leslie's early work and her mentoring on these important concepts. This research and the many other lines of research that arose from these same origins has helped develop our understanding of the visual system, and cognitive systems more generally, as collections of highly organized, specialized, dynamic, and interacting subsystems.

The function(s) of consciousness: an evolutionary perspective

The functions of consciousness, viewed from an evolutionary standpoint, can be categorized as being either general or particular. There are two general functions, meaning those that do not depend on the particulars of how consciousness influences behavior or how and why it first evolved: of (1) expanding the behavioral repertoire of the individual through the gradual accumulation of neurocircuitry innovations incorporating consciousness that would not exist without it, and (2) reducing the time scale over which preprogrammed behaviors can be altered, from evolutionary time, across generations, to real-time. But neither answers Velmans' question, of why consciousness is adaptive in a proximate sense, and hence why it would have evolved, which depends on identifying the particular function it first performed. Memory arguably plays a role here, as a strong case can be made that consciousness first evolved to make motivational control more responsive, though memory, to the past life experiences of the individual. A control mechanism of this kind could, for example, have evolved to consciously inhibit appetitive behaviors, whether consciously instigated or not, that would otherwise expose the individual to harm. There is then the question of whether, for amniote vertebrates, a role in memory formation and access would have led directly to a wider role for consciousness in the way the brain operates, or if some other explanation is required. Velmans' question might then have two answers, the second having more to do with the advantages of global oversight for the control of behavior, as in a global workspace, or for conferring meaning on sensory experience in a way that non-conscious neural processes cannot. Meaning in this context refers specifically to the way valence is embodied in the genomic instructions for assembling the neurocircuitry responsible for phenomenal contents, so it constitutes an embodied form of species memory, and a way of thinking about the adaptive utility of consciousness that is less concerned with real-time mechanistic events than with information storage on an evolutionary time scale.

Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness

A primary aim of computational psychiatry is to establish predictive models linking individual differences in brain functioning with symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and associated with poor outcomes. Recent work suggests that thousands of participants may be necessary for the accurate and reliable prediction of cognition, questioning the utility of most patient collection efforts. Here, using a transfer learning framework, we train a model on functional neuroimaging data from the UK Biobank to predict cognitive functioning in three transdiagnostic samples (ns = 101 to 224). We demonstrate prediction performance in all three samples comparable to that reported in larger prediction studies and a boost of up to 116% relative to classical models trained directly in the smaller samples. Critically, the model generalizes across datasets, maintaining performance when trained and tested across independent samples. This work establishes that predictive models derived in large population-level datasets can boost the prediction of cognition across clinical studies.

Connectome-based prediction modeling of cognitive control using functional and structural connectivity

BACKGROUND

Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks.

METHODS

The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control.

RESULTS

Our results showed that both structural (r values 0.263-0.375) and functional (r values 0.336-0.503) connectomes can significantly predict individuals' cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns.

CONCLUSIONS

The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.

Multimodal neuroimaging of hierarchical cognitive control

Cognitive control enables us to translate our knowledge into actions, allowing us to flexibly adjust our behavior, according to environmental contexts, our internal goals, and future plans. Multimodal neuroimaging and neurostimulation techniques have proven essential for advancing our understanding of how cognitive control emerges from the coordination of distributed neuronal activities in the brain. In this review, we examine the literature on multimodal studies of cognitive control. We explore how these studies provide converging evidence for a novel, multiplexed model of cognitive control, in which neural oscillations support different levels of control processing along a functionally hierarchical organization of distinct frontoparietal networks.

Computer-assisted training of executive functions in adult patients with non-progressive acquired brain damage - a pilot study on efficacy of a new therapeutic application

Executive dysfunction is most often caused by post-traumatic or post-stroke damage to the prefrontal regions of the brain. The aim of this study was to compare the efficacy of two computer-assisted therapy programs for executive dysfunctions in patients with acquired brain injury. Patients were trained using either a newly developed application ExeSystem (designed to help improve the ability to manage and control one's own behavior by performing tasks imitating natural, everyday situations) or a combination of two commercial applications RehaCom and CogniPlus. Data collected after a three-week period of therapy conducted in two 15-person groups of participants indicated comparable efficacy of both therapy programs in improving quality of daily functioning, executive attention, as well as planning and problem-solving but not memory. The improvement in social competence (p = .028) was the only advantage of therapy with the ExeSystem. Therapeutic interactions using computer programs were shown to be positively evaluated by patients (p < .01). This study confirmed at least equal efficacy of computer-based executive function therapy using ExeSystem compared to RehaCom and CogniPlus. However, despite the implementation of a more ecological and comprehensive approach to the content of a new application, the benefits of this approach were limited.

Apathy and effort-based decision-making in Alzheimer's disease and subjective cognitive impairment

INTRODUCTION

Apathy is a significant feature in Alzheimer's disease (AD) and subjective cognitive impairment (SCI), though its mechanisms are not well established.

METHODS

An effort-based decision-making (EBDM) framework was applied to investigate apathy in 30 AD patients, 41 SCI participants, and 55 healthy controls (HC). Data were analyzed using a drift-diffusion model (DDM) to uncover latent psychological processes.

RESULTS

SCI participants reported higher apathy than AD patients and HC. However, informant reports of apathy in AD patients were higher than self-reports and indicated significant apathy compared to HC. Both the AD and SCI groups showed reduced sensitivity to effort changes, linked to executive dysfunction in AD and apathy in SCI. Increased resting functional cortical connectivity with the nucleus accumbens (NA) was associated with higher apathy in SCI.

DISCUSSION

These results highlight a similar disruption of EBDM in AD and SCI, differentially related to executive functioning in AD and apathy in SCI.

Highlights

This is the first study investigating apathy using an effort-based decision-making (EBDM) framework in Alzheimer's disease (AD) and subjective cognitive impairment (SCI).Self-reports underestimate apathy in AD patients when compared to informant reports and healthy controls (HC). SCI participants, in whom self and informant reports were more concordant, also showed higher degrees of apathy.Both AD and SCI groups showed reduced sensitivity to effort.Reduced sensitivity to effort correlates with executive dysfunction in AD and apathy, but not depression, in SCI.Increased nucleus accumbens (ventral striatum) connectivity with the frontoparietal network was associated with higher apathy scores in SCI.The results thus suggest that while AD and SCI can have similar deficits in EBDM, these deficits correlate with distinct clinical manifestations: executive dysfunction in AD and apathy in SCI.

Understanding dual process cognition via the minimum description length principle

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in domains ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.

Pre-supplementary motor area strengthens reward sensitivity in intertemporal choice

Previous investigations on the causal neural mechanisms underlying intertemporal decision making focused on the dorsolateral prefrontal cortex as neural substrate of cognitive control. However, little is known, about the causal contributions of further parts of the frontoparietal control network to delaying gratification, including the pre-supplementary motor area (pre-SMA) and posterior parietal cortex (PPC). Conflicting previous evidence related pre-SMA and PPC either to evidence accumulation processes, choice biases, or response caution. To disentangle between these alternatives, we combined drift diffusion models of decision making with online transcranial magnetic stimulation (TMS) over pre-SMA and PPC during an intertemporal decision task. While we observed no robust effects of PPC TMS, perturbation of pre-SMA activity reduced preferences for larger over smaller rewards. A drift diffusion model of decision making suggests that pre-SMA increases the weight assigned to reward magnitudes during the evidence accumulation process without affecting choice biases or response caution. Taken together, the current findings reveal the computational role of the pre-SMA in value-based decision making, showing that pre-SMA promotes choices of larger, costly rewards by strengthening the sensitivity to reward magnitudes.

Thalamocortical contributions to hierarchical cognitive control

Cognitive flexibility relies on hierarchically structured task representations that organize task contexts, relevant environmental features, and subordinate decisions. Despite ongoing interest in the human thalamus, its role in cognitive control has been understudied. This study explored thalamic representation and thalamocortical interactions that contribute to hierarchical cognitive control in humans. We found that several thalamic nuclei, including the anterior, mediodorsal, ventrolateral, and pulvinar nuclei, exhibited stronger evoked responses when subjects switch between task contexts. Decoding analysis revealed that thalamic activity encodes task contexts within the hierarchical task representations. To determine how thalamocortical interactions contribute to task representations, we developed a thalamocortical functional interaction model to predict task-related cortical representation. This data-driven model outperformed comparison models, particularly in predicting activity patterns in cortical regions that encode context representations. Collectively, our findings highlight the significant contribution of thalamic activity and thalamocortical interactions for contextually guided hierarchical cognitive control.

Lateral prefrontal cortex controls interplay between working memory and actions

Humans must often keep multiple task goals in mind, at different levels of priority and immediacy, while also interacting with the environment. We might need to remember information for an upcoming task while engaged in more immediate actions. Consequently, actively maintained working memory (WM) content may bleed into ongoing but unrelated motor behavior. Here, we experimentally test the impact of WM maintenance on action execution, and we transcranially stimulate lateral prefrontal cortex (PFC) to parse its functional contributions to WM-motor interactions. We first created a task scenario wherein human participants (both sexes) executed cued hand movements during WM maintenance. We manipulated the compatibility between WM and movement goals at the trial level and the statistical likelihood that the two would be compatible at the block level. We found that remembering directional words (e.g., 'left', 'down') biased the trajectory and speed of hand movements that occurred during the WM delay, but the bias was dampened in blocks when WM content predictably conflicted with movement goals. Then we targeted left lateral PFC with two different transcranial magnetic stimulation (TMS) protocols before participants completed the task. We found that an intermittent theta-burst protocol, which is thought to be excitatory, dampened sensitivity to block-level control demands (i.e., proactive control), while a continuous theta-burst protocol, which is thought to be inhibitory, dampened adaptation to trial-by-trial conflict (i.e., reactive control). Therefore, lateral PFC is involved in controlling the interplay between WM content and manual action, but different PFC mechanisms may support different time-scales of adaptive control.

The Lifespan Trajectories of Brain Activities Related to Cognitive Control

Cognitive control plays a pivotal role in guiding human goal-directed behavior. While existing studies have documented an inverted U-shaped trajectory of cognitive control both behaviorally and anatomically, little is known about the corresponding changes in functional brain activation with age. To bridge this gap, we conducted a comprehensive meta-analysis of 129 neuroimaging studies using conflict tasks, encompassing 3,388 participants aged from 5 to 85 years old. We have three major findings: 1) The inverted U-shaped trajectory is the predominant pattern; 2) Cognitive control-related brain regions exhibit heterogeneous lifespan trajectories: the frontoparietal control network follows inverted U-shaped trajectories, peaking between 24 and 40 years, while the dorsal attention network demonstrates no clear trajectories; 3) Both the youth and the elderly show weaker brain activities and greater left laterality than young to middle-aged adults. These results reveal the lifespan trajectories of cognitive control, highlighting heterogeneous fluctuations in brain networks with age.

Timescales of learning in prefrontal cortex

The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.

Dissociable functional responses along the posterior-anterior gradient of the frontal and parietal cortices revealed by parametric working memory and training

While the storage capacity is limited, accumulating studies have indicated that working memory (WM) can be improved by cognitive training. However, understanding how exactly the brain copes with limited WM capacity and how cognitive training optimizes the brain remains inconclusive. Given the hierarchical functional organization of WM, we hypothesized that the activation profiles along the posterior-anterior gradient of the frontal and parietal cortices characterize WM load and training effects. To test this hypothesis, we recruited 51 healthy volunteers and adopted a parametric WM paradigm and training method. In contrast to exclusively strengthening the activation of posterior areas, a broader range of activation concurrently occurred in the anterior areas to cope with increased memory load for all subjects at baseline. Moreover, there was an imbalance in the responses of the posterior and anterior areas to the same increment of 1 item at different load levels. Although a general decrease in activation after adaptive training, the changes in the posterior and anterior areas were distinct at different memory loads. Particularly, we found that the activation gradient between the posterior and anterior areas was significantly increased at load 4-back after adaptive training, and the changes were correlated with improvement in WM performance. Together, our results demonstrate a shift in the predominant role of posterior and anterior areas in the frontal and parietal cortices when approaching WM capacity limits. Additionally, the training-induced performance improvement likely benefits from the elevated neural efficiency reflected in the increased activation gradient between the posterior and anterior areas.

Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning

Notification systems that convey urgency without adding cognitive burden are crucial in human-computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing desirable levels of urgency depending on the application. High-risk applications require an evaluation of the urgency level elicited during critical notifications. Traditional evaluations of perceived urgency rely on subjective self-reporting and performance metrics, which, while useful, are not real-time and can be distracting from the task at hand. In contrast, EEG technology offers a direct, non-intrusive method of assessing the user's cognitive state. Leveraging deep learning, this study introduces a novel approach to evaluate perceived urgency from single-trial EEG data, induced by vibration stimuli on the upper body, utilizing our newly collected urgency-via-vibration dataset. The proposed model combines a 2D convolutional neural network with a temporal convolutional network to capture spatial and temporal EEG features, outperforming several established EEG models. The proposed model achieves an average classification accuracy of 83% through leave-one-subject-out cross-validation across three urgency classes (not urgent, urgent, and very urgent) from a single trial of EEG data. Furthermore, explainability analysis showed that the prefrontal brain region, followed by the central brain region, are the most influential in predicting the urgency level. A follow-up neural statistical analysis revealed an increase in event-related synchronization (ERS) in the theta frequency band (4-7 Hz) with the increased level of urgency, which is associated with high arousal and attention in the neuroscience literature. A limitation of this study is that the proposed model's performance was tested only the urgency-via-vibration dataset, which may affect the generalizability of the findings.

Thalamocortical architectures for flexible cognition and efficient learning

The brain exhibits a remarkable ability to learn and execute context-appropriate behaviors. How it achieves such flexibility, without sacrificing learning efficiency, is an important open question. Neuroscience, psychology, and engineering suggest that reusing and repurposing computations are part of the answer. Here, we review evidence that thalamocortical architectures may have evolved to facilitate these objectives of flexibility and efficiency by coordinating distributed computations. Recent work suggests that distributed prefrontal cortical networks compute with flexible codes, and that the mediodorsal thalamus provides regularization to promote efficient reuse. Thalamocortical interactions resemble hierarchical Bayesian computations, and their network implementation can be related to existing gating, synchronization, and hub theories of thalamic function. By reviewing recent findings and providing a novel synthesis, we highlight key research horizons integrating computation, cognition, and systems neuroscience.

Neural dynamics underlying minute-timescale persistent behavior in the human brain

The ability to pursue long-term goals relies on a representations of task context that can both be maintained over long periods of time and switched flexibly when goals change. Little is known about the neural substrate for such minute-scale maintenance of task sets. Utilizing recordings in neurosurgical patients, we examined how groups of neurons in the human medial frontal cortex and hippocampus represent task contexts. When cued explicitly, task context was encoded in both brain areas and changed rapidly at task boundaries. Hippocampus exhibited a temporally dynamic code with fast decorrelation over time, preventing cross-temporal generalization. Medial frontal cortex exhibited a static code that decorrelated slowly, allowing generalization across minutes of time. When task context needed to be inferred as a latent variable, hippocampus encoded task context with a static code. These findings reveal two possible regimes for encoding minute-scale task-context representations that were engaged differently based on task demands.