A neurocomputational theory of the dopaminergic modulation of working memory functions

Background synaptic input modulates the visuospatial working memory

It is generally thought that persistent firing of neurons in the prefrontal cortex underlies working memory. Previous studies have focused on the influence of recurrent synaptic connectivity in local circuits on memory storage. Given neurons in the neocortex are extensively connected, individual neural circuits should receive synaptic inputs from other areas. Here we explore how background synaptic inputs (BSIs) modulate the visuospatial working memory in an oculomotor delayed response task. In a local recurrent network composed of pyramidal cells and interneurons, a bump attractor persists across the delay period, encoding the cue location. Under independent BSIs, the spontaneous network state before the cue presentation can be classified as inactive, active, or overactive, occurring successively with increasing the BSI strength, and the active state facilitates the memory storage. Under spatially correlated BSIs, optimal scenarios, in terms of accuracy of representation and resistance to distraction, involve the BSIs with intermediate strength and low correlation or high strength and moderate correlation. Our results demonstrate how the memory storage is regulated via tuning the balance between local excitation and global inhibition in the network. The current work reveals the functional importance of background input and suggests that robust memory storage could be accomplished over a variety of network states.

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.

Amphetamine reduces reward encoding and stabilizes neural dynamics in rat anterior cingulate cortex

Psychostimulants such as d-amphetamine (AMPH) often have behavioral effects that appear paradoxical within the framework of optimal choice theory. AMPH typically increases task engagement and the effort animals exert for reward, despite decreasing reward valuation. We investigated neural correlates of this phenomenon in the anterior cingulate cortex (ACC), a brain structure implicated in signaling cost-benefit utility. AMPH decreased signaling of reward, but not effort, in the ACC of freely-moving rats. Ensembles of simultaneously recorded neurons generated task-specific trajectories of neural activity encoding past, present, and future events. Low-dose AMPH contracted these trajectories and reduced their variance, whereas high-dose AMPH expanded both. We propose that under low-dose AMPH, increased network stability balances moderately increased excitability, which promotes accelerated unfolding of a neural 'script' for task execution, despite reduced reward valuation. Noise from excessive excitability at high doses overcomes stability enhancement to drive frequent deviation from the script, impairing task execution.

Neural Substrates of Inhibitory Control Maturation in Adolescence

Inhibitory control matures through adolescence and into early adulthood, impacting decision-making. Impairments in inhibitory control are associated with various psychopathologies, many of which emerge during adolescence. In this review, we examine the neural basis of developmental improvements in inhibitory control by integrating findings from humans and non-human primates, identifying the structural and functional specialization of executive brain systems that mediates cognitive maturation. Behavioral manifestations of response inhibition suggest that adolescents are capable of producing adult level responses on occasion, but lack the ability to engage systems mediating response inhibition in a consistent fashion. Maturation is associated with changes in structural anatomy as well as local and systems-level connectivity. Functional changes revealed by neuroimaging and neurophysiology indicate that maturation of inhibitory control is achieved through improvements in response preparation, error processing, and planned responses.

Temporal Dynamics of Hippocampal and Medial Prefrontal Cortex Interactions During the Delay Period of a Working Memory-Guided Foraging Task

Connections between the hippocampus (HC) and medial prefrontal cortex (mPFC) are critical for working memory; however, the precise contribution of this pathway is a matter of debate. One suggestion is that it may stabilize retrospective memories of recently encountered task-relevant information. Alternatively, it may be involved in encoding prospective memories, or the internal representation of future goals. To explore these possibilities, simultaneous extracellular recordings were made from mPFC and HC of rats performing the delayed spatial win-shift on a radial maze. Each trial consisted of a training-phase (when 4 randomly chosen arms were open) and test phase (all 8 arms were open but only previously blocked arms contained food) separated by a 60-s delay. Theta power was highest during the delay, and mPFC units were more likely to become entrained to hippocampal theta as the delay progressed. Training and test phase performance were accurately predicted by a linear classifier, and there was a transition in classification for training-phase to test-phase activity patterns throughout the delay on trials where the rats performed well. These data suggest that the HC and mPFC become more strongly synchronized as mPFC circuits preferentially shift from encoding retrospective to prospective information.

Is the Capacity for Vocal Learning in Vertebrates Rooted in Fish Schooling Behavior?

The capacity to learn and reproduce vocal sounds has evolved in phylogenetically distant tetrapod lineages. Vocal learners in all these lineages express similar neural circuitry and genetic factors when perceiving, processing, and reproducing vocalization, suggesting that brain pathways for vocal learning evolved within strong constraints from a common ancestor, potentially fish. We hypothesize that the auditory-motor circuits and genes involved in entrainment have their origins in fish schooling behavior and respiratory-motor coupling. In this acoustic advantages hypothesis, aural costs and benefits played a key role in shaping a wide variety of traits, which could readily be exapted for entrainment and vocal learning, including social grouping, group movement, and respiratory-motor coupling. Specifically, incidental sounds of locomotion and respiration (ISLR) may have reinforced synchronization by communicating important spatial and temporal information between school-members and extending windows of silence to improve situational awareness. This process would be mutually reinforcing. Neurons in the telencephalon, which were initially involved in linking ISLR with forelimbs, could have switched functions to serve vocal machinery (e.g. mouth, beak, tongue, larynx, syrinx). While previous vocal learning hypotheses invoke transmission of neurons from visual tasks (gestures) to the auditory channel, this hypothesis involves the auditory channel from the onset. Acoustic benefits of locomotor-respiratory coordination in fish may have selected for genetic factors and brain circuitry capable of synchronizing respiratory and limb movements, predisposing tetrapod lines to synchronized movement, vocalization, and vocal learning. We discuss how the capacity to entrain is manifest in fish, amphibians, birds, and mammals, and propose predictions to test our acoustic advantages hypothesis.

Enhanced switching and familial susceptibility for psychosis

INTRODUCTION

Working Memory and Task-Switching are essential components of cognitive control, which underlies many symptoms evident across multiple neuropsychiatric disorders, including psychotic and mood disorders. Vulnerability to these disorders has a substantial genetic component, suggesting that clinically unaffected first-degree relatives may carry some vulnerability-related traits. Converging evidence from animal and human studies demonstrates that dopamine transmission, striatal and frontal brain regions, and attention and switching behaviors are essential components of a multilevel circuit involved in salience, and disruptions in that circuit may lead to features of psychosis. Yet, it is possible that unaffected relatives may also possess characteristics that protect against development of illness. We hypothesized that reduced switch cost in a cued task-switching task, may be a behavioral expression of this "resilience" phenotype that will be observable in unaffected relatives.

METHODS

We tested a large community sample (n = 536) via the web, to assess different subcomponents of cognitive control, including task-switching and working memory, as well as risk-taking, among individuals who report having an affected relative with a psychotic or mood disorder.

RESULTS

Healthy individuals with suspected genetic risk due to a self-reported familial history of a psychotic disorder demonstrated better task-switching performance compared to healthy people without a psychiatrically ill relative and those with a relative with a mood disorder. This result was specific to illness status and task domain, in that individuals with a personal history of depression or anxiety did not show improved task-switching performance, and this improvement was selective to task-switching and not seen in other putative cognitive control domains (working memory or risk taking).

CONCLUSIONS

Although this study has limitations and independent replication is needed, these preliminary findings suggest a potential avenue for understanding susceptibility to these disorders by highlighting possible protective as well as vulnerability-related aspects of risk phenotypes.

Prefronto-cortical dopamine D1 receptor sensitivity can critically influence working memory maintenance during delayed response tasks

The dopamine (DA) hypothesis of cognitive deficits suggests that too low or too high extracellular DA concentration in the prefrontal cortex (PFC) can severely impair the working memory (WM) maintenance during delay period. Thus, there exists only an optimal range of DA where the sustained-firing activity, the neural correlate of WM maintenance, in the cortex possesses optimal firing frequency as well as robustness against noisy distractions. Empirical evidences demonstrate changes even in the D1 receptor (D1R)-sensitivity to extracellular DA, collectively manifested through D1R density and DA-binding affinity, in the PFC under neuropsychiatric conditions such as ageing and schizophrenia. However, the impact of alterations in the cortical D1R-sensitivity on WM maintenance has yet remained poorly addressed. Using a quantitative neural mass model of the prefronto-mesoprefrontal system, the present study reveals that higher D1R-sensitivity may not only effectuate shrunk optimal DA range but also shift of the range to lower concentrations. Moreover, higher sensitivity may significantly reduce the WM-robustness even within the optimal DA range and exacerbates the decline at abnormal DA levels. These findings project important clinical implications, such as dosage precision and variability of DA-correcting drugs across patients, and failure in acquiring healthy WM maintenance even under drug-controlled normal cortical DA levels.

Age-dependent expression of Nav1.9 channels in medial prefrontal cortex pyramidal neurons in rats

Developmental changes that occur in the prefrontal cortex during adolescence alter behavior. These behavioral alterations likely stem from changes in prefrontal cortex neuronal activity, which may depend on the properties and expression of ion channels. Nav1.9 sodium channels conduct a Na⁺ current that is TTX resistant with a low threshold and noninactivating over time. The purpose of this study was to assess the presence of Nav1.9 channels in medial prefrontal cortex (mPFC) layer II and V pyramidal neurons in young (20-day old), late adolescent (60-day old), and adult (6- to 7-month old) rats. First, we demonstrated that layer II and V mPFC pyramidal neurons in slices obtained from young rats exhibited a TTX-resistant, low-threshold, noninactivating, and voltage-dependent Na⁺ current. The mRNA expression of the SCN11a gene (which encodes the Nav1.9 channel) in mPFC tissue was significantly higher in young rats than in late adolescent and adult rats. Nav1.9 protein was immunofluorescently labeled in mPFC cells in slices and analyzed via confocal microscopy. Nav1.9 immunolabeling was present in layer II and V mPFC pyramidal neurons and was more prominent in the neurons of young rats than in the neurons of late adolescent and adult rats. We conclude that Nav1.9 channels are expressed in layer II and V mPFC pyramidal neurons and that Nav1.9 protein expression in the mPFC pyramidal neurons of late adolescent and adult rats is lower than that in the neurons of young rats. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 77: 1371-1384, 2017.

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements

The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series, and directly link these to computational properties.

Schizophrenia is associated with a pattern of spatial working memory deficits consistent with cortical disinhibition

Schizophrenia is associated with severe cognitive deficits, including impaired working memory (WM). A neural mechanism that may contribute to WM impairment is the disruption in excitation-inhibition (E/I) balance in cortical microcircuits. It remains unknown, however, how these alterations map onto quantifiable behavioral deficits in patients. Based on predictions from a validated microcircuit model of spatial WM, we hypothesized two key behavioral consequences: i) increased variability of WM traces over time, reducing performance precision; and ii) decreased ability to filter out distractors that overlap with WM representations. To test model predictions, we studied N=27 schizophrenia patients and N=28 matched healthy comparison subjects (HCS) who performed a spatial WM task designed to test the computational model. Specifically, we manipulated delay duration and distractor distance presented during the delay. Subjects used a high-sensitivity joystick to indicate the remembered location, yielding a continuous response measure. Results largely followed model predictions, whereby patients exhibited increased variance and less WM precision as the delay period increased relative to HCS. Schizophrenia patients also exhibited increased WM distractibility, with reports biased toward distractors at specific spatial locations, as predicted by the model. Finally, the magnitude of the WM drift and distractibility were significantly correlated, indicating a possibly shared underlying mechanism. Effects are consistent with elevated E/I ratio in schizophrenia, establishing a framework for translating neural circuit computational model of cognition to human experiments, explicitly testing mechanistic behavioral hypotheses of cellular-level neural deficits in patients.

Prefrontal Markers and Cognitive Performance Are Dissociated during Progressive Dopamine Lesion

Dopamine is thought to directly influence the neurophysiological mechanisms of both performance monitoring and cognitive control-two processes that are critically linked in the production of adapted behaviour. Changing dopamine levels are also thought to induce cognitive changes in several neurological and psychiatric conditions. But the working model of this system as a whole remains untested. Specifically, although many researchers assume that changing dopamine levels modify neurophysiological mechanisms and their markers in frontal cortex, and that this in turn leads to cognitive changes, this causal chain needs to be verified. Using longitudinal recordings of frontal neurophysiological markers over many months during progressive dopaminergic lesion in non-human primates, we provide data that fail to support a simple interaction between dopamine, frontal function, and cognition. Feedback potentials, which are performance-monitoring signals sometimes thought to drive successful control, ceased to differentiate feedback valence at the end of the lesion, just before clinical motor threshold. In contrast, cognitive control performance and beta oscillatory markers of cognitive control were unimpaired by the lesion. The differing dynamics of these measures throughout a dopamine lesion suggests they are not all driven by dopamine in the same way. These dynamics also demonstrate that a complex non-linear set of mechanisms is engaged in the brain in response to a progressive dopamine lesion. These results question the direct causal chain from dopamine to frontal physiology and on to cognition. They imply that biomarkers of cognitive functions are not directly predictive of dopamine loss.

A neurological perspective on the enhancement debate: Lessons learned from Parkinson's disease

Cognitive enhancement is signified by adaptive behavioural change following an intervention that targets the brain. Although much of the discussion and research into cognitive enhancement focuses on the effects of neural interventions in healthy individuals, it is useful to consider evidence from clinical populations. Diseases of the central nervous system represent the primary and richest source of evidence on the effects of brain manipulations, which are in the first instance therapeutic. Parkinson's disease (PD) is used as a model for understanding the effects of pharmacological agents that target systems with a central role in cognition. The mixed outcomes of deep brain stimulation on cognition will also be discussed. By illustrating the psychopharmacological principle of diverse and malleable neurochemical optima for different cognitive functions, and the role of individual differences, it will be argued that the entire spectrum of cognitive effects in any one individual following any given manipulation, such as the administration of a drug, often includes enhancement as well as impairment. Predicting these effects represents a complex multivariate problem, and the accuracy of this predictive effort, as well as the harm prevention it connotes, is determined by our evolving understanding of the brain and cognition. A manipulation can be said to confer cognitive enhancement; however, it is argued that using the global term cognitive enhancer to refer to such a manipulation without qualification is of limited utility.

Stochastic Mesocortical Dynamics and Robustness of Working Memory during Delay-Period

The role of prefronto-mesoprefrontal system in the dopaminergic modulation of working memory during delayed response tasks is well-known. Recently, a dynamical model of the closed-loop mesocortical circuit has been proposed which employs a deterministic framework to elucidate the system's behavior in a qualitative manner. Under natural conditions, noise emanating from various sources affects the circuit's functioning to a great extent. Accordingly in the present study, we reformulate the model into a stochastic framework and investigate its steady state properties in the presence of constant background noise during delay-period. From the steady state distribution, global potential landscape and signal-to-noise ratio are obtained which help in defining robustness of the circuit dynamics. This provides insight into the robustness of working memory during delay-period against its disruption due to background noise. The findings reveal that the global profile of circuit's robustness is predominantly governed by the level of D1 receptor activity and high D1 receptor stimulation favors the working memory-associated sustained-firing state over the spontaneous-activity state of the system. Moreover, the circuit's robustness is further fine-tuned by the levels of excitatory and inhibitory activities in a way such that the robustness of sustained-firing state exhibits an inverted-U shaped profile with respect to D1 receptor stimulation. It is predicted that the most robust working memory is formed possibly at a subtle ratio of the excitatory and inhibitory activities achieved at a critical level of D1 receptor stimulation. The study also paves a way to understand various cognitive deficits observed in old-age, acute stress and schizophrenia and suggests possible mechanistic routes to the working memory impairments based on the circuit's robustness profile.

Modulating the map: dopaminergic tuning of hippocampal spatial coding and interactions

Salient events activate the midbrain dopaminergic system and have important impacts on various aspects of mnemonic function, including the stability of hippocampus-dependent memories. Dopamine is also central to modulation of neocortical memory processing, particularly during prefrontal cortex-dependent working memory. Here, we review the current state of the circuitry and physiology underlying dopamine's actions, suggesting that--alongside local effects within hippocampus and prefrontal cortex--dopamine released from the midbrain ventral tegmental area is well positioned to dynamically tune interactions between limbic-cortical circuits through modulation of rhythmic network activity.

Dopaminergic modulation of distracter-resistance and prefrontal delay period signal

Dopamine has long been implicated in the online maintenance of information across short delays. Specifically, dopamine has been proposed to modulate the strength of working memory representations in the face of intervening distracters. This hypothesis has not been tested in humans. We fill this gap using pharmacological neuroimaging. Healthy young subjects were scanned after intake of the dopamine receptor agonist bromocriptine or placebo (in a within-subject, counterbalanced, and double-blind design). During scanning, subjects performed a delayed match-to-sample task with face stimuli. A face or scene distracter was presented during the delay period (between the cue and the probe). Bromocriptine altered distracter-resistance, such that it impaired performance after face relative to scene distraction. Individual differences in the drug effect on distracter-resistance correlated negatively with drug effects on delay period signal in the prefrontal cortex, as well as on functional connectivity between the prefrontal cortex and the fusiform face area. These results provide evidence for the hypothesis that dopaminergic modulation of the prefrontal cortex alters resistance of working memory representations to distraction. Moreover, we show that the effects of dopamine on the distracter-resistance of these representations are accompanied by modulation of the functional strength of connections between the prefrontal cortex and stimulus-specific posterior cortex.

Bridging Levels of Understanding in Schizophrenia Through Computational Modeling

Schizophrenia is an illness with a remarkably complex symptom presentation that has thus far been out of reach of neuroscientific explanation. This presents a fundamental problem for developing better treatments that target specific symptoms or root causes. One promising path forward is the incorporation of computational neuroscience, which provides a way to formalize experimental observations and, in turn, make theoretical predictions for subsequent studies. We review three complementary approaches: (a) biophysically based models developed to test cellular-level and synaptic hypotheses, (b) connectionist models that give insight into large-scale neural-system-level disturbances in schizophrenia, and (c) models that provide a formalism for observations of complex behavioral deficits, such as negative symptoms. We argue that harnessing all of these modeling approaches represents a productive approach for better understanding schizophrenia. We discuss how blending these approaches can allow the field to progress toward a more comprehensive understanding of schizophrenia and its treatment.

Stochastic cortical neurodynamics underlying the memory and cognitive changes in aging

The relatively random spiking times of individual neurons provide a source of noise in the brain. We show how this noise interacting with altered depth in the basins of attraction of networks involved in short-term memory, attention, and episodic memory provide an approach to understanding some of the cognitive changes in normal aging. The effects of the neurobiological changes in aging that are considered include reduced synaptic modification and maintenance during learning produced in part through reduced acetylcholine in normal aging, reduced dopamine which reduces NMDA-receptor mediated effects, reduced noradrenaline which increases cAMP and thus shunts excitatory synaptic inputs, and the effects of a reduction in acetylcholine in increasing spike frequency adaptation. Using integrate-and-fire simulations of an attractor network implementing memory recall and short-term memory, it is shown that all these changes associated with aging reduce the firing rates of the excitatory neurons, which in turn reduce the depth of the basins of attraction, resulting in a much decreased probability in maintaining in short-term memory what has been recalled from the attractor network. This stochastic dynamics approach opens up new ways to understand and potentially treat the effects of normal aging on memory and cognitive functions.

Dopamine modulation of learning and memory in the prefrontal cortex: insights from studies in primates, rodents, and birds

In this review, we provide a brief overview over the current knowledge about the role of dopamine transmission in the prefrontal cortex during learning and memory. We discuss work in humans, monkeys, rats, and birds in order to provide a basis for comparison across species that might help identify crucial features and constraints of the dopaminergic system in executive function. Computational models of dopamine function are introduced to provide a framework for such a comparison. We also provide a brief evolutionary perspective showing that the dopaminergic system is highly preserved across mammals. Even birds, following a largely independent evolution of higher cognitive abilities, have evolved a comparable dopaminergic system. Finally, we discuss the unique advantages and challenges of using different animal models for advancing our understanding of dopamine function in the healthy and diseased brain.

Anatomical constraints on lateral competition in columnar cortical architectures

Competition is a well-studied and powerful mechanism for information processing in neuronal networks, providing noise rejection, signal restoration, decision making and associative memory properties, with relatively simple requirements for network architecture. Models based on competitive interactions have been used to describe the shaping of functional properties in visual cortex, as well as the development of functional maps in columnar cortex. These models require competition within a cortical area to occur on a wider spatial scale than cooperation, usually implemented by lateral inhibitory connections having a longer range than local excitatory connections. However, measurements of cortical anatomy reveal that the spatial extent of inhibition is in fact more restricted than that of excitation. Relatively few models reflect this, and it is unknown whether lateral competition can occur in cortical-like networks that have a realistic spatial relationship between excitation and inhibition. Here we analyze simple models for cortical columns and perform simulations of larger models to show how the spatial scales of excitation and inhibition can interact to produce competition through disynaptic inhibition. Our findings give strong support to the direct coupling effect-that the presence of competition across the cortical surface is predicted well by the anatomy of direct excitatory and inhibitory coupling and that multisynaptic network effects are negligible. This implies that for networks with short-range inhibition and longer-range excitation, the spatial extent of competition is even narrower than the range of inhibitory connections. Our results suggest the presence of network mechanisms that focus on intra-rather than intercolumn competition in neocortex, highlighting the need for both new models and direct experimental characterizations of lateral inhibition and competition in columnar cortex.

Long-term memory and the control of attentional control

Task-switch costs and in particular the switch-cost asymmetry (i.e., the larger costs of switching to a dominant than a non-dominant task) are usually explained in terms of trial-to-trial carry-over of task-specific control settings. Here we argue that task switches are just one example of situations that trigger a transition from working-memory maintenance to updating, thereby opening working memory to interference from long-term memory. We used a new paradigm that requires selecting a spatial location either on the basis of a central cue (i.e., endogenous control of attention) or a peripheral, sudden onset (i.e., exogenous control of attention). We found a strong cost asymmetry that occurred even after short interruptions of otherwise single-task blocks (Exp. 1-3), but that was much stronger when participants had experienced the competing task under conditions of conflict (Exp. 1-2). Experiment 3 showed that the asymmetric costs were due to interruptions per se, rather than to associative interference tied to specific interruption activities. Experiment 4 generalized the basic pattern across interruptions varying in length or control demands and Experiment 5 across primary tasks with response-selection conflict rather than attentional conflict. Combined, the results support a model in which costs of selecting control settings arise when (a) potentially interfering memory traces have been encoded in long-term memory and (b) working-memory is forced from a maintenance mode into an updating mode (e.g., through task interruptions), thereby allowing unwanted retrieval of the encoded memory traces.

Conflict adaptation in prefrontal cortex: now you see it, now you don't

Daily life requires people to monitor and resolve conflict arising from distracting information irrelevant to current goals. The highly influential conflict monitoring theory (CMT) holds that the anterior cingulate cortex (ACC) detects conflict and subsequently triggers the dorsolateral prefrontal cortex (DLPFC) to regulate that conflict. Multiple lines of evidence have provided support for CMT. For example, performance is faster on incongruent trials that follow other incongruent trials (iI), and is accompanied by reduced ACC and increased DLPFC activation (the conflict adaptation effect). In this fMRI study, we explored whether ACC-DLPFC conflict signaling can result in behavioral adjustments beyond on-line contexts. Participants completed a modified version of the Stroop conflict adaptation paradigm which tested for conflict adaptation effects on the current (N) trial associated with not only the immediately preceding (N - 1) trial, but also 2-back (N - 2) trials. Results demonstrated evidence for direct relationships between ACC activity on N - 2 trials and both N trial DLPFC activity and behavioral adjustment when intervening trials were congruent (i.e., icI). In contrast, when N - 1 trials were incongruent (i.e., iiI), ACC-DLPFC signaling failed and conflict adaptation was absent. These results provide new evidence demonstrating that the conflict monitor-controller maintains previously experienced conflict in the service of subsequent behavioral adjustment. However, the processing of multiple, temporally proximal conflict signals takes a toll on the working memory (WM) system, which appears to require resetting in order to adapt our behavior to frequently changing environmental demands.

Development of a simple and rapid solid phase microextraction-gas chromatography-triple quadrupole mass spectrometry method for the analysis of dopamine, serotonin and norepinephrine in human urine

The work aims at developing a simple and rapid method for the quantification of dopamine (DA), serotonin (5-HT) and norepinephrine (NE) in human urine. The urinary levels of these biogenic amines can be correlated with several pathological conditions concerning heart disease, stress, neurological disorders and cancerous tumors. The proposed analytical approach is based on the use of solid phase microextraction (SPME) combined with gas chromatography-triple quadrupole mass spectrometry (GC-QqQ-MS) after a fast derivatization of both aliphatic amino and phenolic moieties by propyl chloroformate. The variables influencing the derivatization reaction were reliably optimized by the multivariate approach of "Experimental design". The optimal conditions were obtained by performing derivatization with 100μL of propyl chloroformate and 100μL of pyridine. The extraction ability of five commercially available SPME fibers was evaluated in univariate mode and the best results were obtained using the polyacrylate fiber. The variables affecting the efficiency of SPME analysis were again optimized by the multivariate approach of "Experimental design" and, in particular, a central composite design (CCD) was applied. The optimal values were extraction in 45min at room temperature, desorption temperature at 300°C, no addition of NaCl. Assay of derivatized analytes was performed by using a gas chromatography-triple quadrupole mass spectrometry (GC-QqQ-MS) system in selected reaction monitoring (SRM) acquisition. An evaluation of all analytical parameters demonstrates that the developed method provides satisfactory results. Indeed, very good linearities were achieved in the tested calibration range with correlation coefficient values of 0.9995, 0.9999 and 0.9997 for DA, 5-HT and NE, respectively. Accuracies and RSDs calculated for between-run and tested at concentrations of 30, 200, and 800μg L(-1) were in the range from 92.8% to 103.0%, and from 0.67 to 4.5%, respectively. Finally, the LOD values obtained can be considered very good (0.587, 0.381 and 1.23μg L(-1) for DA, 5-HT and NE, respectively).

Dopamine and glutamate receptor genes interactively influence episodic memory in old age

Both the dopaminergic and glutamatergic systems modulate episodic memory consolidation. Evidence from animal studies suggests that these two neurotransmitters may interact in influencing memory performance. Given that individual differences in episodic memory are heritable, we investigated whether variations of the dopamine D2 receptor gene (rs6277, C957T) and the N-methyl-D-aspartate 3A (NR3A) gene, coding for the N-methyl-D-aspartate 3A subunit of the glutamate N-methyl-D-aspartate receptor (rs10989591, Val362Met), interactively modulate episodic memory in large samples of younger (20-31 years; n = 670) and older (59-71 years; n = 832) adults. We found a reliable gene-gene interaction, which was observed in older adults only: older individuals carrying genotypes associated with greater D2 and N-methyl-D-aspartate receptor efficacy showed better episodic performance. These results are in line with findings showing magnification of genetic effects on memory in old age, presumably as a consequence of reduced brain resources. Our findings underscore the need for investigating interactive effects of multiple genes to understand individual difference in episodic memory.

Self-generated sounds of locomotion and ventilation and the evolution of human rhythmic abilities

It has been suggested that the basic building blocks of music mimic sounds of moving humans, and because the brain was primed to exploit such sounds, they eventually became incorporated in human culture. However, that raises further questions. Why do genetically close, culturally well-developed apes lack musical abilities? Did our switch to bipedalism influence the origins of music? Four hypotheses are raised: (1) Human locomotion and ventilation can mask critical sounds in the environment. (2) Synchronization of locomotion reduces that problem. (3) Predictable sounds of locomotion may stimulate the evolution of synchronized behavior. (4) Bipedal gait and the associated sounds of locomotion influenced the evolution of human rhythmic abilities. Theoretical models and research data suggest that noise of locomotion and ventilation may mask critical auditory information. People often synchronize steps subconsciously. Human locomotion is likely to produce more predictable sounds than those of non-human primates. Predictable locomotion sounds may have improved our capacity of entrainment to external rhythms and to feel the beat in music. A sense of rhythm could aid the brain in distinguishing among sounds arising from discrete sources and also help individuals to synchronize their movements with one another. Synchronization of group movement may improve perception by providing periods of relative silence and by facilitating auditory processing. The adaptive value of such skills to early ancestors may have been keener detection of prey or stalkers and enhanced communication. Bipedal walking may have influenced the development of entrainment in humans and thereby the evolution of rhythmic abilities.

Variability in the quality of visual working memory

Working memory is a mental storage system that keeps task-relevant information accessible for a brief span of time, and it is strikingly limited. Its limits differ substantially across people but are assumed to be fixed for a given person. Here we show that there is substantial variability in the quality of working memory representations within an individual. This variability can be explained neither by fluctuations in attention or arousal over time, nor by uneven distribution of a limited mental commodity. Variability of this sort is inconsistent with the assumptions of the standard cognitive models of working memory capacity, including both slot- and resource-based models, and so we propose a new framework for understanding the limitations of working memory: a stochastic process of degradation that plays out independently across memories.

Local and global effects of motivation on cognitive control

Motivation has been found to enhance cognitive control, but the mechanisms by which this occurs are still poorly understood. Cued motivational incentives (e.g., monetary rewards) can modulate cognitive processing locally-that is, on a trial-by-trial basis (incentive cue effect). Recently, motivational incentives have also been found to produce more global and tonic changes in performance, as evidenced by performance benefits on nonincentive trials occurring within incentive blocks (incentive context effect). In two experiments involving incentivized cued task switching, we provide systematic evidence that the two effects are dissociable. Through behavioral, diffusion-modeling, and individual-differences analyses, we found dissociations between local and global motivational effects that were linked to specific properties of the incentive signals (i.e., timing), while also ruling out alternative interpretations (e.g., practice and speed-accuracy trade-off effects). These results provide important clues regarding the neural mechanisms by which motivation exerts both global and local influences on cognitive control.

Exercise: applications to childhood ADHD

ADHD is the most common neurobehavioral disorder of childhood, presenting with pervasive and impairing symptoms of inattention, hyperactivity, impulsivity, or a combination. The leading hypothesis of the underlying physiology of this disorder of inattention and/or hyperactivity-impulsivity is based on catecholamine dysfunction. Pharmacotherapy research indicates that psychostimulants, which are catecholamine agonists, show the greatest efficacy for treating the core symptoms of ADHD. Exercise affects the same dopaminergic and noradrenergic systems that stimulant medications target and is a stressor, which elicits measurable physiological changes. The magnitude of these peripheral alterations is posited as a potential biomarker of ADHD. The hypothesis that exercise training alters the underlying physiology present in ADHD and other medical conditions as well as conceptual issues behind its potential clinical utility is reviewed.

Embedding responses in spontaneous neural activity shaped through sequential learning

Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, “memories-as-bifurcations,” that differs from the traditional “memories-as-attractors” viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple Hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in our previous study.

The neural activity without explicit stimuli shows highly structured patterns in space and time, known as spontaneous activity. This spontaneous activity plays a key role in the behavior of the response to external stimuli generated by the interplay between the spontaneous activity and external input. Studying this interplay and how it is shaped by learning is an essential step toward understanding the principles of neural processing. To address this, we proposed a novel viewpoint, memories-as-bifurcations, in which the appropriate changes in the activity upon the input are embedded through learning. Based on this viewpoint, we introduce here an associative memory model with sequential learning by a simple Hebbian-type rule. In spite of its simplicity, the model memorizes the input/output mappings successively, as long as the input is sufficiently large and the synaptic change is slow. The spontaneous neural activity shaped after learning is shown to itinerate over the memorized targets in remarkable agreement with the experimental reports. These dynamics may prepare and facilitate to generate the learned response to the input. Our results suggest that this is the possible functional role of the spontaneous neural activity, while the uncovered network structure inspires a design principle for the memories-as-bifurcations.

Executive control: balancing stability and flexibility via the duality of evolutionary neuroanatomical trends

The concept of executive functions has a rich history and remains current despite increased use of other terms, including working memory and cognitive control. Executive functions have sometimes been equated with functions subserved by the frontal cortex, but this adds little clarity, given that we so far lack a comprehensive theory of frontal function. Pending a more complete mechanistic understanding, clinically useful generalizations can help characterize both healthy cognition and multiple varieties of cognitive impairment. This article surveys several hierarchical and autoregulatory control theories, and suggests that the evolutionary cytoarchitectonic trends theory provides a valuable neuroanatomical framework to help organize research on frontal structure-function relations. The theory suggests that paleocortical/ventrolateral and archicortical/dorsomedial trends are associated with neural network flexibility and stability respectively, which comports well with multiple other conceptual distinctions that have been proposed to characterize ventral and dorsal frontal functions, including the “initiation/inhibition,” “what/where,” and “classification/expectation” hypotheses.

Smooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia

This paper introduces a model of oculomotor control during the smooth pursuit of occluded visual targets. This model is based upon active inference, in which subjects try to minimise their (proprioceptive) prediction error based upon posterior beliefs about the hidden causes of their (exteroceptive) sensory input. Our model appeals to a single principle – the minimisation of variational free energy – to provide Bayes optimal solutions to the smooth pursuit problem. However, it tries to accommodate the cardinal features of smooth pursuit of partially occluded targets that have been observed empirically in normal subjects and schizophrenia. Specifically, we account for the ability of normal subjects to anticipate periodic target trajectories and emit pre-emptive smooth pursuit eye movements – prior to the emergence of a target from behind an occluder. Furthermore, we show that a single deficit in the postsynaptic gain of prediction error units (encoding the precision of posterior beliefs) can account for several features of smooth pursuit in schizophrenia: namely, a reduction in motor gain and anticipatory eye movements during visual occlusion, a paradoxical improvement in tracking unpredicted deviations from target trajectories and a failure to recognise and exploit regularities in the periodic motion of visual targets. This model will form the basis of subsequent (dynamic causal) models of empirical eye tracking measurements, which we hope to validate, using psychopharmacology and studies of schizophrenia.

Animal foraging and the evolution of goal-directed cognition

Foraging- and feeding-related behaviors across eumetazoans share similar molecular mechanisms, suggesting the early evolution of an optimal foraging behavior called area-restricted search (ARS), involving mechanisms of dopamine and glutamate in the modulation of behavioral focus. Similar mechanisms in the vertebrate basal ganglia control motor behavior and cognition and reveal an evolutionary progression toward increasing internal connections between prefrontal cortex and striatum in moving from amphibian to primate. The basal ganglia in higher vertebrates show the ability to transfer dopaminergic activity from unconditioned stimuli to conditioned stimuli. The evolutionary role of dopamine in the modulation of goal-directed behavior and cognition is further supported by pathologies of human goal-directed cognition, which have motor and cognitive dysfunction and organize themselves, with respect to dopaminergic activity, along the gradient described by ARS, from perseverative to unfocused. The evidence strongly supports the evolution of goal-directed cognition out of mechanisms initially in control of spatial foraging but, through increasing cortical connections, eventually used to forage for information.

Neurocircuitry for modeling drug effects

The identification and functional understanding of the neurocircuitry that mediates alcohol and drug effects that are relevant for the development of addictive behavior is a fundamental challenge in addiction research. Here we introduce an assumption-free construction of a neurocircuitry that mediates acute and chronic drug effects on neurotransmitter dynamics that is solely based on rodent neuroanatomy. Two types of data were considered for constructing the neurocircuitry: (1) information on the cytoarchitecture and neurochemical connectivity of each brain region of interest obtained from different neuroanatomical techniques; (2) information on the functional relevance of each region of interest with respect to alcohol and drug effects. We used mathematical data mining and hierarchical clustering methods to achieve the highest standards in the preprocessing of these data. Using this approach, a dynamical network of high molecular and spatial resolution containing 19 brain regions and seven neurotransmitter systems was obtained. Further graph theoretical analysis suggests that the neurocircuitry is connected and cannot be separated into further components. Our analysis also reveals the existence of a principal core subcircuit comprised of nine brain regions: the prefrontal cortex, insular cortex, nucleus accumbens, hypothalamus, amygdala, thalamus, substantia nigra, ventral tegmental area and raphe nuclei. Finally, by means of algebraic criteria for synchronizability of the neurocircuitry, the suitability for in silico modeling of acute and chronic drug effects is indicated. Indeed, we introduced as an example a dynamical system for modeling the effects of acute ethanol administration in rats and obtained an increase in dopamine release in the nucleus accumbens-a hallmark of drug reinforcement-to an extent similar to that seen in numerous microdialysis studies. We conclude that the present neurocircuitry provides a structural and dynamical framework for large-scale mathematical models and will help to predict chronic drug effects on brain function.

Prefrontal cortical mechanisms underlying individual differences in cognitive flexibility and stability

The pFC is critical for cognitive flexibility (i.e., our ability to flexibly adjust behavior to changing environmental demands), but also for cognitive stability (i.e., our ability to follow behavioral plans in the face of distraction). Behavioral research suggests that individuals differ in their cognitive flexibility and stability, and neurocomputational theories of working memory relate this variability to the concept of attractor stability in recurrently connected neural networks. We introduce a novel task paradigm to simultaneously assess flexible switching between task rules (cognitive flexibility) and task performance in the presence of irrelevant distractors (cognitive stability) and to furthermore assess the individual "spontaneous switching rate" in response to ambiguous stimuli to quantify the individual dispositional cognitive flexibility in a theoretically motivated way (i.e., as a proxy for attractor stability). Using fMRI in healthy human participants, a common network consisting of parietal and frontal areas was found for task switching and distractor inhibition. More flexible persons showed reduced activation and reduced functional coupling in frontal areas, including the inferior frontal junction, during task switching. Most importantly, the individual spontaneous switching rate antagonistically affected the functional coupling between inferior frontal junction and the superior frontal gyrus during task switching and distractor inhibition, respectively, indicating that individual differences in cognitive flexibility and stability are indeed related to a common prefrontal neural mechanism. We suggest that the concept of attractor stability of prefrontal working memory networks is a meaningful model for individual differences in cognitive stability versus flexibility.

Attention and visual dysfunction in Parkinson's disease

Visual processing extends from the retinal level to the ventral temporal lobe, and is modified by top-down and bottom-up processing. Complex visual hallucinations (VH) are commonly a feature of disorders which affect temporal lobe structures, frequently in association with impairment of ascending monoaminergic pathways. When Parkinson's disease (PD) is associated with VH, pathological changes characteristically affect the temporal lobes, a finding which is recapitulated by imaging findings. However, a major association of VH is with cognitive decline, and this is typically linked to deficits in attention and working memory, both of which are modulated by dopamine. Similarly, dopamine plays a crucial role in the function of prefrontal cortex, in addition to controlling access to consciousness via gating mechanisms that are dependent on the basal ganglia.

Neurons with inverted tuning during the delay periods of working memory tasks in the dorsal prefrontal and posterior parietal cortex

The dorsolateral prefrontal and posterior parietal cortices are two interconnected brain areas that are coactivated in tasks involving functions such as spatial attention and working memory. The response properties of neurons in the two areas are in many respects indistinguishable, yet only prefrontal neurons are able to resist interference by distracting stimuli when subjects are required to remember an initial stimulus. Several mechanisms have been proposed that could account for this functional difference, including the existence of specialized interneuron types, specific to the prefrontal cortex. Although such neurons with inverted tuning during the delay period of a working memory task have been described in the prefrontal cortex, no comparative data exist from other cortical areas that would establish a unique prefrontal role. To test this hypothesis, we analyzed a large database of recordings obtained in the dorsolateral prefrontal and posterior parietal cortex of the same monkeys as they performed working memory tasks. We found that in the prefrontal cortex, neurons with inverted tuning were more numerous and manifested unique properties. Our results give credence to the idea that a division of labor exists between separate neuron types in the prefrontal cortex and that this represents a functional specialization that is not present in its cortical afferents.

Executive control: balancing stability and flexibility via the duality of evolutionary neuroanatomical trends

The concept of executive functions has a rich history and remains current despite increased use of other terms, including working memory and cognitive control. Executive functions have sometimes been equated with functions subserved by the frontal cortex, but this adds little clarity, given that we so far lack a comprehensive theory of frontal function. Pending a more complete mechanistic understanding, clinically useful generalizations can help characterize both healthy cognition and multiple varieties of cognitive impairment. This article surveys several hierarchical and autoregulatory control theories, and suggests that the evolutionary cytoarchitectonic trends theory provides a valuable neuroanatomical framework to help organize research on frontal structure-function relations. The theory suggests that paleocortical/ventrolateral and archicortical/dorsomedial trends are associated with neural network flexibility and stability respectively, which comports well with multiple other conceptual distinctions that have been proposed to characterize ventral and dorsal frontal functions, including the "initiation/inhibition," "what/where," and "classification/expectation" hypotheses.

Effects of smoking history on selective attention in schizophrenia

Smoking prevalence is highly elevated in schizophrenia compared to the general population and to other psychiatric populations. Evidence suggests that smoking may lead to improvements of schizophrenia-associated attention deficits; however, large-scale studies on this important issue are scarce. We examined whether sustained, selective, and executive attention processes are differentially modulated by long-term nicotine consumption in 104 schizophrenia patients and 104 carefully matched healthy controls. A significant interaction of 'smoking status' × 'diagnostic group' was obtained for the domain of selective attention. Smoking was significantly associated with a detrimental conflict effect in controls, while the opposite effect was revealed for schizophrenia patients. Likewise, a positive correlation between a cumulative measure of nicotine consumption and conflict effect in controls and a negative correlation in patients were found. These results provide evidence for specific directional effects of smoking on conflict processing that critically dissociate with diagnosis. The data supports the self-medication hypothesis of smoking in schizophrenia and suggests selective attention as a specific cognitive domain targeted by nicotine consumption. A potential mechanistic model explaining these findings is discussed.

Modeling neuropathologies as disruption of normal sequence generation in working memory networks

Recurrent networks of cortico-cortical connections have been implicated as the substrate of working memory persistent activity, and patterned sequenced representation as needed in cognitive function. We examine the pathological behavior which may result from specific changes in the normal parameters or architecture in a biologically plausible computational working memory model capable of learning and reproducing sequences which come from external stimuli. Specifically, we examine systematical reductions in network inhibition, excitatory potentiation, delays in excitatory connections, and heterosynaptic plasticity. We show that these changes result in a set of dynamics which may be associated with cognitive symptoms associated with different neuropathologies, particularly epilepsy, schizophrenia, and obsessive compulsive disorders. We demonstrate how cognitive symptoms in these disorders may arise from similar or the same general mechanisms acting in the recurrent working memory networks. We suggest that these pathological dynamics may form a set overlapping states within the normal network function, and relate this to observed associations between different pathologies.