The why, what, where, when and how of goal-directed choice: neuronal and computational principles

Learning and memory processes in behavioural addiction: A systematic review

Similar to addictive substances, addictive behaviours such as gambling and gaming are associated with maladaptive modulation of key brain areas and functional networks implicated in learning and memory. Therefore, this review sought to understand how different learning and memory processes relate to behavioural addictions and to unravel their underlying neural mechanisms. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched four databases - PsycINFO, PubMed, Scopus, and Web of Science using the agreed-upon search string. Findings suggest altered executive function-dependent learning processes and enhanced habit learning in behavioural addiction. Whereas the relationship between working memory and behavioural addiction is influenced by addiction type, working memory aspect, and task nature. Additionally, long-term memory is incoherent in individuals with addictive behaviours. Consistently, neurophysiological evidence indicates alterations in brain areas and networks implicated in learning and memory processes in behavioural addictions. Overall, the present review argues that, like substance use disorders, alteration in learning and memory processes may underlie the development and maintenance of behavioural addictions.

Socially adaptive cognitive architecture for human-robot collaboration in industrial settings

This paper introduces DAC-HRC, a novel cognitive architecture designed to optimize human-robot collaboration (HRC) in industrial settings, particularly within the context of Industry 4.0. The architecture is grounded in the Distributed Adaptive Control theory and the principles of joint intentionality and interdependence, which are key to effective HRC. Joint intentionality refers to the shared goals and mutual understanding between a human and a robot, while interdependence emphasizes the reliance on each other's capabilities to complete tasks. DAC-HRC is applied to a hybrid recycling plant for the disassembly and recycling of Waste Electrical and Electronic Equipment (WEEE) devices. The architecture incorporates several cognitive modules operating at different timescales and abstraction levels, fostering adaptive collaboration that is personalized to each human user. The effectiveness of DAC-HRC is demonstrated through several pilot studies, showcasing functionalities such as turn-taking interaction, personalized error-handling mechanisms, adaptive safety measures, and gesture-based communication. These features enhance human-robot collaboration in the recycling plant by promoting real-time robot adaptation to human needs and preferences. The DAC-HRC architecture aims to contribute to the development of a new HRC paradigm by paving the way for more seamless and efficient collaboration in Industry 4.0 by relying on socially adept cognitive architectures.

Dopamine receptors of the rodent fastigial nucleus support skilled reaching for goal-directed action

The dopaminergic (DA) system regulates both motor function, and learning and memory. The cerebellum supports motor control and the acquisition of procedural memories, including goal-directed behavior, and is subjected to DA control. Its fastigial nucleus (FN) controls and interprets body motion through space. The expression of dopamine receptors has been reported in the deep cerebellar nuclei of mice. However, the presence of dopamine D1-like (D1R) and D2-like (D2R) receptors in the rat FN has not yet been verified. In this study, we first confirmed that DA receptors are expressed in the FN of adult rats and then targeted these receptors to explore to what extent the FN modulates goal-directed behavior. Immunohistochemical assessment revealed expression of both D1R and D2R receptors in the FN, whereby the medial lateral FN exhibited higher receptor expression compared to the other FN subfields. Bilateral treatment of the FN with a D1R antagonist, prior to a goal-directed pellet-reaching task, significantly impaired task acquisition and decreased task engagement. D2R antagonism only reduced late performance post-acquisition. Once task acquisition had occurred, D1R antagonism had no effect on successful reaching, although it significantly decreased reaching speed, task engagement, and promoted errors. Motor coordination and ambulation were, however, unaffected as neither D1R nor D2R antagonism altered rotarod latencies or distance and velocity in an open field. Taken together, these results not only reveal a novel role for the FN in goal-directed skilled reaching, but also show that D1R expressed in FN regulate this process by modulating motivation for action.

Simulated Dopamine Modulation of a Neurorobotic Model of the Basal Ganglia

The vertebrate basal ganglia play an important role in action selection-the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of the neurotransmitter dopamine. We investigated reducing or increasing the tonic level of simulated dopamine in a prior model of the basal ganglia integrated into a robot control architecture engaged in a foraging task inspired by animal behaviour. The main findings were that progressive reductions in the levels of simulated dopamine caused slowed behaviour and, at low levels, an inability to initiate movement. These states were partially relieved by increased salience levels (stronger sensory/motivational input). Conversely, increased simulated dopamine caused distortion of the robot's motor acts through partially expressed motor activity relating to losing actions. This could also lead to an increased frequency of behaviour switching. Levels of simulated dopamine that were either significantly lower or higher than baseline could cause a loss of behavioural integration, sometimes leaving the robot in a 'behavioral trap'. That some analogous traits are observed in animals and humans affected by dopamine dysregulation suggests that robotic models could prove useful in understanding the role of dopamine neurotransmission in basal ganglia function and dysfunction.

Grid cells: the missing link in understanding Parkinson's disease?

The mechanisms underlying Parkinson's disease (PD) are complex and not fully understood, and the box-and-arrow model among other current models present significant challenges. This paper explores the potential role of the allocentric brain and especially its grid cells in several PD motor symptoms, including bradykinesia, kinesia paradoxa, freezing of gait, the bottleneck phenomenon, and their dependency on cueing. It is argued that central hubs, like the locus coeruleus and the pedunculopontine nucleus, often narrowly interpreted in the context of PD, play an equally important role in governing the allocentric brain as the basal ganglia. Consequently, the motor and secondary motor (e.g., spatially related) symptoms of PD linked with dopamine depletion may be more closely tied to erroneous computation by grid cells than to the basal ganglia alone. Because grid cells and their associated central hubs introduce both spatial and temporal information to the brain influencing velocity perception they may cause bradykinesia or hyperkinesia as well. In summary, PD motor symptoms may primarily be an allocentric disturbance resulting from virtual faulty computation by grid cells revealed by dopamine depletion in PD.

How deep is the brain? The shallow brain hypothesis

Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today's dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning and predictive coding networks. We argue that the shallow brain architecture provides several critical benefits over deep hierarchical structures and a more complete depiction of how mammalian brains achieve fast and flexible computational capabilities.

The Motivational Determinants of Human Action, Their Neural Bases and Functional Impact in Adolescents With Obsessive-Compulsive Disorder

Background

Establishing the motivational influences on human action is essential for understanding choice and decision making in health and disease. Here we used tests of value-based decision making, manipulating both predicted and experienced reward values to assess the motivational control of goal-directed action in healthy adolescents and those with obsessive-compulsive disorder (OCD).

Methods

After instrumental training on a two action-two outcome probabilistic task, adolescents (n = 21) underwent Pavlovian conditioning using distinct stimuli predicting either the instrumental outcomes, a third outcome, or nothing. We then assessed functional magnetic resonance imaging during choice tests in which we varied the predicted value, using specific and general Pavlovian-instrumental transfer, and the experienced value, using outcome devaluation. To establish functional significance, we tested a matched cohort of adolescents with OCD (n = 20).

Results

In healthy adolescents, both predicted and experienced values influenced the performance of goal-directed actions, mediated by distinct orbitofrontal-striatal circuits involving the lateral orbitofrontal cortex (OFC) and medial OFC, respectively. However, in adolescents with OCD, choice was insensitive to changes in either predicted or experienced values. These impairments were related to hypoactivity in the lateral OFC and hyperactivity in the medial OFC during specific Pavlovian-instrumental transfer and hypoactivity in the anterior prefrontal cortex, caudate nucleus, and their connectivity in the devaluation test.

Conclusions

We found that predicted and experienced values exerted a potent influence on the performance of goal-directed actions in adolescents via distinct orbitofrontal- and prefrontal-striatal circuits. Furthermore, the influence of these motivational processes was severely blunted in OCD, as was the functional segregation of circuits involving medial and lateral OFC, producing dysregulated action control.

Sensorimotor Integration Supporting Perception Requires Syngap1 Expression in Cortex

Perception, a cognitive construct, emerges through sensorimotor integration (SMI). The molecular and cellular mechanisms that shape SMI within circuits that promote cognition are poorly understood. Here, we demonstrate that expression of the autism/intellectual disability gene, Syngap1, in mouse cortical excitatory neurons promotes touch sensitivity required to elicit perceptual behaviors. Cortical Syngap1 expression enabled touch-induced feedback signals within sensorimotor loops by assembling circuits that support tactile sensitivity. These circuits also encoded correlates of attention that promoted self-generated whisker movements underlying purposeful and sustained object exploration. As Syngap1 deficient animals explored objects with whiskers, relatively weak touch signals were integrated with relatively strong motor signals. This produced a signal-to-noise deficit consistent with impaired tactile sensitivity, reduced tactile exploration, and weak tactile learning. Thus, Syngap1 expression in cortex promotes tactile perception by assembling circuits that integrate touch and whisker motor signals. Deficient Syngap1 expression likely contributes to cognitive impairment through abnormal top-down SMI.

Control systems theory revisited: new insights on the brain clocks of time-to-action

To outline the complex biological rhythms underlying the time-to-action of goal-oriented behavior in the adult brain, we employed a Boolean Algebra model based on Control Systems Theory. This suggested that "timers" of the brain reflect a metabolic excitation-inhibition balance and that healthy clocks underlying goal-oriented behavior (optimal range of signal variability) are maintained by XOR logic gates in parallel sequences between cerebral levels. Using truth tables, we found that XOR logic gates reflect healthy, regulated time-to-action events between levels. We argue that the brain clocks of time-to-action are active within multileveled, parallel-sequence complexes shaped by experience. We show the metabolic components of time-to-action in levels ranging from the atom level through molecular, cellular, network and inter-regional levels, operating as parallel sequences. We employ a thermodynamic perspective, suggest that clock genes calculate free energy versus entropy and derived time-to-action level-wise as a master controller, and show that they are receivers, as well as transmitters of information. We argue that regulated multileveled time-to-action processes correspond to Boltzmann's thermodynamic theorem of micro- and macro-states, and that the available metabolic free-energy-entropy matrix determines the brain's reversible states for its age-appropriate chrono-properties at given moments. Thus, healthy timescales are not a precise number of nano- or milliseconds of activity nor a simple phenotypic distinction between slow vs. quick time-to-action, but rather encompass a range of variability, which depends on the molecules' size and dynamics with the composition of receptors, protein and RNA isoforms.

Understanding brain functional architecture through robotics

Robotics is increasingly seen as a useful test bed for computational models of the brain functional architecture underlying animal behavior. We provide an overview of past and current work, focusing on probabilistic and dynamical models, including approaches premised on the free energy principle, situating this endeavor in relation to evidence that the brain constitutes a layered control system. We argue that future neurorobotic models should integrate multiple neurobiological constraints and be hybrid in nature.

The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines

In this perspective article, we show that a morphospace, based on information-theoretic measures, can be a useful construct for comparing biological agents with artificial intelligence (AI) systems. The axes of this space label three kinds of complexity: (i) autonomic, (ii) computational and (iii) social complexity. On this space, we map biological agents such as bacteria, bees, C. elegans, primates and humans; as well as AI technologies such as deep neural networks, multi-agent bots, social robots, Siri and Watson. A complexity-based conceptualization provides a useful framework for identifying defining features and classes of conscious and intelligent systems. Starting with cognitive and clinical metrics of consciousness that assess awareness and wakefulness, we ask how AI and synthetically engineered life-forms would measure on homologous metrics. We argue that awareness and wakefulness stem from computational and autonomic complexity. Furthermore, tapping insights from cognitive robotics, we examine the functional role of consciousness in the context of evolutionary games. This points to a third kind of complexity for describing consciousness, namely, social complexity. Based on these metrics, our morphospace suggests the possibility of additional types of consciousness other than biological; namely, synthetic, group-based and simulated. This space provides a common conceptual framework for comparing traits and highlighting design principles of minds and machines.

Theory of brain complexity and marital behaviors: The application of complexity science and neuroscience to explain the complexities of marital behaviors

The extant theories on the quality and stability of marital relationships have some difficulties in explaining some of the complexities of marital behaviors. The present article is an initial attempt to explain the complexities of marital behaviors based on the science of complexity and neuroscience. This article proposes a new theoretical framework relying on this simple argument that marital behaviors, as one of the most complex human behaviors, are the product of one's brain's complex adaptive system (CAS). Hence, to understand the complexities of marital behaviors, a movement toward familiarity with the brain's CAS involved in marital behaviors needs to be started. The article presents the theory of brain complexity and marital behaviors (BCM) and outlines its assumptions, concepts, and propositions. Then, BCM is compared with the extant theories on happy and stable marriage, and finally, it was concluded by discussing the testability and the potential application of the theory. This article might inspire interdisciplinary studies of marital relationships, complex systems, and neuroscience and may have considerable practical implications.

A Liaison Brought to Light: Cerebellum-Hippocampus, Partners for Spatial Cognition

This review focuses on the functional and anatomical links between the cerebellum and the hippocampus and the role of their interplay in goal-directed navigation and spatial cognition. We will describe the interactions between the cerebellum and the hippocampus at different scales: a macroscopic scale revealing the joint activations of these two structures at the level of neuronal circuits, a mesoscopic scale highlighting the synchronization of neuronal oscillations, and finally a cellular scale where we will describe the activity of hippocampal neuronal assemblies following a targeted manipulation of the cerebellar system. We will take advantage of this framework to summarize the different anatomical pathways that may sustain this multiscale interaction. We will finally consider the possible influence of the cerebellum on pathologies traditionally associated with hippocampal dysfunction.

Readiness to remember: predicting variability in episodic memory

Learning and remembering are fundamental to our lives, so what causes us to forget? Answers often highlight preparatory processes that precede learning, as well as mnemonic processes during the act of encoding or retrieval. Importantly, evidence now indicates that preparatory processes that precede retrieval attempts also have powerful influences on memory success or failure. Here, we review recent work from neuroimaging, electroencephalography, pupillometry, and behavioral science to propose an integrative framework of retrieval-period dynamics that explains variance in remembering in the moment and across individuals as a function of interactions among preparatory attention, goal coding, and mnemonic processes. Extending this approach, we consider how a 'readiness to remember' (R2R) framework explains variance in high-level functions of memory and mnemonic disruptions in aging.

Anticipatory regulation of cardiovascular system on the emergence of auditory-motor interaction in young infants

Humans develop auditory-motor interaction to produce a variety of rhythmic sounds using body movements, which are often produced and amplified with tools, such as drumming. The extended production of sounds allows us to express a wide range of emotions, accompanied by physiological changes. According to previous studies, even young infants enhance movements in response to auditory feedback. However, their exhibition of physiological adaptation on the emergence of auditory-motor interaction is unclear. We investigated the movement and cardiac changes associated with auditory feedback to spontaneous limb movements in 3-month-old infants. The results showed that infants increased the frequency of limb movements inducing auditory feedback, while they exhibited a more regular rhythm of the limb movements. Furthermore, heart rate increase associated with the limb movement was first inhibited immediately after the timing of the auditory feedback, which may reflect sustained attention to the auditory stimuli. Then, through auditory-motor experience, the heart rate increase was inhibited even prior to the auditory feedback, leading to suppression of the peak intensity of the heart rate increase. These findings suggest that young infants regulate the cardiovascular system as well as limb movements in anticipation of the auditory feedback. The anticipatory regulation associated with movement and attentional changes may contribute to reduced cardiovascular stress in auditory-motor interaction, and provide a developmental basis for more sophisticated goal-directed behavior of producing rhythmic sounds.

Biomaterials via peptide assembly: Design, characterization, and application in tissue engineering

A core challenge in biomaterials, with both fundamental significance and technological relevance, concerns the rational design of bioactive microenvironments. Designed properly, peptides can undergo supramolecular assembly into dynamic, physical hydrogels that mimic the mechanical, topological, and biochemical features of native tissue microenvironments. The relatively facile, inexpensive, and automatable preparation of peptides, coupled with low batch-to-batch variability, motivates the expanded use of assembling peptide hydrogels for biomedical applications. Integral to realizing dynamic peptide assemblies as functional biomaterials for tissue engineering is an understanding of the molecular and macroscopic features that govern assembly, morphology, and biological interactions. In this review, we first discuss the design of assembling peptides, including primary structure (sequence), secondary structure (e.g., α-helix and β-sheets), and molecular interactions that facilitate assembly into multiscale materials with desired properties. Next, we describe characterization tools for elucidating molecular structure and interactions, morphology, bulk properties, and biological functionality. Understanding of these characterization methods enables researchers to access a variety of approaches in this ever-expanding field. Finally, we discuss the biological properties and applications of peptide-based biomaterials for engineering several important tissues. By connecting molecular features and mechanisms of assembling peptides to the material and biological properties, we aim to guide the design and characterization of peptide-based biomaterials for tissue engineering and regenerative medicine. STATEMENT OF SIGNIFICANCE: Engineering peptide-based biomaterials that mimic the topological and mechanical properties of natural extracellular matrices provide excellent opportunities to direct cell behavior for regenerative medicine and tissue engineering. Here we review the molecular-scale features of assembling peptides that result in biomaterials that exhibit a variety of relevant extracellular matrix-mimetic properties and promote beneficial cell-biomaterial interactions. Aiming to inspire and guide researchers approaching this challenge from both the peptide biomaterial design and tissue engineering perspectives, we also present characterization tools for understanding the connection between peptide structure and properties and highlight the use of peptide-based biomaterials in neural, orthopedic, cardiac, muscular, and immune engineering applications.

The evolution of brain architectures for predictive coding and active inference

This article considers the evolution of brain architectures for predictive processing. We argue that brain mechanisms for predictive perception and action are not late evolutionary additions of advanced creatures like us. Rather, they emerged gradually from simpler predictive loops (e.g. autonomic and motor reflexes) that were a legacy from our earlier evolutionary ancestors-and were key to solving their fundamental problems of adaptive regulation. We characterize simpler-to-more-complex brains formally, in terms of generative models that include predictive loops of increasing hierarchical breadth and depth. These may start from a simple homeostatic motif and be elaborated during evolution in four main ways: these include the multimodal expansion of predictive control into an allostatic loop; its duplication to form multiple sensorimotor loops that expand an animal's behavioural repertoire; and the gradual endowment of generative models with hierarchical depth (to deal with aspects of the world that unfold at different spatial scales) and temporal depth (to select plans in a future-oriented manner). In turn, these elaborations underwrite the solution to biological regulation problems faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising-about predictive processing-with evolutionary and comparative data on brain architectures in different animal species. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Whisker trimming during infanthood modifies the development of spike-wave discharges and behavioral sequences in IntelliCage impulsivity paradigm in adult WAG/Rij rats

Whisker system in rats undergoes rapid development during the first postnatal weeks. Neonatal whisker trimming increases excitability in the somatosensory cortex and affects exploratory behavior at adult ages. WAG/Rij rats are genetically predisposed to develop absence seizures in adulthood, and whisker trimming during three postnatal weeks aggravates epileptic activity in these rats. It is assumed that behavioral performance in adult WAG/Rij rats is influenced (1) by absence epilepsy and (2) by whisker trimming during the short period around the onset of active whisker movements, PN9-16. We examined the effect of whisker trimming in WAG/Rij rats during PN9-16 on spike-wave discharges (SWD, EEG hallmark of absence epilepsy). We found that 77% of WAG/Rij rats showed pronounced SWD (epileptic phenotype), and the rest did not (non-epileptic phenotype). At the age of 5 m, epileptic trimmed rats showed more SWD than epileptic control rats. Age-related increase of SWD was found only in the control group, suggesting that whisker trimming during PN9-16 led to an earlier maturation of SWD. Goal-directed behavior was examined in all rats at the age of 4-4.5 m using IntelliCage impulsivity paradigm. In order to optimize the analysis of behavioral data, we combined several Python packages into a single processing pipeline. Early life whisker trimming altered behavioral sequences and strategy of exploration in adulthood, suggesting reduced whisker sensitivity in the trimmed rats. Epileptic WAG/Rij rats at 4-4.5 months showed only a slight learning impairment during later stages of IntelliCage impulsivity paradigm, which may be associated with the early stage of development of SWD.

Permutation Entropy as a Universal Disorder Criterion: How Disorders at Different Scale Levels Are Manifestations of the Same Underlying Principle

What do bacteria, cells, organs, people, and social communities have in common? At first sight, perhaps not much. They involve totally different agents and scale levels of observation. On second thought, however, perhaps they share everything. A growing body of literature suggests that living systems at different scale levels of observation follow the same architectural principles and process information in similar ways. Moreover, such systems appear to respond in similar ways to rising levels of stress, especially when stress levels approach near-lethal levels. To explain such communalities, we argue that all organisms (including humans) can be modeled as hierarchical Bayesian controls systems that are governed by the same biophysical principles. Such systems show generic changes when taxed beyond their ability to correct for environmental disturbances. Without exception, stressed organisms show rising levels of 'disorder' (randomness, unpredictability) in internal message passing and overt behavior. We argue that such changes can be explained by a collapse of allostatic (high-level integrative) control, which normally synchronizes activity of the various components of a living system to produce order. The selective overload and cascading failure of highly connected (hub) nodes flattens hierarchical control, producing maladaptive behavior. Thus, we present a theory according to which organic concepts such as stress, a loss of control, disorder, disease, and death can be operationalized in biophysical terms that apply to all scale levels of organization. Given the presumed universality of this mechanism, 'losing control' appears to involve the same process anywhere, whether involving bacteria succumbing to an antibiotic agent, people suffering from physical or mental disorders, or social systems slipping into warfare. On a practical note, measures of disorder may serve as early warning signs of system failure even when catastrophic failure is still some distance away.

How Pain Shapes Depression and Anxiety: A Hybrid Self-regulatory/Predictive Mind Perspective

Because many persons living with chronic pain achieve a relatively balanced lifestyle without experiencing functional disability, medical psychologists must explain the well-documented co-occurrence of pain complaints and DSM-5-disorders (including but not limited to depression and anxiety) in a significant subset of individuals. The question of differential resilience versus susceptibility has received modest theoretical and empirical attention, but remains open. In this review, I deconstruct the temporally extended pain adaptation process in order to address this vexing question, relying upon two complementary explanatory frames. The first is a motivational/cybernetic systems formulation labeled the Goal-Centered, Self-Regulatory, Automated Social Systems Psychology (GRASSP) model, erected upon feedback sensitive, goal-guided, hierarchically organized self-regulatory processes. Depression and anxiety presumably result from compromised regulatory functions undermining pain processing, goal pursuit, and everyday performance. The second perspective postulates a "Bayesian Brain"/"Predictive Mind" capable of unifying perception, action, and emotion via predictive processing. From a Bayesian perspective, predictive processing implies that our brains evolved to compare, without conscious direction, incoming environmental information against prior, model-based predictions in order to arrive at accurate perceptual representations of the world. Maladjustment results from failures of active inference. When applied to the perception of visceral information, the embodied process, termed interoceptive inference, can also yield pathogenic outcomes. The Bayesian model holds that depression and anxiety in individuals with pain result from error-prone (biased, rigid, or highly certain) prior evaluations of aversive feeling states and their relation to the external milieu. I consider how the hybrid conceptual framework advanced by the two models points to several novel and familiar avenues of intervention.

Enhanced Hippocampus-Nidopallium Caudolaterale Connectivity during Route Formation in Goal-Directed Spatial Learning of Pigeons

Goal-directed spatial learning is crucial for the survival of animals, in which the formation of the route from the current location to the goal is one of the central problems. A distributed brain network comprising the hippocampus and prefrontal cortex has been shown to support such capacity, yet it is not fully understood how the most similar brain regions in birds, the hippocampus (Hp) and nidopallium caudolaterale (NCL), cooperate during route formation in goal-directed spatial learning. Hence, we examined neural activity in the Hp-NCL network of pigeons and explored the connectivity dynamics during route formation in a goal-directed spatial task. We found that behavioral changes in spatial learning during route formation are accompanied by modifications in neural patterns in the Hp-NCL network. Specifically, as pigeons learned to solve the task, the spectral power in both regions gradually decreased. Meanwhile, elevated hippocampal theta (5 to 12 Hz) connectivity and depressed connectivity in NCL were also observed. Lastly, the interregional functional connectivity was found to increase with learning, specifically in the theta frequency band during route formation. These results provide insight into the dynamics of the Hp-NCL network during spatial learning, serving to reveal the potential mechanism of avian spatial navigation.

Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus

Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal-hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems.

Can Attention and Working Memory Impairments of Intimate Partner Perpetrators Explain Their Risky Decision Making?

Intimate partner violence (IPV) perpetrators commonly exhibit deficits in a wide range of cognitive domains, such as attention, memory, and executive functions. Executive dysfunctions tend to be related to a preference for disadvantageous decisions, which could be explained by a pattern of focusing on positive outcomes (gains) while disregarding negative ones. Nonetheless, it is less clear whether risk-taking and decision-making problems should be attributed to motivational and/or emotional causes or to cognitive deficits in attention and/or working memory. The main goal of the present study was to examine whether IPV perpetrators can be distinguished from non-violent controls based on their performance on attention, working memory, and decision-making tests from a computerized battery of tests. In addition, this study investigated the potential relationship between attention and working memory impairments in the decision-making abilities of IPV perpetrators. Our data indicated that IPV perpetrators perform worse than controls on measures of attention and sustained attention, processing speed, working memory, spatial span, and decision making. Moreover, IPV perpetrators' preference for disadvantageous and risky decisions could be partially explained by attention and working memory impairments. Our study has enabled us to explore the cognitive deficits underlying IPV perpetration as key factors in reducing IPV risky decisions. Moreover, it reinforces the need to develop specific neuropsychological training in the attention-switching ability in general and working memory, which could lead to improvements in decision-making processes or other executive functions.

Decoding route selection of pigeon during goal-directed behavior: A joint spike-LFP study

How to reach the goal is one of the core problems that animals must solve to complete goal-directed behavior. Studies have proved the important role of hippocampus (Hp) in spatial navigation and shown that hippocampal neural activities can represent the current location and goal location. However, for the different routes linking these two locations, the neural representation mechanism of the route selection in Hp is not clear. Here, we addressed this question using neural recordings of Hp ensembles and decoding analyses in pigeons performing a goal-directed route selection task known to require Hp participation. The hippocampal spike trains and local field potentials (LFPs) of five pigeons performing the task were acquired and analyzed. We found that the neuron firing rates and power spectrum characteristics in Hp could encode the animal's route selection during goal-directed behavior, suggesting that the representation of route selection was coherent for hippocampal spike and LFP signals. Decoding results further indicated that joint spike-LFP features resulted in a significant improvement in the representation accuracy of the route selection. These findings of this study will help to understand the encoding mechanism of route selection in goal-directed behavior.

A global framework for a systemic view of brain modeling

The brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. A systemic description of the brain is presented here, as a contribution to developing a brain theory and as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the frontal cortex. This framework highlights that responses can be prepared in different ways, from pavlovian reflexes and habitual behavior to deliberations for goal-directed planning and reasoning, and explains that these different kinds of responses coexist, collaborate and compete for the control of behavior. It also lays emphasis on the fact that cognition can be described as a dynamical system of interacting memories, some acting to provide information to others, to replace them when they are not efficient enough, or to help for their improvement. Describing the brain as an architecture of learning systems has also strong implications in Machine Learning. Our biologically informed view of pavlovian and instrumental conditioning can be very precious to revisit classical Reinforcement Learning and provide a basis to ensure really autonomous learning.

Renewal of goal direction with a context change after habit learning

An instrumental action can be goal-directed after a moderate amount of practice and then convert to habit after more extensive practice. Recent evidence suggests, however, that habits can return to action status after different environmental manipulations. The present experiments therefore asked whether habit learning interferes with goal direction in a context-dependent manner like other types of retroactive interference (e.g., extinction, punishment, counterconditioning). In Experiment 1, rats were given a moderate amount of instrumental training to form an action in one context (Context A) and then more extended training of the same response to form a habit in another context (Context B). We then performed reinforcer devaluation with taste aversion conditioning in both contexts, and tested the response in both contexts. The response remained habitual in Context B, but was goal-directed in Context A, indicating renewal of goal direction after habit learning. Experiment 2 expanded on Experiment 1 by testing the response in a third context (Context C). It found that the habitual response also renewed as action in this context. Together, the results establish a parallel between habit and extinction learning: Conversion to habit does not destroy action knowledge, but interferes with it in a context-specific way. They are also consistent with other results suggesting that habit is specific to the context in which it is learned, whereas goal-direction can transfer between contexts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Translation-Focused Approaches to GPCR Drug Discovery for Cognitive Impairments Associated with Schizophrenia

There are no effective therapeutics for cognitive impairments associated with schizophrenia (CIAS), which includes deficits in executive functions (working memory and cognitive flexibility) and episodic memory. Compounds that have entered clinical trials are inadequate in terms of efficacy and/or tolerability, highlighting a clear translational bottleneck and a need for a cohesive preclinical drug development strategy. In this review we propose hippocampal-prefrontal-cortical (HPC-PFC) circuitry underlying CIAS-relevant cognitive processes across mammalian species as a target source to guide the translation-focused discovery and development of novel, procognitive agents. We highlight several G protein-coupled receptors (GPCRs) enriched within HPC-PFC circuitry as therapeutic targets of interest, including noncanonical approaches (biased agonism and allosteric modulation) to conventional clinical targets, such as dopamine and muscarinic acetylcholine receptors, along with prospective novel targets, including the orphan receptors GPR52 and GPR139. We also describe the translational limitations of popular preclinical cognition tests and suggest touchscreen-based assays that probe cognitive functions reliant on HPC-PFC circuitry and reflect tests used in the clinic, as tests of greater translational relevance. Combining pharmacological and behavioral testing strategies based in HPC-PFC circuit function creates a cohesive, translation-focused approach to preclinical drug development that may improve the translational bottleneck currently hindering the development of treatments for CIAS.

Learning to select actions shapes recurrent dynamics in the corticostriatal system

Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are strongly interconnected, are key nodes in this circuitry. Substantial experimental evidence, including neurophysiological recordings, have shown that neurons in these structures represent key aspects of learning. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. We compared the activity generated by the model to activity recorded from monkey dlPFC and dSTR in the same task. This network consisted of a striatal component which encoded action values, and a prefrontal component which selected appropriate actions. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. We found that learning to select the correct actions drove action-sequence representations further apart in activity space, both in the model and in the neural data. The model revealed that learning proceeds by increasing the distance between sequence-specific representations. This makes it more likely that the model will select the appropriate action sequence as learning develops. Our model thus supports the hypothesis that learning in networks drives the neural representations of actions further apart, increasing the probability that the network generates correct actions as learning proceeds. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, revealing how dynamics in the corticostriatal system support task learning.

Age-related decline in executive function as a hallmark of cognitive ageing in primates: an overview of cognitive and neurobiological studies

Executive function (EF) is a complex construct that reflects multiple higher-order cognitive processes such as planning, updating, inhibiting and set-shifting. Decline in these functions is a hallmark of cognitive ageing in humans, and age differences and changes in EF correlate with age-related differences and changes in association cortices, particularly the prefrontal areas. Here, we review evidence for age-related decline in EF and associated neurobiological changes in prosimians, New World and Old World monkeys, apes and humans. While EF declines with age in all primate species studied, the relationship of this decline with age-related alterations in the prefrontal cortex remains unclear, owing to the scarcity of neurobiological studies focusing on the ageing brain in most primate species. In addition, the influence of sex, vascular and metabolic risk, and hormonal status has rarely been considered. We outline several methodological limitations and challenges with the goal of producing a comprehensive integration of cognitive and neurobiological data across species and elucidating how ageing shapes neurocognitive trajectories in primates with different life histories, lifespans and brain architectures. Such comparative investigations are critical for fostering translational research and understanding healthy and pathological ageing in our own species. This article is part of the theme issue 'Evolution of the primate ageing process'.

The Role of Hp-NCL Network in Goal-Directed Routing Information Encoding of Bird: A Review

Goal-directed navigation is a crucial behavior for the survival of animals, especially for the birds having extraordinary spatial navigation ability. In the studies of the neural mechanism of the goal-directed behavior, especially involving the information encoding mechanism of the route, the hippocampus (Hp) and nidopallium caudalle (NCL) of the avian brain are the famous regions that play important roles. Therefore, they have been widely concerned and a series of studies surrounding them have increased our understandings of the navigation mechanism of birds in recent years. In this paper, we focus on the studies of the information encoding mechanism of the route in the avian goal-directed behavior. We first summarize and introduce the related studies on the role of the Hp and NCL for goal-directed behavior comprehensively. Furthermore, we review the related cooperative interaction studies about the Hp-NCL local network and other relevant brain regions supporting the goal-directed routing information encoding. Finally, we summarize the current situation and prospect the existing important questions in this field. We hope this paper can spark fresh thinking for the following research on routing information encoding mechanism of birds.

Modeling the formation of social conventions from embodied real-time interactions

What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into account the role of sensorimotor control loops in embodied decision-making scenarios. For this purpose, we introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded in the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control is modulated through perceptual and behavioral learning in a layered structure. CRL follows these principles by implementing a feedback control loop handling the agent’s reactive behaviors (pre-wired reflexes), along with an Adaptive Layer that uses reinforcement learning to maximize long-term reward. We test our model in a multi-agent game-theoretic task in which coordination must be achieved to find an optimal solution. We show that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution.

Learning, memory and consolidation mechanisms for behavioral control in hierarchically organized cortico-basal ganglia systems

This article aims to provide a synthesis on the question how brain structures cooperate to accomplish hierarchically organized behaviors, characterized by low‐level, habitual routines nested in larger sequences of planned, goal‐directed behavior. The functioning of a connected set of brain structures—prefrontal cortex, hippocampus, striatum, and dopaminergic mesencephalon—is reviewed in relation to two important distinctions: (a) goal‐directed as opposed to habitual behavior and (b) model‐based and model‐free learning. Recent evidence indicates that the orbitomedial prefrontal cortices not only subserve goal‐directed behavior and model‐based learning, but also code the “landscape” (task space) of behaviorally relevant variables. While the hippocampus stands out for its role in coding and memorizing world state representations, it is argued to function in model‐based learning but is not required for coding of action–outcome contingencies, illustrating that goal‐directed behavior is not congruent with model‐based learning. While the dorsolateral and dorsomedial striatum largely conform to the dichotomy between habitual versus goal‐directed behavior, ventral striatal functions go beyond this distinction. Next, we contextualize findings on coding of reward‐prediction errors by ventral tegmental dopamine neurons to suggest a broader role of mesencephalic dopamine cells, viz. in behavioral reactivity and signaling unexpected sensory changes. We hypothesize that goal‐directed behavior is hierarchically organized in interconnected cortico‐basal ganglia loops, where a limbic‐affective prefrontal‐ventral striatal loop controls action selection in a dorsomedial prefrontal–striatal loop, which in turn regulates activity in sensorimotor‐dorsolateral striatal circuits. This structure for behavioral organization requires alignment with mechanisms for memory formation and consolidation. We propose that frontal corticothalamic circuits form a high‐level loop for memory processing that initiates and temporally organizes nested activities in lower‐level loops, including the hippocampus and the ripple‐associated replay it generates. The evidence on hierarchically organized behavior converges with that on consolidation mechanisms in suggesting a frontal‐to‐caudal directionality in processing control.

Multifunctional multi-shank neural probe for investigating and modulating long-range neural circuits in vivo

Investigation and modulation of neural circuits in vivo at the cellular level are very important for studying functional connectivity in a brain. Recently, neural probes with stimulation capabilities have been introduced, and they provided an opportunity for studying neural activities at a specific region in the brain using various stimuli. However, previous methods have a limitation in dissecting long-range neural circuits due to inherent limitations on their designs. Moreover, the large size of the previously reported probes induces more significant tissue damage. Herein, we present a multifunctional multi-shank MEMS neural probe that is monolithically integrated with an optical waveguide for optical stimulation, microfluidic channels for drug delivery, and microelectrode arrays for recording neural signals from different regions at the cellular level. In this work, we successfully demonstrated the functionality of our probe by confirming and modulating the functional connectivity between the hippocampal CA3 and CA1 regions in vivo.

Microelectromechanical neural probes can cause tissue damage and often cannot record from distant brain areas. Here the authors combine electrical recording, optical stimulation and microfluidic drug delivery in one multi-shank probe with thinner shanks to reduce damage and a flexible design to target long-range neural circuits.

Hypothalamic Responses to Cocaine and Food Cues in Individuals with Cocaine Dependence

BACKGROUND

Individuals with cocaine addiction are characterized by under-responsiveness to natural reinforcers. As part of the dopaminergic pathways, the hypothalamus supports motivated behaviors. Rodent studies suggested inter-related roles of the hypothalamus in regulating drug and food intake. However, few studies have investigated hypothalamic responses to drugs and food or related cues in humans.

METHODS

We examined regional responses in 20 cocaine-dependent and 24 healthy control participants exposed to cocaine/food (cocaine dependent) and food (healthy control) vs neutral cues during functional magnetic resonance imaging. We examined the relationship between imaging findings and clinical variables and performed mediation analyses to examine the inter-relationships between cue-related activations, tonic cocaine craving, and recent cocaine use.

RESULTS

At a corrected threshold, cocaine-dependent participants demonstrated higher activation to cocaine than to food cues in the hypothalamus, inferior parietal cortex, and visual cortex. Cocaine-dependent participants as compared with healthy control participants also demonstrated higher hypothalamic activation to food cues. Further, the extent of these cue-induced hypothalamic activations was correlated with tonic craving, as assessed by the Cocaine Craving Questionnaire, and days of cocaine use in the prior month. In mediation analyses, hypothalamic activation to cocaine and food cues both completely mediated the relationship between the Cocaine Craving Questionnaire score and days of cocaine use in the past month.

CONCLUSIONS

The results were consistent with the proposition that the mechanisms of feeding and drug addiction are inter-linked in the hypothalamus and altered in cocaine addiction. The findings provide new evidence in support of hypothalamic dysfunction in cocaine addiction.

Differential neural dynamics underling pragmatic and semantic affordance processing in macaque ventral premotor cortex

Premotor neurons play a fundamental role in transforming physical properties of observed objects, such as size and shape, into motor plans for grasping them, hence contributing to “pragmatic” affordance processing. Premotor neurons can also contribute to “semantic” affordance processing, as they can discharge differently even to pragmatically identical objects depending on their behavioural relevance for the observer (i.e. edible or inedible objects). Here, we compared the response of monkey ventral premotor area F5 neurons tested during pragmatic (PT) or semantic (ST) visuomotor tasks. Object presentation responses in ST showed shorter latency and lower object selectivity than in PT. Furthermore, we found a difference between a transient representation of semantic affordances and a sustained representation of pragmatic affordances at both the single neuron and population level. Indeed, responses in ST returned to baseline within 0.5 s whereas in PT they showed the typical sustained visual-to-motor activity during Go trials. In contrast, during No-go trials, the time course of pragmatic and semantic information processing was similar. These findings suggest that premotor cortex generates different dynamics depending on pragmatic and semantic information provided by the context in which the to-be-grasped object is presented.

Memory and mental time travel in humans and social robots

From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future-mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques-Gaussian process latent variable models-to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human-robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.

A thalamocortical circuit for updating action-outcome associations

The ability to flexibly use knowledge is one cardinal feature of goal-directed behaviors. We recently showed that thalamocortical and corticothalamic pathways connecting the medial prefrontal cortex and the mediodorsal thalamus (MD) contribute to adaptive decision-making (Alcaraz et al., 2018). In this study, we examined the impact of disconnecting the MD from its other main cortical target, the orbitofrontal cortex (OFC) in a task assessing outcome devaluation after initial instrumental training and after reversal of action-outcome contingencies. Crossed MD and OFC lesions did not impair instrumental performance. Using the same approach, we found however that disconnecting the OFC from its other main thalamic afferent, the submedius nucleus, produced a specific impairment in adaptive responding following action-outcome reversal. Altogether, this suggests that multiple thalamocortical circuits may act synergistically to achieve behaviorally relevant functions.

Developmental perspectives on risky and impulsive choice

Epidemiological data suggest that risk taking in the real world increases from childhood into adolescence and declines into adulthood. However, developmental patterns of behaviour in laboratory assays of risk taking and impulsive choice are inconsistent. In this article, we review a growing literature using behavioural economic approaches to understand developmental changes in risk taking and impulsivity. We present findings that have begun to elucidate both the cognitive and neural processes that contribute to risky and impulsive choice, as well as how age-related changes in these neurocognitive processes give rise to shifts in choice behaviour. We highlight how variability in task parameters can be used to identify specific aspects of decision contexts that may differentially influence risky and impulsive choice behaviour across development. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'.

Treating Brain Disorders by Targeting Adult Neural Stem Cells

Adult neurogenesis, a developmental process of generating functionally integrated neurons from neural stem cells, occurs throughout life in the hippocampus of the mammalian brain and highlights the plastic nature of the mature central nervous system. Substantial evidence suggests that new neurons participate in cognitive and affective brain functions and aberrant adult neurogenesis contributes to various brain disorders. Focusing on adult hippocampal neurogenesis, we review recent findings that advance our understanding of the key properties and potential functions of adult neural stem cells. We further discuss the key evidence demonstrating the causal role of aberrant hippocampal neurogenesis and various brain disorders. Finally, we propose strategies aimed at simultaneously correcting stem cells and their niche for treating brain disorders.

The ventral hippocampus is involved in multi-goal obstacle-rich spatial navigation

A large body of evidence shows that the hippocampus is necessary for successful spatial navigation. Various studies have shown anatomical and functional differences between the dorsal (DHC) and ventral (VHC) portions of this structure. The DHC is primarily involved in spatial navigation and contains cells with small place fields. The VHC is primarily involved in context and emotional encoding contains cells with large place fields and receives major projections from the medial prefrontal cortex. In the past, spatial navigation experiments have used relatively simple tasks that may not have required a strong coordination along the dorsoventral hippocampal axis. In this study, we tested the hypothesis that the DHC and VHC may be critical for goal-directed navigation in obstacle-rich environments. We used a learning task in which animals memorize the location of a set of rewarded feeders, and recall these locations in the presence of small or large obstacles. We report that bilateral DHC or VHC inactivation impaired spatial navigation in both large and small obstacle conditions. Importantly, this impairment did not result from a deficit in the spatial memory for the set of feeders (i.e., recognition of the goal locations) because DHC or VHC inactivation did not affect recall performance when there was no obstacle on the maze. We also show that the behavioral performance of the animals was correlated with several measures of maze complexity and that these correlations were significantly affected by inactivation only in the large object condition. These results suggest that as the complexity of the environment increases, both DHC and VHC are required for spatial navigation.

Toward mechanistic models of action-oriented and detached cognition

To be successful, the research agenda for a novel control view of cognition should foresee more detailed, computationally specified process models of cognitive operations including higher cognition. These models should cover all domains of cognition, including those cognitive abilities that can be characterized as online interactive loops and detached forms of cognition that depend on internally generated neuronal processing.

Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis

While the neurobiology of simple and habitual choices is relatively well known, our current understanding of goal-directed choices and planning in the brain is still limited. Theoretical work suggests that goal-directed computations can be productively associated to model-based (reinforcement learning) computations, yet a detailed mapping between computational processes and neuronal circuits remains to be fully established. Here we report a computational analysis that aligns Bayesian nonparametrics and model-based reinforcement learning (MB-RL) to the functioning of the hippocampus (HC) and the ventral striatum (vStr)-a neuronal circuit that increasingly recognized to be an appropriate model system to understand goal-directed (spatial) decisions and planning mechanisms in the brain. We test the MB-RL agent in a contextual conditioning task that depends on intact hippocampus and ventral striatal (shell) function and show that it solves the task while showing key behavioral and neuronal signatures of the HC-vStr circuit. Our simulations also explore the benefits of biological forms of look-ahead prediction (forward sweeps) during both learning and control. This article thus contributes to fill the gap between our current understanding of computational algorithms and biological realizations of (model-based) reinforcement learning.

Hierarchical Active Inference: A Theory of Motivated Control

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.

Conjunctive rehabilitation of multiple cognitive domains for chronic stroke patients in virtual reality

Classically, cognitive deficits have been studied and treated in isolation from each other. A stroke patient is classified as being memory impaired, having executive dysfunction or showing attentional deficits after which a dedicated rehabilitation therapy is given. Studies seldom looked at the relationship between these different cognitive domains and syndromes, although, there is evidence that they might share common neuronal substrates and do not occur in isolation. Here, we propose a novel rehabilitation method in virtual reality to treat cognitive deficits in conjunction and report the preliminary results of an ongoing randomized controlled clinical trial. The current results suggest that in a homogeneous patient group the cognitive deficits are correlated and that the individual impairment level can be optimally addressed through an adaptive training paradigm.

Resting State Functional Connectivity of the Lateral and Medial Hypothalamus in Cocaine Dependence: An Exploratory Study

The role of dopamine in cocaine misuse has been extensively documented for the mesocorticolimbic circuit. Preclinical work from earlier lesion studies to recent multidisciplinary investigations has suggested that the hypothalamus is critically involved in motivated behavior, with the lateral and medial hypothalamus each involved in waking/feeding and resting/satiety. However, little is known of hypothalamus function and dysfunction in cocaine misuse. Here, we examined resting state functional connectivity of the lateral and medial hypothalamus in 70 individuals with cocaine dependence (CD) and 70 age as well as gender matched healthy controls (HC). Image pre-processing and analyses followed published work. Compared to HC, CD showed increased lateral hypothalamic connectivity with dorsolateral prefrontal cortex and decreased functional connectivity with the ventral precuneus. CD showed increased medial hypothalamic connectivity with the inferior parietal lobule and decreased connectivity with the ventromedial prefrontal cortex, temporal gyrus, fusiform gyrus, and ventral striatum. Further, at trend level significance, the connectivity strength between lateral hypothalamus and dorsolateral prefrontal cortex was positively correlated with total amount of cocaine use in the past month (p = 0.004, r = 0.35) and the connectivity strength between medial hypothalamus and ventral striatum was negatively correlated with cocaine craving as assessed by the Tiffany Cocaine Craving Questionnaire (p = 0.008, r = -0.33). Together, the findings demonstrated altered resting state functional connectivity of the hypothalamus and may provide new insight on circuit level deficits in cocaine dependence.