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.

Patient-specific modeling for guided rehabilitation of stroke patients: the BrainX3 use-case.

BrainX3 is an interactive neuroinformatics platform that has been thoughtfully designed to support neuroscientists and clinicians with the visualization, analysis, and simulation of human neuroimaging, electrophysiological data, and brain models. The platform is intended to facilitate research and clinical use cases, with a focus on personalized medicine diagnostics, prognostics, and intervention decisions. BrainX3 is designed to provide an intuitive user experience and is equipped to handle different data types and 3D visualizations. To enhance patient-based analysis, and in keeping with the principles of personalized medicine, we propose a framework that can assist clinicians in identifying lesions and making patient-specific intervention decisions. To this end, we are developing an AI-based model for lesion identification, along with a mapping of tract information. By leveraging the patient's lesion information, we can gain valuable insights into the structural damage caused by the lesion. Furthermore, constraining whole-brain models with patient-specific disconnection masks can allow for the detection of mesoscale excitatory-inhibitory imbalances that cause disruptions in macroscale network properties. Finally, such information has the potential to guide neuromodulation approaches, assisting in the choice of candidate targets for stimulation techniques such as Transcranial Ultrasound Stimulation (TUS), which modulate E-I balance, potentiating cortical reorganization and the restoration of the dynamics and functionality disrupted due to the lesion.

Redefining stroke rehabilitation: Mobilizing the embodied goal-oriented brain.

Advancements in stroke rehabilitation remain limited and call for a reorientation. Based on recent results, this study proposes a network-centric perspective on stroke, positing that it not only causes localized deficits but also affects the brain's intricate network of networks, transiting it into a pathological state. Translating these system-level insights into interventions requires brain theory, and the Distributed Adaptive Control (DAC) theory offers such a framework. When applied in the rehabilitation gaming system, these principles demonstrate superior results over conventional methods. This impact stems from activating extensive brain networks, particularly the executive control network, focused motor learning, and maintaining excitatory-inhibitory balance, which is essential for neural repair and functional reorganization. The analysis stresses uniting preclinical and clinical research and placing the architecture of the embodied volitional brain at the centre of rehabilitation approaches.

Human Brain Project Partnering Projects Meeting: Status Quo and Outlook.

As the European Flagship Human Brain Project (HBP) ends in September 2023, a meeting dedicated to the Partnering Projects (PPs), a collective of independent research groups that partnered with the HBP, was held on September 4-7, 2022. The purpose of this meeting was to allow these groups to present their results, reflect on their collaboration with the HBP and discuss future interactions with the European Research Infrastructure (RI) EBRAINS that has emerged from the HBP. In this report, we share the tour-de-force that the Partnering Projects that were present in the meeting have made in furthering knowledge concerning various aspects of Brain Research with the HBP. We describe briefly major achievements of the HBP Partnering Projects in terms of a systems-level understanding of the functional architecture of the brain and its possible emulation in artificial systems. We then recapitulate open discussions with EBRAINS representatives about the evolution of EBRAINS as a sustainable Research Infrastructure for the Partnering Projects after the HBP, and also for the wider scientific community.

Multiscale effects of excitatory-inhibitory homeostasis in lesioned cortical networks: A computational study.

Stroke-related disruptions in functional connectivity (FC) often spread beyond lesioned areas and, given the localized nature of lesions, it is unclear how the recovery of FC is orchestrated on a global scale. Since recovery is accompanied by long-term changes in excitability, we propose excitatory-inhibitory (E-I) homeostasis as a driving mechanism. We present a large-scale model of the neocortex, with synaptic scaling of local inhibition, showing how E-I homeostasis can drive the post-lesion restoration of FC and linking it to changes in excitability. We show that functional networks could reorganize to recover disrupted modularity and small-worldness, but not network dynamics, suggesting the need to consider forms of plasticity beyond synaptic scaling of inhibition. On average, we observed widespread increases in excitability, with the emergence of complex lesion-dependent patterns related to biomarkers of relevant side effects of stroke, such as epilepsy, depression and chronic pain. In summary, our results show that the effects of E-I homeostasis extend beyond local E-I balance, driving the restoration of global properties of FC, and relating to post-stroke symptomatology. Therefore, we suggest the framework of E-I homeostasis as a relevant theoretical foundation for the study of stroke recovery and for understanding the emergence of meaningful features of FC from local dynamics.

Multi-modal and multi-model interrogation of large-scale functional brain networks.

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

Drive competition underlies effective allostatic orchestration.

Living systems ensure their fitness by self-regulating. The optimal matching of their behavior to the opportunities and demands of the ever-changing natural environment is crucial for satisfying physiological and cognitive needs. Although homeostasis has explained how organisms maintain their internal states within a desirable range, the problem of orchestrating different homeostatic systems has not been fully explained yet. In the present paper, we argue that attractor dynamics emerge from the competitive relation of internal drives, resulting in the effective regulation of adaptive behaviors. To test this hypothesis, we develop a biologically-grounded attractor model of allostatic orchestration that is embedded into a synthetic agent. Results show that the resultant neural mass model allows the agent to reproduce the navigational patterns of a rodent in an open field. Moreover, when exploring the robustness of our model in a dynamically changing environment, the synthetic agent pursues the stability of the self, being its internal states dependent on environmental opportunities to satisfy its needs. Finally, we elaborate on the benefits of resetting the model's dynamics after drive-completion behaviors. Altogether, our studies suggest that the neural mass allostatic model adequately reproduces self-regulatory dynamics while overcoming the limitations of previous models.

From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans.

Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially underlie a spectrum of neuropsychiatric conditions (e.g., Autism, Schizophrenia, Downs' Syndrome, intellectual disability). Hypotheses related to E/I dysfunction have the potential to provide cross-diagnostic explanations and to combine genetic and neurological evidence that exists within and between psychiatric conditions. However, the hypothesis has been difficult to test because: (1) it lacks specificity-an E/I dysfunction could pertain to any level in the neural system- neurotransmitters, single neurons/receptors, local networks of neurons, or global brain balance - most researchers do not define the level at which they are examining E/I function; (2) We lack validated methods for assessing E/I function at any of these neural levels in humans. As a result, it has not been possible to reliably or robustly test the E/I hypothesis of psychiatric disorders in a large cohort or longitudinal patient studies. Currently available, in vivo markers of E/I in humans either carry significant risks (e.g., deep brain electrode recordings or using Positron Emission Tomography (PET) with radioactive tracers) and/or are highly restrictive (e.g., limited spatial extent for Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Spectroscopy (MRS). More recently, a range of novel Electroencephalography (EEG) features has been described, which could serve as proxy markers for E/I at a given level of inference. Thus, in this perspective review, we survey the theories and experimental evidence underlying 6 novel EEG markers and their biological underpinnings at a specific neural level. These cheap-to-record and scalable proxy markers may offer clinical utility for identifying subgroups within and between diagnostic categories, thus directing more tailored sub-grouping and, therefore, treatment strategies. However, we argue that studies in clinical populations are premature. To maximize the potential of prospective EEG markers, we first need to understand the link between underlying E/I mechanisms and measurement techniques.

Chronic cannabis use affects cerebellum dependent visuomotor adaptation.

BACKGROUND: Cannabis is one of the most commonly used substances in the world. However, its effects on human cognition are not yet fully understood. Although the cerebellum has the highest density of cannabinoid receptor type 1 (CB1R) in the human brain, literature on how cannabis use affects cerebellar-dependent learning is sparse. This study examined the effect of chronic cannabis use on sensorimotor adaptation, a cerebellar-mediated task, which has been suggested to depend on endocannabinoid signaling. METHODS: Chronic cannabis users (n = 27) with no psychiatric comorbidities and healthy, cannabis-naïve controls (n = 25) were evaluated using a visuomotor rotation task. Cannabis users were re-tested after 1 month of abstinence (n = 13) to assess whether initial differences in performance would persist after cessation of use. RESULTS: Cannabis users showed lower adaptation rates compared to controls at the first time point. However, this difference in performance did not persist when participants were retested after one month of abstinence (n = 13). Healthy controls showed attenuated implicit learning in the late phase of the adaptation during re-exposure, which was not present in cannabis users. This explains the lack of between group differences in the second time point and suggests a potential alteration of synaptic plasticity required for cerebellar learning in cannabis users. CONCLUSIONS: Overall, our results suggest that chronic cannabis users show alterations in sensorimotor adaptation, likely due to a saturation of the endocannabinoid system after chronic cannabis use.

Bringing rehabilitation home with an e-health platform to treat stroke patients: study protocol of a randomized clinical trial (RGS@home).

BACKGROUND: There is a pressing need for scalable healthcare solutions and a shift in the rehabilitation paradigm from hospitals to homes to tackle the increase in stroke incidence while reducing the practical and economic burden for patients, hospitals, and society. Digital health technologies can contribute to addressing this challenge; however, little is known about their effectiveness in at-home settings. In response, we have designed the RGS@home study to investigate the effectiveness, acceptance, and cost of a deep tech solution called the Rehabilitation Gaming System (RGS). RGS is a cloud-based system for delivering AI-enhanced rehabilitation using virtual reality, motion capture, and wearables that can be used in the hospital and at home. The core principles of the brain theory-based RGS intervention are to deliver rehabilitation exercises in the form of embodied, goal-oriented, and task-specific action. METHODS: The RGS@home study is a randomized longitudinal clinical trial designed to assess whether the combination of the RGS intervention with standard care is superior to standard care alone for the functional recovery of stroke patients at the hospital and at home. The study is conducted in collaboration with hospitals in Spain, Sweden, and France and includes inpatients and outpatients at subacute and chronic stages post-stroke. The intervention duration is 3 months with assessment at baseline and after 3, 6, and 12 months. The impact of RGS is evaluated in terms of quality of life measurements, usability, and acceptance using standardized clinical scales, together with health economic analysis. So far, one-third of the patients expected to participate in the study have been recruited (N = 90, mean age 60, days after stroke ≥ 30 days). The trial will end in July 2023. DISCUSSION: We predict an improvement in the patients' recovery, high acceptance, and reduced costs due to a soft landing from the clinic to home rehabilitation. In addition, the data provided will allow us to assess whether the prescription of therapy at home can counteract deterioration and improve quality of life while also identifying new standards for online and remote assessment, diagnostics, and intervention across European hospitals. TRIAL REGISTRATION: C linicalTrials.gov NCT04620707. Registered on November 3, 2020.

Saccade rate is associated with recall of items in working memory.

Working memory has been shown to rely on theta oscillations' phase synchronicity for item encoding and recall. At the same time, saccadic eye movements during visual exploration have been observed to trigger theta-phase resets, raising the question of whether the neuronal substrates of mnemonic processing rely on motor-evoked responses. To quantify the relationship between saccades and working memory load, we recorded eye tracking and behavioral data from human participants simultaneously performing an n-back Sternberg auditory task and a hue-based catch detection task. In addition to task-specific interference in performance, we also found that saccade rate was modulated by working memory load in the Sternberg task's preresponse stage. Our results support the possibility of interplay between saccades and hippocampal theta during working memory retrieval of items.

Chronic use of cannabis might impair sensory error processing in the cerebellum through endocannabinoid dysregulation.

Chronic use of cannabis leads to both motor deficits and the downregulation of CB1 receptors (CB1R) in the cerebellum. In turn, cerebellar damage is often related to impairments in motor learning and control. Further, a recent motor learning task that measures cerebellar-dependent adaptation has been shown to distinguish well between healthy subjects and chronic cannabis users. Thus, the deteriorating effects of chronic cannabis use in motor performance point to cerebellar adaptation as a key process to explain such deficits. We review the literature relating chronic cannabis use, the endocannabinoid system in the cerebellum, and different forms of cerebellar-dependent motor learning, to suggest that CB1R downregulation leads to a generalized underestimation and misprocessing of the sensory errors driving synaptic updates in the cerebellar cortex. Further, we test our hypothesis with a computational model performing a motor adaptation task and reproduce the behavioral effect of decreased implicit adaptation that appears to be a sign of chronic cannabis use. Finally, we discuss the potential of our hypothesis to explain similar phenomena related to motor impairments following chronic alcohol dependency.

Escaping from the IIT Munchausen method: Re-establishing the scientific method in the study of consciousness.

Integrated information theory (IIT) is an example of "ironic science" and obstructs the scientific study of consciousness. By confusing the ontological status of a method to quantify network complexity with that of a theory of consciousness, IIT has to square the circle and spirals toward its panpsychism conclusion. I analyze the consequences of this fallacy and suggest how the study of consciousness can be brought back into the realm of rational, empirical science.

Telehealth for rehabilitation and recovery after stroke: State of the evidence and future directions.

AIMS: The aim of this rapid review and opinion paper is to present the state of the current evidence and present future directions for telehealth research and clinical service delivery for stroke rehabilitation. METHODS: We conducted a rapid review of published trials in the field. We searched Medline using key terms related to stroke rehabilitation and telehealth or virtual care. We also searched clinical trial registers to identify key ongoing trials. RESULTS: The evidence for telehealth to deliver stroke rehabilitation interventions is not strong and is predominantly based on small trials prone to Type 2 error. To move the field forward, we need to progress to trials of implementation that include measures of adoption and reach, as well as effectiveness. We also need to understand which outcome measures can be reliably measured remotely, and/or develop new ones. We present tools to assist with the deployment of telehealth for rehabilitation after stroke. CONCLUSION: The current, and likely long-term, pandemic means that we cannot wait for stronger evidence before implementing telehealth. As a research and clinical community, we owe it to people living with stroke internationally to investigate the best possible telehealth solutions for providing the highest quality rehabilitation.

Rehabilitation Gaming System for Alcohol-Related Cognitive Impairment: A Pilot Usability Study.

AIMS: Cognitive impairment in patients with alcohol use disorder (AUD) is highly prevalent, and it negatively impacts treatment outcome. However, this condition is neither systematically assessed nor treated. Thus, we aimed to explore the usability of a virtual reality-based protocol ('Rehabilitation Gaming System', RGS) for patients with AUD. METHODS: Twenty AUD patients (50% also cognitive impairment) underwent a single session of the RGS protocol (four cognitive training tasks, 10 minutes each). System Usability Scale (SUS) and Post-Study System Usability Questionnaire (PSSUQ) were applied to assess the RGS usability and patients' satisfaction with it. Also, the Perceived Competence Scale was administered to assess the patients' feelings of competence when using the training protocol. Comparisons of the responses to these questionnaires were performed between AUD patients with cognitive impairment and those without cognitive impairment. RESULTS: RGS usability was very positively rated (median SUS score = 80, Interquartile Range, IQR = 68.13-86-88). No significant differences were found in the median SUS scores for any of the sociodemographic or clinical variables, excepting for gender (women median score = 85; IQR = 80-94.38 vs. men median score = 71.25; IQR = 61.25-89.25; P-value = 0.035). The quality of the information provided by the RGS training scenarios and the usability were positively rated (PSSUQ), and patients experienced high feelings of competence. CONCLUSIONS: The RGS has been found to be usable in the short term and patients with AUD stated to be satisfied with it. Future larger, randomized trials are needed to explore the effectiveness of this tool to help overcome the cognitive deficits in AUD patients.

Challenging the Boundaries of the Physical Self: Distal Cues Impact Body Ownership.

The unique ability to identify one's own body and experience it as one's own is fundamental in goal-oriented behavior and survival. However, the mechanisms underlying the so-called body ownership are yet not fully understood. Evidence based on Rubber Hand Illusion (RHI) paradigms has demonstrated that body ownership is a product of reception and integration of self and externally generated multisensory information, feedforward and feedback processing of sensorimotor signals, and prior knowledge about the body. Crucially, however, these designs commonly involve the processing of proximal modalities while the contribution of distal sensory signals to the experience of ownership remains elusive. Here we propose that, like any robust percept, body ownership depends on the integration and prediction across all sensory modalities, including distal sensory signals pertaining to the environment. To test our hypothesis, we created an embodied goal-oriented Virtual Air Hockey Task, in which participants were to hit a virtual puck into a goal. In two conditions, we manipulated the congruency of distal multisensory cues (auditory and visual) while preserving proximal and action-driven signals entirely predictable. Compared to a fully congruent condition, our results revealed a significant decrease on three dimensions of ownership evaluation when distal signals were incongruent, including the subjective report as well as physiological and kinematic responses to an unexpected threat. Together, these findings support the notion that the way we represent our body is contingent upon all the sensory stimuli, including distal and action-independent signals. The present data extend the current framework of body ownership and may also find applications in rehabilitation scenarios.

Distinguishing Self, Other, and Autonomy From Visual Feedback: A Combined Correlation and Acceleration Transfer Analysis.

In cognitive science, Theory of Mind (ToM) is the mental faculty of assessing intentions and beliefs of others and requires, in part, to distinguish incoming sensorimotor (SM) signals and, accordingly, attribute these to either the self-model, the model of the other, or one pertaining to the external world, including inanimate objects. To gain an understanding of this mechanism, we perform a computational analysis of SM interactions in a dual-arm robotic setup. Our main contribution is that, under the common fate principle, a correlation analysis of the velocities of visual pivots is shown to be sufficient to characterize "the self" (including proximo-distal arm-joint dependencies) and to assess motor to sensory influences, and "the other" by computing clusters in the correlation dependency graph. A correlational analysis, however, is not sufficient to assess the non-symmetric/directed dependencies required to infer autonomy, the ability of entities to move by themselves. We subsequently validate 3 measures that can potentially quantify a metric for autonomy: Granger causality (GC), transfer entropy (TE), as well as a novel "Acceleration Transfer" (AT) measure, which is an instantaneous measure that computes the estimated instantaneous transfer of acceleration between visual features, from which one can compute a directed SM graph. Subsequently, autonomy is characterized by the sink nodes in this directed graph. This study results show that although TE can capture the directional dependencies, a rectified subtraction operation denoted, in this study, as AT is both sufficient and computationally cheaper.

Erratum: Entorhinal mismatch: A model of self-supervised learning in the hippocampus.

[This corrects the article DOI: 10.1016/j.isci.2021.102364.].