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ObjectiveThe compromise of the hippocampal loop is a hallmark of mesial temporal lobe epilepsy (MTLE); particularly, the hippocampal output to the parahippocampal cortex is disrupted by damage of the CA1. While closed-loop deep brain stimulation (DBS) is the latest frontier to improve drug-refractory MTLE, current approaches do not restore the hippocampal loop, are designed by trial-and-error and heavily rely on seizure detection or prediction algorithms. The objective of this study is to evaluate the efficacy and robustness of bridging hippocampus and cortex via closed-loop stimulation to achieve the functional restoration of the hippocampal loop and control limbic seizures.ApproachIn hippocampus-cortex slices treated with 4-aminopyridine and in which the Schaffer Collaterals are severed, we used interictal discharges originating in the CA3 to trigger stimulation in the subiculum and re-establish the hippocampus output to the cortex. Combining tools from information theory with quantification of ictal activity, we addressed the efficacy of the bridge in restoring the functional connectivity of the hippocampal loop and controlling ictogenesis.Main resultsBridging hippocampus and cortex recovered the functional connectivity of the hippocampal loop, controlled ictogenesis and proved robust to failure mimicking the functional impairment of the CA3 seen in MTLE rodent models and patients. The efficacy and robustness of the bridge stem in mirroring the adaptive properties of the CA3, which acts as biological neuromodulator.SignificanceA DBS device that does not depend on seizure detection/prediction algorithms but relies on endogenous interictal patterns presents the key to advance the conceptual design of current DBS paradigms for epilepsy treatment.

Hippocampal dysfunction is the hallmark of mesial temporal lobe epilepsy (MTLE), the most common epileptic syndrome in adults and the most often refractory to medical therapy. Deep brain stimulation (DBS) may ameliorate drug-refractory MTLE, but it still cannot guarantee a seizure-free life. One major drawback is that the stimulation policy is informed by trial-and-error rather than by the operating mode of the brain. Thus, optimizing DBS parameters is still an unmet clinical need.Here, we propose the deployment of hippocampal interictal activity in a biohybrid approach to control limbic ictogenesis. Specifically, an electronic bridge establishes a graft-host interaction between the hippocampal subfield CA3 (graft) and the parahippocampal cortex (CTX – host) of distinct rodent brain slices, both treated with 4-aminopyridine; the electronic bridge relays the graft interictal events to the host via electrical pulses. We show that interictal activity generated by the graft CA3 controls limbic ictogenesis in the host CTX even in the absence of feedback from it, thus likely reflecting an intrinsic anti-ictogenic clock of this brain region.This work opens a translational perspective for MTLE treatment via biohybrid neuroprostheses relying on the intrinsic clock of incorporated hippocampal cells.

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

The correct identi cation of burst events is crucial in many scenarios, ranging from basic neuroscience to biomedical applications. However, none of the burst detection methods that can be found in the literature have been widely adopted for this task. As an alternative to conventional techniques, a novel neuromorphic approach for real-time burst detection is proposed and tested on acquisitions from in vitro cultures. The system consists of a Neuromorphic Auditory Sensor, which converts the input signal obtained from electrophysiological recordings into spikes and decomposes them into di erent frequency bands. The output of the sensor is sent to a trained spiking neural network implemented on a SpiNNaker board that discerns between bursting and non-bursting activity. This data-driven approach was compared with 8 di erent conventional spike-based methods, addressing some of their drawbacks, such as being able to detect both high and low frequency events and working in an online manner. Similar results in terms of number of detected events, mean burst duration and correlation as current state-ofthe- art approaches were obtained with the proposed system, also bene ting from its lower power consumption and computational latency. Therefore, our neuromorphic-based burst detection paves the road to future implementations for neuroprosthetic applications. Spanish Ministry of Education, Culture and Sport "Formación de Personal Universitario Scholarship" European Regional Development Fund COFNET TEC2016-77785-P

As our understanding of volitional motor function increases, it is clear that complex movements are the result of the interactions of multiple cortical regions rather than just the output properties of primary motor cortex. However, our understanding of the interactions among these regions is limited. In this study, we used the activity-dependent stimulation (ADS) technique to determine the short/long-term effects on network activity and neuroplasticity of intracortical connections. ADS uses the intrinsic neural activity of one region to trigger stimulations in a separate region of the brain and can manipulate neuronal connectivity in vivo. Our aim was to compare single-unit neuronal activity within premotor cortex (rostral forelimb area, [RFA] in rats) in response to ADS (triggered from RFA) and randomly-generated stimulation in the somatosensory area (S1) within single sessions and across 21 consecutive days of stimulation. We examined firing rate and correlation between spikes and stimuli in chronically-implanted healthy ambulatory rats during spontaneous and evoked activity. At the end of the treatment, we evaluated changes of synaptophysin expression. Our results demonstrated the ability of ADS to modulate RFA firing properties and to promote synaptogenesis in S1, strengthening the idea that this Hebbian-inspired protocol can be used to modulate cortical connectivity.

As our understanding of how motor output is generated increases, it is clear that there is a need to understand the interactions of multiple distinct regions rather than just the output properties of primary motor cortex. This becomes even more imperative when trying to understand how different regions may contribute to recovery following injury. In this study we used a technique that promotes functional motor recovery after injury, activity-dependent stimulation (ADS), to determine the short- and long-term effects on network activity and neuroplasticity of intracortical connections. ADS uses recorded neural activity to trigger stimulation of the brain and may be utilized to manipulate neuronal connectivity in vivo, representing a novel technique to shape intrinsic neuroplasticity. The aim of this work was to compare the effect of ADS to randomly-generated stimulation (RS) of the somatosensory area (S1) on the single units’ patterns of activity taking place in the premotor cortex (RFA) and to investigate whether synaptic plasticity changes occur in S1 as a consequence of 21 consecutive days of stimulation. In particular, we examined both firing rate changes and correlation between spiking activity and stimuli in chronically-implanted healthy ambulatory rats during both spontaneous and evoked activity, resulting from the two stimulation paradigms. Finally, we evaluated changes in expression of synaptophysin at the end of the treatment. This experimental procedure demonstrated the ability of ADS to modulate firing properties of RFA within daily recording sessions and to promote synaptogenesis in S1, further strengthening the idea that this Hebbian-inspired protocol can be used to effectively modulate cortical connectivity and thus suggesting its translational potential for promoting recovery after brain injury.

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrates a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks. ispartof: IEEE Open Journal of Engineering in Medicine and Biology vol:1 pages:57-64 ispartof: location:United States status: published

OBJECTIVE Activity-dependent stimulation (ADS) is designed to strengthen the connections between neuronal circuits and therefore may be a promising tool for promoting neurophysiological reorganization following a brain injury. To successfully perform this technique, two criteria must be met: (1) spikes in the extracellular electrical field potential must be detected accurately at one site of interest, and (2) stimulation pulses generated at fixed (

One of the main limitations preventing the realization of a successful dialogue between the brain and a putative enabling device is the intricacy of brain signals. In this perspective, closed-loop in vitro systems can be used to investigate the interactions between a network of neurons and an external system, such as an interacting environment or an artificial device. In this chapter, we provide an overview of closed-loop in vitro systems, which have been developed for investigating potential neuroprosthetic applications. In particular, we first explore how to modify or set a target dynamical behavior in a network of neurons. We then analyze the behavior of in vitro systems connected to artificial devices, such as robots. Finally, we provide an overview of biological neuronal networks interacting with artificial neuronal networks, a configuration currently offering a promising solution for clinical applications.

Recent advances in the field of human brain imaging by electrophysiological recordings allow to reliably quantify the connectivity patterns of spontaneous oscillatory activity. This has provided novel tools to investigate the neural basis underlying complex human behavior and to unravel the mechanisms of brain function and reorganization in response to neurological diseases. In this context, we present a pipeline integrating high-density electroencephalography (hd-EEG) analysis and graph theory. To test our pipeline, we conducted a feasibility study on hd-EEG analysis to recordings from healthy individuals, examining the frequency-specific small-world organization of brain connectivity. Here we present the preliminary results on a small sample size. The ultimate goal of our study is to extract graph-theory related metrics, such as small-worldness index, from injured patients and use them as electrophysiological biomarkers of the recovery.

Recent advances in neurotechnology allow neurological impairments to be treated or reduced by brain machine interfaces and neuroprostheses. To develop energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation ‘neurobiohybrid’ systems for brain repair. We demonstrate here the first exploitation of a neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or completely missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for two specific applications with future clinical implications: bidirectional bridging to artificially reconnect two disconnected neuronal modules and hybrid bidirectional bridging to replace the activity of one module with a neuromorphic spiking neural network. Our neuroprosthetic system opens up new avenues for the development of novel bioelectrical therapeutics for human applications.

One of the main limitations preventing the realization of a successful dialogue between the brain and a putative enabling device is the intricacy of brain signals. In this perspective, closed-loop in vitro systems can be used to investigate the interactions between a network of neurons and an external system, such as an interacting environment or an artificial device. In this chapter, we provide an overview of closed-loop in vitro systems, which have been developed for investigating potential neuroprosthetic applications. In particular, we first explore how to modify or set a target dynamical behavior in a network of neurons. We then analyze the behavior of in vitro systems connected to artificial devices, such as robots. Finally, we provide an overview of biological neuronal networks interacting with artificial neuronal networks, a configuration currently offering a promising solution for clinical applications.

The combination of recording and stimulation into closed-loop electrophysiological systems is one of the most challenging topics in neuroscience and neural engineering. The application of such systems can range from basic neuroscientific questions to neurorehabilitation purposes. In this review, we summarize the main results obtained with a hybrid neurorobotic platform that we developed in the past to perform closed-loop experiments. To minimize the complexity of the biological network, we used cultured neuronal networks kept alive over Micro Electrode Arrays. The robot is characterized by proximity sensors and wheels, allowing it to navigate within an arena with obstacles. The neurorobotic architecture can become an experimental environment for studying the interaction between brains and machines. We also discuss other possible applications of similar closed-loop architectures related to neuroprosthetics and neurorehabilitation.

A hybrid neuro-robotic architecture was developed, where a biological neuronal network was able to communicate with a small wheeled robot. The robot had to perform an obstacle-avoidance task and every time the robot hits an obstacle, the biological network receives a tetanic stimulation, aimed at strengthening connectivity. Here, we investigated the impact of tetanic stimulation on network activity of hippocampal culture grown on grids of 60 substrate-embedded microelectrode arrays. We found that closed-loop tetanic stimulation produced an enhancement of activity in terms of evoked response, not seen in control and sham (i.e. open-loop tetanic stimulation) experiments. The obtained results demonstrate that activity-dependent interaction with the environment is essential to significantly impact network behavior. Our study constitutes a further step towards the development of neuro-artificial interfaces.