Disruption of layer-specific visual processing in a model of focal neocortical epilepsy.

The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and, ultimately, of behavior. However, how hyperexcitability affects information processing in cortical sensory circuits is not yet fully understood. Here, we investigated interlaminar alterations in sensory processing of the visual cortex in a mouse model of focal epilepsy. We found three main circuit dynamics alterations in epileptic mice: (i) a spreading of visual contrast-driven gamma modulation across layers, (ii) an increase in firing rate that is layer-unspecific for excitatory units and localized in infragranular layers for inhibitory neurons, and (iii) a strong and contrast-dependent locking of firing units to network activity. Altogether, our data show that epileptic circuits display a functional disruption of layer-specific organization of visual sensory processing, which could account for visual dysfunction observed in epileptic subjects. Understanding these mechanisms paves the way to circuital therapeutic interventions for epilepsy.

Predicting party switching through machine learning and open data.

Parliament dynamics might seem erratic at times. Predicting future voting patterns could support policy design based on the simulation of voting scenarios. The availability of open data on legislative activities and machine learning tools might enable such prediction. In our paper, we provide evidence for this statement by developing an algorithm able to predict party switching in the Italian Parliament with over 70% accuracy up to two months in advance. The analysis was based on voting data from the XVII (2013-2018) and XVIII (2018-2022) Italian legislature. We found party switchers exhibited higher participation in secret ballots and showed a progressive decrease in coherence with their party's majority votes up to two months before the actual switch. These results show how machine learning combined with political open data can support predicting and understanding political dynamics.

Bioelectronic medicine for the autonomic nervous system: clinical applications and perspectives.

Bioelectronic medicine (BM) is an emerging new approach for developing novel neuromodulation therapies for pathologies that have been previously treated with pharmacological approaches. In this review, we will focus on the neuromodulation of autonomic nervous system (ANS) activity with implantable devices, a field of BM that has already demonstrated the ability to treat a variety of conditions, from inflammation to metabolic and cognitive disorders. Recent discoveries about immune responses to ANS stimulation are the laying foundation for a new field holding great potential for medical advancement and therapies and involving an increasing number of research groups around the world, with funding from international public agencies and private investors. Here, we summarize the current achievements and future perspectives for clinical applications of neural decoding and stimulation of the ANS. First, we present the main clinical results achieved so far by different BM approaches and discuss the challenges encountered in fully exploiting the potential of neuromodulatory strategies. Then, we present current preclinical studies aimed at overcoming the present limitations by looking for optimal anatomical targets, developing novel neural interface technology, and conceiving more efficient signal processing strategies. Finally, we explore the prospects for translating these advancements into clinical practice.

Computational approaches to decode grasping force and velocity level in upper-limb amputee from intraneural peripheral signals.

Objective. Recent results have shown the potentials of neural interfaces to provide sensory feedback to subjects with limb amputation increasing prosthesis usability. However, their advantages for decoding motor control signals over current methods based on electromyography (EMG) are still debated. In this study we compared a standard EMG-based method with approaches that use peripheral intraneural data to infer distinct levels of grasping force and velocity in a trans-radial amputee.Approach. Surface EMG (three channels) and intraneural signals (collected with transverse intrafascicular multichannel electrodes, TIMEs, 56 channels) were simultaneously recorded during the amputee's intended grasping movements. We sorted single unit activity (SUA) from each neural signal and then we identified the most informative units. EMG envelopes were extracted from the recorded EMG signals. A reference support vector machine (SVM) classifier was used to map EMG envelopes into desired force and velocity levels. Two decoding approaches using SUA were then tested and compared to the EMG-based reference classifier: (a) SVM classification of firing rates into desired force and velocity levels; (b) reconstruction of covariates (the grasp cue level or EMG envelopes) from neural data and use of covariates for classification into desired force and velocity levels.Main results.Using EMG envelopes as reconstructed covariates from SUA yielded significantly better results than the other approaches tested, with performance similar to that of the EMG-based reference classifier, and stable over three different recording days. Of the two reconstruction algorithms used in this approach, a linear Kalman filter and a nonlinear point process adaptive filter, the nonlinear filter gave better results.Significance.This study presented a new effective approach for decoding grasping force and velocity from peripheral intraneural signals in a trans-radial amputee, which relies on using SUA to reconstruct EMG envelopes. Being dependent on EMG recordings only for the training phase, this approach can fully exploit the advantages of implanted neural interfaces and potentially overcome, in the medium to long term, current state-of-the-art methods. (Clinical trial's registration number: NCT02848846).

Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals.

Objective. Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nervous system and visceral organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop VN stimulation (VNS) would be crucial to increase effectiveness of this approach. Therefore, the extrapolation of useful physiological information from VN electrical activity would represent an invaluable source for single-target applications. Here, we present an advanced decoding algorithm novel to VN studies and properly detecting different functional changes from VN signals.Approach. VN signals were recorded using intraneural electrodes in anaesthetized pigs during cardiovascular and respiratory challenges mimicking increases in arterial blood pressure, tidal volume and respiratory rate. We developed a decoding algorithm that combines discrete wavelet transformation, principal component analysis, and ensemble learning made of classification trees.Main results. The new decoding algorithm robustly achieved high accuracy levels in identifying different functional changes and discriminating among them. Interestingly our findings suggest that electrodes positioning plays an important role on decoding performances. We also introduced a new index for the characterization of recording and decoding performance of neural interfaces. Finally, by combining an anatomically validated hybrid neural model and discrimination analysis, we provided new evidence suggesting a functional topographical organization of VN fascicles.Significance. This study represents an important step towards the comprehension of VN signaling, paving the way for the development of effective closed-loop VNS systems.

Decoding of grasping tasks from intraneural recordings in trans-radial amputee.

OBJECTIVE: A major challenge in neuroprosthetics is the restoration of sensory-motor hand functions in upper-limb amputees. Neuroprostheses based on the direct re-connection of the peripheral nerves may be an interesting approach for re-establishing the natural and effective bidirectional control of hand prostheses. Recent results have shown that transverse intrafascicular multi-channel electrodes (TIMEs) can restore natural and sophisticated sensory feedback. However, the potential of using TIME-recorded motor intraneural signals to decode grasping tasks has not as yet been explored. APPROACH: In this study, we show that several hand-movement intentions can be decoded from intraneural signals recorded using four TIMEs implanted in the median and ulnar nerves of an upper limb amputee. Experimental sessions were performed over a week, from day 16 to day 23 after the surgical operation. Intraneural activity was recorded during several hand motor tasks imagined by the subject and processed offline. MAIN RESULTS: We obtained a very high decoding accuracy considering 11 class states (up to 83%). These results confirm that neural signals recorded by multi-channel intraneural electrodes can be used to decode several movement intentions with high accuracy. Moreover, we were able to use same TIME channels for decoding over one week within the first month, even if the stability has to be confirmed during long-term experiments. SIGNIFICANCE: Therefore, TIMEs could be used in the future to achieve a complete bidirectional approach exploiting neural pathways, to make a more natural and intuitive new generation of hand prostheses that have a closer resemblance to a healthy hand.

Quantitative estimation of nerve fiber engagement by vagus nerve stimulation using physiological markers.

BACKGROUND: Cervical vagus nerve stimulation (VNS) is an emerging bioelectronic treatment for brain, metabolic, cardiovascular and immune disorders. Its desired and off-target effects are mediated by different nerve fiber populations and knowledge of their engagement could guide calibration and monitoring of VNS therapies. OBJECTIVE: Stimulus-evoked compound action potentials (eCAPs) directly provide fiber engagement information but are currently not feasible in humans. A method to estimate fiber engagement through common, noninvasive physiological readouts could be used in place of eCAP measurements. METHODS: In anesthetized rats, we recorded eCAPs while registering acute physiological response markers to VNS: cervical electromyography (EMG), changes in heart rate (ΔHR) and breathing interval (ΔBI). Quantitative models were established to capture the relationship between A-, B- and C-fiber type activation and those markers, and to quantitatively estimate fiber activation from physiological markers and stimulation parameters. RESULTS: In bivariate analyses, we found that EMG correlates with A-fiber, ΔHR with B-fiber and ΔBI with C-fiber activation, in agreement with known physiological functions of the vagus. We compiled multivariate models for quantitative estimation of fiber engagement from these markers and stimulation parameters. Finally, we compiled frequency gain models that allow estimation of fiber engagement at a wide range of VNS frequencies. Our models, after calibration in humans, could provide noninvasive estimation of fiber engagement in current and future therapeutic applications of VNS.

Anodal block permits directional vagus nerve stimulation.

Vagus nerve stimulation (VNS) is a bioelectronic therapy for disorders of the brain and peripheral organs, and a tool to study the physiology of autonomic circuits. Selective activation of afferent or efferent vagal fibers can maximize efficacy and minimize off-target effects of VNS. Anodal block (ABL) has been used to achieve directional fiber activation in nerve stimulation. However, evidence for directional VNS with ABL has been scarce and inconsistent, and it is unknown whether ABL permits directional fiber activation with respect to functional effects of VNS. Through a series of vagotomies, we established physiological markers for afferent and efferent fiber activation by VNS: stimulus-elicited change in breathing rate (ΔBR) and heart rate (ΔHR), respectively. Bipolar VNS trains of both polarities elicited mixed ΔHR and ΔBR responses. Cathode cephalad polarity caused an afferent pattern of responses (relatively stronger ΔBR) whereas cathode caudad caused an efferent pattern (stronger ΔHR). Additionally, left VNS elicited a greater afferent and right VNS a greater efferent response. By analyzing stimulus-evoked compound nerve potentials, we confirmed that such polarity differences in functional responses to VNS can be explained by ABL of A- and B-fiber activation. We conclude that ABL is a mechanism that can be leveraged for directional VNS.

An impedance matching algorithm for common-mode interference removal in vagus nerve recordings.

BACKGROUND: The peripheral nervous system is involved in a multitude of physiological functions. Recording neural signals provides information that can be used by diagnostic bioelectronic medicine devices, closed-loop neuromodulation therapies and other neuroprosthetic applications. The ability to accurately record these signals is challenging, due to the presence of various biological and instrument-related interference sources. NEW METHOD: We developed a common-mode interference rejection algorithm based on an impedance matching approach for bipolar cuff electrodes. Two unipolar channels were recorded from the two electrode contacts of a bipolar cuff. The impedance mismatch was estimated and used to correct one of the two channels. RESULTS: When applied to electrocardiographic (ECG) artifacts collected from three mice using CorTec electrodes, the algorithm reduced the interference to noise ratio (INR) over simple subtraction by 12 dB on average. The algorithm also reduced the INR of stimulation artifacts in recordings from three rats collected using flexible electrodes by an additional 2.4 dB. In the same experiments evoked electromyographic (EMG) interference was suppressed by 1.3 dB. COMPARISON WITH EXISTING METHODS: Simple subtraction is the common approach for reducing common-mode interference in bipolar recordings, however impedance mismatches that exist or emerge compromise its efficiency. CONCLUSIONS: The algorithm significantly reduced the common-mode interference from ECG artifacts, stimulation artifacts, and evoked EMG interference, while retaining neural signals, in two animal models and two recording setups. This approach can be used in a variety of different neurophysiological setups to remove common-mode interference from a variety of sources.

Decoding Neural Metabolic Markers From the Carotid Sinus Nerve in a Type 2 Diabetes Model.

Recent studies showed that the carotid sinus nerve (CSN) and the sympathetic nervous system (SNS) are overactivated in type 2 diabetes and that restoring the correct CSN neural activity can re-establish the proper metabolism. However, a robust characterization of the relationship between CSN and SNS neural activities and metabolism in type 2 diabetes is still missing. Here, we investigated the relationship between neural activity of CSN and SNS in control rats and in rats with diet-induced type 2 diabetes and the animal condition during metabolic challenges. We found that the diabetic condition can be discriminated on the basis of CSN and SNS neural activities due to a high-frequency shift in both spectra. This shift is suppressed in the SNS in case of CSN denervation, confirming the role of CSN in driving sympathetic overactivation in type 2 diabetes. Interestingly, the Inter-Burst-Intervals (IBIs) calculated from CSN bursts strongly correlate with perturbations in glycaemia levels. This finding, held for both control and diabetic rats, indicates the possibility of detecting metabolic information from neural recordings even in pathological conditions. Our results suggest that CSN activity could serve as a marker to monitor glycaemic alterations and, therefore, it could be used for closed-loop control of CSN neuromodulation. This paves the way to the development of novel and effective bioelectronic therapies for type 2 diabetes.

A Robotic System for Adaptive Training and Function Assessment of Forelimb Retraction in Mice.

Rodent models are decisive for translational research in healthy and pathological conditions of motor function thanks to specific similarities with humans. Here, we present an upgraded version of the M-Platform, a robotic device previously designed to train mice during forelimb retraction tasks. This new version significantly extends its possibilities for murine experiments during motor tasks: 1) an actuation system for friction adjustment allows to automatically adapt pulling difficulty; 2) the device can be used both for training, with a retraction task, and for assessment, with an isometric task; and 3) the platform can be integrated with a neurophysiology systems to record simultaneous cortical neural activity. Results of the validation experiments with healthy mice confirmed that the M-Platform permits precise adjustments of friction during the task, thus allowing to change its difficulty and that these variations induce a different improvement in motor performance, after specific training sessions. Moreover, simultaneous and high quality (high signal-to-noise ratio) neural signals can be recorded from the rostral forelimb area (RFA) during task execution. With the novel features presented herein, the M-Platform may allow to investigate the outcome of a customized motor rehabilitation protocol after neural injury, to analyze task-related signals from brain regions interested by neuroplastic events and to perform optogenetic silencing or stimulation during experiments in transgenic mice.