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Objectives To evaluate the correlation between the pallidal local field potentials (LFPs) activity and the cortical oscillations (at rest and during several motor tasks) in two freely moving patients with generalized dystonia and pallidal deep brain stimulation (DBS). Materials and methods Two women with isolated generalized dystonia were selected for bilateral globus pallidus internus (GPi) DBS. After the electrodes' implantation, cortical activity was recorded by a portable electroencephalography (EEG) system simultaneously with GPi LFPs activity, during several motor tasks, gait, and rest condition. Recordings were not performed during stimulation. EEG and LFPs signals relative to each specific movement were coupled together and grouped in neck/upper limbs movements and gait. Power spectral density (PSD), EEG-LFP coherence (through envelope of imaginary coherence operator), and 1/f exponent of LFP-PSD background were calculated. Results In both patients, the pallidal LFPs PSD at rest was characterized by prominent 4-12 Hz activity. Voluntary movements increased activity in the theta (θ) band (4-7 Hz) compared to rest, in both LFPs and EEG signals. Gait induced a drastic raise of θ activity in both patients' pallidal activity, less marked for the EEG signal. A coherence peak within the 8-13 Hz range was found between pallidal LFPs and EEG recorded at rest. Conclusions Neck/upper limbs voluntary movements and gait suppressed the GPi-LFPs-cortical-EEG coherence and differently impacted both EEG and LFPs low frequency activity. These findings suggest a selective modulation of the cortico-basal ganglia network activity in dystonia.

The process of recording local fields potentials can be influenced by many internal and external sources of electrical noise. To successfully use these recordings, noise must be removed, for which an automatic detection tool is needed to speed up the reviewing process. This research aims to develop an interpretable model, and among the many machine learning models, decision trees stand out due to the innate ability to allow insight into the classification criteria. As they require extracted features instead of the raw signal to achieve good performance, the adaptation of features was proposed originally intended for electroencephalography classification for the detection of artefacts in local fields potentials. Afterwards, they are grouped, and three different filtering feature selection algorithms are applied to obtain the most relevant ones and compare their choices. Classification accuracy of 88.1% is obtained with a single feature. Hence, it is concluded that interpretable models can be obtained with a performance similar to that of deep learning approaches.

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

Local fields potentials (LFP) can be contaminated by different internal and external sources of noise during their recordings. In cases where artefacts are present in them, automatic detection tools are needed to speed up the high accuracy detection process, followed by their removal to successfully use these recordings. This process is facilitated by a pool of supervised machine learning based tools which require labelled data for training. These algorithms have the capacity to distinguish between normal brain patterns and artefacts from an individual or a group, which is more flexible than template matching and subtraction. In addition, their portability has seen developments from both the software and hardware perspective. For many tools, LFP signal power is used as a gauge to measure and eventually label the artefacts portion of the LFP. This work explores how signal power affects the detection and classification accuracy of artefacts. Results show that a higher threshold value impacts positively the accuracy, due to less false positives in the data, without compromising the specificity in balanced datasets.

Acquired brain injuries, such as stroke, are a major cause of long-term disability worldwide. When an injury occurs within primary motor cortex (M1) there is both a loss of descending information to the spinal cord and a disruption in the communication within and between spared regions, which can lead to hemiparesis and other motor dysfunctions. Recently, activity-dependent stimulation (ADS), a closed loop intracortical neurostimulation technique, was shown to significantly improve behavioural recovery in rodent models following a unilateral traumatic brain injury in the primary motor cortex. While behavioural benefits of this approach have been described, the neurophysiological changes have not yet been fully characterized. In this study, we investigated how neural activity in the form of local field potential (LFP) was impacted by an ischemic lesion and how it changed following prolonged ADS sessions. Intracortical microelectrodes arrays were used to record and stimulate from the rostral forelimb area (RFA) and the primary somatosensory cortex (S1) of 12 rats (Long Evans). An ischemic injury induced with Endothelin-1 microinjections in the caudal forelimb area (CFA) was used as an injury model. We found that the ET-1 caused lesion led to widespread drop of LFP power across all the frequency bands in both the monitored areas. The lesion did not change the functional connectivity within the brain areas, while it increased the synchronization between them. Following stimulation, LFP power generally increased in both areas. Stimulation also led to a decrement in connectivity between S1 and RFA, while causing the within S1 connectivity to increase.

Summary Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or 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 bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care., Graphical Abstract, Highlights • We developed a novel real-time neuromorphic system acting as a neuroprosthesis • The neuroprosthesis can bidirectionally reconnect two disconnected networks • The neuroprosthesis implements a real-time spiking neural network • The spiking network can successfully replace the activity of a neural population, Neuroscience; Systems Neuroscience; Computer Science; Evolvable Hardware; Electronic Materials

Neural recording, known as local field potentials, offer valuable knowledge on how neural processes work and contribute to neural circuits. The recording can be contaminated by different internal and external sources of noise, because of the involvement of complex electronic apparatuses and the natural electrical activity throughout the body. In order to successfully utilize these signal, artifacts must be identified and removed. Thus, in this paper, an artifact detection method using a one-dimensional convolutional network referred to as 1D-CNN is proposed. The presented method achieved an improved accuracy and reduced computational time over the existing methods which use a multi-layered feed-forward neural network and a long-short term memory network. It also provided insight of the criteria behind the classification with gradient attribution maps.

The process of recording local fields potentials (LFP) can be contaminated by different internal and external sources of noise. To successfully use these recordings, noise must be removed, for which an automatic detection tool is needed to speed up the detection process. This work presents the use of a specific configuration of the recurrent neural network based machine learning approach, known as the long-short term memory (LSTM), in two different settings to identify artifacts and compares the obtained results to a feed forward neural network both in terms of classification performance and computational time. Using spontaneous LFP signals recorded chronically by multisite neuronal probes in behaving rats, our results show that the LSTM model with and without drop out can achieve an accuracy of 87.1%.

Background: Italy's first outbreak of COVID-19 was confirmed on 21 st February 2020;this virus has quickly become a global pandemic, causing a health emergen

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.

This study assesses the feasibility of latent factor analysis via dynamical systems (LFADS) for evaluating differences in the observed spiking response dynamics imposed by two electrical microstimulation regimes in awake rats. LFADS is a recently-developed deep learning method that uses stimulus-aligned neural spiking data to determine the initial neural state of each trial, as well as infer a set of time-dependent perturbations to the learned neural dynamics within trials. We show that time-dependent perturbations inferred by an LFADS model trained on spikes from trials on a single session can distinguish between different stimulation conditions. Furthermore, we use these data to exemplify how LFADS inferences track the evolution of stimulus-related spiking responses during chronic microstimulation experiments.