Trends and challenges in neuroengineering: toward "Intelligent" neuroprostheses through brain-"brain inspired systems" communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace “intelligent” neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes

Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data

An automated method for characterization of evoked single-trial local field potentials recorded from rat barrel cortex under mechanical whisker stimulation

Rodents explore their surroundings through whisking by localizing objects and detecting textures very precisely. During such tactile exploration, whisker deflection is first mechanically transduced by receptors and then information encoded throughout the somatosensory pathway ending in the somatosensory ‘barrel’ cortex. In the barrel cortex, tactile information from a single whisker is segregated and processed in a cortical column corresponding to the deflected whisker. Local Field Potentials (LFPs) generated by whisker deflection in the barrel cortex present typical signatures in terms of shape and amplitude that are related to the activation of the local neuronal populations. Therefore, rigorous analysis of such responses may reveal important features about the function of underlying neuronal microcircuits. In this context, software methods for characterizing single-trial LFPs are needed that are also suitable for online extraction of LFP features and for brain–machine interfacing applications. In this work, we present an automated and efficient method to analyze evoked LFP responses in the rat barrel cortex through automatic removal of stimulation artifacts, detection of single events and characterization of their relevant parameters. Evoked single-trial LFPs recorded under two different anesthetics are examined to demonstrate the feasibility, accuracy and applicability of the method

SigMate: a MATLAB-based automated tool for extracellular neuronal signal processing and analysis

Rapid advances in neuronal probe technology for multisite recording of brain activity have posed a significant challenge to neuroscientists for processing and analyzing the recorded signals. To be able to infer meaningful conclusions quickly and accurately from large datasets, automated and sophisticated signal processing and analysis tools are required. This paper presents a Matlab-based novel tool, “SigMate”, incorporating standard methods to analyze spikes and EEG signals, and in-house solutions for local field potentials (LFPs) analysis. Available modules at present are – 1. In-house developed algorithms for: data display (2D and 3D), file operations (file splitting, file concatenation, and file column rearranging), baseline correction, slow stimulus artifact removal, noise characterization and signal quality assessment, current source density (CSD) analysis, latency estimation from LFPs and CSDs, determination of cortical layer activation order using LFPs and CSDs, and single LFP clustering; 2. Existing modules: spike detection, sorting and spike train analysis, and EEG signal analysis. SigMate has the flexibility of analyzing multichannel signals as well as signals from multiple recording sources. The in-house developed tools for LFP analysis have been extensively tested with signals recorded using standard extracellular recording electrode, and planar and implantable multi transistor array (MTA) based neural probes. SigMate will be disseminated shortly to the neuroscience community under the open-source GNU-General Public License

An automated method for detection of layer activation order in information processing pathway of rat barrel cortex under mechanical whisker stimulation

Rodents perform object localization, texture and shape discrimination very precisely through whisking. During whisking, microcircuits in corresponding barrel columns get activated to segregate and integrate tactile information through the information processing pathway. Sensory signals are projected through the brainstem and thalamus to the corresponding ‘barrel columns’ where different cortical layers are activated during signal projection. Therefore, having precise information about the layer activation order is desirable to better understand this signal processing pathway. This work proposes an automated, computationally efficient and easy to implement method to determine the cortical layer activation from intracortically recorded local field potentials (LFPs) and derived current source density (CSD) profiles: 1. Barrel cortex LFPs are represented by a template of four subsequent events: small positive/negative (E1) → large negative (E2) → slow positive (E3)→ slow long negative (E4). The method exploits the layer specific characteristics of LFPs to obtain latencies of the individual events (E1–E4), then taking the latency of E2 for calculating the layer activation order. 2. The corresponding CSD profile is calculated from the LFPs and the first sink’s peak is considered as a reference point to calculate latencies and evaluate the layer activation order. Other reference points require manual calculation. Similar results of layer activation sequence are found using LFPs and CSDs. Extensive tests on LFPs recorded using standard borosilicate micropipettes demonstrated the method’s workability. An interpretation of layer activation order and CSD profiles on the basis of a simplified interacortical barrel column architecture is also provided

An automated classification method for single sweep local field potentials recorded from rat barrel cortex under mechanical whisker stimulation

Understanding brain signals as an outcome of brain’s information processing is a challenge for the neuroscience and neuroengineering community. Rodents sense and explore the environment through whisking. The local field potentials (LFPs) recorded from the barrel 28 columns of the rat somatosensory cortex (S1) during whisking provide information about the tactile information processing pathway. Particularly when using large-scale high-resolution neuronal probes, during each experiment many single LFPs are recorded as an outcome of underlying neuronal network activation and averaged to extract information. However, single LFP signals are frequently very different from each other and extracting information provided by their shape is a useful way to better decode information transmitted by the network. In this work, we propose an automated method capable of classifying these signals based on their shapes. We used template matching approach to recognize single LFPs and extracted the contour information from the recognized signal to generate a feature matrix, which is then classified using the intelligent K–means clustering. As an application example, shape specific information (e.g., latency, and amplitude) of LFPs evoked in the rat somatosensory barrel cortex and used in decoding the rat whiskers information processing pathway is provided by the method

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel \u201cbiohybrid\u201d experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering. \ua9 2020 The Author(s

Mechanical and Electrophysiological Properties of the Sarcolemma of Muscle Fibers in Two Murine Models of Muscle Dystrophy: Col6a1−/− and Mdx

This study aimed to analyse the sarcolemma of Col6a1−/− fibers in comparison with wild type and mdx fibers, taken as positive control in view of the known structural and functional alterations of their membranes. Structural and mechanical properties were studied in single muscle fibers prepared from FDB muscle using atomic force microscopy (AFM) and conventional electrophysiological techniques to measure ionic conductance and capacitance. While the sarcolemma topography was preserved in both types of dystrophic fibers, membrane elasticity was significantly reduced in Col6a1−/− and increased in mdx fibers. In the membrane of Col6a1−/− fibers ionic conductance was increased likely due to an increased leakage, whereas capacitance was reduced, and the action potential (ap) depolarization rate was reduced. The picture emerging from experiments on fibers in culture was consistent with that obtained on intact freshly dissected muscle. Mdx fibers in culture showed a reduction of both membrane conductance and capacitance. In contrast, in mdx intact FDB muscle resting conductance was increased while resting potential and ap depolarization rate were reduced, likely indicating the presence of a consistent population of severely altered fibers which disappear during the culture preparation

On the way to large-scale and high-resolution brain-chip interfacing

Brain-chip-interfaces (BCHIs) are hybrid entities where chips and nerve cells establish a close physical interaction allowing the transfer of information in one or both directions. Typical examples are represented by multi-site-recording chips interfaced to cultured neurons, cultured/acute brain slices, or implanted “in vivo”. This paper provides an overview on recent achievements in our laboratory in the field of BCHIs leading to enhancement of signals transmission from nerve cells to chip or from chip to nerve cells with an emphasis on in vivo interfacing, either in terms of signal-to-noise ratio or of spatiotemporal resolution. Oxide-insulated chips featuring large-scale and high-resolution arrays of stimulation and recording elements are presented as a promising technology for high spatiotemporal resolution interfacing, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on an automated tool for processing and analysis of acquired signals by BCHIs

Prognostic Value of Indeterminable Anaerobic Threshold in Heart Failure.

Background In patients with heart failure (HF), during maximal cardiopulmonary exercise test, anaerobic threshold (AT) is not always identified. We evaluated whether this finding has a prognostic meaning. Methods and Results We recruited and prospectively followed up, in 14 dedicated HF units, 3058 patients with systolic (left ventricular ejection fraction <40%) HF in stable clinical conditions, New York Heart Association class I to III, who underwent clinical, laboratory, echocardiographic, and cardiopulmonary exercise test investigations at study enrollment. We excluded 921 patients who did not perform a maximal exercise, based on lack of achievement of anaerobic metabolism (peak respiratory quotient 1.05). Primary study end point was a composite of cardiovascular death and urgent cardiac transplant, and secondary end point was all-cause death. Median follow-up was 3.01 (1.39-4.98) years. AT was identified in 1935 out of 2137 patients (90.54%). At multivariable logistic analysis, failure in detecting AT resulted significantly in reduced peak oxygen uptake and higher metabolic exercise and cardiac and kidney index score value, a powerful prognostic composite HF index (P<0.001). At multivariable analysis, the following variables were significantly associated with primary study end point: peak oxygen uptake (% pred; P<0.001; hazard ratio [HR]=0.977; confidence interval [CI]=0.97-0.98), ventilatory efficiency slope (P=0.01; HR=1.02; CI=1.01-1.03), hemoglobin (P<0.05; HR=0.931; CI=0.87-1.00), left ventricular ejection fraction (P<0.001; HR=0.948; CI=0.94-0.96), renal function (modification of diet in renal disease; P<0.001; HR=0.990; CI=0.98-0.99), sodium (P<0.05; HR=0.967; CI=0.94-0.99), and AT nonidentification (P<0.05; HR=1.41; CI=1.06-1.89). Nonidentification of AT remained associated to prognosis also when compared with metabolic exercise and cardiac and kidney index score (P<0.01; HR=1.459; CI=1.09-1.10). Similar results were obtained for the secondary study end point. Conclusions The inability to identify AT most often occurs in patients with severe HF, and it has an independent prognostic role in HF