Alessandro Vato

Objective. Accurate identification of functional cortical regions is essential in neurological resection. The central sulcus (CS) is an important landmark that delineates functional cortical regions. Median nerve stimulation (MNS) is a standard procedure to identify the position of the CS intraoperatively. In this paper, we introduce an automated procedure that uses MNS to rapidly localize the CS and create functional somatotopic maps. Approach. We recorded electrocorticographic signals from 13 patients who underwent MNS in the course of an awake craniotomy. We analyzed these signals to develop an automated procedure that determines the location of the CS and that also produces functional somatotopic maps. Main results. The comparison between our automated method and visual inspection performed by the neurosurgeon shows that our procedure has a high sensitivity (89%) in identifying the CS. Further, we found substantial concordance between the functional somatotopic maps generated by our method and passive functional mapping (92% sensitivity). Significance. Our automated MNS-based method can rapidly localize the CS and create functional somatotopic maps without imposing additional burden on the clinical procedure. With additional development and validation, our method may lead to a diagnostic tool that guides neurosurgeons and reduces postoperative morbidity in patients undergoing resective brain surgery.

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link 4 between the brain and the external world. A decoder translates recorded neural activity into motor 5 commands and an encoder delivers sensory information collected from the environment directly 6 to the brain creating a closed-loop system. These two modules are typically integrated in bulky 7 external devices. However, the clinical support of patients with severe motor and sensory deficits 8 requires compact, low-power, and fully implantable systems that can decode neural signals to 9 control external devices. As a first step toward this goal, we developed a modular bidirectional BMI 10 setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented 11 a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. 12 On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn 13 to decode neural signals recorded from the brain into motor outputs controlling the movements 14 of an external device. The modularity of the BMI allowed us to tune the individual components 15 of the setup without modifying the whole system. In this paper we present the features of 16 this modular BMI, and describe how we configured the network of spiking neuron circuits to 17 implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm 18 that connects bidirectionally the brain of an anesthetized rat with an external object. We show that 19 the chip learned the decoding task correctly, allowing the interfaced brain to control the object’s 20 trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is 21 mature enough for the development of BMI modules that are sufficiently low-power and compact, 22 while being highly computationally powerful and adaptive.

Motor brain-machine interfaces (BMIs) transform neural activities recorded directly from the brain into motor commands to control the movements of an external object by establishing an interface between the central nervous system (CNS) and the device. Bidirectional BMIs are closed-loop systems that add a sensory channel to provide the brain with an artificial feedback signal produced by the interaction between the device and the external world. Taking inspiration from the functioning of the spinal cord in mammalians, in our previous works we designed and developed a bidirectional BMI that uses the neural signals recorded form rats' motor cortex to control the movement of an external object. We implemented a decoding interface based on the approximation of a predefined force field with a central attractor point. Now we consider a non-linear transformation that allows to design a decoder approximating force fields with arbitrary attractors. We describe here the non-linear mapping algorithm and preliminary results of its use with behaving rats.

This study explored the psychophysical effects of intracortical microstimulation (ICMS) coupled to auditory stimulation during a behavioral detection task in rats. ICMS directed to the sensory areas of the cortex can be instrumental in facilitating operant conditioning behavior. Moreover, multisensory stimulation promotes learning by enabling the subject to access multiple information channels. However, the extent to which multisensory information can be used as a cue to make decisions has not been fully understood. This study addressed the exploration of the parameters of multisensory stimulation delivered to behaving rats in an operant conditioning task. Preliminary data indicate that animal decisions can be shaped by online changing the stimulation parameters.

Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

We present a novel experimental framework that implements a bidirectional brain-machine interface inspired by the operation of the spinal cord in vertebrates that generates a control policy in the form of a force field. The proposed experimental set-up allows connecting the brain of freely moving rats to an external device. We tested this apparatus in a preliminary experiment with an alert rat that used the interface for acquiring a food reward. The goal of this approach to bidirectional interfaces is to explore the role of voluntary neural commands in controlling a dynamical system represented by a small cart moving on vertical plane and connected to a water/pellet dispenser.

Extending bidirectional brain-machine interfaces (BMI) tailored for specific experiments with additional software and hardware tools can be very onerous, if not impossible. To overcome this problem, we developed a modular configurable system by modifying the architecture of an existing bidirectional BMI. This modular system enables the seamless and efficient inclusion of new features and the integration of new protocols without changing the native system's overall structure. By introducing a platform for the implementation of BMI algorithms on neuromorphic chips, this method represents a step towards the development of low-power, compact and computationally powerful tools for clinical applications.

A portable 16-channels microcontroller-based wireless system for a bi-directional interaction with the central nervous system is presented in this work. The device is designed to be used with freely behaving small laboratory animals and allows recording of spontaneous and evoked neural activity wirelessly transmitted and stored on a personal computer. Biphasic current stimuli with programmable duration, frequency and amplitude may be triggered in real-time on the basis of the recorded neural activity as well as by the animal behavior within a specifically designed experimental setup. An intuitive graphical user interface was developed to configure and to monitor the whole system. The system was successfully tested through bench tests and in vivo measurements on behaving rats chronically implanted with multi-channels microwire arrays.

One of the most difficult tasks for the surgeon during the removal of low-grade gliomas is to identify as precisely as possible the borders between functional and non-functional brain tissue with the aim of obtaining the maximal possible resection which allows to the patient the longer survival. For this purpose, systems for acute extracellular recordings of single neuron and multi-unit activity are considered promising. Here we describe a system to be used with 16 microelectrodes arrays that consists of an autoclavable headstage, a built-in inserter for precise electrode positioning and a system that measures and controls the pressure exerted by the headstage on the brain with a twofold purpose: to increase recording stability and to avoid disturbance of local perfusion which would cause a degradation of the quality of the recording and, eventually, local ischemia. With respect to devices where only electrodes are autoclavable, our design permits the reduction of noise arising from long cable connections preserving at the same time the flexibility and avoiding long-lasting gas sterilization procedures. Finally, size is much smaller and set up time much shorter compared to commercial systems currently in use in surgery rooms, making it easy to consider our system very useful for intra-operatory mapping operations.