Alessandro Vato received the M.Eng. degree in electronics engineering and the Ph.D. degree in Bioengineering and Bioelectronics both from the University of Genova, Genova, Italy in 2000 and 2004, respectively.
His long-term goal is to develop novel invasive adaptive neuromodulation systems that interact directly with the nervous system to treat important neurological disorders, and to test them in appropriate human patient populations.
He has pursued this vision since the beginning of his research experience. He explored different levels of organization in the nervous system, from networks of in-vitro cultured neurons (as PhD student), to anaesthetized or behaving rodents (as post-doc), to human subjects (current research).
As a PhD student, he focused his research on exploring the computational capability and the plastic processes that take place in random networks of cultured neurons of rat cortex coupled to arrays of planar microelectrodes. Together with his colleagues he characterized the spontaneous and electrically-evoked activity of these neural network and developed a hybrid device by establishing a bidirectional connection between cultured neurons and a mobile robot. This work provided the basis for his PhD thesis (“Connecting neurons to artificial devices: a new tool for investigating the neural code”) and his PhD degree in Bioengineering and Bioelectronics.
As postdoctoral fellow at Northwestern University, he contributed to the NIH-funded project “Development of a bidirectional brain-machine interface.” He learned the surgical procedures to chronically implant multi-electrode arrays in rat cortex, the behavioral techniques to train them on sensory-motor tasks, and how to combine electrophysiological recordings with intracortical microstimulation. He was interested in using intracortical micro-stimulation patterns in behaving rats to build an artificial sensory input channel. He explored which stimulation parameters are needed to create an artificial sensation and how to encode information coming from the environment into an electrical signal that can be felt and understood by the subject.
After his postdoctoral training, he set up his own lab at the Italian Institute of Technology. There, he developed a bidirectional neural interface inspired by the operation of the vertebrate spinal cord as the prime biological interface between the brain and the musculoskeletal apparatus. He designed a method to decode force information from motor cortical activity, and to translate this information into patterns of electrical stimuli that were delivered to the somatosensory cortex of anesthetized rats. Based on my initial work in this area, he received an EU-funded grant entitled “SiCode: Towards new BMIs: State dependent information coding” with the goal of understanding the state dependency of neuronal responses to external stimuli and to use this knowledge to improve bi-directional communication between brains and machines. In the context of this work, he also developed a programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals and a modular bidirectional neural interface by using an ultra-low-power neuromorphic.
How to optimally extract relevant information from recorded neural activity, and how to improve the efficacy of current brain stimulation techniques are crucial questions for both basic and applied neuroscience. Thus, addressing these questions is important for understanding how the brain processes information as well as for the development of neural interfaces that directly interact with the nervous system to treat neurological disorders.
1. Develop novel invasive adaptive neuromodulation systems that interact directly with the nervous system
In order to improve the performance of a neural interface, the stimulation parameters need to be adapted in real time according to the current state of the brain. This is supported by the observation that neural responses to a sensory stimulus do not only depend on extrinsic sensory inputs, but also on intrinsic network variables that can be collectively defined as the neural state. The principles that govern these interactions between spontaneous activity, the external stimulation, and the internal state of the brain are still largely unknown.
The goal of my research over the upcoming years is to increase our knowledge about the dynamics of the brain processes with particular attention to understanding: a) how to extract useful information from different frequencies of the recorded neural signals; b) how the brain routes the information flow across different cortical regions; and finally c) how the responses of the brain to external stimuli are affected by the internal neural state changes. My final goal is to use such knowledge in designing new adaptive closed-loop neuromodulation devices for the treatment of neurological disorders such as epilepsy or Parkinson’s disease (PD).
2. Develop of a bidirectional Brain Machine Interface
The main idea of a Brain Machine Interface system consists in extracting neural signals directly from the brain and use them to control external devices. In the framework of building neural prostheses this technique could be useful to better understand how the brain processes the sensory information coming from the environment and uses it to build motor commands. To reach this result a crucial point is to develop a BMI real-time system to create a bidirectional communication channel with the nervous system.
In our lab we are implementing in-vivo techniques using multielectrode microwires arrays chronically implanted in the cortex of awake rodents. These techniques permit to record the neural activity from the cortex while the animal is behaving and simultaneously to deliver Intracortical Micro Stimulation (ICMS) patterns providing an artificial input in a closed-loop system.
In this research topics we are developing a novel bidirectional neural interface emulating the functionalities of the spinal cord. In vertebrates the spinal cord mediates the communications between brain and limb mechanics, combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We embedded a portion of the central nervous system within a closed-loop system controlling the movements of a point mass, whose behavior emerges from the combined dynamical properties of its neural and artificial components. Our system included (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generated a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. The obtained results open new perspectives within the possibility of closing the sensory-motor loop to restore a connection with the world for people with severe paralysis.
3- Intracortical Micro Stimulation as artificial sensory channel
This research theme has been explored by designing two experiments both involving behaving rats with a microwires array chronically implanted in the barrel cortex. In the first experiment we took inspiration from a well-known behavioral paradigm called "gap crossing" and we trained the rats in a dark room to jump between two platforms after inferring the distance of the second platform by information collected by the whiskers. The goal is to provide the same distance-information by using the intracortical microstimulation of the barrel cortex and substituting the natural sensation with an artificial one.
In the second experiment we explored the effects of multimodal stimulations to be used as artificial feedback for a bidirectional BMI system on rats. We focused on the sensory perception investigating which are the best modalities to translate an artificial feedback in a coherent and representative stimulus able to encode information collected form the environment. We developed a novel behavioral paradigm in which behaving rats chronically implanted with an array of microelectrodes are subjected to different multimodal stimulations (audio and intracortical electrical micro-stimulation). Rats are trained to recognize between high-frequency and low-frequency stimulations, using both audio signals and ICMS patterns, by pressing a different lever inside a behavioral box. We used this experimental paradigm to explore which modality is predominant in the case of incongruent stimulation (i.e. hi-freq. audio simultaneous with low-freq. intracortical electrical stimulation) and which is the role of the intracortical microstimulation in the learning process of this multimodal decision making experiment.