IEEE Brain

Over the last few decades, the use of deep brain stimulation (DBS) to improve the treatment of those with neurological movement disorders represents a critical success story in the development of invasive neurotechnology and the promise of brain-computer interfaces (BCI) to improve the lives of those suffering from incurable neurological disorders. In the last decade, investigational devices capable of recording and streaming neural activity from chronically implanted therapeutic electrodes has supercharged research into clinical applications of BCI, enabling in-human studies investigating the use of adaptive stimulation algorithms to further enhance therapeutic outcomes and improve future device performance. In this talk, Dr. Herron will review ongoing clinical research efforts in the field of adaptive DBS systems and algorithms. This will include an overview of DBS in current clinical practice, the development of bidirectional clinical-use research platforms, ongoing algorithm evaluation efforts, a discussion of current adoption barriers to be addressed in future work.

Over the past 30 years bionic devices such as cochlear implants and pacemakers, have used a small number of metal electrodes to restore function and monitor activity in patients following disease or injury of excitable tissues. Growing interest in neurotechnologies, facilitated by ventures such as BrainGate, Neuralink and the European Human Brain Project, has increased public awareness of electrotherapeutics and led to both new applications for bioelectronics and a growing demand for less invasive devices with improved performance. Coupled with the rapid miniaturisation of electronic chips, bionic devices are now being developed to diagnose and treat a wide variety of neural and muscular disorders. Of particular interest is the area of high resolution devices that require smaller, more densely packed electrodes. Due to poor integration and communication with body tissue, conventional metallic electrodes cannot meet these size and spatial requirements. We have developed a range of polymer based electronic materials including conductive hydrogels (CHs), conductive elastomers (CEs) and living electrodes (LEs). These technologies provide synergy between low impedance charge transfer, reduced stiffness and an ability to be provide a biologically active interface. A range of electrode approaches are presented spanning wearables, implantables and drug delivery devices. This talk outlines the materials development and characterisation of both in vitro properties and translational in vivo performance. The challenges for translation and commercial uptake of novel technologies will also be discussed.

A brain-machine interface (BMI) is a system that enables users to interact with computers and robots through the voluntary modulation of their brain activity. Such a BMI is particularly relevant as an aid for patients with severe neuromuscular disabilities, although it also opens up new possibilities in human-machine interaction for able-bodied people. Real-time signal processing and decoding of brain signals are certainly at the heart of a BMI. Yet, this does not suffice for subjects to operate a brain-controlled device. In the first part of my talk I will review some of our recent studies, most involving participants with severe motor disabilities, that illustrate additional principles of a reliable BMI that enable users to operate different devices. In particular, I will show how an exclusive focus on machine learning is not necessarily the solution as it may not promote subject learning. This highlights the need for a comprehensive mutual learning methodology that foster learning at the three critical levels of the machine, subject and application. To further illustrate that BMI is more than just decoding, I will discuss how to enhance subject learning and BMI performance through appropriate feedback modalities. Finally, I will show how these principles translate to motor rehabilitation, where in a controlled trial chronic stroke patients achieved a significant functional recovery after the intervention, which was retained 6-12 months after the end of therapy.

We will highlight recent technological and methodological advances in deploying miniaturized technologies that can monitor the spatial electrophysiologic patterns of the visceral nervous system. As an example, we will discuss recent developments of thin, stretchable, wireless biosensor patches that can be embedded within routinely used medical adhesives for recording electrophysiologic patterns of the GI tract. We will also showcase recent developments in array signal processing that enable non-invasive tracking, and source localization, of the slow wave patterns associated with the GI tract. We will illustrate how such systems can also be used in tandem with novel miniaturized pacing devices to can enable closed-loop neuromodulation of the enteric nervous system. We will conclude with a summary of the knowns and unknowns in how multi-organ physiology research, technology miniaturization, and data science may create unique opportunities for the intersection of electrical engineering and neuroscience.

In this talk I outline the technical challenges and current solutions to layer fMRI. Specifically, I describe our acquisition strategies for maximizing resolution, spatial coverage, time efficiency as well as, perhaps most importantly, vascular specificity. Novel applications from our group, including mapping feedforward and feedback connections to M1 during task and sensory input modulation and S1 during a sensory prediction task are be shown. Layer specific activity in dorsal lateral prefrontal cortex during a working memory task is also demonstrated. Additionally, I’ll show preliminary work on mapping whole brain layer-specific resting state connectivity and hierarchy.

In this talk, I aim to show that the dynamical properties of emerging nanodevices can accelerate the development of smart, and environmentally friendly chips that inherently learn through their physics. The goal of neuromorphic computing is to draw inspiration from the architecture of the brain to build low-power circuits for artificial intelligence. I will first give a brief overview of the state of the art of neuromorphic computing, highlighting the opportunities offered by emerging nanodevices in this field, and the associated challenges. I will then show that the intrinsic dynamical properties of these nanodevices can be exploited at the device and algorithmic level to assemble systems that infer and learn though their physics. I will illustrate these possibilities with examples from our work on spintronic neural networks that communicate and compute through their microwave oscillations, and on an algorithm called Equilibrium Propagation that minimizes both the error and energy of a dynamical system.

The analysis of functional magnetic resonance imaging (fMRI) data can greatly benefit from flexible analytic approaches. In particular, the advent of data-driven approaches to identify whole-brain time-varying connectivity and activity has revealed a number of interesting relevant variation in the data which, when ignored, can provide misleading information. In this lecture I will provide a comparative introduction of a range of data-driven approaches to estimating time-varying connectivity. I will also present detailed examples where studies of both brain health and disorder have been advanced by approaches designed to capture and estimate time-varying information in resting fMRI data. I will review several exemplar data sets analyzed in different ways to demonstrate the complementarity as well as trade-offs of various modeling approaches to answer questions about brain function. Finally, I will review and provide examples of strategies for validating time-varying connectivity including simulations, multimodal imaging, and comparative prediction within clinical populations, among others. As part of the interactive aspect I will provide a hands-on guide to the dynamic functional network connectivity toolbox within the GIFT software, including an online didactic analytic decision tree to introduce the various concepts and decisions that need to be made when using such tools