Proceedings of the Fourth Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies

This paper provides an overview of current progress in the technological advances and the use of deep brain stimulation (DBS) to treat neurological and neuropsychiatric disorders, as presented by participants of the Fourth Annual DBS Think Tank, which was convened in March 2016 in conjunction with the Center for Movement Disorders and Neurorestoration at the University of Florida, Gainesveille FL, USA. The Think Tank discussions first focused on policy and advocacy in DBS research and clinical practice, formation of registries, and issues involving the use of DBS in the treatment of Tourette Syndrome. Next, advances in the use of neuroimaging and electrochemical markers to enhance DBS specificity were addressed. Updates on ongoing use and developments of DBS for the treatment of Parkinson’s disease, essential tremor, Alzheimer’s disease, depression, post-traumatic stress disorder, obesity, addiction were presented, and progress toward innovation(s) in closed-loop applications were discussed. Each section of these proceedings provides updates and highlights of new information as presented at this year’s international Think Tank, with a view toward current and near future advancement of the field

Proceedings of the Third Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies

The proceedings of the 3rd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, imaging, and computational work on DBS for the treatment of neurological and neuropsychiatric disease. Significant innovations of the past year are emphasized. The Think Tank\u27s contributors represent a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers, and members of industry. Presentations and discussions covered a broad range of topics, including policy and advocacy considerations for the future of DBS, connectomic approaches to DBS targeting, developments in electrophysiology and related strides toward responsive DBS systems, and recent developments in sensor and device technologies

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani

Neural Adaptation and the Effect of Interelectrode Spacing on Epidural Electrocorticography for Brain-Computer Interfaces

Electrocorticography: ECoG) is increasingly being identified as a safe and reliable recording technique for both Brain-Computer Interface: BCI) applications as well as neurophysiology studies. This thesis describes some of the first real-time closed-loop BCI studies of chronic ECoG in non-human primates. Epidural microECoG electrodes developed in our lab were implanted in three monkeys with the electrode array centered over primary motor cortex: M1). Monkeys were then trained to perform a one-dimensional BCI task. The BCI control scheme was independent of any prior screening for task-related activity. All three monkeys successfully learned to perform the task with multiple control configurations and each time gained significant performance in 10 days or less. Interelectrode distance between control electrodes was also tested for three different distances. 15 and 9 mm spacing resulted in equivalent performance while 3 mm saw a moderate but significant degradation in performance. Finally, post hoc analysis was performed to analyze various decoding parameters. While decoding parameters were generally well matched to the observed signals, several potential decoding improvements were identified. Overall, these results demonstrate the feasibility of epidural ECoG BCIs, highlight the importance of neural adaptation for BCI control, and quantify various metrics of a current ECoG BCI system to drive further studies

Cortical Dynamics of Language

The human capability for fluent speech profoundly directs inter-personal communication and, by extension, self-expression. Language is lost in millions of people each year due to trauma, stroke, neurodegeneration, and neoplasms with devastating impact to social interaction and quality of life. The following investigations were designed to elucidate the neurobiological foundation of speech production, building towards a universal cognitive model of language in the brain. Understanding the dynamical mechanisms supporting cortical network behavior will significantly advance the understanding of how both focal and disconnection injuries yield neurological deficits, informing the development of therapeutic approaches

Assessing Neuronal Synchrony and Brain Function Through Local Field Potential and Spike Analysis

Studies of neuronal network oscillations and rhythmic neuronal synchronization have led to a number of important insights in recent years, giving us a better understanding of the temporal organization of neuronal activity related to essential brain functions like sensory processing and cognition. Important principles and theories have emerged from these findings, including the communication through coherence hypothesis, which proposes that synchronous oscillations render neuronal communication effective, selective, and precise. The implications of such a theory may be universal for brain function, as the determinants of neuronal communication inextricably shape the neuronal representation of information in the brain. However, the study of communication through coherence is still relatively young. Since its articulation in 2005, the theory has predominantly been applied to assess cortical function and its communication with downstream targets in different sensory and behavioral conditions. The results herein are intended to bolster this hypothesis and explore new ways in which oscillations coordinate neuronal communication in distributed regions. This includes the development of new analytic tools for interpreting electrophysiological patterns, inspired by phase synchronization and spike train analysis. These tools aim to offer fast results with clear statistical and physiological interpretation

OPTIMIZATION OF TIME-RESPONSE AND AMPLIFICATION FEATURES OF EGOTs FOR NEUROPHYSIOLOGICAL APPLICATIONS

In device engineering, basic neuron-to-neuron communication has recently inspired the development of increasingly structured and efficient brain-mimicking setups in which the information flow can be processed with strategies resembling physiological ones. This is possible thanks to the use of organic neuromorphic devices, which can share the same electrolytic medium and adjust reciprocal connection weights according to temporal features of the input signals. In a parallel - although conceptually deeply interconnected - fashion, device engineers are directing their efforts towards novel tools to interface the brain and to decipher its signalling strategies. This led to several technological advances which allow scientists to transduce brain activity and, piece by piece, to create a detailed map of its functions. This effort extends over a wide spectrum of length-scales, zooming out from neuron-to-neuron communication up to global activity of neural populations. Both these scientific endeavours, namely mimicking neural communication and transducing brain activity, can benefit from the technology of Electrolyte-Gated Organic Transistors (EGOTs). Electrolyte-Gated Organic Transistors (EGOTs) are low-power electronic devices that functionally integrate the electrolytic environment through the exploitation of organic mixed ionic-electronic conductors. This enables the conversion of ionic signals into electronic ones, making such architectures ideal building blocks for neuroelectronics. This has driven extensive scientific and technological investigation on EGOTs. Such devices have been successfully demonstrated both as transducers and amplifiers of electrophysiological activity and as neuromorphic units. These promising results arise from the fact that EGOTs are active devices, which widely extend their applicability window over the capabilities of passive electronics (i.e. electrodes) but pose major integration hurdles. Being transistors, EGOTs need two driving voltages to be operated. If, on the one hand, the presence of two voltages becomes an advantage for the modulation of the device response (e.g. for devising EGOT-based neuromorphic circuitry), on the other hand it can become detrimental in brain interfaces, since it may result in a non-null bias directly applied on the brain. If such voltage exceeds the electrochemical stability window of water, undesired faradic reactions may lead to critical tissue and/or device damage. This work addresses EGOTs applications in neuroelectronics from the above-described dual perspective, spanning from neuromorphic device engineering to in vivo brain-device interfaces implementation. The advantages of using three-terminal architectures for neuromorphic devices, achieving reversible fine-tuning of their response plasticity, are highlighted. Jointly, the possibility of obtaining a multilevel memory unit by acting on the gate potential is discussed. Additionally, a novel mode of operation for EGOTs is introduced, enabling full retention of amplification capability while, at the same time, avoiding the application of a bias in the brain. Starting on these premises, a novel set of ultra-conformable active micro-epicortical arrays is presented, which fully integrate in situ fabricated EGOT recording sites onto medical-grade polyimide substrates. Finally, a whole organic circuitry for signal processing is presented, exploiting ad-hoc designed organic passive components coupled with EGOT devices. This unprecedented approach provides the possibility to sort complex signals into their constitutive frequency components in real time, thereby delineating innovative strategies to devise organic-based functional building-blocks for brain-machine interfaces.Nell’ingegneria elettronica, la comunicazione di base tra neuroni ha recentemente ispirato lo sviluppo di configurazioni sempre più articolate ed efficienti che imitano il cervello, in cui il flusso di informazioni può essere elaborato con strategie simili a quelle fisiologiche. Ciò è reso possibile grazie all'uso di dispositivi neuromorfici organici, che possono condividere lo stesso mezzo elettrolitico e regolare i pesi delle connessioni reciproche in base alle caratteristiche temporali dei segnali in ingresso. In modo parallelo, gli ingegneri elettronici stanno dirigendo i loro sforzi verso nuovi strumenti per interfacciare il cervello e decifrare le sue strategie di comunicazione. Si è giunti così a diversi progressi tecnologici che consentono agli scienziati di trasdurre l'attività cerebrale e, pezzo per pezzo, di creare una mappa dettagliata delle sue funzioni. Entrambi questi ambiti scientifici, ovvero imitare la comunicazione neurale e trasdurre l'attività cerebrale, possono trarre vantaggio dalla tecnologia dei transistor organici a base elettrolitica (EGOT). I transistor organici a base elettrolitica (EGOT) sono dispositivi elettronici a bassa potenza che integrano funzionalmente l'ambiente elettrolitico attraverso lo sfruttamento di conduttori organici misti ionici-elettronici, i quali consentono di convertire i segnali ionici in segnali elettronici, rendendo tali dispositivi ideali per la neuroelettronica. Gli EGOT sono stati dimostrati con successo sia come trasduttori e amplificatori dell'attività elettrofisiologica e sia come unità neuromorfiche. Tali risultati derivano dal fatto che gli EGOT sono dispositivi attivi, al contrario dell'elettronica passiva (ad esempio gli elettrodi), ma pongono comunque qualche ostacolo alla loro integrazione in ambiente biologico. In quanto transistor, gli EGOT necessitano l'applicazione di due tensioni tra i suoi terminali. Se, da un lato, la presenza di due tensioni diventa un vantaggio per la modulazione della risposta del dispositivo (ad esempio, per l'ideazione di circuiti neuromorfici basati su EGOT), dall'altro può diventare dannosa quando gli EGOT vengono adoperati come sito di registrazione nelle interfacce cerebrali, poiché una tensione non nulla può essere applicata direttamente al cervello. Se tale tensione supera la finestra di stabilità elettrochimica dell'acqua, reazioni faradiche indesiderate possono manifestarsi, le quali potrebbero danneggiare i tessuti e/o il dispositivo. Questo lavoro affronta le applicazioni degli EGOT nella neuroelettronica dalla duplice prospettiva sopra descritta: ingegnerizzazione neuromorfica ed implementazione come interfacce neurali in applicazioni in vivo. Vengono evidenziati i vantaggi dell'utilizzo di architetture a tre terminali per i dispositivi neuromorfici, ottenendo una regolazione reversibile della loro plasticità di risposta. Si discute inoltre la possibilità di ottenere un'unità di memoria multilivello agendo sul potenziale di gate. Viene introdotta una nuova modalità di funzionamento per gli EGOT, che consente di mantenere la capacità di amplificazione e, allo stesso tempo, di evitare l'applicazione di una tensione all’interfaccia cervello-dispositivo. Partendo da queste premesse, viene presentata una nuova serie di array micro-epicorticali ultra-conformabili, che integrano completamente i siti di registrazione EGOT fabbricati in situ su substrati di poliimmide. Infine, viene proposto un circuito organico per l'elaborazione del segnale, sfruttando componenti passivi organici progettati ad hoc e accoppiati a dispositivi EGOT. Questo approccio senza precedenti offre la possibilità di filtrare e scomporre segnali complessi nelle loro componenti di frequenza costitutive in tempo reale, delineando così strategie innovative per concepire blocchi funzionali a base organica per le interfacce cervello-macchina

Non-Penetrating Microelectrode Interfaces for Cortical Neuroprosthetic Applications with a Focus on Sensory Encoding: Feasibility and Chronic Performance in Striate Cortex

abstract: Growing understanding of the neural code and how to speak it has allowed for notable advancements in neural prosthetics. With commercially-available implantable systems with bi- directional neural communication on the horizon, there is an increasing imperative to develop high resolution interfaces that can survive the environment and be well tolerated by the nervous system under chronic use. The sensory encoding aspect optimally interfaces at a scale sufficient to evoke perception but focal in nature to maximize resolution and evoke more complex and nuanced sensations. Microelectrode arrays can maintain high spatial density, operating on the scale of cortical columns, and can be either penetrating or non-penetrating. The non-penetrating subset sits on the tissue surface without puncturing the parenchyma and is known to engender minimal tissue response and less damage than the penetrating counterpart, improving long term viability in vivo. Provided non-penetrating microelectrodes can consistently evoke perception and maintain a localized region of activation, non-penetrating micro-electrodes may provide an ideal platform for a high performing neural prosthesis; this dissertation explores their functional capacity. The scale at which non-penetrating electrode arrays can interface with cortex is evaluated in the context of extracting useful information. Articulate movements were decoded from surface microelectrode electrodes, and additional spatial analysis revealed unique signal content despite dense electrode spacing. With a basis for data extraction established, the focus shifts towards the information encoding half of neural interfaces. Finite element modeling was used to compare tissue recruitment under surface stimulation across electrode scales. Results indicated charge density-based metrics provide a reasonable approximation for current levels required to evoke a visual sensation and showed tissue recruitment increases exponentially with electrode diameter. Micro-scale electrodes (0.1 – 0.3 mm diameter) could sufficiently activate layers II/III in a model tuned to striate cortex while maintaining focal radii of activated tissue. In vivo testing proceeded in a nonhuman primate model. Stimulation consistently evoked visual percepts at safe current thresholds. Tracking perception thresholds across one year reflected stable values within minimal fluctuation. Modulating waveform parameters was found useful in reducing charge requirements to evoke perception. Pulse frequency and phase asymmetry were each used to reduce thresholds, improve charge efficiency, lower charge per phase – charge density metrics associated with tissue damage. No impairments to photic perception were observed during the course of the study, suggesting limited tissue damage from array implantation or electrically induced neurotoxicity. The subject consistently identified stimulation on closely spaced electrodes (2 mm center-to-center) as separate percepts, indicating sub-visual degree discrete resolution may be feasible with this platform. Although continued testing is necessary, preliminary results supports epicortical microelectrode arrays as a stable platform for interfacing with neural tissue and a viable option for bi-directional BCI applications.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Evaluation and Advancement of Electrocorticographic Brain-Machine Interfaces for Individuals with Upper-Limb Paralysis

Brain-machine interface (BMI) technology aims to provide individuals with movement paralysis a natural and intuitive means for the restoration of function. Electrocorticography (ECoG), in which disc electrodes are placed on either the surface of the dura or the cortex to record field potential activity, has been proposed as a viable neural recording modality for BMI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previous demonstrations of BMI control using ECoG have consisted of short-term periods of control by able-bodied subjects utilizing basic processing and decoding techniques. This dissertation presents work seeking to advance the current state of ECoG BMIs through an assessment of the ability of individuals with movement paralysis to control an ECoG BMI, an investigation into adaptation during BMI skill acquisition, an evaluation of chronic implantation of an ECoG electrode grid, and improved extraction of BMI command signals from ECoG recordings. Two individuals with upper-limb paralysis were implanted with high-density ECoG electrode grids over sensorimotor cortical areas for up to 30 days, with both subjects found to be capable of voluntarily modulating their cortical activity to control movement of a computer cursor with up to three degrees of freedom. Analysis of control signal angular error and the tuning characteristics of ECoG spectral features during the acquisition of brain control revealed that both decoder calibration and fixed-decoder training could facilitate performance improvements. In addition, to better understand the capability of ECoG to provide robust, long-term recordings, work was conducted assessing the effects of chronic implantation of an ECoG electrode grid in a non-human primate, demonstrating that movement-related modulation could be recorded from electrode nearly two years post-implantation despite the presence of substantial fibrotic encapsulation. Finally, it was found that the extraction of command signals from ECoG recordings could be improved through the use of a decoding method incorporating weight-space priors accounting for the expected correlation structure of electrical field potentials. Combined, this work both demonstrates the feasibility of ECoG-based BMI systems as well as addresses some of key challenges that must be overcome before such systems are translated to the clinical realm

Doctor of Philosophy

dissertationThis dissertation describes the use of cortical surface potentials, recorded with dense grids of microelectrodes, for brain-computer interfaces (BCIs). The work presented herein is an in-depth treatment of a broad and interdisciplinary topic, covering issues from electronics to electrodes, signals, and applications. Within the scope of this dissertation are several significant contributions. First, this work was the first to demonstrate that speech and arm movements could be decoded from surface local field potentials (LFPs) recorded in human subjects. Using surface LFPs recorded over face-motor cortex and Wernickes area, 150 trials comprising vocalized articulations of ten different words were classified on a trial-by-trial basis with 86% accuracy. Surface LFPs recorded over the hand and arm area of motor cortex were used to decode continuous hand movements, with correlation of 0.54 between the actual and predicted position over 70 seconds of movement. Second, this work is the first to make a detailed comparison of cortical field potentials recorded intracortically with microelectrodes and at the cortical surface with both micro- and macroelectrodes. Whereas coherence in macroelectrocorticography (ECoG) decayed to half its maximum at 5.1 mm separation in high frequencies, spatial constants of micro-ECoG signals were 530-700 ?m-much closer to the 110-160 ?m calculated for intracortical field potentials than to the macro-ECoG. These findings confirm that cortical surface potentials contain millimeter-scale dynamics. Moreover, these fine spatiotemporal features were important for the performance of speech and arm movement decoding. In addition to contributions in the areas of signals and applications, this dissertation includes a full characterization of the microelectrodes as well as collaborative work in which a custom, low-power microcontroller, with features optimized for biomedical implants, was taped out, fabricated in 65 nm CMOS technology, and tested. A new instruction was implemented in this microcontroller which reduced energy consumption when moving large amounts of data into memory by as much as 44%. This dissertation represents a comprehensive investigation of surface LFPs as an interfacing medium between man and machine. The nature of this work, in both the breadth of topics and depth of interdisciplinary effort, demonstrates an important and developing branch of engineering