Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals

Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This could lead to a new breed of closed-loop responsive and personalised feedback, which we describe as Neuromorphic Neuromodulation. This can empower precise and adaptive modulation strategies by integrating neuromorphic AI as tightly as possible to the site of the sensors and stimulators. This paper presents a perspective on the potential of Neuromorphic Neuromodulation, emphasizing its capacity to revolutionize implantable brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page

A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices

INTRODUCTION: About 30% of epilepsy patients are resistant to treatment with antiepileptic drugs, and only a minority of these are surgical candidates. A recent therapeutic approach is the application of electrical stimulation in the early phases of a seizure to interrupt its spread across the brain. To accomplish this, energy-efficient seizure detectors are required that are able to detect a seizure in its early stages. METHODS: Three patient-specific, energy-efficient seizure detectors are proposed in this study: (i) random forest (RF); (ii) long short-term memory (LSTM) recurrent neural network (RNN); and (iii) convolutional neural network (CNN). Performance evaluation was based on EEG data (n = 40 patients) derived from a selected set of surface EEG electrodes, which mimic the electrode layout of an implantable neurostimulation system. As for the RF input, 16 features in the time- and frequency-domains were selected. Raw EEG data were used for both CNN and RNN. Energy consumption was estimated by a platform-independent model based on the number of arithmetic operations (AOs) and memory accesses (MAs). To validate the estimated energy consumption, the RNN classifier was implemented on an ultra-low-power microcontroller. RESULTS: The RNN seizure detector achieved a slightly better level of performance, with a median area under the precision-recall curve score of 0.49, compared to 0.47 for CNN and 0.46 for RF. In terms of energy consumption, RF was the most efficient algorithm, with a total of 67k AOs and 67k MAs per classification. This was followed by CNN (488k AOs and 963k MAs) and RNN (772k AOs and 978k MAs), whereby MAs contributed more to total energy consumption. Measurements derived from the hardware implementation of the RNN algorithm demonstrated a significant correlation between estimations and actual measurements. DISCUSSION: All three proposed seizure detection algorithms were shown to be suitable for application in implantable devices. The applied methodology for a platform-independent energy estimation was proven to be accurate by way of hardware implementation of the RNN algorithm. These findings show that seizure detection can be achieved using just a few channels with limited spatial distribution. The methodology proposed in this study can therefore be applied when designing new models for responsive neurostimulation

Evolving Refractory Major Depressive Disorder Diagnostic and Treatment Paradigms: Toward Closed-Loop Therapeutics

Current antidepressant therapies do not effectively control or cure depressive symptoms. Pharmaceutical therapies altogether fail to address an estimated 4 million Americans who suffer from a recurrent and severe treatment-resistant form of depression known as refractory major depressive disorder. Subjective diagnostic schemes, differing manifestations of the disorder, and antidepressant treatments with limited theoretical bases each contribute to the general lack of therapeutic efficacy and differing levels of treatment resistance in the refractory population. Stimulation-based therapies, such as vagus nerve stimulation, transcranial magnetic stimulation, and deep brain stimulation, are promising treatment alternatives for this treatment-resistant subset of patients, but are plagued with inconsistent reports of efficacy and variable side effects. Many of these problems stem from the unknown mechanisms of depressive disorder pathogenesis, which prevents the development of treatments that target the specific underlying causes of the disorder. Other problems likely arise due to the non-specific stimulation of various limbic and paralimbic structures in an open-loop configuration. This review critically assesses current literature on depressive disorder diagnostic methodologies, treatment schemes, and pathogenesis in order to emphasize the need for more stringent depressive disorder classifications, quantifiable biological markers that are suitable for objective diagnoses, and alternative closed-loop treatment options tailored to well-defined forms of the disorder. A closed-loop neurostimulation device design framework is proposed, utilizing symptom-linked biomarker abnormalities as control points for initiating and terminating a corrective electrical stimulus which is autonomously optimized for correcting the magnitude and direction of observed biomarker abnormality

Optimizing the neural response to electrical stimulation and exploring new applications of neurostimulation

Electrical stimulation has been successful in treating patients who suffer from neurologic and neuropsychiatric disorders that are resistant to standard treatments. For deep brain stimulation (DBS), its official approved use has been limited to mainly motor disorders, such as Parkinson\u27s disease and essential tremor. Alcohol use disorder, and addictive disorders in general, is a prevalent condition that is difficult to treat long-term. To determine whether DBS can reduce alcohol drinking in animals, voluntary alcohol consumption of alcohol-preferring rats before, during, and after stimulation of the nucleus accumbens shell were compared. Intake levels in the low stimulus intensity group (n=3, 100&mgr;A current) decreased by as much as 43% during stimulation, but the effect did not persist. In the high stimulus intensity group (n=4, 200&mgr;A current), alcohol intake decreased as much as 59%, and the effect was sustained. These results demonstrate the potent, reversible effects of DBS.^ Left vagus nerve stimulation (VNS) is approved for treating epilepsy and depression. However, the standard method of determining stimulus parameters is imprecise, and the patient responses are highly variable. I developed a method of designing custom stimulus waveforms and assessing the nerve response to optimize stimulation selectivity and efficiency. VNS experiments were performed in rats aiming to increase the selectivity of slow nerve fibers while assessing activation efficiency. When producing 50% of maximal activation of slow fibers, customized stimuli were able to activate as low as 12.8% of fast fibers, while the lowest for standard rectangular waveforms was 35.0% (n=4-6 animals). However, the stimulus with the highest selectivity requires 19.6nC of charge per stimulus phase, while the rectangular stimulus required only 13.2nC.^ Right VNS is currently under clinical investigation for preventing sudden unexpected death in epilepsy and for treating heart failure. Activation of the right vagal parasympathetic fibers led to waveform-independent reductions in heart rate, ejection ratio, and stroke volume. Customized stimulus design with response feedback produces reproducible and predictable patterns of nerve activation and physiological effects, which will lead to more consistent patient responses

Technology of deep brain stimulation: current status and future directions

Deep brain stimulation (DBS) is a neurosurgical procedure that allows targeted circuit-based neuromodulation. DBS is a standard of care in Parkinson disease, essential tremor and dystonia, and is also under active investigation for other conditions linked to pathological circuitry, including major depressive disorder and Alzheimer disease. Modern DBS systems, borrowed from the cardiac field, consist of an intracranial electrode, an extension wire and a pulse generator, and have evolved slowly over the past two decades. Advances in engineering and imaging along with an improved understanding of brain disorders are poised to reshape how DBS is viewed and delivered to patients. Breakthroughs in electrode and battery designs, stimulation paradigms, closed-loop and on-demand stimulation, and sensing technologies are expected to enhance the efficacy and tolerability of DBS. In this Review, we provide a comprehensive overview of the technical development of DBS, from its origins to its future. Understanding the evolution of DBS technology helps put the currently available systems in perspective and allows us to predict the next major technological advances and hurdles in the field.ope

Implantable Micro-Device for Epilepsy Seizure Detection and Subsequent Treatment

RÉSUMÉ L’émergence des micro-dispositifs implantables est une voie prometteuse pour le traitement de troubles neurologiques. Ces systèmes biomédicaux ont été exploités comme traitements non-conventionnels sur des patients chez qui les remèdes habituels sont inefficaces. Les récents progrès qui ont été faits sur les interfaces neuronales directes ont permis aux chercheurs d’analyser l’activité EEG intracérébrale (icEEG) en temps réel pour des fins de traitements. Cette thèse présente un dispositif implantable à base de microsystèmes pouvant capter efficacement des signaux neuronaux, détecter des crises d’épilepsie et y apporter un traitement afin de l’arrêter. Les contributions principales présentées ici ont été rapportées dans cinq articles scientifiques, publiés ou acceptés pour publication dans les revues IEEE, et plusieurs autres tels que «Low Power Electronics» et «Emerging Technologies in Computing». Le microsystème proposé inclus un circuit intégré (CI) à faible consommation énergétique permettant la détection de crises d’épilepsie en temps réel. Cet CI comporte une pré-amplification initiale et un détecteur de crises d’épilepsie. Le pré-amplificateur est constitué d’une nouvelle topologie de stabilisateur d’hacheur réduisant le bruit et la puissance dissipée. Les CI fabriqués ont été testés sur des enregistrements d’icEEG provenant de sept patients épileptiques réfractaires au traitement antiépileptique. Le délai moyen de la détection d’une crise est de 13,5 secondes, soit avant le début des manifestations cliniques évidentes. La consommation totale d’énergie mesurée de cette puce est de 51 μW. Un neurostimulateur à boucle fermée (NSBF), quant à lui, détecte automatiquement les crises en se basant sur les signaux icEEG captés par des électrodes intracrâniennes et permet une rétroaction par une stimulation électrique au même endroit afin d’interrompre ces crises. La puce de détection de crises et le stimulateur électrique à base sur FPGA ont été assemblés à des électrodes afin de compléter la prothèse proposée. Ce NSBF a été validé en utilisant des enregistrements d’icEEG de dix patients souffrant d’épilepsie réfractaire. Les résultats révèlent une performance excellente pour la détection précoce de crises et pour l’auto-déclenchement subséquent d’une stimulation électrique. La consommation énergétique totale du NSBF est de 16 mW. Une autre alternative à la stimulation électrique est l’injection locale de médicaments, un traitement prometteur de l’épilepsie. Un système local de livraison de médicament basé sur un nouveau détecteur asynchrone des crises est présenté.----------ABSTRACT Emerging implantable microdevices hold great promise for the treatment of patients with neurological conditions. These biomedical systems have been exploited as unconventional treatment for the conventionally untreatable patients. Recent progress in brain-machine-interface activities has led the researchers to analyze the intracerebral EEG (icEEG) recording in real-time and deliver subsequent treatments. We present in this thesis a long-term safe and reliable low-power microsystem-based implantable device to perform efficient neural signal recording, seizure detection and subsequent treatment for epilepsy. The main contributions presented in this thesis are reported in five journal manuscripts, published or accepted for publication in IEEE Journals, and many others such as Low Power Electronics, and Emerging Technologies in Computing. The proposed microsystem includes a low-power integrated circuit (IC) intended for real-time epileptic seizure detection. This IC integrates a front-end preamplifier and epileptic seizure detector. The preamplifier is based on a new chopper stabilizer topology that reduces noise and power dissipation. The fabricated IC was tested using icEEG recordings from seven patients with drug-resistant epilepsy. The average seizure detection delay was 13.5 sec, well before the onset of clinical manifestations. The measured total power consumption of this chip is 51 µW. A closed-loop neurostimulator (CLNS) is next introduced, which is dedicated to automatically detect seizure based on icEEG recordings from intracranial electrode contacts and provide an electrical stimulation feedback to the same contacts in order to disrupt these seizures. The seizure detector chip and a dedicated FPGA-based electrical stimulator were assembled together with common recording electrodes to complete the proposed prosthesis. This CLNS was validated offline using recording from ten patients with refractory epilepsy, and showed excellent performance for early detection of seizures and subsequent self-triggering electrical stimulation. Total power consumption of the CLNS is 16 mW. Alternatively, focal drug injection is the promising treatment for epilepsy. A responsive focal drug delivery system based on a new asynchronous seizure detector is also presented. The later system with data-dependent computation reduces up to 49% power consumption compared to the previous synchronous neurostimulator