The field of neuromodulation encompasses a wide spectrum of interventional technologies that modify pathological activity within the nervous system to achieve a therapeutic effect. Therapies including deep brain stimulation (DBS), intracranial cortical stimulation (ICS), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (TMS) have all shown promising results across a range of neurological and neuropsychiatric disorders. While the mechanisms of therapeutic action are invariably different amongst these approaches, there are several fundamental neuroengineering challenges that are commonly applicable to improving neuromodulation efficacy. This article reviews the state-of-the-art of neuromodulation for brain disorders and discusses the challenges and opportunities available for clinicians and researchers interested in advancing neuromodulation therapies.

The system identification framework with the new BN-modulated waveform and the clinical HIL simulation testbed can help develop future model-based closed-loop electrical brain stimulation systems for treatment of neurological and neuropsychiatric disorders.

Chronic pain patients receive escalating opioid dosage prior to SCS implant, and high-dose opioid usage is associated with an increased risk of explant. Neuromodulation can stabilize or decrease opioid usage. Earlier consideration of SCS before escalated opioid usage has the potential to improve outcomes in complex chronic pain.

While beta oscillations often occur within the parkinsonian basal ganglia, how these oscillations emerge from a naive state and change with disease severity is not clear. To address this question, a progressive, nonhuman primate model of Parkinson's disease was developed using staged injections of MPTP. Within each parkinsonian state (naive, mild, moderate, and severe), spontaneous local field potentials were recorded throughout the sensorimotor globus pallidus. In the naive state, beta oscillations (11-32 Hz) occurred in half of the recordings, indicating spontaneous beta oscillations in globus pallidus are not pathognomonic. Mild and moderate states were characterized by a narrower distribution of beta frequencies that shifted toward the 8 -15 Hz range. Additionally, coupling between the phase of beta and the amplitude of highfrequency oscillations (256 -362 Hz) emerged in the mild state and increased with severity. These findings provide a novel mechanistic framework to understand how progressive loss of dopamine translates into abnormal information processing in the pallidum through alterations in oscillatory activity. The results suggest that rather than the emergence of oscillatory activity in one frequency spectrum or the other, parkinsonian motor signs may relate more to the development of altered coupling across multiple frequency spectrums.

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Deep brain stimulation (DBS) therapy has become an essential tool for treating a range of brain disorders. In the resting state, DBS is known to regularize spike activity in and downstream of the stimulated brain target, which in turn has been hypothesized to create informational lesions. Here, we specifically test this hypothesis using repetitive joint articulations in two non-human Primates while recording single-unit activity in the sensorimotor globus pallidus and motor thalamus before, during, and after DBS in the globus pallidus (GP) GP-DBS resulted in: (1) stimulus-entrained firing patterns in globus pallidus, (2) a monophasic stimulus-entrained firing pattern in motor thalamus, and (3) a complete or partial loss of responsiveness to joint position, velocity, or acceleration in globus pallidus (75%, 12/16 cells) and in the pallidal receiving area of motor thalamus (ventralis lateralis pars oralis, VLo) (38%, 21/55 cells). Despite loss of kinematic tuning, cells in the globus pallidus (63%, 10/16 cells) and VLo (84%, 46/55 cells) still responded to one or more aspects of joint movement during GP-DBS. Further, modulated kinematic tuning did not always necessitate modulation in firing patterns (2/12 cells in globus pallidus; 13/23 cells in VLo), and regularized firing patterns did not always correspond to altered responses to joint articulation (3/4 cells in globus pallidus, 11/33 cells in VLo). In this context, DBS therapy appears to function as an amalgam of network modulating and network lesioning therapies.

Designing bug-free medical device software is difficult, especially in complex implantable devices that may be used in unanticipated contexts. Safety recalls of pacemakers and implantable cardioverter defibrillators due to firmware problems between 1990 and 2000 affected over 200,000 devices, comprising 41% of the devices recalled and are increasing in frequency. There is currently no formal methodology or open experimental platform to validate and verify the correct operation of medical device software. To this effect, a real-time Virtual Heart Model (VHM) has been developed to model the electrophysiological operation of the functioning (i.e. during normal sinus rhythm) and malfunctioning (i.e. during arrhythmia) heart. We present a methodology to extract timing properties of the heart to construct a timed-automata model. The platform exposes functional and formal interfaces for validation and verification of implantable cardiac devices. We demonstrate the VHM is capable of generating clinically-relevant response to intrinsic (i.e. premature stimuli) and external (i.e. artificial pacemaker) signals for a variety of common arrhythmias. By connecting the VHM with a pacemaker model, we are able to pace and synchronize the heart during the onset of irregular heart rhythms. The VHM has also been implemented on a hardware platform for closed-loop experimentation with existing and virtual medical devices. The VHM allows for exploratory electrophysiology studies for physicians to evaluate their diagnosis and determine the appropriate device therapy. This integrated functional and formal device design approach will potentially help expedite medical device certification for safer operation. Abstract-Designing bug-free medical device software is difficult, especially in complex implantable devices that may be used in unanticipated contexts. Safety recalls of pacemakers and implantable cardioverter defibrillators due to firmware problems between 1990 and 2000 affected over 200,000 devices, comprising 41% of the devices recalled and are increasing in frequency [1]. There is currently no formal methodology or open experimental platform to validate and verify the correct operation of medical device software. To this effect, a real-time Virtual Heart Model (VHM) has been developed to model the electrophysiological operation of the functioning (i.e. during normal sinus rhythm) and malfunctioning (i.e. during arrhythmia) heart. We present a methodology to extract timing properties of the heart to construct a timed-automata model. The platform exposes functional and formal interfaces for validation and verification of implantable cardiac devices. We demonstrate the VHM is capable of generating clinically-relevant response to intrinsic (i.e. premature stimuli) and external (i.e. artificial pacemaker) signals for a variety of common arrhythmias. By connecting the VHM with a pacemaker model, we are able to pace and synchronize the heart during the onset of irregular heart rhythms. The VHM has also been implemented on a hard...

Together, these results suggest that endovascular DBS can serve as a complementary approach to stereotactic DBS in select cases.