A fast solution to robust minimum variance beamformer and application to simultaneous MEG and local field potential

An interesting relationship between the probability-constrained and worst-case optimization based robust minimum variance (MV) beamformers has been discovered. It is shown that both in the cases of circularly symmetric Gaussian and worst-case distributions of the steering vector mismatch, the probability-constrained robust MV beamforming problem can be tightly approximated as a convex second-order cone programming (SOCP) problem. The latter problem is mathematically equivalent to that resulting from the deterministic worst-case approach and, therefore, probability-constrained beamformers can be interpreted and implemented using their deterministic worst-case counterparts. However, an important advantage of the developed probability-constrained MV beamformers with respect to their standard worst-case counterparts is that the former approaches enable to explicitly quantify the parameters of the uncertainty region in terms of the beamformer outage probability.

To the Editor: Modern deep brain stimulation (DBS) devices are often advertized as being “whole-body conditional,” and this label is an important consideration when assessing the suitability of DBS for a given patient (eg, a patient who would likely require spinal imaging in the future). However, we found that unforeseen contradictions between DBS vendor guidelines and MRI manufacturer manuals rendered diagnostic “whole-body” (eg, spine) imaging unfeasible at our center, leading to deleterious effects on patient care. As described previously,1 the main risk of MRI on deep DBS patients is heating at the DBS lead tips. To mitigate this risk, DBS vendor guidelines typically include 2 heating-related thresholds that scans must be acquired within: most commonly (1) specific absorption rate (SAR) or, if available, (2) B1 + rms. Of the 2, SAR thresholds are extremely restrictive while the more permissive B1 + rms metric is less established and not universally available. Although these factors have been useful in preventing adverse events, they also have limited MRI for DBS patients.2 At our institution, our previous MRI (1.5T GE-Signa; software: HDxT 23.0_V021406.a) did not offer B1 + rms, and the SAR thresholds specified in DBS vendor guidelines meant that it was only practical to acquire diagnostic quality imaging of the head. Given the demand for MRI in DBS patients—1 study estimating that up to 75% of patients require MRI within 10 years of DBS surgery, the majority (62%) of whom calling for body and/or spine scans3—we performed rigorous phantom testing to ensure the safety of off-label (ie, outside SAR threshold) spinal scans on our GE machine.4 Recently, we upgraded our MRI to the one enabling B1 + rms measurements (1.5T Siemens-Magnetom; software: XA20), theoretically allowing us to leverage a more permissive heating-related threshold and acquire diagnostic scans within DBS guidelines. However, we found that the Siemens system displayed a warning prohibiting scans based on B1 rms values: “The actual B1 rms value may be much higher than the value displayed. Do not scan patients with implants based on this value.” This is in conflict with the recommendations of DBS vendor guidelines, which even acknowledge potential discrepancies between real and predicted B1 + rms: “The actual B1 + rms value may differ slightly from the predicted value… The MRI Guidelines for Medtronic …requires only that the predicted B1 + rms value for the protocol is <2.0 µT.”5 Consequently, our MRI department was unable to perform clinically practical scans for extracranial pathology (eg, for the spine) without breaching DBS vendor guidelines or MRI manufacturer recommendations. The delays caused by this impasse have compromised patient care and ultimately led us back to square 1, in which we had to reproduce our phantom safety experiments using the Siemens MRI to facilitate off-label whole-body imaging. It is important that the DBS and radiology community are aware of the contradictory guidelines set by major MRI and DBS manufacturers, which we have not previously seen described in the literature and the considerable hurdles that they can impose.

In this paper, eyeblink artifacts of a multi-channel electroencephalogram (EEG) are extracted and removed by employing a spatial filter designed using the proposed efficient method via the robust minimum variance beamforming (RMVB). Unlike the conventional method where the spatial filter is designed using the correlation and prior knowledge of eyeblink from all channels of EEG, the spatial filter is obtained by simultaneously taking into consideration the local correlation and prior knowledge of each groups of EEG channels. The prior knowledge is obtained by taking into account the space-time-frequency information via the STF-TS model. Simulation results show that, by using less computational complexity, the proposed method can efficiently remove eyeblink artifacts from the multi-channel EEG as well as the conventional method.

Background/Objectives: Subcortical local field potentials (LFPs) provide a valuable in vivo window into the neurophysiology of the dystonia network. These signals can be recorded through Deep Brain Stimulation (DBS) devices and combined with whole-head techniques such as magnetoencephalography (MEG) to study cortical–subcortical interactions. However, simultaneous LFP-MEG acquisition poses challenges, including interference from the DBS device and synchronization issues. We present preliminary data on the feasibility and signal quality of concurrent LFP and MEG recordings in dystonia patients. Methods: We assessed simultaneous MEG-LFP recordings in 11 patients with inherited or idiopathic dystonia who underwent bilateral DBS lead implantation in the Globus Pallidus Internus (GPi). Two synchronization strategies were tested: (1) the Tapping method, using an accelerometer placed on the DBS device, and (2) the Stimulation method, which generated detectable artifacts during sham stimulation. Results: Both methods successfully aligned MEG and LFP signals with a mean temporal delay of 91 ± 22 ms for the Tapping method and 288 ± 166 ms for the Stimulation method. Post-implantation signal-to-noise ratio analysis revealed slight degradation but no significant impact on MEG quality (gradiometers: −0.12 ± 1.85 dB; magnetometers: −0.47 ± 2.03 dB). Conclusions: Simultaneous MEG-LFP recordings in dystonic patients are feasible, yielding high-quality signals, and reliable synchronization. Temporal alignment improved with practice, suggesting a short learning curve. This method opens new opportunities to study cortical-subcortical dynamics and strengthens the potential of combining MEG-LFP approaches for investigating dystonia.

Thus far only a few closed-loop deep brain stimulation (DBS) devices have been developed. These devices measure one biomarker for closing the stimulation loop. However, there are many types of neurological disorders requiring different biomarkers to be monitored. Therefore, a multiple-biomarker-based neural sensing device is needed to achieve closed-loop DBS for multiple disorders. This paper presents the design, fabrication, and in vitro evaluation of a miniature multi-biomarker-based neural sensor for closed-loop DBS. The developed device is capable of concurrent sensing and processing of action potentials (APs) and local field potentials (LFPs) from within the brain. A signal conditioning circuit involving the following stages is incorporated into the device: pre-amplification through an instrumentation amplifier, noise and artifact rejection via active filters, and post-amplification by means of micro-power operational amplifiers. It is a portable and miniature device allowing for use in closed-loop DBS of small laboratory animals. The device benefits from a gain of 100 dB in both the biomarker paths. The frequency specifications of the AP and LFP sensing circuits are 300 Hz–10 kHz and 2 Hz–40 Hz, respectively. It requires only $965~\mu \text{A}$ to operate on both channels from a 3-V coin battery. The device has been validated via a bench-test by using both sinusoidal signals and pre-recorded human neural signals. In addition, in vitro validation using saline solution has been carried out on the device confirming its accurate operation. The experimental results are presented and discussed.

In order to solce the problem of lacking robustness against the unknown array steering vector in minimum variance beamformer. A new beamforming algorithm based on spherical uncertainty set is proposed in this paper. In this paper, we investigate the robust beamforming with spherical constraint on steering vector further and provide a novel robust minimum variance beamforming (RMVB) algorithm. Compared with existing methods in this field, the new algorithm is efficient in computation and more numerical stable.

Although the minimum variance (MV) beamformer can provide enhancement in both resolution and contrast of ultrasound images when compared with conventional delay-and-sum (DAS) beamforming, its clinical application is limited by its sensitivity to phase aberrations. Several robust MV beamformers have been proposed, but present limitations when faced with second order phase aberration. Additionally, the information that can be obtained from phase aberration correction (PAC) methods is not utilized and the gain in robustness is usually accompanied by a loss in resolution or contrast. In the current work, we propose a new robust MV beamformer that uses a locally adaptive PAC (LAPAC-MV) in order to cope with the aformentioned difficulties. The method is based on a PAC estimation at every point in the image domain and a profile decision logic is included in the algorithm to take into account regions with low signal power. Preliminary results in full wave simulations and experimental phantom data are reported. In the resulting images, LAPAC-MV achieved enhancement in resolution of 0.03 mm, and reduction in sidelobe levels of around 8 dB when compared with precorrected DAS (DAS-PC) or precorrected MV (MV-PC). Contrast of LAPAC-MV is equivalent to MV-PC in cyst target simulations but the former showed improvements of approximately 1 dB in contrast in experiments with a 20 mm thick tissue aberrator.

Chapter 7 - Magnetic resonance imaging safety of deep brain stimulator devices

Despite the efficiency of deep brain stimulation (DBS) in the treatment of Parkinson’s disease (PD), the respective systems need to be further optimized. The lack of accurate PD-related quantitative biomarkers limits the improvement of adaptive DBS (aDBS) systems. Now, most DBS devices are open-loop sets with fixed stimulation parameters, which prevents them from coping with movement fluctuations and rapid changes of the symptoms. At the same time, ample evidence has shown that abnormal oscillations in the composition of local field potential (LFP) signals recorded in PD patients can reflect, to a certain extent, the crucial symptoms of the desease. Therefore, acquisition of LFP signals, extraction of abnormal oscillations, and acquisition of indicative biomarkers is the core technical issue with respective to the aDBS systems. Here, we review the current feature extraction methods in PD, summarize results of the latest research, analyze the difficulties of extracting the quantitative biomarkers, and discuss the developmental trend of feature extraction for aDBS.

Deep brain stimulation (DBS) of the subthalamic nucleus is an effective treatment for Parkinson disease. However, DBS is not responsive to an individual's disease state, and programming parameters, once established, do not change to reflect disease state. Local field potentials (LFPs) recorded from DBS electrodes are being investigated as potential biomarkers for the Parkinson disease state. However, no patient data exist about what happens to LFPs over the lifetime of the implant. We investigated whether LFP amplitude and response to limb movement differed between patients implanted acutely with subthalamic nucleus DBS electrodes and patients implanted 2 to 7 years previously. We recorded LFPs at DBS surgery time (9 subjects), 3 weeks after initial placement (9 subjects), and 2 to 7 years (median: 3.5) later during implanted programmable generator replacement (11 sides). LFP power-frequency spectra for each of 3 bipolar electrode derivations of adjacent contacts were calculated over 5-minute resting and 30-second movement epochs. Monopolar impedance data were used to evaluate trends over time. There was no significant difference in β-band LFP amplitude between initial electrode implantation (OR) and 3-week post-OR times (P=.94). However, β-band amplitude was lower at implanted programmable generator replacement times than in OR (P=.008) and post-OR recordings (P=.039). Impedance measurements declined over time (P<.001). Postoperative LFP activity can be recorded years after DBS implantation and demonstrates a similar profile in response to movement as during acute recordings, although amplitude may decrease. These results support the feasibility of constructing a closed-loop, patient-responsive DBS device based on LFP activity.

In this paper, a dual-biomarker-based neural sensing and conditioning device is proposed for closing the feedback loop in deep brain stimulation devices. The device explores both local field potentials (LFPs) and action potentials (APs) as measured biomarkers. It includes two channels, each having four main parts: (1) a pre-amplifier with built-in low-pass filter, (2) a ground shifting circuit, (3) an amplifier with low-pass function, and (4) a high-pass filter. The design specifications include miniature-size, light-weight, and 100 dB gain in the LFP and AP channels. This device has been validated through bench and in-vitro tests. The bench tests have been performed using different sinusoidal signals and pre-recorded neural signals. The in-vitro tests have been conducted in the saline solution that mimics the brain environment. The total weight of the device including a 3 V coin battery, and battery holder is 1.2 g. The diameter of the device is 11.2 mm. The device can be used to concurrently sense LFPs and APs for closing the feedback loop in closed-loop deep brain stimulation systems. It provides a tetherless head-mountable platform suitable for pre-clinical trials.

Deep brain stimulation (DBS) of the globus pallidus internus (GPi) can be an effective intervention for severe refractory dystonia. Nonetheless, outcomes are often variable and unpredictable. To improve and predict outcomes, further development of physiomarkers can be useful. In contrast to more established β (±13–30 Hz) local field potential (LFP) oscillations in Parkinson's disease, low-frequency (4–12 Hz) oscillations (LFOs) are present in dystonia.1 LFP recordings perioperatively demonstrate suppression of LFOs related to clinical improvement.2, 3 Furthermore, LFOs have been used as a physiomarker for adaptive DBS (aDBS).3, 4 Although it is understood that LFOs evolve, longitudinal evolution of LFOs and their relation to symptoms are not yet established. Here, we describe a 22-day recording of LFOs with clinical correlations in a pediatric patient with generalized dystonia, who first received bilateral GPi DBS in a status dystonicus (SD) in 2019.5 A 10-year-old patient with GNAO1-related dystonia was presented at our emergency department with an impending SD, triggered by left lead malfunctioning. He underwent DBS revision surgery (day 0) and received a sensing-enabled rechargeable device (Medtronic Percept RC, Minneapolis, MN) and a new left lead on the same position as the former lead. The neurostimulator was activated perioperatively with the following stimulation parameters: bilateral monopolar case (+), contacts 2 (right) and 9 (left), pulse width (90 μs), stimulation frequency 130 Hz, and stimulation amplitude 2.2 mA (right) and 2.7 mA (left). Brain sensing was activated, and a periodogram of LFPs was analyzed to identify a frequency band of interest to record every 10 minutes. Presence of dystonia (0 = absent, 1 = present) was assessed four times daily for each body part (arm, leg, face, and torso) and side (left/right). On day 7 the new left lead needed to be repositioned because his symptoms, predominantly right sided, did not improve sufficiently. LFPs were analyzed, and the periodogram underwent bandpass filtering from 1 to 100 Hz and bandstop filtering at 40 Hz for artifact removal. Subsequently, fitting oscillations and one-over-F6 was applied. Longitudinal LFP band powers were normalized using z scores. After lead replacement on day 7, separate z scores were calculated pre- and postoperatively. Postoperative computed tomography imaging confirmed lead localization. Clinical scores and LFPs were plotted with a 5-day moving average. Spearman's correlation was performed on dystonia severity estimation and mean LFP power. During a 1-minute rest recording (day 0), a clear LFO peak was visible for the left GPi (Fig. 1A). The surrounding 5-Hz spectrum (7.27–12.27 Hz) was used for chronic LFP recording for 22 days. No major mechanical movement artifacts were visible in the signal. Figure 1B shows the relation between severity for right-sided dystonia and left GPi-LFP power, with a significant correlation of −0.69 (P < 0.001). The right GPi is not shown because no clear LFP peak could be detected. Here, we demonstrated a strong correlation between LFOs and severity of dystonia, which further illustrates the importance of this physiomarker.1, 2 With sensing-enabled DBS systems, LFO dynamics might function as a physiomarker to help titrating DBS for dystonia and guide treatment, potentially applied as aDBS in dystonia.3, 4 J.M.D. received funding for research from the Netherlands Organisation for Health Research and Development (ZonMW), Medtronic, and Amsterdam Neuroscience, all paid to the institution. L.A.P. received a research grant from the ForWhishdom Foundation. P.R.S. received TKI-PPP grants from the Ministry of Economic Affairs, Brainlab, and Boston Scientific, and support grants from the Dutch Brain Foundation. R.M.A.B. received research grants from Medtronic, Bial, ZonMw, AMC Foundation, ROMO Foundation, and Stichting ParkinsonFonds, all paid to the institution. M.B received research funding from the EU Joint Programme—Neurodegenerative Disease Research (JPND) project (2020 call), the Amsterdam UMC TKI-PPP grant (2021 and2023 call), Stichting ParkinsonFonds (2023), and Medtronic (2023, 2024). (1) Research project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the first draft, B. Review and Critique. D. H: 1A, 1B, 1C, 2A, 2B, 3A L.R.H: 1A, 1B, 1C, 2C, 3A, 3B M.J.S: 1C, 2C, 3B J.M.D: 2C, 3B P.R.S: 2C, 3B R.M.A.B: 2C, 3B L.A.P: 1B, 2C, 3B M.B: 1A, 1B, 2C, 3B The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Data S1. Supporting Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials

MRI is a valuable clinical and research tool for patients undergoing deep brain stimulation (DBS). However, risks associated with imaging DBS devices have led to stringent regulations, limiting the clinical and research utility of MRI in these patients. The main risks in patients with DBS devices undergoing MRI are heating at the electrode tips, induced currents, implantable pulse generator dysfunction, and mechanical forces. Phantom model studies indicate that electrode tip heating remains the most serious risk for modern DBS devices. The absence of adverse events in patients imaged under DBS vendor guidelines for MRI demonstrates the general safety of MRI for patients with DBS devices. Moreover, recent work indicates that-given adequate safety data-patients may be imaged outside these guidelines. At present, investigators are primarily focused on improving DBS device and MRI safety through the development of tools, including safety simulation models. Existing guidelines provide a standardized framework for performing safe MRI in patients with DBS devices. It also highlights the possibility of expanding MRI as a tool for research and clinical care in these patients going forward.

Deep brain stimulation has emerged as an effective medical procedure that has therapeutic efficacy in a number of neuropsychiatric disorders. Preclinical research involving laboratory animals is being conducted to study the principles, mechanisms, and therapeutic effects of deep brain stimulation. A bottleneck is, however, the lack of deep brain stimulation devices that enable long term brain stimulation in freely moving laboratory animals. Most of the existing devices employ complex circuitry, and are thus bulky. These devices are usually connected to the electrode that is implanted into the animal brain using long fixed wires. In long term behavioral trials, however, laboratory animals often need to continuously receive brain stimulation for days without interruption, which is difficult with existing technology. This paper presents a low power and lightweight portable microdeep brain stimulation device for laboratory animals. Three different configurations of the device are presented as follows: 1) single piece head mountable; 2) single piece back mountable; and 3) two piece back mountable. The device can be easily carried by the animal during the course of a clinical trial, and that it can produce non-stop stimulation current pulses of desired characteristics for over 12 days on a single battery. It employs passive charge balancing to minimize undesirable effects on the target tissue. The results of bench, in-vitro, and in-vivo tests to evaluate the performance of the device are presented.