Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography

Stereo-Electroencephalography-Guided Network Neuromodulation for Psychiatric Disorders: The Neurophysiology Monitoring Unit

BACKGROUND AND OBJECTIVES

Recent advances in stereotactic and functional neurosurgery have brought forth the stereo-electroencephalography approach which allows deeper interrogation and characterization of the contributions of deep structures to neural and affective functioning. We argue that this approach can and should be brought to bear on the notoriously intractable issue of defining the pathophysiology of refractory psychiatric disorders and developing patient-specific optimized stimulation therapies.

METHODS

We have developed a suite of methods for maximally leveraging the stereo-electroencephalography approach for an innovative application to understand affective disorders, with high translatability across the broader range of refractory neuropsychiatric conditions.

RESULTS

This article provides a roadmap for determining desired electrode coverage, tracking high-resolution research recordings across a large number of electrodes, synchronizing intracranial signals with ongoing research tasks and other data streams, applying intracranial stimulation during recording, and design choices for patient comfort and safety.

CONCLUSION

These methods can be implemented across other neuropsychiatric conditions needing intensive electrophysiological characterization to define biomarkers and more effectively guide therapeutic decision-making in cases of severe and treatment-refractory disease.

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising

BACKGROUND

Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis.

NEW METHOD

In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered.

RESULT

SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates.

COMPARISON WITH EXISTING METHODS

SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering.

CONCLUSION

The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM)

Objective.Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals.Approach.To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects.Main results.MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5-10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with94%±1.47%sensitivity and99%±1.01%specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation.Significance.MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering

Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

A novel framework for the removal of pacing artifacts from bio-electrical recordings

BACKGROUND

Electroceuticals provide clinical solutions for a range of disorders including Parkinson's disease, cardiac arrythmias and are emerging as a potential treatment option for gastrointestinal disorders. However, pre-clinical investigations are challenged by the large stimulation artifacts registered in bio-electrical recordings.

METHOD

A generalized framework capable of isolating and suppressing stimulation artifacts with minimal intervention was developed. Stimulation artifacts with different pulse-parameters in synthetic and experimental cardiac and gastrointestinal signals were detected using a Hampel filter and reconstructed using 3 methods: i) autoregression, ii) weighted mean, and iii) linear interpolation.

RESULTS

Synthetic stimulation artifacts with amplitudes of 2 mV and 4 mV and pulse-widths of 50 ms, 100 ms, and 200 ms were successfully isolated and the artifact window size remained uninfluenced by the pulse-amplitude, but was influenced by pulse-width (e.g., the autoregression method resulted in an identical Root Mean Square Error (RMSE) of 1.64 mV for artifacts with 200 ms pulse-width and both 2 mV and 4 mV amplitudes). The performance of autoregression (RMSE = 1.45 ± 0.16 mV) and linear interpolation (RMSE = 1.22 ± 0.14 mV) methods were comparable and better than weighted mean (RMSE = 5.54 ± 0.56 mV) for synthetic data. However, for experimental recordings, artifact removal by autoregression was superior to both linear interpolation and weighted mean approaches in gastric, small intestinal and cardiac recordings.

CONCLUSIONS

A novel signal processing framework enabled efficient analysis of bio-electrical recordings with stimulation artifacts. This will allow the bio-electrical events induced by stimulation protocols to be efficiently and systematically evaluated, resulting in improved stimulation therapies.

Current developments in artificial intelligence from obstetrics and gynecology to urogynecology

In today's medical practice clinicians need to struggle with a huge amount of data to improve the outcomes of the patients. Sometimes one clinician needs to deal with thousands of ultrasound images or hundred papers of laboratory results. To overcome this shortage, computers get in help of human beings and they are educated under the term "artificial intelligence." We were using artificial intelligence in our daily lives (i.e., Google, Netflix, etc.), but applications in medicine are relatively new. In obstetrics and gynecology, artificial intelligence models mostly use ultrasound images for diagnostic purposes but nowadays researchers started to use other medical recordings like non-stress tests or urodynamics study results to develop artificial intelligence applications. Urogynecology is a developing subspecialty of obstetrics and gynecology, and articles about artificial intelligence in urogynecology are limited but in this review, we aimed to increase clinicians' knowledge about this new approach.

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML's popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

Artificial intelligence in functional urology: how it may shape the future

PURPOSE OF REVIEW

The aim of the present manuscript is to provide an overview on the current state of artificial intelligence (AI) tools in either decision making, diagnosis, treatment options, or outcome prediction in functional urology.

RECENT FINDINGS

Several recent studies have shed light on the promising potential of AI in functional urology to investigate lower urinary tract dysfunction pathophysiology but also as a diagnostic tool by enhancing the existing evaluations such as dynamic magnetic resonance imaging or urodynamics. AI may also improve surgical education and training because of its automated performance metrics recording. By bringing prediction models, AI may also have strong therapeutic implications in the field of functional urology in the near future. AI may also be implemented in innovative devices such as e-bladder diary and electromechanical artificial urinary sphincter and could facilitate the development of remote medicine.

SUMMARY

Over the past decade, the enthusiasm for AI has been rising exponentially. Machine learning was well known, but the increasing power of processors and the amount of data available has provided the platform for deep learning tools to expand. Although the literature on the applications of AI technology in the field of functional urology is relatively sparse, its possible uses are countless especially in surgical training, imaging, urodynamics, and innovative devices.

Pattern recognition algorithm to identify detrusor overactivity on urodynamics

AIMS

Detrusor overactivity (DO) of the bladder is a finding on urodynamic studies (UDS) that often correlates with lower urinary tract symptoms and drives management. However, UDS interpretation remains nonstandardized. We sought to develop a mathematical model to reliably identify DO in UDS.

METHODS

We utilized UDS archive files for studies performed at our institution between 2013 and 2019. Raw tracings of vesical pressure, abdominal pressure, detrusor pressure, infused volume, and all annotations during UDS were obtained. Patients less than 1 year old, studies with calibration issues, or those with significant artifacts were excluded. In the training set, five representative DO patterns were identified. Candidate Pdet signal segments were matched to representative DO patterns. Manifold learning and dynamic time warping algorithms were used. Five-fold cross validation (CV) was used to evaluate the performance.

RESULTS

A total of 799 UDS studies were included. The median age was 9 years (range, 1-33). There were 1,742 DO events that did not overlap with annotated artifacts (cough, cry, valsalva, movements). The AUC of the training sets from the five-fold CV was 0.84 ± 0.01. The five-fold CV leads to an overall accuracy 81.35%, and sensitivity and specificity of detecting DO events are 76.92% and 81.41%, respectively, in the testing set.

CONCLUSIONS

Our predictive model using machine learning algorithms provides promising performance to facilitate automated identification of DO in UDS. This would allow for standardization and potentially more reliable UDS interpretation. Signal processing and machine learning interpretation of the other components of UDS are forthcoming.

Network-Targeted, Multi-site Direct Cortical Stimulation Enhances Working Memory by Modulating Phase Lag of Low-Frequency Oscillations

Working memory is mediated by the coordinated activation of frontal and parietal cortices occurring in the theta and alpha frequency ranges. Here, we test whether electrically stimulating frontal and parietal regions at the frequency of interaction is effective in modulating working memory. We identify working memory nodes that are functionally connected in theta and alpha frequency bands and intracranially stimulate both nodes simultaneously in participants performing working memory tasks. We find that in-phase stimulation results in improvements in performance compared to sham stimulation. In addition, in-phase stimulation results in decreased phase lag between regions within working memory network, while anti-phase stimulation results in increased phase lag, suggesting that shorter phase lag in oscillatory connectivity may lead to better performance. The results support the idea that phase lag may play a key role in information transmission across brain regions. Thus, brain stimulation strategies to improve cognition may require targeting multiple nodes of brain networks.

Acute modulation of the limbic network with low and high-frequency stimulation of the human fornix

Targeted stimulation of white matter has opened newer perspectives in the field of neuromodulation, towards an attempt to improve memory or as a therapy for epilepsy. Stimulation of the fornix, being a part of the Papez circuit, is likely to modulate the limbic network excitability. However, the stimulation-frequency dependent variability in network excitability is unknown. In the case study, which involved stereo electroencephalographic (SEEG) recording of field potentials in a 48-year old left-handed woman with suspected temporal lobe epilepsy, we demonstrated the network effects of acute low (1 and 10 Hz) and high (50 Hz) frequency electrical stimulation of fornix. Mapping the short-latency evoked responses to forniceal stimulation confirmed the SEEG target localization within the Papez circuit. Low and high-frequency stimulation of the fornix produced opposite effects in the post-stimuli excitability, with the latter causing increased excitability in the limbic network that culminated in a clinical seizure. A distinct spectral peak around 8 Hz confirmed that sensing field potentials from the forniceal white matter is feasible. This is the first case study that provided an insight into how the temporal patterning of forniceal stimulation altered the downstream limbic network excitability.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

OBJECTIVE

Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available.

APPROACH

We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings.

MAIN RESULTS

We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation.

SIGNIFICANCE

Our work will enable future advances in neural engineering with simultaneous stimulation and recording.