Tractography-Based Modeling Explains Treatment Outcomes in Patients Undergoing Deep Brain Stimulation for Obsessive-Compulsive Disorder.

BACKGROUND: Deep brain stimulation (DBS) is an established and expanding therapy for treatment-refractory obsessive-compulsive disorder. Previous work has suggested that a white matter circuit providing hyperdirect input from the dorsal cingulate and ventrolateral prefrontal regions to the subthalamic nucleus could be an effective neuromodulatory target. METHODS: We tested this concept by attempting to retrospectively explain through predictive modeling the ranks of clinical improvement as measured by the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) in 10 patients with obsessive-compulsive disorder who underwent DBS to the ventral anterior limb of internal capsule with subsequent programming uninformed by the putative target tract. RESULTS: Rank predictions were carried out using the tract model by a team that was completely uninvolved in DBS planning and programming. Predicted Y-BOCS improvement ranks significantly correlated with actual Y-BOCS improvement ranks at the 6-month follow-up (r = 0.75, p = .013). Predicted score improvements correlated with actual Y-BOCS score improvements (r = 0.72, p = .018). CONCLUSIONS: Here, we provide data in a first-of-its-kind report suggesting that normative tractography-based modeling can blindly predict treatment response in DBS for obsessive-compulsive disorder.

Optimizing Obsessive-Compulsive Symptom Measurement With the Yale-Brown Obsessive-Compulsive Scales-Second Edition.

The Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) is a widely used measure for assessing the presence and severity of obsessive-compulsive disorder symptoms. The Yale-Brown Obsessive-Compulsive Scale-Second Edition (Y-BOCS-II) was developed, in part, to more comprehensively evaluate symptom severity, especially in extremely ill patients, and improve consistency in detecting and incorporating avoidance behaviors. We present 3 case studies that demonstrate the enhanced ability of the Y-BOCS-II to: (1) detect fluctuations in symptom severity among extremely ill patients, (2) systematically incorporate avoidance variables for more accurate ratings, and (3) maintain strong convergence with the Y-BOCS in assessing patients presenting with mild to moderate symptoms. In addition, we outline how to obtain both Y-BOCS and Y-BOCS-II scores within 1 administration by adding Y-BOCS item 4 to the Y-BOCS-II assessment, then "back-coding" the Y-BOCS-II ratings to Y-BOCS ratings and using Y-BOCS item 4 in place of Y-BOCS-II item 2. The use of this method allows for more robust data collection while providing comparability across the literature.

PELP: Accounting for Missing Data in Neural Time Series by Periodic Estimation of Lost Packets.

Recent advances in wireless data transmission technology have the potential to revolutionize clinical neuroscience. Today sensing-capable electrical stimulators, known as "bidirectional devices", are used to acquire chronic brain activity from humans in natural environments. However, with wireless transmission come potential failures in data transmission, and not all available devices correctly account for missing data or provide precise timing for when data losses occur. Our inability to precisely reconstruct time-domain neural signals makes it difficult to apply subsequent neural signal processing techniques and analyses. Here, our goal was to accurately reconstruct time-domain neural signals impacted by data loss during wireless transmission. Towards this end, we developed a method termed Periodic Estimation of Lost Packets (PELP). PELP leverages the highly periodic nature of stimulation artifacts to precisely determine when data losses occur. Using simulated stimulation waveforms added to human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Then, we applied PELP to local field potential (LFP) recordings collected using an implantable, bidirectional DBS platform operating at various telemetry bandwidths. By effectively accounting for the timing of missing data, PELP enables the analysis of neural time series data collected via wireless transmission-a prerequisite for better understanding the brain-behavior relationships underlying neurological and psychiatric disorders.

Artifact characterization and mitigation techniques during concurrent sensing and stimulation using bidirectional deep brain stimulation platforms.

Bidirectional deep brain stimulation (DBS) platforms have enabled a surge in hours of recordings in naturalistic environments, allowing further insight into neurological and psychiatric disease states. However, high amplitude, high frequency stimulation generates artifacts that contaminate neural signals and hinder our ability to interpret the data. This is especially true in psychiatric disorders, for which high amplitude stimulation is commonly applied to deep brain structures where the native neural activity is miniscule in comparison. Here, we characterized artifact sources in recordings from a bidirectional DBS platform, the Medtronic Summit RC + S, with the goal of optimizing recording configurations to improve signal to noise ratio (SNR). Data were collected from three subjects in a clinical trial of DBS for obsessive-compulsive disorder. Stimulation was provided bilaterally to the ventral capsule/ventral striatum (VC/VS) using two independent implantable neurostimulators. We first manipulated DBS amplitude within safe limits (2-5.3 mA) to characterize the impact of stimulation artifacts on neural recordings. We found that high amplitude stimulation produces slew overflow, defined as exceeding the rate of change that the analog to digital converter can accurately measure. Overflow led to expanded spectral distortion of the stimulation artifact, with a six fold increase in the bandwidth of the 150.6 Hz stimulation artifact from 147-153 to 140-180 Hz. By increasing sense blank values during high amplitude stimulation, we reduced overflow by as much as 30% and improved artifact distortion, reducing the bandwidth from 140-180 Hz artifact to 147-153 Hz. We also identified artifacts that shifted in frequency through modulation of telemetry parameters. We found that telemetry ratio changes led to predictable shifts in the center-frequencies of the associated artifacts, allowing us to proactively shift the artifacts outside of our frequency range of interest. Overall, the artifact characterization methods and results described here enable increased data interpretability and unconstrained biomarker exploration using data collected from bidirectional DBS devices.

Intraoperative valence testing to adjudicate between ventral capsule/ventral striatum and bed nucleus of the stria terminalis target selection in deep brain stimulation for obsessive-compulsive disorder.

OBJECTIVE: Deep brain stimulation (DBS) is an accepted therapy for severe, treatment-refractory obsessive-compulsive disorder (trOCD). The optimal DBS target location within the anterior limb of the internal capsule, particularly along the anterior-posterior axis, remains elusive. Empirical evidence from several studies in the past decade has suggested that the ideal target lies in the vicinity of the anterior commissure (AC), either just anterior to the AC, above the ventral striatum (VS), or just posterior to the AC, above the bed nucleus of the stria terminalis (BNST). Various methods have been utilized to optimize target selection for trOCD DBS. The authors describe their practice of planning trajectories to both the VS and BNST and adjudicating between them with awake intraoperative valence testing to individualize permanent target selection. METHODS: Eight patients with trOCD underwent awake DBS with trajectories planned for both VS and BNST targets bilaterally. The authors intraoperatively assessed the acute effects of stimulation on mood, energy, and anxiety and implanted the trajectory with the most reliable positive valence responses and least stimulation-induced side effects. The method of intraoperative target adjudication is described, and the OCD outcome at last follow-up is reported. RESULTS: The mean patient age at surgery was 41.25 ± 15.1 years, and the mean disease duration was 22.75 ± 10.2 years. The median preoperative Yale-Brown Obsessive Compulsive Scale (Y-BOCS) score was 39 (range 34-40). Two patients had previously undergone capsulotomy, with insufficient response. Seven (44%) of 16 leads were moved to the second target based on intraoperative stimulation findings, 4 of them to avoid strong negative valence effects. Three patients had an asymmetric implant (1 lead in each target). All 8 patients (100%) met full response criteria, and the mean Y-BOCS score reduction across the full cohort was 51.2% ± 12.8%. CONCLUSIONS: Planning and intraoperatively testing trajectories flanking the AC-superjacent to the VS anteriorly and to the BNST posteriorly-allowed identification of positive valence responses and acute adverse effects. Awake testing helped to select between possible trajectories and identify individually optimized targets in DBS for trOCD.

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method.

Advances in therapeutic neuromodulation devices have enabled concurrent stimulation and electrophysiology in the central nervous system. However, stimulation artifacts often obscure the sensed underlying neural activity. Here, we develop a method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker are used for validation. Performance is found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: (1) it is superior in signal recovery; (2) it is easily adaptable to several neurostimulation paradigms; and (3) it has low complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies.

Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder.

Detection of neural signatures related to pathological behavioral states could enable adaptive deep brain stimulation (DBS), a potential strategy for improving efficacy of DBS for neurological and psychiatric disorders. This approach requires identifying neural biomarkers of relevant behavioral states, a task best performed in ecologically valid environments. Here, in human participants with obsessive-compulsive disorder (OCD) implanted with recording-capable DBS devices, we synchronized chronic ventral striatum local field potentials with relevant, disease-specific behaviors. We captured over 1,000 h of local field potentials in the clinic and at home during unstructured activity, as well as during DBS and exposure therapy. The wide range of symptom severity over which the data were captured allowed us to identify candidate neural biomarkers of OCD symptom intensity. This work demonstrates the feasibility and utility of capturing chronic intracranial electrophysiology during daily symptom fluctuations to enable neural biomarker identification, a prerequisite for future development of adaptive DBS for OCD and other psychiatric disorders.