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Background: Deep brain stimulation (DBS) and other neuromodulatory techniques are being increasingly utilized to treat refractory neurologic and psychiatric disorders., Objective: /Hypothesis: To better understand the circuit-level pathophysiology of treatment-resistant depression (TRD) and treat the network-level dysfunction inherent to this challenging disorder, we adopted an approach of inpatient intracranial monitoring borrowed from the epilepsy surgery field., Methods: We implanted 3 patients with 4 DBS leads (bilateral pair in both the ventral capsule/ventral striatum and subcallosal cingulate) and 10 stereo-electroencephalography (sEEG) electrodes targeting depression-relevant network regions. For surgical planning, we used an interactive, holographic visualization platform to appreciate the 3D anatomy and connectivity. In the initial surgery, we placed the DBS leads and sEEG electrodes using robotic stereotaxy. Subjects were then admitted to an inpatient monitoring unit for depression-specific neurophysiological assessments. Following these investigations, subjects returned to the OR to remove the sEEG electrodes and internalize the DBS leads to implanted pulse generators., Results: Intraoperative testing revealed positive valence responses in all 3 subjects that helped verify targeting. Given the importance of the network-based hypotheses we were testing, we required accurate adherence to the surgical plan (to engage DBS and sEEG targets) and stability of DBS lead rotational position (to ensure that stimulation field estimates of the directional leads used during inpatient monitoring were relevant chronically), both of which we confirmed (mean radial error 1.2±0.9 mm; mean rotation 3.6±2.6°)., Conclusion: This novel hybrid sEEG-DBS approach allows detailed study of the neurophysiological substrates of complex neuropsychiatric disorders., Competing Interests: Declaration of competing interest SAS is a consultant for Boston Scientific, Neuropace, Koh Young, Zimmer Biomet, Varian, Sensoria Therapeutics, and co-founder of Motif Neurotech. NP is a consultant for Boston Scientific and Abbott. WG has received donated devices from Medtronic and is a consultant for Biohaven Pharmaceuticals. SJM has served as a consultant for Alkermes, Allergan, Axsome Therapeutics, Clexio Biosciences, Engrail Therapeutics, Intra-Cellular Therapies, Janssen, Neurocrine, Perception Neurosciences, Praxis Precision Medicines, and Sage Therapeutics. CCM is a consultant for Boston Scientific; receives royalties from Hologram Consultants, Neuros Medical, and Qr8 Health; and is a shareholder in the following companies: Hologram Consultants, Surgical Information Sciences, CereGate, Autonomic Technologies, Cardionomic, Enspire DBS. All other authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The success of deep brain stimulation (DBS) for treating Parkinson's disease has led to its application to several other disorders, including treatment-resistant depression. Results with DBS for treatment-resistant depression have been heterogeneous, with inconsistencies largely driven by incomplete understanding of the brain networks regulating mood, especially on an individual basis. We report results from the first subject treated with DBS for treatment-resistant depression using an approach that incorporates intracranial recordings to personalize understanding of network behavior and its response to stimulation. These recordings enabled calculation of individually optimized DBS stimulation parameters using a novel inverse solution approach. In the ensuing double-blind, randomized phase incorporating these bespoke parameter sets, DBS led to remission of symptoms and dramatic improvement in quality of life. Results from this initial case demonstrate the feasibility of this personalized platform, which may be used to improve surgical neuromodulation for a vast array of neurologic and psychiatric disorders., (Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objective. Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians' experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms. Approach. This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described. Main results. Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target ( P = 0.002). Significance . The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area., (© 2022 IOP Publishing Ltd.)

Background: The efficacy of psychiatric DBS is thought to be driven by the connectivity of stimulation targets with mood-relevant fronto-temporal networks, which is typically evaluated using diffusion-weighted tractography., Objective: Leverage intracranial electrophysiology recordings to better predict the circuit-wide effects of neuromodulation to white matter targets. We hypothesize strong convergence between tractography-predicted structural connectivity and stimulation-induced electrophysiological responses., Methods: Evoked potentials were elicited by single-pulse stimulation to two common DBS targets for treatment-resistant depression - the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VCVS) - in two patients undergoing DBS with stereo-electroencephalographic (sEEG) monitoring. Evoked potentials were compared with predicted structural connectivity between DBS leads and sEEG contacts using probabilistic, patient-specific diffusion-weighted tractography., Results: Evoked potentials and tractography showed strong convergence in both patients in orbitofrontal, ventromedial prefrontal, and lateral prefrontal cortices for both SCC and VCVS stimulation targets. Low convergence was found in anterior cingulate (ACC), where tractography predicted structural connectivity from SCC targets but produced no evoked potentials during SCC stimulation. Further, tractography predicted no connectivity to ACC from VCVS targets, but VCVS stimulation produced robust evoked potentials., Conclusion: The two connectivity methods showed significant convergence, but important differences emerged with respect to the ability of tractography to predict electrophysiological connectivity between SCC and VCVS to regions of the mood-related network. This multimodal approach raises intriguing implications for the use of tractography in surgical targeting and provides new data to enhance our understanding of the network-wide effects of neuromodulation., Competing Interests: Declaration of competing interest CCM is a paid consultant for Boston Scientific Neuromodulation, receives royalties from Hologram Consultants, Neuros Medical, Qr8 Health, and is a shareholder in the following companies: Hologram Consultants, Surgical Information Sciences, CereGate, Autonomic Technologies, Cardionomic, Enspire DBS. NP is a consultant for Abbott. SAS is a consultant for Boston Scientific, Zimmer Biomet, Neuropace, Abbott, and Koh Young. MM and RM are employees of Boston Scientific Corporation. KC is a paid full-time employee of Brainlab, Inc. JAA, ET, DO, BAM, RKM, ACW, MER, ABA, AMN, WG, DB, and KRB do not report biomedical financial interests or potential conflicts of interest., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

The success of deep brain stimulation (DBS) for treating Parkinsons disease has led to its application to several other disorders, including treatment-resistant depression. Results with DBS for treatment-resistant depression have been heterogeneous, with inconsistencies largely driven by incomplete understanding of the brain networks regulating mood, especially on an individual basis. We report results from the first subject treated with DBS for treatment-resistant depression using an approach that incorporates intracranial recordings to personalize understanding of network behavior and its response to stimulation. These recordings enabled calculation of individually optimized DBS stimulation parameters using a novel inverse solution approach. In the ensuing double-blind, randomized phase incorporating these bespoke parameter sets, DBS led to remission of symptoms and dramatic improvement in quality of life. Results from this initial case demonstrate the feasibility of this personalized platform, which may be used to improve surgical neuromodulation for a vast array of neurologic and psychiatric disorders.