Deep Brain Stimulation for Depression Informed by Intracranial Recordings

Abstract

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.

Keywords: Deep brain stimulation; Depression; Epilepsy; Network; Neuromodulation; Stereo-EEG.

Figure 1.. Network-guided implant planning.

A) Interactive trajectory planning for 4 DBS leads and 10 sEEG electrodes using structural connectivity hypotheses to guide the electrode positions. This process was performed using holographic augmented reality facilitated by custom software (HoloSEEG). B) VC/VS and SCC DBS leads target regions defined by patient-specific tractography (Supplementary Information). For clarity, we only show the left VC/VS and right SCC target regions. C, D) We used axonal pathway activation estimates of stimulation through the DBS leads (white arrowheads) to guide placement of the sEEG electrodes. Streamlines from VC/VS (C) and SCC (D) connect to distinct but partially overlapping frontotemporal regions. For sEEG trajectory planning, dorsal and lateral cortical termination regions of streamline estimates (e.g., dlPFC, vlPFC) were chosen as sEEG entry points. Ventral, medial, and orbital streamline termination regions (e.g., dACC, vmPFC, OFC) were chosen as sEEG target points. We thus tried to maximize sampling of relevant cortical areas while minimizing the number of sEEG electrodes. E,F) Frontal view and mesial view(26) showing actual implant locations in this subject. Stereo-EEG recording contacts (white) sample regions within depression-relevant frontotemporal networks, including dlPFC and dmPFC (blue), dACC (green), vmPFC (yellow), OFC (pink), and MTL (red). DBS lead contacts are also shown in pink (SCC) and orange (VC/VS).

Figure 2.. “Inverse” DBS programming strategy and clinical outcome.

A) The NMU recordings uniquely enable us to generate data-driven “Inverse” solutions for DBS programming. Selection of programming parameters in conventional DBS progresses in the “Forward” (upper arrow) direction: 1) Using a trial-and-error strategy, the clinician chooses different combinations of input stimulation parameters, including contact configuration, frequency, pulse width, and amplitude. 2) These parameters produce unknown changes in the brain, which in turn lead to (3) measurable behaviors (e.g., mood changes in the case of DBS for TRD). As described in the text, optimizing this Forward solution is challenging even in conventional DBS for TRD because of the mismatch in time constants and inconsistencies between programming adjustments and behavioral changes. Exploring the vast possible stimulation parameter space using this brute force approach is extremely time consuming and inefficient. Our trial uses NMU-derived intracranial recordings to pioneer the “Inverse solution” (lower arrow). The recordings allow us to measure and define various “network states”, electrographic patterns characterizing various mood states, and the network’s response to stimulation (4). Armed with this information, we can select a desired behavioral outcome (3), identify its associated network state (4), and then compute the combination of stimulation parameters (1) that are most likely to achieve it. This approach will become progressively more effective with future improvements in our understanding of brain-behavior relationships – in particular, the neural encoding of mood states. It is also readily translatable to a non-invasive future, as less invasive methods of network state measurement improve and can be substituted in (4). B) We tested the stimulation parameters derived from the NMU (yellow bar) during the outpatient open-label optimization phase (pink). Depression scores decreased steadily to remission (HAM-D<=7, blue dashed line; MADRS<10, orange dashed line). At week 37 the subject initiated the double-blind, randomized discontinuation phase. He was randomized to SCC discontinuation first. Stimulation amplitude was reduced from 100% to 0% in 25% increments per week (corresponding to shades of dark to light blue). Only the unblinded programmer knew the stimulation amplitude; the subject and remainder of the research team, including symptom rater, were blinded. For the first three weeks when stimulation was maintained at 100% (dark blue), the subject’s scores did not appreciably change, indicating lack of a nocebo effect that would have confounded interpretation. As amplitude was reduced over subsequent weeks his scores worsened, suggesting that his response to DBS was a true response, not a sham/placebo response. He met rescue criteria at week 44 (MADRS >25% increase and CGI-I [values shown along x-axis] of 6 [‘much worse’] relative to pre-discontinuation). At this point the discontinuation phase ended and unblinded stimulation resumed. VC/VS stimulation was not tapered, as dictated by our study protocol, to reduce risk to the subject (see Supplement). He again quickly remitted following DBS reinstatement. Abbreviations: HAM-A, Hamilton Anxiety Inventory (magenta); MADRS, Montgomery-Asberg Depression Rating Scale (orange); HAM-D, Hamilton Depression Inventory (blue); QIDS-SR, Quick Inventory of Depressive Symptomatology Self Report (green); CGI-I, Clinical Global Impression-Improvement.