Closed-loop neuromodulation in an individual with treatment-resistant depression

Abstract

Deep brain stimulation is a promising treatment for neuropsychiatric conditions such as major depression. It could be optimized by identifying neural biomarkers that trigger therapy selectively when symptom severity is elevated. We developed an approach that first used multi-day intracranial electrophysiology and focal electrical stimulation to identify a personalized symptom-specific biomarker and a treatment location where stimulation improved symptoms. We then implanted a chronic deep brain sensing and stimulation device and implemented a biomarker-driven closed-loop therapy in an individual with depression. Closed-loop therapy resulted in a rapid and sustained improvement in depression. Future work is required to determine if the results and approach of this n-of-1 study generalize to a broader population.

Extended Data Fig. 1 |. Network Activity and Connectivity.

a. biomarker identification was performed at two levels of spatial resolution (see also Fig. 1c). In the brain region level model, spectral power was averaged across all contacts within brain regions (60 features). Top neural features (defined by F-score, ANOVA) that discriminated high vs. low symptom severity states are shown. Gamma power in the bilateral AMY, right OFC, left SGC, and right HPC had high state discriminative potential (Accuracy: mean 0.73, std 0.08; AUC: mean 0.76, std 0.10). ROC curves reflect mean ± SEM over n=1000 randomly sampled features for the true model (blue) and the shuffled model (gray). b. Evoked potentials (z-scored relative to baseline) across the corticolimbic network due to single pulse stimulation in the right VC/VS, OFC and SGC is shown overlayed on brain as heatmap. Warmer colors indicate a larger N1 amplitude. c. Location of right sided sEEG leads targeting AMY (pink), VC/VS (orange), SGC (green), and OFC (blue). Fiber tracts (color coded by orientation) show putative structural connections between candidate pairs of stimulation and sensing contacts (VC/VS-AMY, VC/VS-OFC, SGC-AMY, OFC-AMY) from deterministic tractography using 3 mm ROIs centered on each contact. Tractography parameters were the same for all pairs: minimum FA = 0.1; minimum fiber length = 80 mm; maximum angulation = 20 degrees.

Extended Data Fig. 2 |. Overall Approach to Stimulation and Sensing Target Selection.

Multimodal method for personalized responsive stimulation multi-lead targeting began with clinical mapping to identify candidate sites where stimulation reliably led to symptom improvement across a range of doses and symptom severity states. Candidate sensing locations were identified by pairing resting state neural activity with symptom severity ratings to identify spectral power biomarkers that correlated with depression. The relationship between candidate stimulation and sensing targets were then tested using three approaches. First, effective network connectivity was assessed by examining the evoked response at nodes across the network due to single pulse stimulation at candidate targets. Second, structural connectivity between candidate contact pairs was assessed using tractography. Influential tracts were identified to help with retargeting during implantation of chronic stimulation device. Finally, the feasibility of closed-loop control was assessed by examining the effect of stimulation in candidate stimulation sites on putative biomarkers identified in candidate sensing locations. This personalized approach enabled us to identify one best stimulation and sensing target pair which were then utilized for the implantation of the RNS System and delivery of closed-loop neurostimulation therapy.

Fig. 1 |. Neural biomarker and limbic subnetwork of depression.

a, Location of intracranial electrodes and overall approach for biomarker detection. b, Clustering clinical reports on anxiety and depression dimensions resulted in two symptom states. Shading has been added to aid visualization. c, Top neural features (defined by F-score, ANOVA) that discriminated high versus low symptom severity states. Receiver operating characteristic curves reflect the mean ± s.e.m. over n = 1,000 randomly sampled features for the true (blue) and shuffled models (gray). d, Directed network graph of right hemisphere where percentage circumference indicates the strength of connection between any two brain regions. The color indicates the starting location for each set of connections. e, Connectivity strength from the network graph was quantified by calculating the weighted indegree and outdegree. The values represent the sum of incoming/outgoing evoked potential waveform N1 (10–50 ms poststimulation) amplitudes, averaged over n = 20 repeated pulses. f, Example of dose-dependent mean-evoked potentials (left). Summary of evoked potentials across the corticolimbic network due to single pulses in the right VC/VS is shown overlayed on the brain as a heatmap (right). Warmer colors indicate a larger N1 amplitude. g, Location of right-sided iEEG leads with fiber tracts (color-coded by orientation) showing structural connections between the amygdala and clinically responsive VC/VS electrodes. Lead location is indicated by color as in d,e. h, Change in gamma biomarker after a period of continuous stimulation for those trials that led to a reduction in symptom severity (red) and those that did not reduce symptom severity (blue).

Fig. 2 |. Implementation of closed-loop neuromodulation.

a, Fully implantable DbS system (illustrated by K. X. Probst). b, Reproducibility of targeting showing robust engagement of the stria terminalis and ansa peduncularis. VC/VS lead, yellow; amygdala lead, pink. c, Distribution of symptom severity scores in relation to clusters identified in the mapping study. d, Positive correlation between gamma power in amygdala contacts 1/2 and 3/4 within the 10-min trials and HAMD-6 score. The linear regression model was evaluated using a two-sided F-test and P values were adjusted for multiple comparisons. The linear model fit is presented as the mean ± s.e.m. over n = 16 symptom ratings. e, Reproducibility of clinical effects. Each point represents a stimulation trial (n = 6, 2, 4, 5 for VAS-D; n = 6, 2, 3, 4 for HAMD) at different bipolar configurations across the right VC/VS and amygdala. f, Left: Effect of burst duration on clinical measures. Stimulation parameters: contact 3⁺/4⁻, 1 mA, 36 s total stimulation across 15.6 min in 6–36 s intervals. Highlighted condition (6-s burst duration) selected for implementation of closed-loop therapy. Right: Effect of increasing dose (1–2 mA) and changing contacts (3⁺/4⁻ to 2⁺/3⁻) on clinical measures. The faded colors indicate that ON/OFF states were detected by the patient (one trial per condition). g, Schematic of closed-loop control. h, Positive correlation between number of gamma detections by the NeuroPace RNS System within 10-min trials as in d and HAMD-6 score. The linear regression model was evaluated using a two-sided F-test and P values were adjusted for multiple comparisons. The linear model fit is presented as the mean ± s.e.m. over n = 18 gamma detections. i, Symptom severity scores in the week pre- versus postclosed-loop stimulation onset (n = 3, 31 for VAS-D, n = 2, 30 for HAMD-6). j, Relationship between daily mood ratings (purple) and number of daily biomarker detections (gray). The dotted lines indicate the DTW-computed distance between VAS-D scores and daily biomarker detection numbers (left). Significance was assessed by comparing the DTW distance to that computed from 10,000 randomly scrambled biomarker time series (right).