Helen S. Mayberg

Humans are remarkable at adapting to changing norms, yet we know little about how the human brain encodes these social signals at the single neuron level. Here, we recorded the activity of single neurons in the substantia nigra and globus pallidus in neurosurgical participants as they played an iterative ultimatum game versus human and computer avatars. Using computational modeling of behavior, we found that putative dopaminergic units in the substantia nigra tracked the valence of norm prediction errors, with greater encoding during gameplay with human versus computer avatars. Analogous analyses in the globus pallidus, a nucleus adjacent to the substantia nigra, found no significant effect. Our results identify a novel role for the substantia nigra in encoding social learning signals. AUTHOR SUMMARY: An important skill within social functioning is the ability to learn and adapt to changing social norms, or rules of behavior among social groups. While computational modeling of behavior has advanced our understanding of how humans learn social norms, little is known about what neural mechanisms support this process. Here, we leverage computational modeling of behavior in conjunction with human intracranial recording to investigate how the human brain encodes norm learning signals at the single neuron level. We recorded the activity of single neurons in the human substantia nigra pars reticulata (SNr) and globus pallidus internus (GPi) in neurosurgical participants as they played a social exchange game with human and computer avatars. We estimated trial-by-trial norm prediction errors (nPEs), or mentally computed errors in social prediction, and revealed that putative dopaminergic SNr neurons encoded the valence of nPEs, with greater encoding observed during gameplay with human versus computer avatar partners. This finding was not observed in the GPi. Together, our results expand upon existing knowledge about the SNr by identifying a novel role for SNr neurons in social norm learning.

Background: Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods: We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results: Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions: Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

Brain connectivity analysis is crucial for understanding brain structure and neurological function, shedding light on the mechanisms of mental illness. To study the association between individual brain connectivity networks and the clinical characteristics, we develop BSNMani: a Bayesian scalar-on-network regression model with manifold learning. BSNMani comprises two components: the network manifold learning model for brain connectivity networks, which extracts shared connectivity structures and subject-specific network features, and the joint predictive model for clinical outcomes, which studies the association between clinical phenotypes and subject-specific network features while adjusting for potential confounding covariates. For posterior computation, we develop a novel two-stage hybrid algorithm combining Metropolis-Adjusted Langevin Algorithm (MALA) and Gibbs sampling. Our method is not only able to extract meaningful subnetwork features that reveal shared connectivity patterns but can also reveal their association with clinical phenotypes, further enabling clinical outcome prediction. We demonstrate our method through simulations and through its application to real resting-state fMRI data from a study focusing on Major Depressive Disorder (MDD). Our approach sheds light on the intricate interplay between brain connectivity and clinical features, offering insights that can contribute to our understanding of psychiatric and neurological disorders as well as mental health.

Deep brain stimulation (DBS) is an emerging therapy for treatment-resistant neurological and psychiatric disorders. Despite this, little is known about the anatomical and functional mechanisms that underlie this therapy. We targeted DBS to white matter adjacent to the subcallosal anterior cingulate cortex (SCC-DBS) in macaques, modeling the approach proven effective for depression in humans. SCC-DBS caused a selective increase in fractional anisotropy, linked to white matter microstructure, in the cingulum bundle. At the cellular level, this was associated with an increase in both myelinated oligodendrocytes and the degree of myelination in the mid-cingulum bundle. SCC-DBS also altered brain-wide functional connectivity, changing interactions between the SCC and multiple brain networks, most notably the default mode network that has been implicated in depression. Overall, our data indicate that white matter remodeling as well as selective changes in multiple brain networks may contribute to DBS's therapeutic efficacy.

Subcallosal cingulate cortex (SCC) deep brain stimulation (DBS) can provide relief for individuals with Treatment Resistant Depression (TRD), but ongoing clinical management remains challenging due to nonspecific symptom fluctuations that can obscure core depression recovery on standard rating scales. Objective, stable biomarkers that selectively track the therapeutic effects of SCC DBS are therefore essential for developing principled decision support systems to guide stimulation adjustments. Recent bidirectional DBS systems enable chronic recording of local field potentials (LFPs) and prior work using the Activa PC+S device identified an electrophysiological signature of stable clinical recovery. However, translation to practical clinical deployment requires demonstrating that this biomarker is robustly generalizable, specific to the impact of the DBS therapy, and deployable in real-world recording contexts. To address this need, we developed an at-home SCC LFP data collection platform (built on the Medtronic Summit RC+S system) enabling at home data collection for a new cohort of ten SCC DBS participants with TRD (ClinicalTrials.gov identifier NCT04106466 ). Using longitudinal LFP recordings collected from this system, we report findings demonstrating that the previously reported biomarker of stable recovery generalizes across subject cohorts and devices, is robust to common potential confounds (including time of day and stimulation status), and shows symptom specificity, sensitivity and stability necessary to support clinical decision making. Across both cohorts, biomarker changes show relationships to pre-DBS white matter structure and network function measured using diffusion MRI and resting-state functional MRI (rsFMRI). These findings replicating and extending previous findings support the biomarker’s utility as a foundation for scalable, electrophysiology-informed decision support in SCC DBS.

Subcallosal cingulate (SCC) deep brain stimulation (DBS) is an established intervention for treatment-resistant depression (TRD) that modulates white-matter tracts within the central autonomic network (CAN). In eight patients with TRD, SCC DBS produced robust antidepressant effects but no group-level change in heart-rate variability (HRV), a marker of vagal flexibility. However, patients whose mood and HRV covaried over time showed greater HRV improvement, suggesting that SCC DBS enhances autonomic flexibility only in individuals with intact mood–vagal coupling.

Recent advances in deep brain stimulation (DBS) of the subcallosal cingulate (SCC) show promise in mitigating the symptoms of treatment-resistant depression (TRD) in humans ^1–3 . Monoamines, such as dopamine and serotonin, mediate the effects of pharmacological treatments of depression. However, their roles in recovery following DBS remain elusive, largely due to technical limitations of measuring these neurotransmitters in the living human brain. Here, by leveraging machine learning-enhanced electrochemistry ^4–7 , we show that dopamine and serotonin signaling following DBS to the SCC predicted later depressive symptom relief in humans with TRD. We found that both dopamine and serotonin levels increased following subtherapeutic intraoperative SCC stimulation, with each neurotransmitter showing selective responses to distinct decision-making tasks. Furthermore, acute dopamine increases predicted later mood improvements during a social decision-making task, while serotonin enhancement predicted faster responses during a non-social learning task longitudinally. Critically, changes in dopamine and serotonin levels during the social decision-making task jointly predicted depressive symptom remission at 6-month follow-up. These findings illustrate the contribution of both dopamine and serotonin signaling in predicting behavioral improvement and depressive symptom remission in humans with TRD. Such neurochemical plasticity may serve as potential mechanistic biomarkers for SCC DBS mechanism and TRD treatment response. Significance statement: Dopamine and serotonin levels increased following acute DBS to the SCC in humans. Acute dopamine and serotonin changes predicted later mood and response speed changes. Sustained TRD recovery was predicted by acute increases in both dopamine and serotonin estimates.

Deep brain stimulation (DBS) of the subcallosal cingulate cortex (SCC) is a promising intervention for treatment-resistant depression, yet objective biomarkers that track recovery remain limited. This study examined longitudinal changes in stimulation-evoked potentials (SEPs) to characterize how SCC-driven cortical communication evolves during treatment. Ten patients across three SCC-DBS trials underwent high-density EEG recordings at 4 and 24 weeks. SEP features were extracted from source-localized SCC signals and related to clinical outcomes and fractional anisotropy (FA) of midcingulate cingulum. Cortical responses showed a consistent reduction in latency and an increase in magnitude over time, indicating faster and stronger electrocortical signaling with chronic stimulation. Higher baseline midcingulate FA predicted greater latency acceleration, linking SEP timing to white-matter integrity. These findings identify temporal SEP dynamics as candidate mechanistic biomarkers that reflect circuit engagement during SCC-DBS and offer a pathway toward physiology-guided optimization of neuromodulation for depression. Highlights: Reduced latency of stimulation evoked potential after 6 months of DBS Larger cortical evoked response to single pulse perturbation after 6 months of DBS Baseline myelin integrity in MCC predicts magnitude of change in EP latency