Fig. 1: Overview of study procedures.

a, Coronal view of the DBS lead targeting bilateral SCC in an example patient. The red sphere indicates the volume of tissue activated (VTA) with the final stimulation parameters. The black circles indicate the volume of tissue recorded (VTR) from each electrode contact, showing coverage of grey matter that are the likely sources of the recorded LFP. b, Common activation pathway patterns from chronic stimulation VTA seed of the six participants at 6 months. c, Trajectory of HDRS-17 scores over 24 weeks for five participants (of six total) who were typical responders. Grey lines indicate individuals and the black line indicates the mean. Error bars indicate standard deviation (n = 5 participants). Clinical consensus was that all five were ‘sick’ during weeks 1–4 and in ‘stable response’ during weeks 20–24. d, Schematic of deriving the SDC from LFP features. A neural network classifier is first trained with data from the ‘sick’ and ‘stable response’ states of all typical responders. Next, separate neural networks are trained to compress the data from the spectral feature space to a low-dimensional latent space and then reconstruct the data from that compressed version. Using recent advances in XAI techniques, one of these latent dimensions is a discriminative component constrained to represent the simultaneous data changes (the SDC) used by the classifier to distinguish ‘sick’ from ‘stable response’. e, Illustration of the utility of an objective biomarker. When patients experience instability in symptom scores, decisions about treatment (for example, stimulation voltage adjustment) must be made by inferring whether the instability is due to transient distress (scenario 1) or depression relapse (scenario 2). A biomarker that reflects progress of the brain towards ‘stable response’ will enable more effective clinical decision-making about interventions. CB, cingulum bundle; UF, uncinate fasciculus; FM, forceps minor; F-ST, frontostriatal fibres.