Background Deep brain stimulation (DBS) of subcallosal cingulate white matter (SCC) is an evolving investigational treatment for major depression. Mechanisms of action are hypothesized to involve modulation of activity within a structurally defined network of brain regions involved in mood regulation. Diffusion tensor imaging (DTI) was used to model white matter connections within this network to identify those critical for successful antidepressant response to SCC DBS. Methods Pre-operative high-resolution MRI data, including DTI, were acquired in 16 patients with treatment-resistant depression who then received SCC DBS. Computerized tomography was used post-operatively to locate DBS contacts. The activation volume around the active contacts used for chronic stimulation was modeled for each patient retrospectively. Probabilistic tractography was used to delineate the white matter tracts that traveled through each activation volume. Patient-specific tract maps were calculated using whole-brain analysis. Clinical evaluations of therapeutic outcome from SCC DBS were defined at 6 months and 2 years. Results Whole brain activation volume tractography (AVT) demonstrated that all DBS responders at six months (n=6) and 2 years (n=12) shared bilateral pathways from their activation volumes to (1) medial frontal cortex via forceps minor and uncinate fasciculus, (2) rostral and dorsal cingulate cortex via the cingulum bundle, and (3) subcortical nuclei. Non-responders did not consistently show these connections. Specific anatomical coordinates of the active contacts did not discriminate responders from non-responders. Conclusions Patient-specific AVT modeling may identify critical tracts that mediate SCC DBS antidepressant response. This suggests a novel method for patient-specific target and stimulation parameter selection.

Target identification and contact selection are known contributors to variability in efficacy across different clinical indications of deep brain stimulation surgery. A retrospective analysis of responders to subcallosal cingulate deep brain stimulation (SCC DBS) for depression demonstrated the common impact of the electrical stimulation on a stereotypic connectome of converging white matter bundles (forceps minor, uncinate fasciculus, cingulum and fronto-striatal fibers). To test the utility of a prospective connectomic approach for SCC DBS surgery, this pilot study used the four-bundle tractography “connectome blueprint” to plan surgical targeting in eleven participants with treatment-resistant depression. Before surgery, targets were selected individually using deterministic tractography. Selection of contacts for chronic stimulation was made by matching the postoperative probabilistic tractography map to the presurgical deterministic tractography map for each subject. Intraoperative behavioral responses were used as a secondary verification of location. A probabilistic tract map of all participants demonstrated inclusion of the four bundles as intended, matching the connectome blueprint previously defined. Eight of 11 patients (72.7%) were responders and 5 were remitters after 6 months of open-label stimulation. At one year, nine of 11 patients (81.8%) were responders, with six of them in remission. These results support the utility of a group probabilistic tractography map as a connectome blueprint for individualized, patient-specific, deterministic tractography targeting, confirming retrospective findings previously published. This new method represents a connectomic approach to guide future SCC DBS studies.

Objective To inform the first-line treatment choice between cognitive behavior therapy (CBT) or an antidepressant medication for treatment-naïve adults with major depressive disorder by defining a neuroimaging biomarker that differentially identifies the outcomes of remission and treatment failure to these interventions. Method Functional magnetic resonance imaging resting state functional connectivity analyses using a bilateral subcallosal cingulate cortex (SCC) seed was applied to 122 patients from the Prediction of Remission to Individual and Combined Treatments (PReDICT) study who completed 12 weeks of randomized treatment with CBT or antidepressant medication. Of the 122, 58 achieved remission (Hamilton Depression Rating Scale, HDRS) ≤7 at weeks 10 and 12); 24 were treatment failures (HDRS <30% decrease from baseline). A 2×2 ANOVA using voxel-wise subsampling permutation tests compared the interaction of treatment and outcome. ROC curves constructed using brain connectivity measures were used to determine possible classification rates for differential treatment outcomes. Results The resting state functional connectivity of three regions with the SCC was differentially associated with outcomes of remission and treatment failure to CBT and antidepressant medication, and survived application of the subsample permutation tests: left anterior ventrolateral/insula prefrontal cortex, dorsal midbrain, and left ventromedial prefrontal cortex. Using the summed SCC functional connectivity scores for these three regions, we demonstrated overall classification rates of 72–78% for remission and 75–89% for treatment failure. Positive summed functional connectivity was associated with remission with CBT and treatment failure with medication, whereas negative summed functional connectivity scores were associated with remission to medication and treatment failure with CBT. Conclusions Imaging-based depression subtypes defined using resting state functional connectivity differentially identified an individual’s probability of remission or treatment failure with first-line treatment options for major depression. This biomarker should be explored in future research through prospective testing and as a component of multivariate treatment prediction models.