Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging

Diffusion tensor imaging (DTI) is a noninvasive MRI technique used to assess white matter (WM) integrity, fiber orientation, and structural connectivity (SC) using water diffusion properties. DTI techniques are rapidly evolving and are now having a dramatic effect on depression research. Major depressive disorder (MDD) is highly prevalent and a leading cause of worldwide disability. Despite decades of research, the neurobiology of MDD remains poorly understood. MDD is increasingly viewed as a disorder of neural circuitry in which a network of brain regions involved in mood regulation is dysfunctional. In an effort to better understand the neurobiology of MDD and develop more effective treatments, much research has focused on delineating the structure of this mood regulation network. Although many studies have focused on the structural connectivity of the mood regulation network, findings using DTI are highly variable, likely due to many technical and analytical limitations. Further, structural connectivity pattern analyses have not been adequately utilized in specific clinical contexts where they would likely have high relevance, e.g., the use of white matter deep brain stimulation (DBS) as an investigational treatment for depression. In this dissertation, we performed a comprehensive analysis of structural WM integrity in a large sample of depressed patients and demonstrated that disruption of WM does not play a major role in the neurobiology of MDD. Using graph theory analysis to assess organization of neural network, we elucidated the importance of the WM network in MDD. As an extension of this WM network analysis, we identified the necessary and sufficient WM tracts (circuit) that mediate the response of subcallosal cingulate cortex DBS treatment for depression; this work showed that such analyses may be useful in prospective target selection. Collectively, these findings contribute to better understanding of depression as a neural network disorder and possibly will improve efficacy of SCC DBS.Ph.D

Treatment-Specific Hippocampal Subfield Volume Changes With Antidepressant Medication or Cognitive-Behavior Therapy in Treatment-Naive Depression.

Background: Hippocampal atrophy has been consistently reported in major depressive disorder with more recent focus on subfields. However, literature on hippocampal volume changes after antidepressant treatment has been limited. The first-line treatments for depression include antidepressant medication (ADM) or cognitive-behavior therapy (CBT). To understand the differential effects of CBT and ADM on the hippocampus, we investigated the volume alterations of hippocampal subfields with treatment, outcome, and chronicity in treatment-naïve depression patients. Methods: Treatment-naïve depressed patients from the PReDICT study were included in this analysis. A total of 172 patients who completed 12 weeks of randomized treatment with CBT (n = 45) or ADM (n = 127) were included for hippocampal subfield volume analysis. Forty healthy controls were also included for the baseline comparison. Freesurfer 6.0 was used to segment 26 hippocampal substructures and bilateral whole hippocampus from baseline and week 12 structural MRI scans. A generalized linear model with covariates of age and gender was used for group statistical tests. A linear mixed model for the repeated measures with covariates of age and gender was used to examine volumetric changes over time and the contributing effects of treatment type, outcome, and illness chronicity. Results: Of the 172 patients, 85 achieved remission (63/127 ADM, 22/45 CBT). MDD patients showed smaller baseline volumes than healthy controls in CA1, CA3, CA4, parasubiculum, GC-ML-DG, Hippocampal Amygdala Transition Area (HATA), and fimbria. Over 12 weeks of treatment, further declines in the volumes of CA1, fimbria, subiculum, and HATA were observed regardless of treatment type or outcome. CBT remitters, but not ADM remitters, showed volume reduction in the right hippocampal tail. Unlike ADM remitters, ADM non-responders had a decline in volume in the bilateral hippocampal tails. Baseline volume of left presubiculum (regardless of treatment type) and right fimbria and HATA in CBT patients were correlated with a continuous measure of clinical improvement. Chronicity of depression had no effect on any measures of hippocampal subfield volumes. Conclusion: Two first-line antidepressant treatments, CBT and ADM, have different effects on hippocampal tail after 12 weeks. This finding suggests that remission achieved via ADM may protect against progressive hippocampal atrophy by altering neuronal plasticity or supporting neurogenesis. Studies with multimodal neuroimaging, including functional and structural analysis, are needed to assess further the impact of two different antidepressant treatments on hippocampal subfields

Indoxyl sulfate, a gut microbiome-derived uremic toxin, is associated with psychic anxiety and its functional magnetic resonance imaging-based neurologic signature

Background: It is unknown whether indoles, metabolites of tryptophan that are derived entirely from bacterial metabolism in the gut, are associated with symptoms of depression and anxiety. Methods: Serum samples (baseline, 12 weeks) were drawn from participants (n=196) randomized to treatment with cognitive behavioral therapy (CBT), escitalopram, or duloxetine for major depressive disorder. Results: Baseline indoxyl sulfate abundance was positively correlated with severity of psychic anxiety and total anxiety and with resting state functional connectivity to a network that processes aversive stimuli (which includes the subcallosal cingulate cortex (SCC-FC), bilateral anterior insula, right anterior midcingulate cortex, and the right premotor areas). The relation between indoxyl sulfate and psychic anxiety was mediated only through the metabolite's effect on the SCC-FC with the premotor area. Baseline indole abundances were unrelated to post-treatment outcome measures, which suggests that CBT and antidepressant medications relieve anxiety via mechanisms unrelated to gut microbiota. Conclusions: A peripheral gut microbiome-derived metabolite was associated with altered neural processing and with psychiatric symptom (anxiety) in humans, which provides further evidence that gut microbiome disruption can contribute to neuropsychiatric disorders that may require different therapeutic approaches

Indoxyl sulfate, a gut microbiome-derived uremic toxin, is associated with psychic anxiety and its functional magnetic resonance imaging-based neurologic signature

Background: It is unknown whether indoles, metabolites of tryptophan that are derived entirely from bacterial metabolism in the gut, are associated with symptoms of depression and anxiety. Methods: Serum samples (baseline, 12 weeks) were drawn from participants (n=196) randomized to treatment with cognitive behavioral therapy (CBT), escitalopram, or duloxetine for major depressive disorder. Results: Baseline indoxyl sulfate abundance was positively correlated with severity of psychic anxiety and total anxiety and with resting state functional connectivity to a network that processes aversive stimuli (which includes the subcallosal cingulate cortex (SCC-FC), bilateral anterior insula, right anterior midcingulate cortex, and the right premotor areas). The relation between indoxyl sulfate and psychic anxiety was mediated only through the metabolite's effect on the SCC-FC with the premotor area. Baseline indole abundances were unrelated to post-treatment outcome measures, which suggests that CBT and antidepressant medications relieve anxiety via mechanisms unrelated to gut microbiota. Conclusions: A peripheral gut microbiome-derived metabolite was associated with altered neural processing and with psychiatric symptom (anxiety) in humans, which provides further evidence that gut microbiome disruption can contribute to neuropsychiatric disorders that may require different therapeutic approaches

Left versus right subcallosal cingulate deep brain stimulation for treatment-resistant depression

Deep brain stimulation (DBS) of the subcallosal cingulate has emerged as a promising therapy for treatment-resistant depression (TRD). To date, all studies have employed bilateral stimulation; however, the physiology of affect and pathophysiology of depression are known to be asymmetric across hemispheres. Unilateral stimulation may provide efficacy while decreasing risk. Five patients were exposed to unilateral open-label DBS to the subcallosal cingulate for 12 weeks each to the left and then right hemispheres in a double-blind, crossover fashion. After 12 weeks of stimulation to each hemisphere, bilateral stimulation was initiated, and patients were followed for 12 additional weeks. Additionally, nine months of long-term follow up data were collected. Left, but not right, unilateral stimulation was associated with significant decrease in depression scores; with bilateral stimulation, all patients improved and one patient remitted. No serious adverse events were associated with surgery or acute or chronic stimulation. This small study suggests that unilateral DBS to the subcallosal cingulate may be an effective treatment for TRD. All patients improved with bilateral stimulation, though antidepressant effects following 12 weeks were modest. These findings contrast somewhat with prior open-label trials, though duration of bilateral stimulation was shorter in this trial. The current study continues to confirm safety of implantation and use of DBS to the subcallosal cingulate for patients with TRD and highlights the importance of personalization of therapy, for example by hemisphere, in future trials

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project