No increase in inflammation in late-life major depression screened to exclude physical illness.

Depression is a common and debilitating disorder in the elderly. Late-life depression (LLD) has been associated with inflammation and elevated levels of proinflammatory cytokines including interleukin (IL)-1ß, tumor necrosis factor-alpha, and IL-6, but often depressed individuals have comorbid medical conditions that are associated with immune dysregulation. To determine whether depression has an association with inflammation independent of medical illness, 1120 adults were screened to identify individuals who had clinically significant depression but not medical conditions associated with systemic inflammation. In total, 66 patients with LLD screened to exclude medical conditions associated with inflammation were studied in detail along with 26 age-matched controls (HC). At baseline, circulating cytokines were low and similar in LLD and HC individuals. Furthermore, cytokines did not change significantly after treatment with either an antidepressant (escitalopram 20 mg/day) or an antidepressant plus a COX-2 inhibitor or placebo, even though depression scores improved in the non-placebo treatment arms. An analysis of cerebrospinal fluid in a subset of individuals for IL-1ß using an ultrasensitive digital enzyme-linked immunosorbent assay revealed low levels in both LLD and HC at baseline. Our results indicate that depression by itself does not result in systemic or intrathecal elevations in cytokines and that celecoxib does not appear to have an adjunctive antidepressant role in older patients who do not have medical reasons for having inflammation. The negative finding for increased inflammation and the lack of a treatment effect for celecoxib in this carefully screened depressed population taken together with multiple positive results for inflammation in previous studies that did not screen out physical illness support a precision medicine approach to the treatment of depression that takes the medical causes for inflammation into account.

Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment.

Resting state functional connectivity (rsFC) offers promise for individualizing stimulation targets for transcranial magnetic stimulation (TMS) treatments. However, current targeting approaches do not account for non-focal TMS effects or large-scale connectivity patterns. To overcome these limitations, we propose a novel targeting optimization approach that combines whole-brain rsFC and electric-field (e-field) modelling to identify single-subject, symptom-specific TMS targets. In this proof of concept study, we recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms (MADRS/HAMD) and recorded rsFC. We used a PCA regression to predict symptoms from rsFC and estimate the parameter vector, for input into our e-field augmented model. We modeled 17 left dlPFC and 7 M1 sites using 24 equally spaced coil orientations. We computed single-subject predicted ΔMADRS/HAMD scores for each site/orientation using the e-field augmented model, which comprises a linear combination of the following elementwise products (1) the estimated connectivity/symptom coefficients, (2) a vectorized e-field model for site/orientation, (3) rsFC matrix, scaled by a proportionality constant. In AM patients, our connectivity-based model predicted a significant decrease depression for sites near BA9, but not M1 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant predicted change. These results corroborate previous work suggesting the efficacy of left dlPFC stimulation for depression treatment, and predict better outcomes with individualized targeting. They also suggest that our novel connectivity-based e-field modelling approach may effectively identify potential TMS treatment responders and individualize TMS targeting to maximize the therapeutic impact.

Dimensional connectomics of anxious misery, a human connectome study related to human disease: Overview of protocol and data quality.

Disparate diagnostic categories from the Diagnostic and Statistical Manual of Mental Disorders (DSM), including generalized anxiety disorder, major depressive disorder and post-traumatic stress disorder, share common behavioral and phenomenological dysfunctions. While high levels of comorbidity and common features across these disorders suggest shared mechanisms, past research in psychopathology has largely proceeded based on the syndromal taxonomy established by the DSM rather than on a biologically-informed framework of neural, cognitive and behavioral dysfunctions. In line with the National Institute of Mental Health's Research Domain Criteria (RDoC) framework, we present a Human Connectome Study Related to Human Disease that is intentionally designed to generate and test novel, biologically-motivated dimensions of psychopathology. The Dimensional Connectomics of Anxious Misery study is collecting neuroimaging, cognitive and behavioral data from a heterogeneous population of adults with varying degrees of depression, anxiety and trauma, as well as a set of healthy comparators (to date, n = 97 and n = 24, respectively). This sample constitutes a dataset uniquely situated to elucidate relationships between brain circuitry and dysfunctions of the Negative Valence construct of the RDoC framework. We present a comprehensive overview of the eligibility criteria, clinical procedures and neuroimaging methods of our project. After describing our protocol, we present group-level activation maps from task fMRI data and independent components maps from resting state data. Finally, using quantitative measures of neuroimaging data quality, we demonstrate excellent data quality relative to a subset of the Human Connectome Project of Young Adults (n = 97), as well as comparable profiles of cortical thickness from T1-weighted imaging and generalized fractional anisotropy from diffusion weighted imaging. This manuscript presents results from the first 121 participants of our full target 250 participant dataset, timed with the release of this data to the National Institute of Mental Health Data Archive in fall 2020, with the remaining half of the dataset to be released in 2021.