- Tue, Jul 24th 2018 at 15:00 - 16:00 UTC (Other timezones)
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Although depression and anxiety are prevalent and crippling diseases, we lack quantifiable, clinically-actionable targets that are deeply rooted in biology, as is the case for disabling diseases in other areas of medicine. Large-scale studies and meta-analyses of human functional brain imaging have identified at least six macro-scale circuits and that are implicated in self-generated thought, detection of salience, processing of positive and negative emotions, attentional vigilance and cognitive control. Major barriers to utilizing knowledge of these circuits for depression and anxiety include the lack of a standardized protocols for quantifying their function and dysfunction in a reproducible manner, the fragmentation of knowledge due to the focus on dysfunction in one circuit at a time or in a small subset, and the resulting lack of a model for quantifying how brain circuits dysfunctions are expressed in symptoms, behaviors, real world functions and treatment outcomes. A through line in addressing these barriers is the need to quantify rather than smear the heterogeneity in the presentation of depression and anxiety.
In this talk, I demonstrate a new system for deriving clinic-ready metrics that quantify variations in the extent of brain circuit dysfunction in each individual patient with depression and anxiety. This system is anchored in a theoretical model hypothesizing that distinct dysfunctions in brain circuits underlie particular subtypes of depression and anxiety, and that these subtypes cut across our existing broad diagnostic categories. To develop and test this new system we analyzed train, test and generalizability samples of patients with clinically significant symptoms of depression and anxiety. Anchored in a theoretically motivated taxonomy, regression modelling was used to test whether specific circuit dysfunctions predict specific profiles of symptoms, behavior and function. Each regression model was compared against the null distribution of t-scores derived by 10,000 permutations. In the exploratory phase, lasso-penalized regression models were used to explore the more complex structure of how the multiple circuits may combine and interact to predict more nuanced combinations of symptoms, behavior and function.
To advance the actionable significance of our system I include clinical illustrations of how circuit dysfunction targets inform response to different forms of intervention, both pharmacological and non-pharmacological.
Leanne Williams, MD PhD
Professor of Psychiatry and Behavioral Sciences
Department of Psychiatry
Major Laboratories and Clinical & Translational Neurosciences Incubator