Technical Program

Paper Detail

Paper: PS-1B.58
Session: Poster Session 1B
Location: H Fläche 1.OG
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Modeling attention impairments in major depression
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Arielle Keller, Shi Qiu, Jason Li, Leanne Williams, Stanford University, United States
Abstract: Attention impairments are a debilitating symptom of Major Depressive Disorder, yet the neurobiological mechanisms underlying this cognitive dysfunction are poorly understood. Moreover, we currently have no method for predicting how individuals’ attention function may change with antidepressant treatment. Our goal was twofold: First, we modeled the effects of both stress and neural factors implicated in attention impairments and their interactions. To do so, we leveraged a large sample of depressed individuals from the international Study to Predict Optimized Treatment for Depression (iSPOT-D) assessed for attention impairments using a behavioral test, for stress using history of early life stress exposure, and for neural function using electroencephalography (EEG). Second, we developed models for predicting whether attention function changes over time as a function of an eight-week course of antidepressant treatment. Our models demonstrate that 1) early life stress interacts with oscillatory EEG signals to produce attention impairment, and 2) gradient boosted trees can be leveraged to predict changes in attention behavior with treatment. Our models provide novel insight into potential biomarkers of attention impairments in depressed individuals as well as how these impairments may change over time.