Research at the MDL generally examines factors that contribute vulnerability to depression, maintain depression, or ameliorate depression. Research tends to focus on cognitive factors; however, recent work has begun to integrate approaches from other disciplines as well (e.g., genetics, imaging, hormones). Integrating ideas and methodology from other domains holds the promise of developing a more comprehensive understanding of the disorder. MDL research has been generously supported by funding from the National Institute of Mental Health (NIMH), Department of Defense (DoD), and The University of Texas at Austin.

There are many active research protocols currently underway at the MDL. Examples of current studies include:

Contribution of Genome-Wide Variation to Cognitive Vulnerability to Negative Valence System
In this study, we aim to comprehensively measure intermediate phenotypes related to cognitive vulnerability to depression, and to use genome-wide complex trait analysis (GCTA) to determine how much variance in these key phenotypes is due to variation in measured polymorphisms. Phenotypes measured include negative attention bias, pupil dilation during exposure to emotional stimuli, working memory, resting frontal asymmetry in EEG, as well as clinically determined and self-reported symptoms of negative valence.

Augmenting Internet-Based Cognitive Behavioral Therapy for Major Depressive Disorder with Low-Level Light Therapy
Cognitive behavioral therapy is a common treatment for Major Depressive Disorder. Several studies now indicate that using Deprexis, an internet-based cognitive behavioral therapy for depression, can significantly improve symptoms of depression among adults with Major Depressive Disorder. In this study, we aim to better understand how low-level light therapy, a near-infrared light that facilitates neuronal energy production and may enhance cognitive changes, can be used to augment the antidepressant effects of Deprexis.

Depression from Speech Patterns
In this study, we aim to use unique combinations of prosodic and linguistic features of human speech to predict depression diagnoses. The project involves developing cutting-edge audio segmentation, transcription, Natural Language Processing (NLP), and acoustical feature selection algorithms, as well as applying those algorithms to large databases of speech recordings on the Hikari supercomputer at the Texas Advanced Computing Center (TACC). The project is highly collaborative and includes contributions from experts in machine learning, sound engineering, computational linguistics, affective computing, and clinical psychology.

Development of Attention Bias Modification for Depression
The overall goal of this project is to continue the development of an attention bias modification (ABM) intervention that targets and reduces negative attention bias among adults with elevated symptoms of depression. Our prior work indicates that attention bias for negative information is associated with the maintenance of depression and that neural circuitry within frontal-parietal brain networks supports biased attention for negative information, thus allowing us to develop specific and targeted interventions that directly alter the neurobiology of negative attention bias.