While the term cognitive control encompasses a broad range of human abilities, a significant portion of what constitutes cognitive control is attentional control. We can influence how we process and interact with the environment around us by what we attend to, including how effectively we can engage and disengage our focus. Currently, the most exciting experimental work in the domain of attention control is focused on the neurocognitive systems associated with attention control.
Attentional control has been shown to play a major role in the vulnerability to and maintenance of mental illness and has been a focus of the NIMH RDoc framework as applied to mental disorders. Moreover, there are individual difference factors that have been shown to influence attentional control, such as genetics, sleep/circadian rhythms and structural and functional brain characteristics. These findings have led to exploring the use of behavioral, eye-tracking and real-time fMRI in a closed-loop neurofeedback approach. The purpose is to develop a training that can help improve attention control and the symptoms associated with its dysfunction. Working in collaboration with Dr. Chris Beevers, we have explored the neural basis of cognitive control of emotion. The focus of this work has been primarily assessing how an individual regulates attention to emotional stimuli. Using as a framework a relatively simple model of PFC-limbic system interactions (Frontiers in Neuroscience, 2009), we have examined the role that lateral PFC plays in shifting attention away from emotionally salient stimuli. This work has revealed that higher depressive symptoms are associated with not effectively engaging regions of right PFC when required to shift attention (Neuroscience, 2010). Furthermore, we’ve examined the influence of a genetic polymorphism on the promoter region of the serotonin transporter gene (5HTTLPR ), which has previously been associated with difficulties in mood regulation. Findings indicate that prefrontal morphology was associated with attentional bias towards emotional stimuli but only for individuals with the genetic profile previously associated with elevated depression risk (Genes, Brain and Behavior, 2010). Functional connectivity in portions of this same attention control network was found to be less in adolescent females with parental history of depression or with (Development Cognitive Neuroscience, 2015) family risk for mood disorders. Moreover, examining the brain’s white matter architecture connecting critical PFC control regions with the limbic system, pathways associated with 5HTTLPR genotype from adolescence to young adulthood (Journal of Neuroscience, 2009), exhibited significant differences. Finally, we were able to apply support vector machine learning to effectively classify individuals with depression using whole brain measures of white matter derived from diffusion tensor MRI (Psychiatry Research: Neuroimaging, 2017). Our work in this area was recently boosted by a R56 award from NIMH (R56MH108650) that will constitute year 1 of a 5-year RO1. The overarching aim is to identify linkages between genetics and sustained attention to negative affective information in 800 community dwelling adults. We plan to use rare (exomic) and common genetic variations to build biologically plausible cumulative genetic scores (CGS) to examine against a broad array of behavioral, eye-tracking and EEG measures.
Working in collaboration with Dr. John Allen at the University of Arizona, we have been focused on better understanding the associated functional neural architecture of a well-established EEG biomarker of depression risk – alpha asymmetry using simultaneously recorded EEG/fMRI (NIMH R21MH101398) in 60 normal individuals with and without history of depression. This work utilizes the BrainVision MR compatible EEG system purchased with a Navy funded equipment grant.
The primary focus of my lab in this area has been to highlight the importance of developing techniques to train attentional control. The purpose is to help individuals suffering from mood disorders or at risk for developing mood disorders how better regulate their attention to emotional information. In a recently completed study (Journal of Abnormal, 2015), we examined the efficacy of a 4-week computer based attention-training paradigm as an intervention for depression. Incorporated into this study is pre and post fMRI scanning that gave us extensive information about a) baseline structural and functional characteristics that predict treatment response and b) neural changes associated with changes in attention bias and reduction of depressive symptoms. Further development and testing of this approach to treatment, has been recently been funded through an R33 grant (R33MH109600). While the computer based training task has shown promise, there are also aspects of that approach that are suboptimal, such as the non-individualized paradigm that does not dynamically adapt to a person’s ability/status. To respond to this, we have worked to create closed-looped feedback approaches to attention training. In collaboration with Dr. Turk-Browne and Dr. Norman at Princeton University we developed and piloted a paradigm using real-time fMRI neurofeedback protocol to train attentional bias in depressed individuals (Biology of Mood and Anxiety Disorders, 2015). This work has resulted in some exciting new findings. There is ample evidence, however, that less expensive and more readily available modalities could be used to derive the feedback signal and we are currently doing pilot work using eye-tracking in a similar closed loop feedback approach as our previously descried real-time fMRI studies.
Training and disrupting attentional control. Working primarily with Dr. Trujillo, we have examined the malleability of attention control. We have done this by demonstrating the ability to rapidly learn novel spatial attention cues that result in effects in the very early ERP attention components normally associated with automatic processes (Trujillo, L.T. & Schnyer, D.M. Psychophysiology, 2011). Moreover, in a series of studies we have examined the effects of sleep deprivation (SD) on cognition. The results of the SD studies provide support for a particular theoretical perspective that postulates that most of the cognitive changes associated with SD result from transient disruptions of attention control. We demonstrated that SD effects endogenous before exogenously controlled attention (Trujillo, L.T., Kornguth, K., & Schnyer, D.M. Sleep, 2009), that widespread differences in the brain’s white matter architecture support resilience in attention capacity in the face of SD (Rocklage, M, Williams, V, Pacheco, J. & Schnyer, D.M., Sleep, 2009) and finally, that changes in prototype learning capacity during SD are associated with a narrowing of attentional focus (Maddox, W.T., Glass, B.C., Wolosin, S.M., Savarie, Z.R., Bowen, C., Mathews, M.D., & Schnyer, D.M., Sleep, 2009; Maddox, W.T., Glass, B.C., Zeithamova, D., Savarie, Z.R., Bowen, C., Mathews, M.D., & Schnyer, D.M., Sleep, 2011).