Opti Lab Research
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Research in the Human Performance Optimization Lab uses psychometric, neuroimaging, electrophysiological, neurostimulation and eye tracking approaches, done in conjunction with computational and biophysiological modeling techniques. These methodologies are applied in both laboratory and real-world contexts to investigate the brain mechanisms that enable visual cognition and to develop interventions that speed learning and improve the quality of rehabilitation. This research adheres to Open Science principles with pre-registered clinical trials and multiple pre-registered systematic reviews and meta-analyses. Specific areas of research include:
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Clinical Cognitive Neuroscience
The field of cognitive neuroscience has revolutionized our ability to measure the activity and connectivity of the brain while people behave and think in order to understand how physiology gives rise to psychology. By identifying the mechanisms and processes at work in healthy brains and determining how they are impaired in neuropsychiatric and neurologic disorders, we are able to use this knowledge to create better diagnostic tools and more potent therapeutic treatments. OptiLab research has embraced these goals with a particular emphasis on the use of network-neuroscience approaches to measure healthy and dysregulated brain activity and the application of targeted forms of Transcranial Magnetic Stimulation (TMS) with the goal of restoring healthy brain dynamics and optimal behavior. Examples include:
- TMS to enhance memory abilities in older adults (Beynel et al 2019; Beynel et al 2020; Beynel et al. 2020)
- TMS to improve emotional regulation (Powers et al 2020; Beynel et al 2020; 2018 DIBS Germinator) and treatment of substance use disorder (Young 2020; Addicott 2019)
- Meta analysis of online rTMS (Beynel et al 2019)
- Systematic review of rTMS effects on fMRI resting-state functional connectivity (Beynel et al 2020)
This is what fMRI-guided, electric field-modeled, robot-controlled, brain stimulation looks like
Perceptual-Motor Learning and Expertise
Humans are remarkable at performing visually guided movements. We are able to achieve and master actions as simple as reaching for a cup and as complex as executing a bicycle kick in a soccer match. A great deal of our brain is devoted to this skill with rich multidirectional communication between systems that are responsible for visual perception, goal-oriented decision-making, and control of motor actions. Developing perceptual-motor expertise in highly demanding activities, such as competing in sports, conducting surgery, and performing music is accompanied by extensive reorganization in the nervous system and a major goal of the OptiLab has been to understand the nature of such perceptual-motor learning and expertise. Examples include:
- Sensorimotor expertise in athletes (Klemish, 2017; Burris et al, 2018; Liu et al 2020)
- Vision training in athletes (Appelbaum & Erickson 2016; Liu et al 2020; Appelbaum 2011, Appelbaum 2012)
- Surgical training studies (Cox et al, 2020; Liu et al 2020)
- Visual (Clements et al 2018) and motor (Rao et al, 2018) learning in virtual reality simulations
Strobe Training
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Rhodes Information Initiative DATA+ project with USA Baseball
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Vision training with the Duke Basketball Team
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Mechanisms of Visual Cognition
Vision plays a central role in the way we perceive, locomote, conceptualize, and remember the world around us. This experience is built from sensory inputs that create perceptual representations, selective attention that prioritizes processing of some elements over others, working memory to maintain and manipulate this information in our mind, and executive functions that decide what to respond to and how. Research in OptiLab seeks to understand how the brain creates these abilities, seemingly without effort. To gain this knowledge we combine psychometric measurement of behavior combined with advanced methods for recording the activity and connectivity of the brain to create a detailed mechanistic account of behavior and the physiology that enables visual cognition. Examples include:
- fMRi BOLD, resting-state and functional connectivity studies of working memory (Davis et al 2018; Crowell et al 2020)
- "Frequency-tagged" EEG to study scene perception (Appelbaum et al 2006; 2008; 2009; 2010; 2012; Norcia et al 2015)
- Reward influences on attention and decision making (San Martin 2013; San Martin 2015)
- The mechanisms of executive control in the brain (Appelbaum et al. 2009; 2012; 2014; Donohue et al 2016; Krebs et al. 2013)
- Multisensory integration (Donohue et al. 2013; Appelbaum et al. 2013)
- Response inhibition (Boehler et al. 2010; 2011; 2012)