Assessment of Stability of Behavioral and Neural Measures of Attention Networks Across Multiple Sessions

Stability of Attentional Networks Over Multiple Sessions of Computerized Task

cognitive process, visuospatial attention

The purpose of this research study is to learn more about the stability over multiple recording sessions of brainwave activity during a computerized task that measures different types of attention.

Attention is the backbone of cognitive systems and is requisite for other cognitive processes vital to everyday functioning, such as memory, problem solving, language skills, and the cognitive control of behavior. Impairments in attention can result from a variety of factors, such as brain injury, and neurodegenerative and neurodevelopmental disorders, as well as various psychopathologies. A prominent multi-component model of visuospatial attention developed by Posner and colleagues conceptualizes visuospatial attention as an organ system comprised of three independent, yet interactive networks which include: 1) Alerting, the generation and maintenance of a vigilant state that facilitates the processing of an upcoming stimulus, 2) Orienting, the disengagement, shifting, and reallocation of attention to a spatial location, and 3) Executive control, the resolution of conflict among competing mutually-exclusive responses that enables selective focus. To aid in understanding of attention, the Attention Network Test (ANT) was developed as a brief computerized measure of the efficiency of each of these networks and has been extensively used in a variety of healthy and clinical populations. Several studies have examined differences in ERPs during the ANT in healthy aging and clinical populations such as ADHD, schizophrenia, and traumatic brain injury (TBI) but have not conducted repeated sessions of the task to evaluate changes over time. While the ANT has been shown to be robustly sensitive to a range of cognitive and/or neuropsychological disorders, the reliability of the ANT across multiple sessions, particularly in terms of neural activity measures, has not been previously examined. An important focus of the proposed research regards the stability of neural measures reflecting activity of each of three different attention networks. One method for examining neural activity involves time locked electroencephalographic (EEG) recordings of brain activity or scalp-recorded brain event-related potentials (ERPs). ERPs can provide insights into the neural correlates of cognitive processes elicited by an experimental task as well as the neural time course. Particularly relevant to this study, the visual N1, a negative-going ERP deflection occurring \ 150-200 milliseconds (ms) post-stimulus. N1 amplitude is considered to index attentional allocation. Additionally, a later-occurring positive-going deflection occurring \ 300-ms post-stimulus, P3, reflects processes associated with stimulus evaluation and categorization. Previous research in our lab has shown that N1 and P3 are differentially sensitive to attention network functioning probed using the ANT in neurologically-healthy controls and are sensitive to attentional impairment in TBI survivors. Thus, the study team will focus on the stability of N1 and P3 waveforms across the 4 sessions. Previous studies have examined reliability and stability of behavioral measures of the ANT by evaluating the reliability of reaction time (RT) and error rates over multiple sessions. Ishigami reported good temporal stability of network scores across 10 sessions, although some practice effects were seen in orienting and executive-control attention networks. Importantly, no prior work has examined the temporal stability of ERPs with repeated ANT performance, which is among the primary aims of the proposed research. Finally, in an exploratory manner, the investigators will extend findings from one of our earlier studies demonstrating altitudinal attentional biases for upper vertical space in healthy participants and the absence of such biases in TBI survivors. Specifically, the investigators will examine which of three attentional networks are sensitive to such biases in healthy controls and determine the ERP signatures of these biases.

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