Name: Sakshi Dhawan
Ph.D. Dissertation Defense Meeting
Date: Tuesday, April 8, 2025
Time: 2:00 pm – 3:30 pm EST
Location: Virtual (https://gatech.zoom.us/j/95564138706 )

Dissertation Chairs/Advisor:
Eric Schumacher, Ph.D. (Georgia Tech)

Dissertation Committee Members:
Mark Wheeler, Ph.D. (Georgia Tech)
Thackery Brown, Ph.D. (Georgia Tech)
Shella Keilholz, Ph.D. (Emory University/ Georgia Institute of Technology)
Audrey Duarte, Ph.D. (University of Texas at Austin, Texas)

Title: The effect of working memory load and sleep quality on dynamic network connectivity during rest and n-back task performance 

Summary: Quasi-periodic patterns (QPPs) are recurrent low-frequency fluctuations that play an important role in the functional connectivity between Default Mode (DMN) and Task Positive Networks (TPN). Research indicates that attentional focus and arousal fluctuations impact QPPs during rest and tasks (Abbas et al., 2019a; Abbas et al., 2019b). Abbas and colleagues (2019a) found that the anti-correlation between DMN-TPN was stronger in the task (0-back and 2-back combined) as compared to the rest condition. Previous research has shown that an increase in working memory load affects performance and the functional activations in the brain (Jonides et al., 1997; Owen et al., 2005; Miri Ashtiani & Daliri, 2023;) so it is unknown from Abbas et al. (2019a) if QPPs were affected differently at different levels of n-back. Neurocognitive performance is also impacted by the individual’s sleep duration with daytime sleepiness reducing DMN connectivity in young adults (Ward et al., 2013), thereby producing altered connectivity between the DMN and the TPN. Past sleep behaviors including experimentally induced, acute sleep deprivation (Gujar, Yoo, Hu, & Walker, 2010; De Havas, Parimal, Soon, & Chee, 2012; Yeo, Tandi, & Chee, 2015) and habitual sleep duration estimated from wrist actigraphy (Khalsa et al., 2016) have also been linked with reductions in the functional connectivity of DMN during wakefulness. Since QPP is affected by task performance, arousal, and sleep quality, I investigated the interaction among these factors at different levels of n-back and sleep quality. I addressed the limitations of Abbas et al. (2019a) and used separate 0-back and 2-back tasks to examine the differences between them. Participants’ sleep quality and duration were measured for three days before scanning. During scanning, they performed rest, 0-back, and 2-back runs. The results demonstrated that the anti-correlation between DMN and TPN is higher in task as compared to rest, however no difference was found between 0-back and 2-back task. Within subnetworks of TPN, the fronto-parietal control network (FPCN) was potentially driving the anti-correlation between DMN and TPN by flexibly switching between DMN and the dorsal attention network (DAN) to meet internal or external processing demands. The ventral attention network (VAN) was observed to decouple with DMN during periods of low activity (rest and 0-back) and synchronize during periods of high activity (2-back). During rest and 2-back tasks, VAN behaved opposite to the FPCN. These findings suggest that FPCN synchronizes with DAN during periods of high activity in opposition to VAN and DMN to possibly suppress distractions and implement executive control and goal-directed attention. I did not observe any clear differences between the DMN and TPN connectivity between good and poor sleepers. Therefore, my study suggests that the relationship between working memory and QPP is intricate and the QPP patterns change across different brain networks based on rest vs task state. A deeper look into the four attentional networks would provide better insights into the underlying mechanism at play.