Organizational Unit:
College of Sciences
College of Sciences
Permanent Link
Research Organization Registry ID
Description
Previous Names
Parent Organization
Parent Organization
Includes Organization(s)
Organizational Unit
Organizational Unit
Organizational Unit
Organizational Unit
Organizational Unit
ArchiveSpace Name Record
Publication Search Results
Now showing
1 - 6 of 6
-
ItemTime-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task(Georgia Institute of Technology, 2023-05-01) Seeburger, DollyThere is ambiguity in the literature about how large-scale brain networks contribute to focused attention. Part of the problem comes from the methods of analyses that treat the correlates of attention as a static and discrete measure when in actuality, attention fluctuates from moment to moment. This continuous change in attention is consistent with the dynamic changes in functional connectivity between brain regions involved in the internal and external allocation of attention (Liu & Dyun, 2013). Namely, the default mode network (DMN) and the task positive network (TPN)(Fox et al., 2005). In this study, I investigated how brain network activity varied across different levels of attentional focus (e.g., “zones”). Participants performed a finger-tapping task and, guided by previous research (Esterman et al., 2013), in-the-zone was marked by low reaction time variability and out-of-the-zone as the inverse. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliably observed spontaneous low-frequency fluctuations), I found that the activity between DMN and TPN was more anti-correlated during in-the-zone states versus out-of-the-zone states. Further investigation showed that it is the fronto-parietal control network (FPCN) of the TPN that drives the differentiation. During in-the-zone periods, FPCN synchronized with the dorsal attention network, while during out-of-the-zone periods, FPCN synchronized with DMN. In contrast, the ventral attention network synchronized more closely with DMN during in-the-zone periods compared to out-of-the-zone periods. These findings suggest that time-varying functional connectivity in the low-frequency can tell us how different networks of the brain work together during periods of sustained attention.
-
ItemImproving Online Instructional Design using Memory, Attention, and Engagement(Georgia Institute of Technology, 2022-05) Chanda, RitikaNeuroscience research supports a relationship between the psychological constructions of attention and engagement. The level of selective attention and engagement present during the learning process correlates with increased memory and recall. With the recent rise in online learning, new questions regarding the improvement of educational design, teaching techniques, and learning have created a new avenue of investigation within the field of Neuroeducation. The objective of this study is to identify whether attentional brain networks related to Gagné’s Nine Events of Instruction and engagement can predict learning in an online setting by using fMRI and behavioral techniques. Overall, we found fMRI evidence of engagement, verified engagement’s role in memory and retrieval, and identified three Gagné events (Events 5, 6 and 7) that increase learning among students. This investigation allows for further advancements in online educational design as it will provide instructors with guidance on how to properly build their curriculum and modify the content structure of online classes to highlight techniques that promote successful learning.
-
ItemSleep and Quasi-Periodic Patterns During Rest(Georgia Institute of Technology, 2022-05) Karkare, Maya C.In this preliminary study, researchers attempted to determine the relationship between sleep and quasi-periodic pattern strength. Three participants wore actigraphy watches for three nights prior to a resting-state functional MRI (rs-fMRI) scan. Actigraphy data was analyzed using the Cole Kripke analysis method. Functional connectivity was analyzed for quasi-periodic patterns (QPPs) between the default mode network (DMN) and the task-positive network (TPN). Due to errors involving preprocessing of rs-fMRI data, proper QPP analyses were unable to be conducted as the QPP template was abnormal. Further analysis of the data collected in the future will yield more conclusive results.
-
ItemThe Impact of Sleep on Quasi-Periodic Patterns During Working Memory Tasks(Georgia Institute of Technology, 2022-05) Huell, Derek TerrellQuasi-periodic patterns (QPPs) are a form of low-frequency neural activity that include the interactions of both default mode and task positive networks. As this brain activity occurs constantly in our brains, they are suspected to contribute to the brain's functional connectivity. This is critical to our understanding understanding of the coordination of activity between multiple brain regions over time to accomplish tasks. Thus, this cognitive neuroscience study will seek to illuminate the effects of underlying brain mechanisms on QPPs, representing functional connectivity. Previous literature has shown that the pattern of QPPs may vary between individuals and with levels of sleep, and this variability may impact the brain's functional connectivity. In this study, we will analyze neural activity while participants complete 0-back, 2-back, and flanker tasks and a resting state fMRI scan, and pair these results with a wearable accelerometer to evaluate how sleep levels affect QPPs.
-
ItemIntegrating Neuroimaging and Behavioral Data Using The Multidimensional Generalized Graded Unfolding Model.(Georgia Institute of Technology, 2021-04-26) Barrett, Matthew E.A study investigating the relationship between two distinct data structures resulting from the same stimulus was examined. Participants made attractiveness judgments to computer generated models in two phases. Phase 1 of the study was conducted in the laboratory (behavioral) while phase 2 was conducted in the fMRI scanner (neuroimaging). Data from the behavioral component was composed of attractiveness ratings for computer generated models, whereas the neuroimaging component was composed of signal change in five pre-specified ROIs when responding to the identical stimulus. It was hypothesized that both of these outcomes were a function of the distance between a subject’s ideal point and the stimulus location in a latent multidimensional preference space. The attractiveness ratings were modeled with the multidimensional generalized graded unfolding model (MGGUM), which is an item response theory model for proximity-based data presumed to underlie the general preference ratings. The signal change data was simultaneously modeled as a function of the estimated distance between a subject and stimulus derived from the MGGUM. Estimation of models for both types of data was conducted simultaneously using a system of two simultaneous equations with parameters that are updated using a Markov chain Monte Carlo procedure. Information about signal change and its relationship to person-stimulus distances (i.e., idealness) in the multidimensional latent space was utilized to update estimates of the individual’s location in that space and this, in turn, lead to updated predictions of signal change in each ROI. This project was predicated on the notion that both behavioral and neural signal data are a function of the proximity between a given individual and stimulus, and was the first study to integrate models for neural signal into an item response theory framework.
-
ItemBroad Effects of Arousal on Quasi-Periodic Patterns of Brain Activity(Georgia Institute of Technology, 2020-12) Humm, Erek MatthewQuasi Periodic Patterns (QPPs) are recurring patterns of brain activity found in brain imaging data that last approximately 20 seconds and occur at no regular interval. In this experiment, researchers aim to establish a link between the level of mental arousal and the strength and frequency of QPPs. It was thought that increased levels of arousal would result in an increase in the strength and frequency of QPPs. To test this, subjects from three different contrasting experimental groups conducted tasks while in a functional magnetic resonance imaging (fMRI) scanner: (1) young subjects vs. old subjects, (2) task-engaged vs. resting-state, and (3) sleep disorder vs. no disorder. QPPs were regressed from the fMRI scans using an extensive processing and analysis pipeline. It was generally found that increased arousal levels led to an increase in the incidence and strength of QPPs. Increased arousal is present in young subjects, task-engaged subjects, and subjects without sleeping disorders. These results open the door for future experiments to quantify the link between arousal and QPPs. Establishing a link between these two can be vital to future research involving therapeutic devices, diagnostic tools, and even human-computer interfaces.