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posters/_posts/2023-07-24-smith-physics-learning-ohbm.md

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+---
+layout: poster
+title: "Task-based attention & default mode connectivity linked to STEM anxiety in university students"
+nickname: 2023-07-24-smith-physics-learning-ohbm
+authors: "Donisha D. Smith, Alan Meca, Katherine L. Bottenhorn, Jessica E. Bartley, Michael C. Riedel, Taylor Salo, Julio A. Peraza, Robert W. Laird, Shannon M. Pruden, Matthew T. Sutherland, Eric Brewe, Angela R. Laird"
+year: "2023"
+conference: "OHBM"
+image: /assets/images/posters/2023-07-24-smith-physics-learning-ohbm.png
+projects: ["mmmm"]
+tags: []
+
+# Content
+fulltext:
+pdf: 
+
+# Links
+doi:
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+f1000:
+---
+
+{% include JB/setup %}
+
+# Abstract
+
+## Introduction
+
+Students in science, technology, engineering, and mathematics (STEM) disciplines often encounter STEM-associated anxiety, which may hinder their learning, performance, and retention, impacting graduation rates negatively According to attentional control theory (ACT), heightened anxiety redirects the cognitive resources required to perform a task toward processing anxiety-induced stimuli. This diversion could lead to impaired processing efficiency and diminished performance effectiveness.The interaction among the dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN) play a pivotal role in attentional control.Our study aimed to expand the ACT framework by exploring the neurobiological correlations between STEM-related anxiety and cognitive performance among 123 undergraduate physics students. We examined the within- and between-network connectivity of the DAN, VAN, and DMN during two in-scanner tasks related to physics cognition, focusing on two subgroups with distinct profiles of science and math anxiety.
+
+## Methods
+
+The sample included 123 right-handed undergraduate students (mean age=19.8±1.5, range=18-26; 56 females) enrolled in physics courses at Florida International University (FIU), Miami. Self-report instruments assessing science, math, and general anxiety were completed during behavioral sessions. Latent profile analysis (LPA) grouped participants based on science/math anxiety profiles, and regression analysis assessed connectivity differences within/between DAN, VAN, DMN. Participants engaged in two fMRI tasks: The Force Concept Inventory (FCI) task, a self-paced conceptual reasoning task consisting of three phases (Scenario, Question, and Answer), and the Physics Knowledge (PK) task, a general knowledge task of semantic retrieval. Four-week behavioral and imaging sessions were conducted, and anatomical and functional images were preprocessed with fMRIPrep. DAN, VAN, DMN were identified through Kong's 17-network parcellation [5], yielding 400x400 region-wise correlation matrices per participant.
+
+## Results
+
+LPA results identified two dominant science and math anxiety profiles that accounted for most of the sample: Low Science and Math Anxiety (59.3%) and High Math Anxiety (21.9%) (Fig 3). Less common profiles, High Science and Math Anxiety (6.5%) and High Science Anxiety (4.1%), were not analyzed in this study. No significant differences between High Math Anxiety' and Low Science and Math Anxiety groups regarding age, sex, ethnicity, income, GPA, FIU enrollment years, general anxiety, FCI/PK tasks accuracy, and Phase II/III FCI task reaction times. However, High Math Anxiety group spent significantly more time on Phase I (Scenario) of the FCI task. In the FCI task, we observed significant variations in between-network connectivity. Specifically, High Math Anxiety participants showed reduced connectivity (DAN-VAN, DAN-DMN, VAN-DMN) exclusively in Phase III (Answer) compared to Low Science and Math Anxiety students. Notably, Phases I (Scenario) and II (Question) revealed no substantial connectivity difference. All phases were unaffected by covariates. During the PK task, High Math Anxiety participants presented significantly reduced DAN-VAN between-network connectivity compared to their Low Science and Math Anxiety counterparts. Covariates did not significantly influence connectivity. The FCI task showed significant alterations in within-network connectivity. High Math Anxiety participants had reduced connectivity (DAN, VAN, DMN) within networks only in Phase III (Answer) compared to Low Science and Math Anxiety participants. No significant difference was noted in within-network connectivity during Phase I (Scenario) and II (Question). Covariates had no impact across all phases. During the PK task, we found significant differences in within-network connectivity. Specifically, High Math Anxiety participants demonstrated a decrease in DAN within-network connectivity compared to Low Science and Math Anxiety participants. No covariate influence on connectivity variation was noted.
+
+## Discussion and Conclusions
+
+The study supports the attentional control theory, demonstrating that High Math Anxiety in students corresponds with reduced task-based connectivity in attention-control-related brain networks during physics conceptual reasoning and knowledge tasks. Future research should investigate how STEM-related anxiety impacts performance across various STEM courses and learning experiences.

team/_posts/2017-04-09-smith-donisha.md

@@ -10,19 +10,19 @@ image: /assets/images/team/donisha-smith.jpg
 alumni: false
 
 # social
-cv: "https://docs.google.com/document/d/12O2ONsc9HpKWgd-ouq4bnEDpqFVJC_NsQIWIJm3YBLI/edit?usp=sharing"
+cv: "https://docs.google.com/document/d/1I3UWsxO400DbDG1iUgHi5EoB-Xi1zSCnDaIH8LScvaU/edit?usp=sharing"
 nih_biosketch:
 email: [email protected]
-github: donishasmith
+github: donishadsmith
 orcid:
 osf:
 figshare:
 publons:
 researchgate:
 impactstory:
 scholar:
-site: "https://github.com/donishasmith"
+site: "https://github.com/donishadsmith"
 twitter:
 ---
 
-Currently, I am a second-year Cognitive Neuroscience graduate student at Florida International University, where I also obtained my B.S. in Biology. My current research interests includes using dynamic functional connectivity techniques to better identify subpopulations in highly heterogeneous neuropsychiatric populations and the use of computational cognitive neuroscience to simulate cognitive phenomena and neurocognitive disease/disorder processes.
+Currently, I am a fifth-year Cognitive Neuroscience PhD candidate at Florida International University, where I also obtained my B.S. in Biology. My current research interests includes using dynamic functional connectivity techniques to better identify subpopulations in highly heterogeneous neuropsychiatric populations and the use of computational cognitive neuroscience to simulate cognitive phenomena and neurocognitive disease/disorder processes.