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1 Task-Based Functional Connectivity and Network Segregation of the Useful Field of View (UFOV) fMRI task

Published online by Cambridge University Press:  21 December 2023

Jessica N Kraft*
Affiliation:
University of Florida, Gainesville, FL, USA.
Hanna K Hausman
Affiliation:
University of Florida, Gainesville, FL, USA.
Cheshire Hardcastle
Affiliation:
University of Florida, Gainesville, FL, USA.
Alejandro Albizu
Affiliation:
University of Florida, Gainesville, FL, USA.
Andrew O’Shea
Affiliation:
University of Florida, Gainesville, FL, USA.
Nicole D Evangelista
Affiliation:
University of Florida, Gainesville, FL, USA.
Emanuel M Boutzoukas
Affiliation:
University of Florida, Gainesville, FL, USA.
Emily J Van Etten
Affiliation:
University of Arizona, Tucson, AZ, USA
Pradyumna K Bharadwaj
Affiliation:
University of Arizona, Tucson, AZ, USA
Hyun Song
Affiliation:
University of Arizona, Tucson, AZ, USA
Samantha G Smith
Affiliation:
University of Arizona, Tucson, AZ, USA
Steven T DeKosky
Affiliation:
University of Florida, Gainesville, FL, USA.
Georg A Hishaw
Affiliation:
University of Arizona, Tucson, AZ, USA
Samuel Wu
Affiliation:
University of Florida, Gainesville, FL, USA.
Michael Marsiske
Affiliation:
University of Florida, Gainesville, FL, USA.
Ronald Cohen
Affiliation:
University of Florida, Gainesville, FL, USA.
Eric Porges
Affiliation:
University of Florida, Gainesville, FL, USA.
Adam J Woods
Affiliation:
University of Florida, Gainesville, FL, USA.
*
Correspondence: Jessica N. Kraft, University of Florida, Gainesville, FL, jkraft@ufl.edu
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Abstract

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Objective:

Interventions using a cognitive training paradigm called the Useful Field of View (UFOV) task have shown to be efficacious in slowing cognitive decline. However, no studies have looked at the engagement of functional networks during UFOV task completion. The current study aimed to (a) assess if regions activated during the UFOV fMRI task were functionally connected and related to task performance (henceforth called the UFOV network), (b) compare connectivity of the UFOV network to 7 resting-state functional connectivity networks in predicting proximal (UFOV) and near-transfer (Double Decision) performance, and (c) explore the impact of network segregation between higher-order networks and UFOV performance.

Participants and Methods:

336 healthy older adults (mean age=71.6) completed the UFOV fMRI task in a Siemens 3T scanner. UFOV fMRI accuracy was calculated as the number of correct responses divided by 56 total trials. Double Decision performance was calculated as the average presentation time of correct responses in log ms, with lower scores equating to better processing speed. Structural and functional MRI images were processed using the default pre-processing pipeline within the CONN toolbox. The Artifact Rejection Toolbox was set at a motion threshold of 0.9mm and participants were excluded if more than 50% of volumes were flagged as outliers. To assess connectivity of regions associated with the UFOV task, we created 10 spherical regions of interest (ROIs) a priori using the WFU PickAtlas in SPM12. These include the bilateral pars triangularis, supplementary motor area, and inferior temporal gyri, as well as the left pars opercularis, left middle occipital gyrus, right precentral gyrus and right superior parietal lobule. We used a weighted ROI-to-ROI connectivity analysis to model task-based within-network functional connectivity of the UFOV network, and its relationship to UFOV accuracy. We then used weighted ROI-to-ROI connectivity analysis to compare the efficacy of the UFOV network versus 7 resting-state networks in predicting UFOV fMRI task performance and Double Decision performance. Finally, we calculated network segregation among higher order resting state networks to assess its relationship with UFOV accuracy. All functional connectivity analyses were corrected at a false discovery threshold (FDR) at p<0.05.

Results:

ROI-to-ROI analysis showed significant within-network functional connectivity among the 10 a priori ROIs (UFOV network) during task completion (all pFDR<.05). After controlling for covariates, greater within-network connectivity of the UFOV network associated with better UFOV fMRI performance (pFDR=.008). Regarding the 7 resting-state networks, greater within-network connectivity of the CON (pFDR<.001) and FPCN (pFDR=. 014) were associated with higher accuracy on the UFOV fMRI task. Furthermore, greater within-network connectivity of only the UFOV network associated with performance on the Double Decision task (pFDR=.034). Finally, we assessed the relationship between higher-order network segregation and UFOV accuracy. After controlling for covariates, no significant relationships between network segregation and UFOV performance remained (all p-uncorrected>0.05).

Conclusions:

To date, this is the first study to assess task-based functional connectivity during completion of the UFOV task. We observed that coherence within 10 a priori ROIs significantly predicted UFOV performance. Additionally, enhanced within-network connectivity of the UFOV network predicted better performance on the Double Decision task, while conventional resting-state networks did not. These findings provide potential targets to optimize efficacy of UFOV interventions.

Type
Poster Session 01: Medical | Neurological Disorders | Neuropsychiatry | Psychopharmacology
Copyright
Copyright © INS. Published by Cambridge University Press, 2023