Poster No:
1415
Submission Type:
Abstract Submission
Authors:
Juliet Fowler1, Vasily Vakorin1, Sam Doesburg1, Sylvain Moreno1
Institutions:
1Simon Fraser University, Vancouver, British Columbia
First Author:
Juliet Fowler
Simon Fraser University
Vancouver, British Columbia
Co-Author(s):
Sam Doesburg
Simon Fraser University
Vancouver, British Columbia
Introduction:
Neuroplasticity is the brain's capacity to adapt and reorganize. Experience can influence neural changes, with the potential for lasting impact on intrinsic brain function (Olsewska et al., 2021). In turn, these changes in brain dynamics can be investigated with functional magnetic resonance imaging (fMRI) and assessed by computing resting-state functional connectivity (rsFC), a measure of coactivation across brain regions (Orwig et al., 2023). The nature of neuroplasticity can be explored through examining the rsFC of individuals who have undergone extensive exposures, such as prolonged occupational experience (Orwig et al., 2023; Wu et al., 2020). Previous research has focused on domain-specific experts, including professional musicians, taxi drivers, mathematicians, and pilots (Wu et al., 2020). Typically, a highly-trained group of professionals are compared to untrained controls. This framework has revealed neural differences in rsFC dynamics thought to relate to sustained engagement in the trained domain (Orwig et al., 2023; Wu et al., 2020).
While foundational evidence of neuroplastic changes within specific occupational expertise domains has been established, analyses across domains may reveal global effects of expertise itself. We hypothesized that experts across domains would exhibit greater connectivity within the control network compared to novices.
Methods:
Participants were drawn from the UK Biobank and categorized into either a novice (n=3,544) or expert group (n=2,539) based on their employment data. The novice group included individuals whose longest-held job required minimal preparation (i.e. high school diploma or on-the-job training), while the expert group consisted of individuals whose longest-held job required extensive preparation (i.e. typically graduate-level training). We obtained information on the training required for each occupation from the Occupational Information Network (O*NET). UKB job titles were represented with numerical codes which were mapped to O*NET job codes (Yanik et al., 2021). Groups were then filtered to ensure validity of assignment. Participants in the final cohort were aged 45 to 82, and there was no significant difference in age between groups.
fMRI data were collected at UKB imaging centres. fMRI signals were mapped to the Schaefer cortical atlas with 200 parcellations (Schaefer et al., 2018). We considered 6 networks, separately for the left and right hemispheres (12 in total). We computed inter-network connectivity for each pair of networks (66 pairs in total). We assessed group differences in connectivity, separately for each network pairing. To correct for multiple comparisons, a false discovery rate (FDR) correction was applied at 0.05.
Results:
Statistically significant differences in connectivity between the expert and novice groups were observed in 27 out of 66 network pairs (all p<.018). Notably, these included pairs involving the default mode network (DMN), control network (CN), dorsal attention network (DAN), and ventral attention network (VAN). The polarity (correlation or anticorrelation) and magnitude of connectivity varied across significant network pairs.
Conclusions:
Our findings reveal significant differences in RSN connectivity between expert and novice groups, suggesting that occupational expertise is associated with distinct functional dynamics measurable even at rest. Previously identified neural features of domain-specific expertise have been thought to emerge from sustained engagement of a specific skill set requiring extensive preparation, and are highly individualized towards that skillset (Wu et al., 2020). In contrast, the observed connectivity patterns shown in our expert group may reflect a domain-general functional adaptation of expertise. By evaluating RSN connectivity across a range of occupations, we provide insight on the potential for neural changes shaped by expert-level experience, emphasizing a role for training in guiding the brain's neuroplastic capacity.
Learning and Memory:
Neural Plasticity and Recovery of Function
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Keywords:
FUNCTIONAL MRI
Plasticity
Other - occupational neuroplasticity; functional connectivity; resting-state networks
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
1T
Which processing packages did you use for your study?
AFNI
SPM
FSL
Provide references using APA citation style.
Olszewska, A. M. (2021). How musical training shapes the adult brain: Predispositions and neuroplasticity. Frontiers in Neuroscience. 15, 630829.
Orwig, W. (2023). Creativity at rest: Exploring functional network connectivity of creative experts. Network Neuroscience, 7(3), 1022–1033.
Schaefer, A. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28(9), 3095-3114.
Wu, H. (2020). Occupational neuroplasticity in the human brain: A critical review and meta-analysis of neuroimaging studies. Frontiers in Human Neuroscience, 14, 215.
Yanik, E. L. (2021). Physical work exposure matrix for use in the UK Biobank. Occupational medicine, 72(2), 132–141.
No