Linking cognitive load with mental fatigue: a resting-state functional connectivity approach
Poster No:
932
Submission Type:
Abstract Submission
Authors:
John Read1, Camille Guillemin1, Maëlle Charonitis1, Nikita Beliy1, Florence Requier1, Mohamed Bahri1, Laurent Lamalle2, Mikhail Zubkov1, Pierre Maquet1, Christophe Phillips1, Gilles Vandewalle2, Fabienne Collette1
Institutions:
1University of Liège, Liège, Belgium, 2Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège, Liège, Belgium
First Author:
Co-Author(s):
Laurent Lamalle
Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège
Liège, Belgium
Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège
Liège, Belgium
Gilles Vandewalle
Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège
Liège, Belgium
Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège
Liège, Belgium
Introduction:
Technological advance drastically changed our working life, leading to significant increases in work demand and effort requirement (Hockey, 2013). Eventually, higher cognitive load comes with higher mental fatigue (MF) and its negative impact on human performance (Borragán et al., 2017). Along with behavioral paradigms also came an explosion of imaging studies seeking to unravel the neural bases of the disease of modern age. While their results are often discrepant, it has been suggested that some MF effects on the brain are task-related (Ioannucci et al., 2023). In the meantime, increased activity in the Default Mode Network (DMN) has also been observed, which was interpreted as compensatory mechanisms occurring when the effort needed increases with time on task (Gergelyfi et al., 2021).
Fortunately, MF experience tends to attenuate while resting (Gilsoul et al., 2021). However, recent work proposed that fatigue after-effect on short timescales could be separated into two components (Matthews et al., 2023; Müller et al., 2021): one that is recoverable with rest and another that is not. In fact, few studies showed that changes in neural functioning could persist at rest after MF induction (Esposito et al., 2014; Gergelyfi et al., 2021). Our goal was to investigate if induction of MF with administration of a task requiring high cognitive load could lead to subtle alterations in the functional connectivity at rest in two networks of interest, DMN and task-related.
Fortunately, MF experience tends to attenuate while resting (Gilsoul et al., 2021). However, recent work proposed that fatigue after-effect on short timescales could be separated into two components (Matthews et al., 2023; Müller et al., 2021): one that is recoverable with rest and another that is not. In fact, few studies showed that changes in neural functioning could persist at rest after MF induction (Esposito et al., 2014; Gergelyfi et al., 2021). Our goal was to investigate if induction of MF with administration of a task requiring high cognitive load could lead to subtle alterations in the functional connectivity at rest in two networks of interest, DMN and task-related.
Methods:
A sample of 19 healthy volunteers (age: 31.42y ± 5.76y; 14 women) realized a 3-session fatiguing protocol (see Fig1 for the detailed protocol). With this paradigm, we induced MF by varying the cognitive load in two conditions of the Time Load Dual Back task (TLDB, Borragán et al., 2017): the Low or the High Cognitive Load (LCL and HCL, respectively). Following both TLDB experimental conditions, participants underwent a 3T resting-state fMRI acquisition of 7 minutes (TR = 1170 ms; voxel size 3x3x3 mm³). They were asked to keep their eyes open and stare at a white fixation cross. Respiration and pulse signals were recorded during fMRI time series in order to correct for physiological noise in the signal.
So far, we conducted exploratory resting-state functional connectivity (rs-FC) analyses – in the HCL condition only – using CONN22.a. More precisely, ROI-to-ROI connectivity matrices were estimated characterizing the rs-FC between each pair of regions among the Control Executive Networks (CEN) as well as the DMN (Schaefer et al., 2018). Group-level analyses were performed using General Linear Models with random-effects across subjects and sample covariance estimation across age and sex. Finally, network-level inferences were based on nonparametric statistics from Network Based Statistics analyses (Zalesky et al., 2010).
So far, we conducted exploratory resting-state functional connectivity (rs-FC) analyses – in the HCL condition only – using CONN22.a. More precisely, ROI-to-ROI connectivity matrices were estimated characterizing the rs-FC between each pair of regions among the Control Executive Networks (CEN) as well as the DMN (Schaefer et al., 2018). Group-level analyses were performed using General Linear Models with random-effects across subjects and sample covariance estimation across age and sex. Finally, network-level inferences were based on nonparametric statistics from Network Based Statistics analyses (Zalesky et al., 2010).
Results:
We extracted connectograms from our two networks of interest at a corrected threshold of p-FDR < 0.001 after the HCL condition (Fig2). Regarding the DMN, preliminary results showed high inter-hemispheric functional connectivity between the precuneus posterior cingulate cortices as well as anti-correlation between the right precuneus and the left prefrontal cortex. Moreover, we observed high connectivity between the left prefrontal cortex and the right dorso-medial prefrontal cortex. Regarding the CEN, we observed an overall high connectivity inside the network, with high inter-hemispheric connectivity for the cingulate and lateral prefrontal cortices.
Conclusions:
For the HCL condition inducing high MF level, our results suggest 1) high patterns of rs-FC in the CEN with a tendency to look like small world topology; 2) high inter-hemispheric connectivity between precuneal DMN areas. Further statistical analyses remain to be done to show if these patterns persist when the cognitive load is low (LCL condition). Besides, future analyses should also focus on detailed network properties.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
Cognition
FUNCTIONAL MRI
Other - Mental Fatigue, Functional Connectivity
1|2Indicates the priority used for review
Provide references using author date format
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