Elsevier

Brain Research

Volume 1270, 13 May 2009, Pages 95-106
Brain Research

Research Report
The influence of mental fatigue and motivation on neural network dynamics; an EEG coherence study

https://doi.org/10.1016/j.brainres.2009.03.015Get rights and content

Abstract

The purpose of the present study is to examine the effects of mental fatigue and motivation on neural network dynamics activated during task switching. Mental fatigue was induced by 2 h of continuous performance; after which subjects were motivated by using social comparison and monetary reward as motivating factors to perform well for an additional 20 min. EEG coherence was used as a measure of synchronization of brain activity. Electrodes of interest were identified using a data-driven pre-processing method (ten Caat, M., Lorist, M.M., Bezdan, E., Roerdink, J.B.T.M., Maurits, N.M., 2008a. High-density EEG coherence analysis using functional units applied to mental fatigue. J. Neurosci. Meth. 171, 271–278; ten Caat, M., Maurits, N.M. and Roerdink, J.B.T.M., 2008b. Data-driven visualization and group analysis of multichannel EEG coherence with functional units. IEEE T. Vis. Comp. Gr. 14, 756–771). Performance on repetition trials was faster and more accurate than on switch trials. EEG data revealed more pronounced, frequency specific fronto-parietal network activation in switch trials, while power density was higher in repetition trials. The effects of mental fatigue on power and coherence were widespread, and not limited to specific frequency bands. Moreover, these effects were independent of specific task manipulations. This increase in neuronal activity and stronger synchronization between neural networks did not result in more efficient performance; response speed decreased and the number of errors increased in fatigued subjects. A modulation of the dopamine system is proposed as a common mechanism underlying the observed the fatigue effects.

Introduction

Mental fatigue influences nearly all aspects of cognitive and emotional functioning in humans. It induces sub-optimal functioning, which may even lead to accidents with severe consequences. Concerning the impact of mental fatigue on daily life it is surprising that only little is known about neuro-cognitive mechanisms underlying the effects of mental fatigue.

Previous studies have indicated that higher level control functions, which orchestrate more basic cognitive functions, are especially sensitive to mental fatigue (Holding, 1983, Lorist et al., 2000, Lorist et al., 2005, van der Linden et al., 2003). Lorist et al. (2000), for example, showed that control mechanisms involved in planning and preparation for future activities became less adequate and subjects made more errors with increasing mental fatigue. Moreover, fatigue was found to be associated with compromised performance monitoring, and the ability to use information from previous trials to strategically adjust behavior was severely deteriorated already after half an hour of continuous task performance (Boksem et al., 2006, Lorist et al., 2005).

These studies mainly focused on effects of mental fatigue on specific cognitive functions reflected in event-related potential (ERP) components in the EEG. However, adequate performance requires the integration of information processed in functionally specialized brain regions. Integration or binding of information processed in separate regions takes place through synchronization of brain activity in these different brain areas. Simultaneous activation in two or more functionally and anatomically distinguished brain areas is expected in case cognitive control functions are active, whereas neural synchronous oscillations occurring in more localized brain areas specialized for specific functions seem to be associated with more basic cognitive functions (Engel et al., 2001, Sauseng and Klimesch, 2008).

The purpose of the present study is to examine whether mental fatigue affects neural network dynamics as indexed by neural synchronization. To verify this we used a switch task in which cognitive control processes, involved in rapidly switching between tasks, play a crucial role, and which has shown to elicit mental fatigue (Lorist et al., 2000). Lorist et al. (2000) limited their analysis of brain activity in that study to ERP components. To investigate whether the observed effects of mental fatigue can be related to changes in network dynamics we focused on EEG coherence in the present study. The EEG signal measured at a specific electrode is supposed to reflect network activity under the electrode (in particular synchronous neural oscillations of pyramidal neurons) and EEG coherence can be regarded as a measure of interaction between two of these neuronal populations (Nunez, 2000). It is generally accepted that more synchronous activity in different brain regions will be reflected in increased coherence between EEG signals obtained from recordings directly above these areas.

Coherence in specific frequency bands has been related to different cognitive functions (Knyazev, 2007). For example, Sauseng et al. (2005) reported that global synchronization in the theta band (4–8 Hz) increases with increasing demands on cognitive control. In addition, they found that local parietal synchronization in the theta band increases with increasing task difficulty. However, the frequency specificity of these results should be interpreted carefully because Sauseng et al. limited their analysis to the alpha and theta range a priori. It is important to realize that changes in coherence values observed in a specific frequency range do not imply that a similar change in power will be observed at single electrodes involved in the interaction. An increase in EEG power indicates an increase in the number of synchronously active neurons underlying an electrode, while stronger coherence without increased power necessarily indicates increased phase locking between neural activities recorded from two cortical areas. Kiroi and Aslanyan (2006) showed that prolonged monotonous task performance for 3–3.5 h increased power in the lower frequency bands (delta and theta) and in the beta band. Coherence levels, however, did not change with time on task, which was interpreted as a reflection of the stability of long-range neuronal relationships during continuous task performance. Similar effects of mental fatigue on EEG power were reported by Boksem et al. (2005); they found an increase in spectral power in the alpha, theta and beta band during 3 h of task performance. The increase in alpha and theta power was interpreted as a reflection of decreased arousal levels, while the increase in beta power was linked to the investment of mental effort to stay awake.

Existing evidence concerning the effects of mental fatigue on EEG synchronization is rather limited; therefore it is difficult to formulate firm hypotheses about effects of mental fatigue on brain synchronization and to identify the regions (i.e., electrodes) of interest or frequency bands of interest to be studied. Discarding a major part of information beforehand might leave brain regions or frequency bands with potentially significant contributions to the cognitive mechanism under observation undetected, which seriously limits the interpretation of research findings. Because of these disadvantages of the hypothesis-driven method under the present circumstances we applied a data-driven pre-processing method instead to identify electrodes of interest and subsequent analyses were performed over a wide range of frequency bands. The method to identify relevant electrodes of interest was developed by ten Caat et al., 2008a, ten Caat et al., 2008b), and visualizes the results of a calculation encompassing all possible coherences between electrodes. Local synchronicity is represented by functional units (FUs), representing spatially connected sets of electrode pairs of which coherence values exceed a predefined threshold level and which are presumed to represent functionally distinct brain areas. Global coherence is represented as coherence between FUs. The individual FU maps are used to calculate group mean coherence maps, visualizing dominant features from individual maps, and this information can be the basis of subsequent conventional coherence and power analysis.

The major issue in the present study is to gain more understanding of the origins and nature of changes in cognition with increasing mental fatigue. From this perspective, it is important to realize that effects of mental fatigue have been related to a lack of motivation (Boksem et al., 2006, Chaudhuri and Behan, 2000). Boksem et al. (2006) showed that after adequate motivation the adverse effects of mental fatigue could be partially reduced. They hypothesized that motivation resulted in improvements in cognitive control. As argued earlier, cognitive control processes are reflected in global synchronization between diverse cortical sites, therefore, an increase in coherence might be expected in motivated subjects compared to mentally fatigued subjects. To test this assumption, subjects were motivated in the present study after 2 h of task performance by using social comparison and monetary reward as motivating factors.

In summary, in the present study we sought to determine the effects of mental fatigue and motivation on neural network dynamics activated during task switching, using EEG coherence as a measure of synchronous activation in the brain. Electrodes of interest were identified using a data-driven pre-processing method (ten Caat et al., 2008a, ten Caat et al., 2008b).

Section snippets

Subjective measures

Subjects reported increasing levels of aversion to continue task performance with increasing time on task (F(5,125) = 44.54, p  < .001); scores increased from 4.5 (SD = 2.0) at the start of the experimental session to 8.8 (SD = 1.7) at the end of task performance. Activation levels, as derived from the Activation–Deactivation Adjective Check List (AD–ACL; Thayer, 1989), decreased during the session (Fig. 1; F(1,25) = 29.47, p  < .001 and F(1,25) = 8.76, p =  .007 for general activation and high activation,

Discussion

The effects of mental fatigue on neural network dynamics related to stimulus processing were examined using a switch paradigm, in which cognitive control processes play a crucial role, and which has shown to elicit mental fatigue (Lorist et al., 2000).

Subjects

Twenty-six healthy subjects (14 women) participated in this study, ranging in age from 18 to 28 years (M = 21.4, SD = 3.0). All subjects reported to have normal sleep patterns, not to work night shifts, and not to use prescription medication. They were right-handed and had normal or corrected to normal visual acuity. The experiment was performed in compliance with relevant laws and institutional guidelines, and was approved by the ethical committee of the University Medical Center Groningen. The

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