About this Research Topic
The traditional paradigm of probing brain function involves a physical or mental challenge, which causes transient perturbations in the electrical and hemodynamic brain activity. Recently, a new type of activation study has been proposed by several research groups that break away from this traditional paradigm. Instead of short-duration, low-effort tasks being performed repeatedly, these newer studies demand an increased level of physical or mental effort from subjects with the intention of inducing physical and/or mental fatigue. Continued task performance over prolonged periods of time with no rest intervals presents a challenging imaging paradigm that requires the development of new imaging protocols and data analysis methods to deduce meaningful conclusions about brain circuit dynamics under fatigue.
The accuracy, sensitivity, and reproducibility of traditional activation studies rely strongly on the identification of a stable baseline not only during resting state before the task but also between task repetition blocks. The latter is necessary for attaining epoch averaging and adequate signal to noise ratios. Fatigue studies, on the other hand, present a unique challenge as the very nature of the protocol does not allow for averaging. Furthermore, as fatigue builds up the physiological state of brain and body change continuously, making it challenging to decide how to define time interval durations for signal averaging. In addition, every subject will likely have a different physiological trajectory while resisting fatigue to maintain task performance due to differences in their level of cardiovascular fitness or mental persistence attitudes, to name a few possibilities. These subject-to-subject variations make it challenging to attain group averages when analyzing data. Despite the new problems that fatigue-inducing protocols present, these studies also present new and exciting opportunities. Mapping the temporal trajectories of brain activity and network dynamics while a subject reaches the edge of their physical or mental performance abilities may open new avenues of inquiry for researchers.
There are two types of manuscripts that would be of interest to this Research Topic. This first type would propose new approaches that improve upon the state of the art in neuroimaging data analyses. Fatigue studies utilizing noninvasive neuroimaging such as fNIRS, EEG, fMRI data would be of interest. These studies would be acquiring any relevant task performance and peripheral physiological data concurrently with brain imaging. For example:
• New computational methods to help parse time-series data in an unbiased manner, e.g., low versus high fatigue, to help guide signal averaging.
• Computational methods to help align different temporal trajectories found between subjects to reduce the large variabilities occurring at the group averaging level.
The second type of manuscript of interest could entail the use of fatigue protocols to help classify physiological differences between patient populations. For example:
• How do brain network trajectories change dynamically during fatigue as a function of subject age?
• Is it feasible to define ‘brain network capacity’ metrics as novel biomarkers to help study the effects of exercise on healthy subjects or on subjects affected, say by stroke or Alzheimer’s?
The accuracy, sensitivity, and reproducibility of traditional activation studies rely strongly on the identification of a stable baseline not only during resting state before the task but also between task repetition blocks. The latter is necessary for attaining epoch averaging and adequate signal to noise ratios. Fatigue studies, on the other hand, present a unique challenge as the very nature of the protocol does not allow for averaging. Furthermore, as fatigue builds up the physiological state of brain and body change continuously, making it challenging to decide how to define time interval durations for signal averaging. In addition, every subject will likely have a different physiological trajectory while resisting fatigue to maintain task performance due to differences in their level of cardiovascular fitness or mental persistence attitudes, to name a few possibilities. These subject-to-subject variations make it challenging to attain group averages when analyzing data. Despite the new problems that fatigue-inducing protocols present, these studies also present new and exciting opportunities. Mapping the temporal trajectories of brain activity and network dynamics while a subject reaches the edge of their physical or mental performance abilities may open new avenues of inquiry for researchers.
There are two types of manuscripts that would be of interest to this Research Topic. This first type would propose new approaches that improve upon the state of the art in neuroimaging data analyses. Fatigue studies utilizing noninvasive neuroimaging such as fNIRS, EEG, fMRI data would be of interest. These studies would be acquiring any relevant task performance and peripheral physiological data concurrently with brain imaging. For example:
• New computational methods to help parse time-series data in an unbiased manner, e.g., low versus high fatigue, to help guide signal averaging.
• Computational methods to help align different temporal trajectories found between subjects to reduce the large variabilities occurring at the group averaging level.
The second type of manuscript of interest could entail the use of fatigue protocols to help classify physiological differences between patient populations. For example:
• How do brain network trajectories change dynamically during fatigue as a function of subject age?
• Is it feasible to define ‘brain network capacity’ metrics as novel biomarkers to help study the effects of exercise on healthy subjects or on subjects affected, say by stroke or Alzheimer’s?
Keywords: fNIRS, EEG, fMRI, fatigue, connectivity
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