Finally, we computed mean rating of perceived exertion scores for each condition by averaging all of the scores recorded for each condition. Statistical differences between conditions for each measure were computed using a one-way, repeated-measures ANOVA. An outline of the processing pipeline is depicted in Fig. Next, we computed the average reference using the remaining channels and applied an adaptive mixture independent component analysis AMICA 32 , 33 to the cleaned channel time series to parse the data into spatially fixed, maximally temporally independent component signals To generate event-related potentials, we removed bad components as identified above and back projected the remaining components to the channels.
Next, we interpolated the channels that were previously removed using the remaining cleaned channels to produce a consistent channel montage for each subject. Finally, target and non-target ERPs were computed by averaging across remaining epochs at each channel for each subject. Data processing pipeline.
Data processing workflow. EEG data were first processed using fairly standard cleaning methods grey boxes. After running AMICA, the data were processed in two different ways in order to perform both the channel-level analysis orange boxes and component-level analysis green boxes.
We determined the location and timing of significant differences between ERPs of each condition by submitting pair-wise data to a repeated measures, two-tailed cluster mass permutation t-test 39 using a family-wise alpha level of 0. This permutation test analysis was used in lieu of more conventional analysis because it allowed us to test for differences in amplitude, topography, and timing of the event related potential while correcting for the large number of multiple comparisons.
We chose the cluster mass statistic because it has been suggested to have good power for event related potentials such as the P 42 , To determine feasibility of detecting expected differences in brain responses during the physically demanding task, we tested for differences between the non-target and target ERPs during the Loaded condition.
We linearly mixed the independent components based on dipole source location using a modified measure-projection analysis 45 , 46 to isolate activity in 56 regions of interest as specified by the Loni atlas We used the modified measure-projection approach to represent the equivalent dipoles for each independent component as a three-dimensional Gaussian density function.
We computed the independent component contribution to a given region of interest by determining the overlap between the region of interest and the dipole density function for that independent component and normalized such that each dipole could only be represented at unity across all 56 regions. We then computed the event-related spectral perturbation ERSP and event related potential ERP for each region of interest by summing the relative contribution of each independent component to a given region of interest.
This was performed for each subject separately, such that each subject had an ERSP and ERP for each region, if activity was found in that region. We determined timing, location, and frequency of significant differences between ERSPs by submitting pair-wise comparisons to a repeated measures, two-tailed cluster mass permutation t-test with the family-wise alpha set to 0. Similarly, we determined differences in source ERPs between conditions by submitting data to a repeated measures, two-tailed cluster mass permutation test t-test with the family-wise alpha set to 0. As expected, rating of perceived exertion significantly increased with increasing physical load Fig.
The seated condition had a mean score of 6. The unloaded walking condition had a mean score of 7. The loaded walking condition had a mean score of Psychological and Behavioral Results. It was administered before and after every bout of the cognitive task.
Panel B shows the reaction time to respond to the visual target during the seated early and late and walking early and late cognitive task bouts. We found significant differences between non-target and target ERPs during the Loaded walking condition Fig. There was also a decrease in amplitude over the frontal regions for the target stimulus but not the non-target. We submitted the data to a cluster mass permutation test and found significant differences between the target and non-target stimuli as demonstrated by Fig.
Feasibility of measuring ERPs during loaded walking. Panel A shows topographical plots of the channel-level ERPs across time for non-target vs. Data were time-locked to the presentation of the stimulus.
Panel B displays the t-scores testing the significant differences between the non-target and target trials across all channels using cluster mass permutation. Channels are ordered based on the Biosemi electrode cap A1 — H32 and displayed by quadrant. Non-significant values were set to zero green. We did not find any statistical differences in the amplitude of early and late event-related potentials in either the seated or walking conditions Fig.
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Given this, all following channel level and cortical source analyses are collapsed into conditions called Seated, Loaded and Unloaded. Early and late task channel-level ERPs. The plots show ERP traces averaged across subjects in the central-parietal region of interest for the seated early and late and walking early and late cognitive bouts for the unloaded and loaded conditions left and right panels, respectively. Analysis of the event-related potentials at the channel level revealed large differences between walking and seated conditions, and some small but significant differences between unloaded and loaded conditions.
Unloaded and Loaded walking condition ERPs demonstrate early perceptual components very similar to the Seated condition.
The loaded walking condition appears to have an even smaller amplitude than the unloaded walking condition in the same time frame. Significance-masked t-values are shown across time for all channels in the top plots of Fig. We found that there were significant differences, particularly in scalp areas near the sensorimotor and parietal regions. Spatially there were significant differences across conditions. The amplitude of the responses to the oddball target were smaller in the unloaded compared to the loaded conditions in these areas. We found similar differences when comparing seated and loaded in the sensorimotor and parietal regions, but with differences also present on the left side of the brain, thus less lateralization was found when comparing the seated and loaded conditions.
Unlike in the sensorimotor and parietal regions, the amplitude of the response in the frontal regions was greater during the loaded walking compared to seated condition. Overall, the channel level results suggest changes in cortical dynamics of the brain during target detection under varying levels of locomotor demand. Channel-level ERPs across conditions. Panel A displays the channel-level ERP in response to the target stimulus averaged across all subjects during seated, unloaded, and loaded conditions for the Central-Parietal ROI see location shown in panel inset.
Non-significant cluster t-values were set to zero green. Differences between seated and walking conditions were more robust in the left with respect to right superior temporal gyrus Fig.
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Component-level ERPs across conditions. Under each plot, the red and blue bars denote significant differences between Seated vs. Unloaded blue bar and Seated vs. Loaded red bar over time as determined by the cluster mass permutation test. No significant differences were found between the Unloaded and Loaded conditions.
The seated condition ERP had nearly twice the amplitude during this time period compared to both the unloaded and loaded walking conditions.
The middle frontal gyrus ERPs had the lowest stimulus-related amplitude of all the regions of interest and there were no differences between waveforms for each condition. We did not detect any significant differences between the loaded and unloaded condition ERPs for any regions of interest. There was more cortical desynchrony during the seated condition compared to the walking conditions Fig. Since the left and right side ERSPs were nearly mirror images of each other, we have presented only the six regions of interest from the right side of the cortex here.
During both the unloaded and loaded walking conditions Fig. Alpha and beta desynchrony is still present in the parietal and occipital areas, but to a much lesser degree than in the seated condition. Component-level ERSPs. Panel A displays the event related spectral perturbations ERSPs for the seated, unloaded, and loaded conditions left, right and middle columns, respectively for six relevant ROIs across the scalp.
Panel B shows the significance-masked cluster mass permutation results for Seated compared to Unloaded and Seated compared to Loaded conditions left and right columns, respectively for the same ROIs as in Panel A. Note that these ROIs are the same as those depicted in Fig. Also noteworthy is the alpha synchrony Fig. This activity is more prominent in the walking conditions particularly the loaded condition in the frontal, cingulate, precentral, and postcentral.