Brain signal variability relates to stability of behavior after recovery from diffuse brain injury

Abstract

Variability or noise is an unmistakable feature of neural signals; however such fluctuations have been regarded as not carrying meaningful information or as detrimental for neural processes. Recent empirical and computational work has shown that neural systems with a greater capacity for information processing are able to explore a more varied dynamic repertoire, and the hallmark of this is increased irregularity or variability in the neural signal. How this variability in neural dynamics affects behavior remains unclear. Here, we investigated the role of variability of magnetoencephalography signals in supporting healthy cognitive functioning, measured by performance on an attention task, in healthy adults and in patients with traumatic brain injury. As an index of variability, we calculated multiscale entropy, which quantifies the temporal predictability of a time series across progressively more coarse time scales. We found lower variability in traumatic brain injury patients compared to controls, arguing against the idea that greater variability reflects dysfunctional neural processing. Furthermore, higher brain signal variability indicated improved behavioral performance for all participants. This relationship was statistically stronger for people with brain injury, demonstrating that those with higher brain signal variability were also those who had recovered the most cognitive ability. Rather than impede neural processing, cortical signal variability within an optimal range enables the exploration of diverse functional configurations, and may therefore play a vital role in healthy brain function.

Figure 1. Depiction of the single-feature, multi-feature, and redundant task conditions

For each condition, examples of a target and of nontargets are shown. For the single-feature condition, subjects had to make the decision based on shape only. For the multi-feature condition, the target and the non-targets may share one or more features (shape, color, and orientation), requiring subjects to integrate all three visual features. For the redundant condition, the target and the non-targets do not share any features, allowing the subject to make the decision based on one feature only.

Figure 2. Group behavioral results for mean reaction time, coefficient of variation, and accuracy

No significant differences were found between TBI patients and controls on all three behavioral measures for all three task conditions, indicating that the recovered post-TBI patients showed relatively normal behavioral function. Error bars indicate group standard errors.

Figure 3

Group average results for spectral power distribution (SPD) during the baseline period at the left middle occipital gyrus channel. Error bars indicate group standard errors

Figure 4. The relationship between brain signal variability and behavioral performance

A. Brain regions robustly expressing the relationship between brain signal variability and the behavioral measures are indicated with red circles. The anatomical labels for these regions are as follows: 1) R Paracentral Lobule (2 −37 55); L Precentral Gyrus (−35 −16 55); L Postcentral Gyrus (−43 −21 55) 2) R Precentral Gyrus (31 −16 50; 23 −19 50); R Postcentral Gyrus (23 −26 50); R Precuneus (17 −48 50) 3) L Precuneus (−14 −79 40) 4) L Cingulate Gyrus (−7 −8 35); L Precentral Gyrus (−50 −4 35) 5) R Precentral Gyrus (37 −2 28) 6) L Anterior Cingulate (−1 34 24) 7) L Caudate/L Anterior Cingulate (−2 19 8) 8) L Anterior Cingulate (−2 22 −8). All coordinates are given according to Talairach and Tournoux (Talairach and Tournoux, 1988). B. Group averages for MSE estimates in a representative brain region (left precuneus) plotted against the sampling period of each time scale. Similar MSE curves were obtained for all regions and showed lower variability for traumatic brain injury patients compared to controls, particularly in the coarser sampling periods, corresponding to larger sampling period windows. Error bars indicate group standard errors for each sampling period. C. Regions and sampling periods coded in warm colors showed a reliable relationship between brain signal variability and the behavioral measures. In these regions, higher brain signal variability robustly related to better accuracy in comparison subjects and stability in performance in both groups. D. The relationship between brain signal variability and each group behavioral measure. Correlations were reliable if confidence intervals did not cross zero (i.e. for accuracy in controls and variability in reaction time (cvRT) in both groups). The relationships between greater variability and more stable and accurate performance for both groups are shown in E. The MSE score indicates how strongly the subject expresses the pattern identified in the latent variable. There was no reliable relation to performance speed.

Figure 5. The relationship between MSE and variability in reaction time for the multi-feature condition

A. Brain regions robustly expressing an inverse relationship between MSE and variability in reaction time (cvRT) in the multi-feature condition are indicated with red circles. The anatomical labels for these regions are as follows: 1) R Paracentral Lobule (2 −37 55); L Precentral Gyrus (−35 −16 55); L Postcentral Gyrus (−43 −21 55) 2) R Precentral Gyrus (31 −16 50; 23 −19 50); R Postcentral Gyrus (23 −26 50); R Precuneus (17 −48 50) 3) L Inferior Parietal Lobule/L Precuneus (−28 −51 45) 4) L Precuneus (−14 −79 40) 5) L Precentral Gyrus (−50 −4 35) 6) R Precentral Gyrus (37 −2 28) 7) L Anterior Cingulate (−1 34 24) 8) L Middle Occipital Gyrus (−29 −76 12) 9) L Caudate/L Anterior Cingulate (−2 19 8) 10) L Anterior Cingulate (−2 22 −8). All coordinates are given according to Talairach and Tournoux (Talairach and Tournoux, 1988). B. Regions and sampling periods coded in warm colors expressed a reliable relationship between variability and variability in reaction times in controls and TBI patients. Regions and sampling periods coded in cool colors expressed the inverse relationship. In these regions, higher brain signal variability robustly related to stability in performance for the multi-feature condition, and this relationship was expressed significantly more strongly in traumatic brain injury patients compared to controls. The group correlations are shown in C. D. The relationships between greater variability and more stable performance during the multi-feature condition for both groups. The MSE score indicates how strongly the subject expresses the pattern identified in the latent variable.