The interaction between endogenous GABA, functional connectivity and behavioral flexibility is critically altered with advanced age
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Abstract
The flexible adjustment of ongoing behavior challenges the nervous system’s dynamic control mechanisms and has shown to be specifically susceptible to age-related decline. Previous work links endogenous gamma-aminobutyric acid (GABA) with behavioral efficiency across perceptual and cognitive domains, with potentially the strongest impact on those behaviors that require a high level of dynamic control. Based on the integrated analyses of behavior and modulation of interhemispheric phase-based connectivity during dynamic motor state transitions and endogenous GABA concentration, we provide converging evidence for age-related differences in the behaviorally more beneficial state of endogenous GABA concentration. We suggest that the increased interhemispheric connectivity seen in the older adults represents a compensatory mechanism caused by rhythmic entrainment of neural populations in homotopic motor cortices. This mechanism appears to be most relevant in the presence of a less optimal tuning of the inhibitory tone to uphold the required flexibility of behavioral action.
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Reviewer #3:
The authors hypothesized lower GABA levels in older adults would influence cortico-cortical phase relationships more than cortico-muscular phase relationships during performance of a bimanual motor task. To this end, they evaluated the mediating role of endogenous bilateral sensorimotor cortex GABA content in relation to behavioral performance and patterns of interhemispheric and cortico-muscular electrophysiological phase coherence during a bimanual motor control task. The central finding was that the mediating influence of right M1 GABA on the relationship between cortico-cortical electrophysiology and behavior diverged between the younger and older groups, with lower endogenous GABA concentrations potentially benefitting bimanual motor performance in young adults and hindering performance in older adults. The result was …
Reviewer #3:
The authors hypothesized lower GABA levels in older adults would influence cortico-cortical phase relationships more than cortico-muscular phase relationships during performance of a bimanual motor task. To this end, they evaluated the mediating role of endogenous bilateral sensorimotor cortex GABA content in relation to behavioral performance and patterns of interhemispheric and cortico-muscular electrophysiological phase coherence during a bimanual motor control task. The central finding was that the mediating influence of right M1 GABA on the relationship between cortico-cortical electrophysiology and behavior diverged between the younger and older groups, with lower endogenous GABA concentrations potentially benefitting bimanual motor performance in young adults and hindering performance in older adults. The result was specific to right M1 GABA, raising questions about hemispheric asymmetry, and behavioral performance differed substantially between groups, possibly influencing the sensitivity of the analyses of the electrophysiological phase relationships. Moreover, several earlier studies suggest endogenous M1 GABA content relates to cortico-muscular excitability measurements, other than phase synchrony, and it is unclear what distinguishes phase synchrony from these other measurements. The behavioral, MRS, and electrophysiological methods employed are fairly well-established and are combined in a novel manner. The Bayesian moderated mediation analysis represents a new approach to evaluating relationships between these measures under the moderating influence of age. The central questions concerning the roles of cortical endogenous GABA in bimanual control, and in age-related changes in motor control more generally, are important for determining the neural computations underlying flexible and precise behavior.
The total number of finger taps within the 2000 ms transition epoch likely differed between groups and could influence the ISPC measures. It would be helpful to rule out this possibility by examining relationships between ISPC measures and the total number of taps.
The differences between right and left M1 are somewhat surprising and merit further attention, particularly given the cortico-cortical ISPC results. The interpretation provided in the discussion (lines 607-618) is not particularly satisfying since this asymmetry is a critical feature of a key result. Can the authors leverage their own data to provide further insight into why RM1 GABA+ may be more likely to exhibit a relationship than LM1 GABA+? Would analyzing the behavioral data separately for the left and right hands provide further insight? Does the non-dominant hand lag behind the dominant hand, and/or is it more susceptible to errors?
There were some general issues concerning the GABA+ data:
a. Figure 2a suggests an interaction in the pattern of variance in the GABA+ data between the Young and Older groups for the LM1 and RM1 voxels. Is this interaction in variance significant, and if so, what might this mean for the M1 GABA+ results? Specifically, Young show greater variance for LM1, and Older show greater variance for RM1. Also, Young appear to show considerably lower variance for RM1 than LM1. However, the data in Figure 2 supplement 2 suggest that variance in the Young is similar between LM1 and RM1. Do these numbers accurately reflect the data depicted in Figure 2a?
b. It would be helpful to show the difference spectra in Figure 2 supplement 1b with separate plots for Young and Older.
c. Figure 2, supplement 1a: Was the LM1 voxel more dorsal and medial than the RM1 voxel?
The authors interpret the decrease in failure and increase in error rate across the task in the Older group as an indication of a loss of precision over time. Alternatively, might this pattern also arise because these participants are becoming faster at correcting their errors (i.e. within 2000 ms), avoiding trials from being categorized as a failure? More generally speaking, it would be helpful if the authors provided additional information about the cumulative error rate trials and what behavior looked like on these trials.
The authors should provide further justification for the assignment of age as the moderator and GABA+ as the mediator in their statistical model. Conceptually, it seems these factors could be reversed.
Several studies have established relationships between transcranial magnetic stimulation measures of cortico-muscular excitability and endogenous GABA+ content in the dominant M1. The manuscript would benefit from further discussion of the relationship of the phase connectivity measurements used here in comparison to these other previous studies.
It is not clear that data or analysis code are available.
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Reviewer #2:
I like this type of multimodal study, and I think that the rationale for the study is good. I am not, however, convinced about the results/conclusions provided. Here are my main points:
I don't agree with your conclusion that the mediating role of GABA changes in aging. This requires longitudinal data, the cross-sectional approach in this study can only conclude differences between groups since only 1 time point is available.
No age interaction, this is surprising to me since there are age differences?
Compensatory explanation: Is there a correlation with performance? If there isn't, the proposal of compensatory mechanisms is unclear since it is then not obvious what the compensation is for?
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Reviewer #1:
The authors have acquired a substantial multimodal dataset and have used careful statistical approaches throughout. The data are acquired and analysed using appropriate methods.
Overall, this is an impressive body of work that aims to answer an interesting question. However, a number of questions over the methods and interpretation make the authors' conclusions difficult to justify.
When comparing between older and younger adults it would also be helpful to know the amount of grey matter in the voxels of interest. It might be expected that older adults might have more atrophy and therefore lower GABA+, than younger adults and this should be controlled for in the statistical models. The authors have put assumptions into their quantification, which are reasonable but are still assumptions. It would be helpful to directly test …
Reviewer #1:
The authors have acquired a substantial multimodal dataset and have used careful statistical approaches throughout. The data are acquired and analysed using appropriate methods.
Overall, this is an impressive body of work that aims to answer an interesting question. However, a number of questions over the methods and interpretation make the authors' conclusions difficult to justify.
When comparing between older and younger adults it would also be helpful to know the amount of grey matter in the voxels of interest. It might be expected that older adults might have more atrophy and therefore lower GABA+, than younger adults and this should be controlled for in the statistical models. The authors have put assumptions into their quantification, which are reasonable but are still assumptions. It would be helpful to directly test for a difference in grey matter fraction in the voxel between the two groups, and include this in the model if necessary.
The authors then look at behaviour, where they use a previously described task which consists of bimanual tapping, with switching between two patterns. The results are complex as there are a number of behavioural metrics, and no clear pattern emerges. While older adults produced more errors in continuation, they also produced more fully correct switching transitions. Older subjects were slower than younger adults in all trials. While this task produces a very rich dataset, which is helpful for analysing complex behaviour, it is not clear how each metric should be interpreted in terms of the underlying neural mechanisms, and how they can be usefully combined, could be given.
In terms of connectivity, the authors found no significant group X task difference between in-phase and anti-phase conditions. They therefore look at the groups and tasks separately. They show different changes in connectivity between age groups in different frequency bands, for example between left and right M1 in the alpha/mu and beta, between EMG and left M1 in the theta band. I am not sure that describing EEG-EMG connectivity as cortico-spinal is strictly accurate - there may be a number of other factors in this -corticomuscular would seem to be more precise. The frequency bands used are not typical, and it would be helpful to have an a priori explanation of which are being tested and why - as well as details about correction for multiple comparisons across these bands.
Finally, the authors bring their GABA, behaviour and connectivity metrics together in a number of mediation analyses. They demonstrate a relationship between cortico-cortical connectivity and behaviour, which is mediated by age.
The authors describe their finding of higher GABA+ in the occipital cortex as a posterior-anterior gradient, which I think is not justified by the results - there could be a number of other reasons for this, for example that different functional networks have different GABA+ levels, which is not related to their anatomical position. With only three voxels it is difficult to make a general claim such as this, and this should probably be reworded.
The authors state that higher GABA+ indicated neural system integrity and better functioning in the older group. This seems to be rather over-interpreting their results - there are many other metrics of integrity and functioning that have not been assessed here. I would suggest rewording.
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