Skip to main content

Main menu

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
    • Podcast
  • ALERTS
  • FOR AUTHORS
    • Information for Authors
    • Fees
    • Journal Clubs
    • eLetters
    • Submit
    • Special Collections
  • EDITORIAL BOARD
    • Editorial Board
    • ECR Advisory Board
    • Journal Staff
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
    • Accessibility
  • SUBSCRIBE

User menu

  • Log out
  • Log in
  • My Cart

Search

  • Advanced search
Journal of Neuroscience
  • Log out
  • Log in
  • My Cart
Journal of Neuroscience

Advanced Search

Submit a Manuscript
  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
    • Podcast
  • ALERTS
  • FOR AUTHORS
    • Information for Authors
    • Fees
    • Journal Clubs
    • eLetters
    • Submit
    • Special Collections
  • EDITORIAL BOARD
    • Editorial Board
    • ECR Advisory Board
    • Journal Staff
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
    • Accessibility
  • SUBSCRIBE
PreviousNext
Research Articles, Behavioral/Cognitive

Dynamic Recovery: GABA Agonism Restores Neural Variability in Older, Poorer Performing Adults

Poortata Lalwani, Douglas D. Garrett and Thad A. Polk
Journal of Neuroscience 10 November 2021, 41 (45) 9350-9360; https://doi.org/10.1523/JNEUROSCI.0335-21.2021
Poortata Lalwani
1Department of Psychology, University of Michigan, Ann Arbor, Michigan 48109-1043
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Douglas D. Garrett
2Max Planck Institute for Human Development, 14195 Berlin, Germany
3Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Thad A. Polk
1Department of Psychology, University of Michigan, Ann Arbor, Michigan 48109-1043
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF
Loading

Abstract

Aging is associated with cognitive impairment, but there are large individual differences in these declines. One neural measure that is lower in older adults and predicts these individual differences is moment-to-moment brain signal variability. Testing the assumption that GABA should heighten neural variability, we examined whether reduced brain signal variability in older, poorer performing adults could be boosted by increasing GABA pharmacologically. Brain signal variability was estimated using fMRI in 20 young and 24 older healthy human adults during placebo and GABA agonist sessions. As expected, older adults exhibited lower signal variability at placebo, and, crucially, GABA agonism boosted older adults' variability to the levels of young adults. Furthermore, poorer performing older adults experienced a greater increase in variability on drug, suggesting that those with more to gain benefit the most from GABA system potentiation. GABA may thus serve as a core neurochemical target in future work on aging- and cognition-related human brain dynamics.

SIGNIFICANCE STATEMENT Prior research indicates that moment-to-moment brain signal variability is lower in older, poorer performing adults. We found that this reduced brain signal variability could be boosted through GABA agonism in older adults to the levels of young adults and that this boost was largest in the poorer performing older adults. These results provide the first evidence that brain signal variability can be restored by increasing GABAergic activity and suggest the promise of developing interventions targeting inhibitory systems to help slow cognitive declines in healthy aging.

  • aging
  • brain signal variability
  • lorazepam
  • pharmacological intervention
  • resting-state fMRI
  • SDBOLD

Introduction

Functional magnetic resonance imaging (fMRI) has become a predominant method for noninvasively estimating brain activity in human beings. Most fMRI studies treat moment-to-moment variability in the blood oxygenation level-dependent (BOLD) fMRI signal as noise, but research demonstrates that such variability is associated with better behavioral performance and is a more powerful predictor of cognitive abilities than mean BOLD signal (McIntosh et al., 2008; Garrett et al., 2011, 2013b; Grady and Garrett, 2014; Burzynska et al., 2015). Consistent with these findings, theoretical, experimental, and computational modeling work suggests that greater brain signal variability typifies younger, higher performing adults and well-functioning cortical networks capable of greater complexity and flexibility, increased dynamic range and information transfer, and stronger long-range functional connectivity (Li et al., 2006; Faisal et al., 2008; McIntosh et al., 2008, 2010; Shew et al., 2009; Deco et al., 2011; Garrett et al., 2011, 2013b; Misic et al., 2011; Vakorin et al., 2011; Beharelle et al., 2012; Grady and Garrett, 2014; Nomi et al., 2017). However, little is known about the underlying basis of performance-related deficits in brain signal variability, and even less is known about how to reverse these deficits.

GABA, the brain's major inhibitory neurotransmitter, plays a role in many of the same functions brain signal variability has been associated with, including cortical plasticity (Jones, 1993; Hensch et al., 1998; Fagiolini et al., 2004), the synchronization of neural oscillations (functional connectivity; Fingelkurts et al., 2004; Bonifazi et al., 2009; Kapogiannis et al., 2013), information capacity (Shew et al., 2011; Puzerey and Galán, 2014), efficiency (Sengupta et al., 2013; Zhou and Yu, 2018), pattern complexity (Monteforte and Wolf, 2010; Lajoie et al., 2014; Agrawal et al., 2018), and dynamic range (Shew et al., 2009; Agrawal et al., 2018) of neural networks. Manipulating the strength of inhibitory connections in artificial neural networks also dramatically influences the number of different states the network can sample (Agrawal et al., 2018). Crucially, decreasing GABA activity pharmacologically in healthy young rats and monkeys has been found to decrease network signal variability and to reduce the number of states that can be visited by the cortical network (Shew et al., 2011). Inspired by these results and by previous work showing that older adults express lower GABA levels (Gao et al., 2013; Porges et al., 2017; Cuypers et al., 2018; Cassady et al., 2019; Chamberlain et al., 2019; Lalwani et al., 2019) and lower brain signal variability in a host of cortical regions (Garrett et al., 2011, 2013a; Grady and Garrett, 2014, 2018; Waschke et al., 2021), we hypothesized that pharmacologically increasing GABA activity might causally reverse deficient brain signal variability levels in older adults. Furthermore, given that lower brain signal variability is typically associated with poorer cognitive performance even in older adults (Garrett et al., 2011, 2013a; Grady and Garrett, 2014; Burzynska et al., 2015), we also hypothesized that GABA agonism-related upregulation of brain signal variability should be largest in poorer performing older adults. However, some studies suggest that region- and measure-specific brain signal variability can also be higher in older, poorer performing adults (Samanez-Larkin et al., 2010; Boylan et al., 2021). We thus examined whether there were any regions exhibiting higher signal variability with older adult age and the role of GABA agonism in the whole brain regardless of sign using multivariate partial least squares (PLS; McIntosh et al., 1996).

In the current study, we analyzed data from 21 young (age 18–25 years) and 25 older (age 65–85 years) adults who had previously participated in the Michigan Neural Distinctiveness (MiND) study (Gagnon et al., 2019). Specifically, we investigated (1) the effect of age on resting-state brain signal variability, (2) the effect of a pharmacological manipulation of GABA activity on brain signal variability, and (3) the association among individual differences in composite cognitive scores and changes in brain signal variability on drug compared with placebo.

Materials and Methods

This dataset was collected as part of the MiND study. Here, we only describe the portions of the study that are relevant to this analysis. Gagnon et al. (2019) has details about the entire study protocol. The ethical approval for the study was granted by the Institutional Review Board of the University of Michigan (HUM00103117).

Participants

We analyzed data from 25 young (age 18–29 years) and 21 older (age 65 years and above) male and female human adults who completed the entire MiND study and received the drug manipulation. All participants were recruited from Ann Arbor and the surrounding area and were right-handed, native English speakers, and had normal or corrected-to-normal vision. Participants completed an initial telephone screening interview and were determined to be eligible. We screened out participants who scored 23 or lower on the Montreal Cognitive Assessment (Carson et al., 2018). All sessions described took place at the University of Michigan Functional MRI Laboratory. We present data collected during two sessions, each on a separate day.

Behavior testing

Participants completed an extensive cognitive and behavioral task battery including tasks from the National Institutes of Health (NIH) Toolbox for Assessment of Neurologic and Behavioral Function (Weintraub et al., 2014). The toolbox tasks were administered using an iPad, and the associated software automatically generates a standardized composite cognitive score for each participant. Here, we provide a brief description of the tasks that contributed to this composite measure (Weintraub et al., 2014).

Pattern Comparison Processing Speed Test

Two simple side-by-side pictures were presented on an iPad, and participants were instructed to discern, as fast as they could, whether the two pictures were the same or different. Participants pressed buttons on the iPad screen to indicate their response. The score was calculated based on the number of items they correctly answered in 85 s.

List Sorting Working Memory Test

Participants were presented with a few pictures from a specific category (e.g., animals) one at a time on the iPad. Participants were then asked to list the items in increasing order of size. Participants' response was marked correct if they listed all the items in the correct order.

Flanker Inhibitory Control and Attention Test

Participants were presented with a row of arrows on the iPad and instructed to indicate the direction of the middle arrow as quickly as they could. They pressed a left or right arrow button located on the iPad screen to indicate their response. The middle arrow could point in the same direction as the arrows surrounding it (congruent trials) or in the opposite direction (incongruent trials). There was a total of 20 trials, 40% of which were incongruent. The score was based on a combination of reaction time and accuracy.

Dimensional Change Card Sort Test

In this task, participants were presented with one target image and two response images that matched the target image in either shape or color. During each trial, participants were first presented with the word “shape” or “color” and were asked to choose the response image that matched the target image based on that dimension. There were 30 trials, and 23% of these were color trials. The score computed for this task was also based on a combination of reaction time and accuracy.

Picture Sequence Memory Test

Participants first had to recall the order of 15 images displayed in sequence on the iPad screen. They moved images on the screen to match the order they remembered them being presented. Participants were then presented with 18 images, including the first 15 images and three new images presented in the middle of the sequence. Again, they were asked to recall the order of the 18 images. The score was based on the total number of correct adjacent pairs.

Picture Vocabulary Test

Participants listened to an audio recording of a word and four pictures displayed on the iPad screen. They were instructed to select the picture that best matched the meaning of the word they heard. This test used the Computerized Adaptive System, whereby the difficulty of the next question was determined by the previous answer. A raw score was computed using Item Response Theory.

Oral Reading Recognition Test

Participants were presented a word on the iPad screen and asked to read the word out loud. Using a pronunciation guide, the examiner scored the response as correct or incorrect. This test also used Computerized Adaptive Testing, and the score was computed using Item Response Theory.

fMRI scans

Participants were given a low-dose benzodiazepine (lorazepam) or a placebo pill ∼1 h before the scan on 2 separate days. The order of the sessions (on and off drug) was counterbalanced across participants. Participants were not told which pill they received on which day; they were blind to the drug administration order. During the drug session, participants were administered a 0.5 or 1 mg oral dose of lorazepam. The drug dosage (0.5 or 1 mg) was randomly assigned across participants for the following reasons: (1) to maximize the chances of including a dose strong enough to produce observable effects without producing significant sedation and (2) to make it possible to analyze the effect of dosage. The participants were screened for use of medications that might interact with lorazepam or affect GABA levels. They also had no history of claustrophobia or mood disorders.

The functional scanning parameters (detailed below) were identical during both sessions. Functional MRI data were collected using a 3T General Electric Discovery MRI system with a volumetric quadrature birdcage head coil and two 32-channel receive arrays. The functional scans were T2*-weighted images collected with a 2D gradient echo pulse sequence with the following parameters: repetition time = 2000 ms, echo time = 30 ms, flip angle = 90°, field of view (FOV) = 220 × 220 mm, 43 axial slices with thickness of 3 mm and no spacing, collected in an ascending sequential sequence (voxels were 3 × 3 × 3 mm). The total acquisition time for the resting-state functional scan was 8 min 10 s, with 245 volumes. Participants were instructed to relax, keep their eyes open, and focus on a fixation cross presented for the duration of the scan. Using an eye-tracking system (in view mode only), we ensured that participants indeed had their eyes open and fixated. Heart rate was collected via a pulse oximeter placed on the left middle finger. We also obtained a T1-weighted image using the spoiled gradient-recalled (3D BRAVO) echo sequence during this session, with the following parameters: inversion time = 500 ms, flip angle = 15°, FOV = 256 × 256 mm.

fMRI data preprocessing

The fMRI data were preprocessed and analyzed using a combination of Functional MRI of the Brain (FMRIB) Software Library (FSL), SPM12, and MATLAB-based scripts. The first five volumes of each scan were discarded. Heart rate was collected via the pulse oximeter placed on the left middle finger, and the data were physio corrected during preprocessing. We performed first-level preprocessing using the FSL FEAT (FMRIB fMRI Expert Analysis Tool; Woolrich et al., 2001) with default parameters for motion correction, normalization, and smoothing (7 mm). We used the SPM12 function spm_detrend to remove linear, quadratic, and cubic trends in the time series and also applied a Butterworth filter (0.01–0.1 Hz). We then ran FSL MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components) to perform independent component analysis. Three separate raters identified noise components through manual visual inspection based on Kelly et al. (2010). These components reflected noise related to sinus activity, vascular and ventricle activations, and motion. We then removed the components identified as noise by at least two of the three raters using the FSL regfilt function. Subsequently, we performed linear registration of the functional and anatomic images of each participant and the Montreal Neurological Institute (MNI)152 template using the FSL FLIRT (FMRIB Linear Image Registration Tool).

Quantification of brain signal variability (SDBOLD)

After preprocessing the fMRI data, the SD in the fMRI signal during the entire resting-state scan was computed at each voxel for each participant and scanning session separately. We also computed SDBOLD-Change at each voxel as the difference between SDBOLD-Drug and SDBOLD-Placebo. All analyses were restricted to voxels in a MNI152 gray matter volume mask.

Statistical analysis

To examine the regional distribution of the effects on brain signal variability, we used multivariate PLS analyses (McIntosh et al., 1996).

We employed behavior-PLS for investigating effects of age group on SDBOLD-Placebo. In this simple one-condition, one-behavior PLS, a correlation matrix between age group and the signal of each voxel (SDBOLD-Placebo) was first computed across subjects. This correlation matrix was then decomposed using singular value decomposition (SVD). This resulted in a singular (S; reflecting the correlation strength) value, and brain voxel (V; i.e., a weighting pattern across brain voxels that optimally expresses the correlation) weights—in this case, the original voxelwise correlations with age but scaled to be unit length. We then calculated individual brainscores by taking the dot product of the brain voxel weights and a given subject's brain measures. Thus, brainscores indicate the degree to which a subject expresses the multivariate spatial pattern captured by an age-driven latent variable.

Next, we used task-PLS with two groups for investigating the effects of drug on SDBOLD. The task-PLS is similar to the behavior-PLS but involves a singular value decomposition of a between-subject covariance (COV) matrix instead of the correlation matrix. We first computed a COV matrix between drug and placebo conditions and the SDBOLD of each voxel within each age group. Then, using SVD, we estimated a left singular vector of experimental condition (U) weights for each age group, a right singular vector of brain V weights, and a diagonal matrix of S values. This produced four latent variables (LVs). Only the first LV was significant and represented greater variability during the drug condition than during placebo in both age groups. Brainscore was computed separately for each condition as the dot product of brain voxel weights and each subjects' SDBOLD-Placebo and SDBOLD-Drug.

We used simple task-PLS for investigating effects of drug on SDBOLD within each age group separately. This is similar to the previously described task-PLS except there are no age groups. Thus, SVD results in only two latent variables. The first LV, representing greater variability during the drug condition compared with placebo, was significant in older adults. The LV in younger adults alone was not significant.

We used rank-based behavior-PLS with two groups for investigating effects of cognitive processing on SDBOLD-Change. Similar to the previously described one-condition, one-behavior PLS, first a rank-based correlation matrix was computed between the composite cognitive scores and the signal of each voxel (SDBOLD-Placebo) across subjects within each age group and then stacked into a single matrix. An SVD of this matrix results in two latent variables defined by S value and brain V weights. This PLS identifies a single latent space in the brain between the two groups that best captures the relationship between voxel signals and the behavioral measure for each of the groups. Thus, the relationship between SDBOLD-Change and cognitive processing could be different in older adults and younger adults. Brainscores were computed as a dot product between V and SDBOLD-Change at each voxel for each subject.

We used a rank-based behavior PLS for investigating the effect of baseline composite cognitive performance on variability during all three conditions (placebo, drug, and change). Like the previously described one-condition, one-behavior PLS, a rank-based correlation matrix is computed based on baseline composite cognitive task scores and the signal of each voxel (SDBOLD) across subjects for each of the three conditions. An SVD on this matrix resulted in three (equal to the number of conditions) latent variables defined by S values, brain V weights, and U weights. Only one latent variable was significant. Brainscore for each condition was computed as the dot product between V weights and variability in each condition (SDBOLD-Placebo, SDBOLD-Drug, SDBOLD-Change) for each subject.

Finally, we used a rank-based behavior-PLS for investigating the effects of several cognitive processing tasks on SDBOLD-Change in older adults. It is very similar to the previously described one-condition, one-behavior PLS. First a rank-based correlation matrix is computed based on each of the cognitive task scores and the signal of each voxel (SDBOLD-Placebo) across subjects. An SVD is performed on this matrix. In our model it resulted in seven (equal to the number of cognitive tasks) latent variables defined by S values, brain V weights, and U weights. Only one latent variable was significant. All the U weights of this LV were negative, suggesting a negative correlation between change in variability and all cognitive tasks. Similar to brainscore, a cognitive score was computed as the dot product between U weights and the cognitive score in each task for each subject.

For all the PLS models, significance of the detected relations was assessed using 1000 permutation tests of the singular value, and the robustness of voxel saliences was computed using 1000 bootstrapped resamples. By dividing the mean salience of each voxel by the bootstrapped SE, we obtained bootstrap ratios (BSRs) as normalized estimates of robustness. We thresholded the BSRs at a value of ≥3.00, which approximates a 99.9% confidence interval. We then used the Harvard-Oxford cortical atlas to identify the regional identity of the significant clusters (Table 1–Table 6) in the cortical regions and the automated anatomical atlas (AAL) to identify the regional identity of significant clusters in the subcortical regions. All the other statistical analyses were conducted using R (https://www.r-project.org/). The lme4 package was used to perform the linear mixed-effects analyses, and figures were plotted using the ggplot2 package (Wickham, 2016) (https://cran.r-project.org/web/packages/lme4/index.html).

View this table:
  • View inline
  • View popup
Table 1.

Brain regions that exhibited a reliable association between age and SDBOLD on placebo

Data availability

Data were collected as a part of a bigger study in Gagnon et al. (2019) and will be publicly available within 1 year of completion of the study. The code used for analysis is available from GitHub (https://github.com/umich-tpolklab/DynamicRecoveryPaper).

Results

Aging and brain signal variability

Consistent with previous results (Grady and Garrett, 2014), resting-state brain signal variability (operationalized as the SD of BOLD fMRI signal, SDBOLD) was significantly lower in older versus younger adults (Welch two-sample t test, t(35.1) = 3.98, p = 0.0003). There were two outliers (one young and one older adult) with a Cook's distance >0.087 (four/sample size). Even after excluding these two subjects, brain signal variability was significantly lower in older adults (permuted p = 0.02) compared with younger adults (Welch two-sample t test of brainscore, t(32.4) = 3.96, p = 0.0004; Fig. 1, Table 1). All the results presented below are based on excluding the two outlier subjects.

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Effect of age on SDBOLD. A, Resting-state SDBOLD at placebo is significantly lower in older adults (purple) compared with younger adults (green). B, Spatial pattern expressing the effect of age. Yellow/red regions showed a reliable decrease in variability with age, whereas blue regions showed a reliable increase. Bootstrap ratios increase from red to yellow and from dark to light blue and are thresholded at a value of ≥3.00.

Increasing GABA activity boosts brain signal variability in older adults

We also investigated the effect of a small dose of a positive allosteric modulator (lorazepam) of the GABAA receptor on brain signal variability. We used PLS with younger and older adults as two separate groups and found one significant latent variable (permuted p < 0.001) showing higher brain signal variability on drug compared with placebo. The spatial pattern indicated a reliable increase in variability on drug in several regions including the cingulate gyrus, cerebellum, frontal, temporal, sensorimotor, and occipital regions. No regions showed a reliable reduction in variability on drug compared with placebo (Figure 2B, Table 2).

View this table:
  • View inline
  • View popup
Table 2.

Brain regions that exhibited a reliable increase in variability on drug compared with placebo, dominantly in older compared with younger adults

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

Effect of GABA agonism on SDBOLD. A, SDBOLD increases on drug significantly in older adults but not in younger adults. Error bars indicate SD. B, Spatial pattern expressing the effect of drug on variability. Yellow/red regions exhibited a reliable increase in variability on drug (bootstrap ratio increases from red to yellow). No regions showed a reliable decline in variability on drug (absence of blue/green regions). Bootstrap ratios are thresholded at a value of ≥3.00.

We used a linear mixed-effects model to investigate the effect of age group, drug, and the age group × drug interaction on brain signal variability estimates produced from the above model after accounting for the subjects as random effects. Brain signal variability was significantly larger on drug than on placebo (F(1,42) = 14.8, p = 0.0004), and the age group × drug interaction was also significant (F(1,42) = 5.6, p = 0.02). On investigating the interaction further, we found that there was a significant increase in variability on drug within older adults (t(23) = −4.04, p = 0.0005), but not within younger adults (t(19) = −0.97, p = 0.34; Fig. 2A). Furthermore, variability was not significantly different between older adults on drug and younger adults on placebo (t(42) = −1.6, p = 0.11), consistent with the hypothesis that the drug restored the older adults' brain signal variability to young adult levels.

Given that the drug effect was only robust within the older group, we then reran an older-adult-only PLS model to isolate relevant brain regions (Fig. 3A). We found that variability reliably increased on drug (permuted p < 0.001) in several regions including the parahippocampal gyrus, fusiform cortex, sensorimotor regions, cerebellum, cingulate gyrus, and frontal and occipital regions (Fig. 3B, Table 3). No regions showed a reliable reduction on drug compared with placebo. As a further set of controls for this older-adult-only PLS result, we used a linear mixed-effects model to account for main effects of session order (counterbalanced across participants), gray matter volume, days between sessions (mean, 16 d; range, 2–79 d), dosage (0.5 vs 1 mg), self-reported drowsiness before and after the drug session, age (mean, 69.9; range, 65–81), and drug on brain signal variability estimates from this model, as well as the age × drug interaction after accounting for subjects as random effects. Only the main effect of drug (F(1,23) = 16.62, p = 0.0005) was significant, and this remained the only significant effect after excluding one participant with a Cook's distance >0.17 (four/number of older adults; F(1,22) = 18.14, p = 0.0003).

View this table:
  • View inline
  • View popup
Table 3.

Brain regions that exhibited a reliable increase in variability on drug compared with placebo in older adults alone

Figure 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 3.

Effect of GABA agonism on SDBOLD in older adults. A, Resting-state SDBOLD in older adults was significantly higher on drug (red) compared with placebo (blue). B, Spatial pattern expressing the effect of the drug on brain signal variability. Yellow/red regions exhibited a reliable increase in variability on drug versus placebo, whereas blue regions exhibited a reliable decrease. Bootstrap ratios were thresholded at a value of more than or equal to ±3.00.

Increasing GABA activity leads to greater boost in brain signal variability in poorer performing older adults

To examine the relationship between cognitive performance and change in brain signal variability on drug, we obtained the composite cognition score from the NIH Toolbox for the Assessment of Neurologic and Behavioral Function (Weintraub et al., 2014) and computed change in variability at each voxel (SDBOLD-change = SDBOLD-Drug – SDBOLD-Placebo). Consistent with the previous literature (Garrett et al., 2011, 2013a; Grady and Garrett, 2014; Burzynska et al., 2015), we found that the composite cognition scores were significantly lower in older adults compared with those of young adults (t(40.7) = 4.7, p <0.001). Using PLS with two age groups modeled separately, we found a single significant latent variable (permuted p = 0.03) that captured the relationship between change in brain signal variability on drug versus placebo (SDBOLD-change) and the composite cognitive score from the NIH battery. The relationship was negative and significant in older adults (r(22) = −0.51, bootstrap CI: 0.46, 0.85; Fig. 4A) but not in younger adults (r(18) = 0.17, bootstrap CI: −0.1, 0.6). The age group × cognition interaction (computed using ANOVA on the brainscore estimates) was also significant (F(1,40) = 7.2, p = 0.01). There were no outliers based on Cook's distance. The negative association between cognitive performance and GABA drug-related change in variability in older adults suggests that poorer performers experience a greater drug-related boost in SDBOLD than higher performers, an effect that was reliable in several brain regions, including the cingulate and middle frontal gyrus, thalamus, superior parietal lobule, sensorimotor, lateral occipital, and temporal regions (Fig. 4B, Table 4).

View this table:
  • View inline
  • View popup
Table 4.

Brain regions that exhibited a reliable association between overall cognitive processing score and drug-related shifts in variability, mainly expressed within the older adult group

Figure 4.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 4.

Cognition and boost in variability on drug. A, Change in SDBOLD on drug is negatively correlated with composite cognitive score in older adults but not in younger adults. Error bars indicate bootstrapped 95% confidence intervals (see above, Materials and Methods). B, Spatial pattern expressing the relationship between change in variability on drug and baseline composite cognitive score. Yellow/red regions exhibited a reliable negative relationship (bootstrap ratio increases from red to yellow). No regions exhibited a reliable positive relationship (absence of blue/green regions). Bootstrap ratios were thresholded at a value of more than or equal to ±3.00.

These results support the hypothesis that older adults with poorer cognitive processing benefit more from the drug-induced increase in GABA activity. Within older adults only, we further investigated the relationship between composite cognitive score and variability during all three conditions, namely, (1) baseline variability (SDBOLD-Placebo), (2) variability on drug (SDBOLD-Drug), and (3) change in variability on drug from baseline (SDBOLD-Change; Fig. 5A,B). We found that change in variability (SDBOLD-Change) explained significant variance in cognition even after accounting for baseline variability (SDBOLD-Placebo; F(1,21) = 17.4, p = 0.0004) and variability on drug (SDBOLD-Drug; F(1,21) = 9.1, p = 0.007).

Figure 5.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 5.

Cognition and SDBOLD in older adults. A, Baseline variability is positively correlated with baseline cognitive performance (but not significantly), whereas variability on drug and change in variability from placebo to drug are negatively correlated with baseline cognitive performance. Error bars indicate bootstrapped 95% confidence intervals (see above, Materials and Methods). B, Spatial pattern expressing the relationship between variability and baseline cognitive performance in all three conditions. Yellow/red regions exhibited a reliable effect (bootstrap ratio increases from red to yellow). No regions exhibited a reliable effect in the opposite direction (absence of blue/green regions). Bootstrap ratios were thresholded at a value of more than or equal to ±3.00.

Finally, we examined the role of change in variability on drug and performance on the individual cognitive tasks (during baseline) that make up the composite cognitive score from the NIH toolbox. We found that all the tasks were negatively associated (significantly in five of seven using 1000 bootstraps) with change in variability on drug (Fig. 6A). This relationship was reliable in several regions of the brain including the middle frontal gyrus, superior parietal lobule, thalamus, parahippocampal gyrus, cingulum, sensorimotor, lateral occipital, and temporal regions (Fig. 6C, Table 5). Figure 6B shows the overall latent relationship between cognitive performance and SDBOLD-change derived from this PLS model (r(22) = −0.58, p = 0.003). This relationship was also significant, even after accounting for self-reported drowsiness before and after the on-drug scan, psychomotor vigilance score before and after the on-drug scan, age, and dosage (0.5 vs 1 mg; F(1,16) = 10.03, p = 0.006). This correlation was negative, suggesting that older adults with high cognitive processing (during baseline) showed the smallest changes on drug, whereas those with the lowest scores experienced a greater boost.

View this table:
  • View inline
  • View popup
Table 5.

Brain regions that exhibited a reliable association between cognitive processing score from the NIH toolbox and brain signal variability measure during placebo and drug condition as well as drug-related change in brain signal variability in older adults alone

View this table:
  • View inline
  • View popup
Table 6.

Brain regions that exhibited a reliable association between various cognitive processing tasks from the NIH toolbox and drug-related shifts in brain signal variability in older adults alone

Figure 6.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 6.

Cognition and boost in variability on drug in older adults. A, Drug-related change in SDBOLD in older adults is negatively correlated with baseline performance on several cognitive tasks. Error bars indicate bootstrapped 95% confidence intervals (see above, Materials and Methods). B, Drug-related change in SDBOLD in older adults is negatively correlated with baseline cognitive score. C, Spatial pattern expressing the relationship between change in variability on drug and baseline cognitive performance. Yellow/red regions exhibit reliable effects (bootstrap ratio increases from red to yellow). No regions exhibited reliable effects in the opposite direction (absence of blue/green regions). Bootstrap ratios were thresholded at a value of more than or equal to ±3.00.

GABA-driven upregulation of brain signal variability in poorer performing older adults is present in regions showing robust age reductions in variability

To examine whether the drug-related boost in brain signal variability was directly present in brain regions showing age-related reductions in SDBOLD, we created a mask using clusters showing robust age-related declines in brain signal variability (Fig. 1, Table 1). We then computed average brain signal variability within this mask during both the placebo and drug conditions. We found that brain signal variability in these regions was significantly higher on drug compared with placebo in older adults (t(23)=3.7, p = 0.001), effects similar to those found using the whole-brain PLS model (t(23) = 4.1, p = 0.0004). Likewise, there was a negative correlation between composite cognitive score and average change in variability within the mask in older adults (r(22) = −0.48, p = 0.02), similar to that found using the whole-brain PLS model (r(22) = −0.58, p = 0.002).

Discussion

In the present study, we found that brain signal variability was significantly lower in older adults than in younger adults, that increasing GABA activity pharmacologically increased brain signal variability in older adults to the level of healthy young adults, and that GABA-related boosts in variability were largest in the poorest performing older adults. These results provide evidence that GABA may provide a crucial basis for understanding associations among brain signal variability, aging, and cognition in humans.

Our finding that brain signal variability was significantly reduced in older adults (e.g., in the superior frontal regions, bilateral superior parietal lobule, and occipital, sensorimotor, and auditory regions) is consistent with a number of previous studies. For example, fMRI signal variability in cortex is often found to be lower in older adults compared with young adults in various studies using fixation baseline periods as a resting-state proxy (Grady and Garrett, 2014) and in those using entire resting-state data (Kielar et al., 2016; Nomi et al., 2017; Grady and Garrett, 2018). Age-related differences in resting-state variability are also robust to multiple vascular controls at the voxel level (Garrett et al., 2017). Further in line with previous work (Garrett et al., 2011; Nomi et al., 2017), relatively few clusters (∼6% of the overall spatial pattern in cerebellum and inferior temporal cortex; Table 1) exhibited greater variability in the older adults. These results add to a growing body of literature suggesting that brain signal variability levels are lower overall in older versus younger adults. However, other studies using different preprocessing pipelines, experimental designs, and variability measures such as the mean squared successive differences approach (Samanez-Larkin et al., 2010; Boylan et al., 2021) have found mainly positive effects between BOLD variability and adult age. Future work could investigate such differences by aggregating various datasets, performing direct comparisons of effects, and unifying the preprocessing pipelines.

GABA levels have been found to decline with age in human visual (Chamberlain et al., 2019), sensorimotor (Cassady et al., 2019; Cuypers et al., 2020), auditory (Lalwani et al., 2019), parietal (Gao et al., 2013), and frontal cortices (Porges et al., 2017), and are associated with individual differences in cognitive and sensorimotor abilities (Porges et al., 2017; Simmonite et al., 2019; Cassady et al., 2019; Levin et al., 2019). Moreover, the amplitude of low-frequency fluctuations, which is mathematically equivalent to SDBOLD when computed on the exact same band-limited time series (median correlation >0.92 in the present dataset), is also positively associated with GABA-binding potential in healthy young adults (Nugent et al., 2015). In the present work, we found that agonizing GABA activity led to an increase in cortical brain signal variability, especially in older, poorer performers.

So how does boosting GABA lead to higher moment-to-moment variability in brain activity? Computational modeling suggests a possible explanation. Having sufficient inhibitory activity to offset excitatory activity has been found to be crucial to allow artificial neural networks to operate near so-called criticality, an operating point near the edge of instability where it is easier to switch from one network state to another (Shew et al., 2011; Deco and Jirsa, 2012; Poil et al., 2012; Agrawal et al., 2018). If inhibitory connections are too weak, then excitatory activity dominates and many neurons fire synchronously, resulting in redundant coding and deep attractor states that are very stable and harder to transition from (Agrawal et al., 2018). In this hyperexcited regime, the network only samples a few synchronous configurations and therefore does not exhibit as much variability. Conversely, if inhibitory connections are too strong, then fewer neurons fire, which can also lead to a reduction in the number of states the network visits. Indeed, increasing GABA activity using propofol (a general anesthetic that also modulates activity at GABAA receptors) leads to a decrease in power spectral density (another measure of signal variance) in monkey electrocorticography recordings (Gao et al., 2017). In short, inhibitory connections should be strong enough to balance the excitatory connections but not so strong that they dampen the entire network. Networks function at criticality and optimally when network dynamics are stabilized by sufficient inhibition (for review, see Sadeh and Clopath, 2021); that is, there is an inverted-U relationship between brain dynamics and GABA that is similar to that observed with other neurotransmitters.

It is plausible that older adults with poorer cognitive performance may reside on the lower left half of a GABA-variability inverted–U function, whereas younger and better performing older adults may be closer to the peak. Increasing GABA activity in older adults with worse cognitive performance should then allow their neural networks to operate nearer to criticality and visit different states more frequently, leading to increased brain signal variability. Conversely, younger adults and older adults with better cognitive performance would not be expected to show as much change in network dynamics/variability on drug. Consistent with these expectations, we found that older adults showed robust increases in variability through GABA agonism and reached levels comparable to younger adults' variability levels at placebo. Additionally, within older adults, change in variability explained significant variance in cognition even after accounting for variance explained by variability on placebo or drug alone. Finally, we found that the poorest performers were most likely to have their signal variability levels boosted on drug, and that association could not be attributed to individual differences in drowsiness, age, or drug dosage. These results provide the first evidence that brain signal variability can be restored by increasing activity of the GABAergic system, particularly for older, poorer cognitive performers.

Limitations and future work

It is important to note that this study was cross-sectional rather than longitudinal, and so the observed age differences could be influenced by cohort or period effects (Hofer et al., 2002). Another limitation was the absence of a middle-age group, making it difficult to disentangle whether the age-related changes in variability were because of aging versus maturation (Editorial Board of the Journal of Neuroscience, 2019). However, previous research has shown that brain signal variability increases during development (from ages 8 to 15 years; McIntosh et al., 2010), and the role of individual differences in variability on cognition among older adults indicates that it is more likely to be an aging process. Additionally, we do not have behavioral data on drug and therefore cannot assess how manipulating GABA activity affected behavior in our sample. However, promisingly, several drugs targeting the GABAergic system have been shown previously to attenuate or even reverse features and symptoms of Alzheimer's disease (for review, see Guzmán et al., 2018). Further research in healthy older adults is thus needed to assess whether cognitive function can also be jointly boosted with brain signal variability via GABA agonism. Finally, we need to better understand how the GABA system is associated with other candidate neurotransmitter systems that have also been proposed previously as plausible bases of moment-to-moment brain signal dynamics (e.g., noradrenaline, dopamine; Garrett et al., 2015; Alavash et al., 2018; Kosciessa et al., 2021) and, in particular, whether the inhibitory system is a more or less effective pharmacological target than the excitatory (glutamatergic) system in future work linking aging, cognition, and neural variability in humans.

Conclusion

Overall, we found that GABA agonism can increase brain signal variability in older poorer performing adults. These results suggest the critical role of the GABAergic system in neural variability and the importance of both in aging and cognition. Potentiating GABAergic signaling represents a potentially promising direction to pursue in efforts to mitigate age-related deficits in brain function and behavior.

Footnotes

  • This work was supported by a grant from the National Institutes of Health to T.A.P (R01AG050523). We thank Holly Gagnon for participant recruitment and data collection.

  • The authors declare no competing financial interests.

  • Correspondence should be addressed to Thad A. Polk at tpolk{at}umich.edu

SfN exclusive license.

References

  1. ↵
    1. Agrawal V,
    2. Cowley AB,
    3. Alfaori Q,
    4. Larremore DB,
    5. Restrepo JG,
    6. Shew WL
    (2018) Robust entropy requires strong and balanced excitatory and inhibitory synapses. Chaos 28:103115. doi:10.1063/1.5043429 pmid:30384653
    OpenUrlCrossRefPubMed
  2. ↵
    1. Alavash M,
    2. Lim S-J,
    3. Thiel C,
    4. Sehm B,
    5. Deserno L,
    6. Obleser J
    (2018) Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance. NeuroImage 172:341–356. doi:10.1016/j.neuroimage.2018.01.048 pmid:29410219
    OpenUrlCrossRefPubMed
  3. ↵
    1. Beharelle AR,
    2. Kovačević N,
    3. McIntosh AR,
    4. Levine B
    (2012) Brain signal variability relates to stability of behavior after recovery from diffuse brain injury. Neuroimage 60:1528–1537. doi:10.1016/j.neuroimage.2012.01.037 pmid:22261371
    OpenUrlCrossRefPubMed
  4. ↵
    1. Bonifazi P,
    2. Goldin M,
    3. Picardo MA,
    4. Jorquera I,
    5. Cattani A,
    6. Bianconi G,
    7. Represa A,
    8. Ben-Ari Y,
    9. Cossart R
    (2009) GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326:1419–1424. doi:10.1126/science.1175509 pmid:19965761
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Boylan MA,
    2. Foster CM,
    3. Pongpipat EE,
    4. Webb CE,
    5. Rodrigue KM,
    6. Kennedy KM
    (2021) Greater BOLD variability is associated with poorer cognitive function in an adult lifespan sample. Cereb Cortex 31:562–574. doi:10.1093/cercor/bhaa243 pmid:32915200
    OpenUrlCrossRefPubMed
  6. ↵
    1. Burzynska AZ,
    2. Wong CN,
    3. Voss MW,
    4. Cooke GE,
    5. McAuley E,
    6. Kramer AF
    (2015) White matter integrity supports BOLD signal variability and cognitive performance in the aging human brain. PLoS One 10:e0120315. doi:10.1371/journal.pone.0120315 pmid:25853882
    OpenUrlCrossRefPubMed
  7. ↵
    1. Carson N,
    2. Leach L,
    3. Murphy KJ
    (2018) A re-examination of Montreal Cognitive Assessment (MoCA) cutoff scores. Int J Geriatr Psychiatry 33:379–388. doi:10.1002/gps.4756 pmid:28731508
    OpenUrlCrossRefPubMed
  8. ↵
    1. Cassady K,
    2. Gagnon H,
    3. Lalwani P,
    4. Simmonite M,
    5. Foerster B,
    6. Park D,
    7. Peltier SJ,
    8. Petrou M,
    9. Taylor SF,
    10. Weissman DH,
    11. Seidler RD,
    12. Polk TA
    (2019) Sensorimotor network segregation declines with age and is linked to GABA and to sensorimotor performance. NeuroImage 186:234–244. doi:10.1016/j.neuroimage.2018.11.008 pmid:30414983
    OpenUrlCrossRefPubMed
  9. ↵
    1. Chamberlain JD,
    2. Gagnon H,
    3. Lalwani P,
    4. Cassady KE,
    5. Simmonite M,
    6. Foerster BR,
    7. Petrou M,
    8. Seidler RD,
    9. Taylor SF,
    10. Weissman DH,
    11. Park DC,
    12. Polk TA
    (2019) GABA levels in ventral visual cortex decline with age and are associated with neural distinctiveness. Neurobiol Aging 102:170–177.
    OpenUrl
  10. ↵
    1. Cuypers K,
    2. Maes C,
    3. Swinnen SP
    (2018) Aging and GABA. Aging (Albany NY) 10:1186–1187. doi:10.18632/aging.101480 pmid:29905530
    OpenUrlCrossRefPubMed
  11. ↵
    1. Cuypers K,
    2. Verstraelen S,
    3. Maes C,
    4. Hermans L,
    5. Hehl M,
    6. Heise K-F,
    7. Chalavi S,
    8. Mikkelsen M,
    9. Edden R,
    10. Levin O,
    11. Sunaert S,
    12. Meesen R,
    13. Mantini D,
    14. Swinnen SP
    (2020) Task-related measures of short-interval intracortical inhibition and GABA levels in healthy young and older adults: a multimodal TMS-MRS study. Neuroimage 208:116470. doi:10.1016/j.neuroimage.2019.116470 pmid:31863914
    OpenUrlCrossRefPubMed
  12. ↵
    1. Deco G,
    2. Jirsa VK
    (2012) Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. J Neurosci 32:3366–3375. doi:10.1523/JNEUROSCI.2523-11.2012
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Deco G,
    2. Jirsa VK,
    3. McIntosh AR
    (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12:43–56. doi:10.1038/nrn2961 pmid:21170073
    OpenUrlCrossRefPubMed
  14. ↵
    Editorial Board of the Journal of Neuroscience (2019) Considerations for design of studies of normal aging, accelerated aging, and neurodegeneration. J Neurosci 39:7032–7033.
    OpenUrlFREE Full Text
  15. ↵
    1. Fagiolini M,
    2. Fritschy J-M,
    3. Löw K,
    4. Möhler H,
    5. Rudolph U,
    6. Hensch TK
    (2004) Specific GABAA circuits for visual cortical plasticity. Science 303:1681–1683. doi:10.1126/science.1091032 pmid:15017002
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Faisal AA,
    2. Selen LPJ,
    3. Wolpert DM
    (2008) Noise in the nervous system. Nat Rev Neurosci 9:292–303. doi:10.1038/nrn2258 pmid:18319728
    OpenUrlCrossRefPubMed
  17. ↵
    1. Fingelkurts AA,
    2. Fingelkurts AA,
    3. Kivisaari R,
    4. Pekkonen E,
    5. Ilmoniemi RJ,
    6. Kähkönen S
    (2004) Enhancement of GABA-related signalling is associated with increase of functional connectivity in human cortex. Hum Brain Mapp 22:27–39. doi:10.1002/hbm.20014 pmid:15083524
    OpenUrlCrossRefPubMed
  18. ↵
    1. Gagnon H,
    2. Simmonite M,
    3. Cassady K,
    4. Chamberlain J,
    5. Freiburger E,
    6. Lalwani P,
    7. Kelley S,
    8. Foerster B,
    9. Park DC,
    10. Petrou M,
    11. Seidler RD,
    12. Taylor SF,
    13. Weissman DH,
    14. Polk TA
    (2019) Michigan Neural Distinctiveness (MiND) study protocol: investigating the scope, causes, and consequences of age-related neural dedifferentiation. BMC Neurol 19:61. doi:10.1186/s12883-019-1294-6 pmid:30979359
    OpenUrlCrossRefPubMed
  19. ↵
    1. Gao F,
    2. Edden RAE,
    3. Li M,
    4. Puts NAJ,
    5. Wang G,
    6. Liu C,
    7. Zhao B,
    8. Wang H,
    9. Bai X,
    10. Zhao C,
    11. Wang X,
    12. Barker PB
    (2013) Edited magnetic resonance spectroscopy detects an age-related decline in brain GABA levels. Neuroimage 78:75–82. doi:10.1016/j.neuroimage.2013.04.012
    OpenUrlCrossRefPubMed
  20. ↵
    1. Gao R,
    2. Peterson EJ,
    3. Voytek B
    (2017) Inferring synaptic excitation/inhibition balance from field potentials. Neuroimage 158:70–78. doi:10.1016/j.neuroimage.2017.06.078 pmid:28676297
    OpenUrlCrossRefPubMed
  21. ↵
    1. Garrett DD,
    2. Kovacevic N,
    3. McIntosh AR,
    4. Grady CL
    (2011) The importance of being variable. J Neurosci 31:4496–4503. doi:10.1523/JNEUROSCI.5641-10.2011 pmid:21430150
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Garrett DD,
    2. Kovacevic N,
    3. McIntosh AR,
    4. Grady CL
    (2013a) The modulation of BOLD variability between cognitive states varies by age and processing speed. Cereb Cortex 23:684–693. doi:10.1093/cercor/bhs055 pmid:22419679
    OpenUrlCrossRefPubMed
  23. ↵
    1. Garrett DD,
    2. Samanez-Larkin GR,
    3. MacDonald SWS,
    4. Lindenberger U,
    5. McIntosh AR,
    6. Grady CL
    (2013b) Moment-to-moment brain signal variability: a next frontier in human brain mapping? Neurosci Biobehav Rev 37:610–624. doi:10.1016/j.neubiorev.2013.02.015 pmid:23458776
    OpenUrlCrossRefPubMed
  24. ↵
    1. Garrett DD,
    2. Nagel IE,
    3. Preuschhof C,
    4. Burzynska AZ,
    5. Marchner J,
    6. Wiegert S,
    7. Jungehülsing GJ,
    8. Nyberg L,
    9. Villringer A,
    10. Li S-C,
    11. Heekeren HR,
    12. Bäckman L,
    13. Lindenberger U
    (2015) Amphetamine modulates brain signal variability and working memory in younger and older adults. Proc Natl Acad Sci U S A 112:7593–7598. doi:10.1073/pnas.1504090112 pmid:26034283
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Garrett DD,
    2. Lindenberger U,
    3. Hoge RD,
    4. Gauthier CJ
    (2017) Age differences in brain signal variability are robust to multiple vascular controls. Sci Rep 7:10149. doi:10.1038/s41598-017-09752-7 pmid:28860455
    OpenUrlCrossRefPubMed
  26. ↵
    1. Grady CL,
    2. Garrett DD
    (2014) Understanding variability in the BOLD signal and why it matters for aging. Brain Imaging Behav 8:274–283. doi:10.1007/s11682-013-9253-0 pmid:24008589
    OpenUrlCrossRefPubMed
  27. ↵
    1. Grady CL,
    2. Garrett DD
    (2018) Brain signal variability is modulated as a function of internal and external demand in younger and older adults. Neuroimage 169:510–523. doi:10.1016/j.neuroimage.2017.12.031 pmid:29253658
    OpenUrlCrossRefPubMed
  28. ↵
    1. Guzmán BC-F,
    2. Vinnakota C,
    3. Govindpani K,
    4. Waldvogel HJ,
    5. Faull RLM,
    6. Kwakowsky A
    (2018) The GABAergic system as a therapeutic target for Alzheimer's disease. J Neurochem 146:649–669. doi:10.1111/jnc.14345 pmid:29645219
    OpenUrlCrossRefPubMed
  29. ↵
    1. Hensch TK,
    2. Fagiolini M,
    3. Mataga N,
    4. Stryker MP,
    5. Baekkeskov S,
    6. Kash SF
    (1998) Local GABA circuit control of experience-dependent plasticity in developing visual cortex. Science 282:1504–1508. doi:10.1126/science.282.5393.1504 pmid:9822384
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Hofer SM,
    2. Sliwinski MJ,
    3. Flaherty BP
    (2002) Understanding ageing: further commentary on the limitations of cross-sectional designs for ageing research. GER 48:22–29. doi:10.1159/000048920
    OpenUrlCrossRef
  31. ↵
    1. Jones E
    (1993) GABAergic neurons and their role in cortical plasticity in primates. Cereb Cortex 3:361–372. doi:10.1093/cercor/3.5.361-a pmid:8260806
    OpenUrlCrossRefPubMed
  32. ↵
    1. Kapogiannis D,
    2. Reiter DA,
    3. Willette AA,
    4. Mattson MP
    (2013) Posteromedial cortex glutamate and GABA predict intrinsic functional connectivity of the default mode network. Neuroimage 64:112–119. doi:10.1016/j.neuroimage.2012.09.029 pmid:23000786
    OpenUrlCrossRefPubMed
  33. ↵
    1. Kelly RE,
    2. Alexopoulos GS,
    3. Wang Z,
    4. Gunning FM,
    5. Murphy CF,
    6. Morimoto SS,
    7. Kanellopoulos D,
    8. Jia Z,
    9. Lim KO,
    10. Hoptman MJ
    (2010) Visual inspection of independent components: defining a procedure for artifact removal from fMRI data. J Neurosci Methods 189:233–245. doi:10.1016/j.jneumeth.2010.03.028 pmid:20381530
    OpenUrlCrossRefPubMed
  34. ↵
    1. Kielar A,
    2. Deschamps T,
    3. Chu RK,
    4. Jokel R,
    5. Khatamian YB,
    6. Chen JJ,
    7. Meltzer JA
    (2016) Identifying dysfunctional cortex: dissociable effects of stroke and aging on resting state dynamics in MEG and fMRI. Front Aging Neurosci 8:40. doi:10.3389/fnagi.2016.00040 pmid:26973515
    OpenUrlCrossRefPubMed
  35. ↵
    1. Kosciessa JQ,
    2. Lindenberger U,
    3. Garrett DD
    (2021) Thalamocortical excitability modulation guides human perception under uncertainty. Nat Commun 12:2430.doi:10.1038/s41467-021-22511-7
    OpenUrlCrossRef
  36. ↵
    1. Lajoie G,
    2. Thivierge J-P,
    3. Shea-Brown E
    (2014) Structured chaos shapes spike-response noise entropy in balanced neural networks. Front Comput Neurosci 8:123. doi:10.3389/fncom.2014.00123 pmid:25324772
    OpenUrlCrossRefPubMed
  37. ↵
    1. Lalwani P,
    2. Gagnon H,
    3. Cassady K,
    4. Simmonite M,
    5. Peltier S,
    6. Seidler RD,
    7. Taylor SF,
    8. Weissman DH,
    9. Polk TA
    (2019) Neural distinctiveness declines with age in auditory cortex and is associated with auditory GABA levels. Neuroimage 201:116033. doi:10.1016/j.neuroimage.2019.116033 pmid:31326572
    OpenUrlCrossRefPubMed
  38. ↵
    1. Levin O,
    2. Weerasekera A,
    3. King BR,
    4. Heise KF,
    5. Sima DM,
    6. Chalavi S,
    7. Maes C,
    8. Peeters R,
    9. Sunaert S,
    10. Cuypers K,
    11. Van Huffel S,
    12. Mantini D,
    13. Himmelreich U,
    14. Swinnen SP
    (2019) Sensorimotor cortex neurometabolite levels as correlate of motor performance in normal aging: evidence from a 1H-MRS study. NeuroImage 202:116050. doi:10.1016/j.neuroimage.2019.116050 pmid:31349070
    OpenUrlCrossRefPubMed
  39. ↵
    1. Li S-C,
    2. von Oertzen T,
    3. Lindenberger U
    (2006) A neurocomputational model of stochastic resonance and aging. Neurocomputing 69:1553–1560. doi:10.1016/j.neucom.2005.06.015
    OpenUrlCrossRefPubMed
  40. ↵
    1. McIntosh AR,
    2. Bookstein FL,
    3. Haxby JV,
    4. Grady CL
    (1996) Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 3:143–157. doi:10.1006/nimg.1996.0016 pmid:9345485
    OpenUrlCrossRefPubMed
  41. ↵
    1. McIntosh AR,
    2. Kovacevic N,
    3. Itier RJ
    (2008) Increased brain signal variability accompanies lower behavioral variability in development. PLOS Comput Biol 4:e1000106. doi:10.1371/journal.pcbi.1000106 pmid:18604265
    OpenUrlCrossRefPubMed
  42. ↵
    1. McIntosh AR,
    2. Kovacevic N,
    3. Lippe S,
    4. Garrett D,
    5. Grady C,
    6. Jirsa V
    (2010) The development of a noisy brain. Arch Ital Biol 148:323–337. pmid:21175017
    OpenUrlPubMed
  43. ↵
    1. Misic B,
    2. Vakorin VA,
    3. Paus T,
    4. McIntosh AR
    (2011) Functional embedding predicts the variability of neural activity. Front Syst Neurosci 5:90. pmid:22164135
    OpenUrlCrossRefPubMed
  44. ↵
    1. Monteforte M,
    2. Wolf F
    (2010) Dynamical entropy production in spiking neuron networks in the balanced state. Phys Rev Lett 105:268104. doi:10.1103/PhysRevLett.105.268104 pmid:21231716
    OpenUrlCrossRefPubMed
  45. ↵
    1. Nomi JS,
    2. Bolt TS,
    3. Ezie CEC,
    4. Uddin LQ,
    5. Heller AS
    (2017) Moment-to-moment BOLD signal variability reflects regional changes in neural flexibility across the lifespan. J Neurosci 37:5539–5548. doi:10.1523/JNEUROSCI.3408-16.2017 pmid:28473644
    OpenUrlAbstract/FREE Full Text
  46. ↵
    1. Nugent AC,
    2. Martinez A,
    3. D'Alfonso A,
    4. Zarate CA,
    5. Theodore WH
    (2015) The relationship between glucose metabolism, resting-state fMRI BOLD signal, and GABAA-binding potential: a preliminary study in healthy subjects and those with temporal lobe epilepsy. J Cereb Blood Flow Metab 35:583–591. doi:10.1038/jcbfm.2014.228 pmid:25564232
    OpenUrlCrossRefPubMed
  47. ↵
    1. Poil S-S,
    2. Hardstone R,
    3. Mansvelder HD,
    4. Linkenkaer-Hansen K
    (2012) Critical-state dynamics of avalanches and oscillations jointly emerge from balanced excitation/inhibition in neuronal networks. J Neurosci 32:9817–9823. doi:10.1523/JNEUROSCI.5990-11.2012 pmid:22815496
    OpenUrlAbstract/FREE Full Text
  48. ↵
    1. Porges EC,
    2. Woods AJ,
    3. Edden RAE,
    4. Puts NAJ,
    5. Harris AD,
    6. Chen H,
    7. Garcia AM,
    8. Seider TR,
    9. Lamb DG,
    10. Williamson JB,
    11. Cohen RA
    (2017) Frontal gamma-aminobutyric acid concentrations are associated with cognitive performance in older adults. Biol Psychiatry Cogn Neurosci Neuroimaging 2:38–44. doi:10.1016/j.bpsc.2016.06.004 pmid:28217759
    OpenUrlCrossRefPubMed
  49. ↵
    1. Puzerey PA,
    2. Galán RF
    (2014) On how correlations between excitatory and inhibitory synaptic inputs maximize the information rate of neuronal firing. Front Comput Neurosci 8:59. doi:10.3389/fncom.2014.00059 pmid:24936182
    OpenUrlCrossRefPubMed
  50. ↵
    1. Sadeh S,
    2. Clopath C
    (2021) Inhibitory stabilization and cortical computation. Nat Rev Neurosci : 22:21–37. doi:10.1038/s41583-020-00390-z pmid:33177630
    OpenUrlCrossRefPubMed
  51. ↵
    1. Samanez-Larkin GR,
    2. Kuhnen CM,
    3. Yoo DJ,
    4. Knutson B
    (2010) Variability in nucleus accumbens activity mediates age-related suboptimal financial risk taking. J Neurosci 30:1426–1434. doi:10.1523/JNEUROSCI.4902-09.2010 pmid:20107069
    OpenUrlAbstract/FREE Full Text
  52. ↵
    1. Sengupta B,
    2. Laughlin SB,
    3. Niven JE
    (2013) Balanced excitatory and inhibitory synaptic currents promote efficient coding and metabolic efficiency. PLOS Comput Biol 9:e1003263. doi:10.1371/journal.pcbi.1003263 pmid:24098105
    OpenUrlCrossRefPubMed
  53. ↵
    1. Shew WL,
    2. Yang H,
    3. Petermann T,
    4. Roy R,
    5. Plenz D
    (2009) Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J Neurosci 29:15595–15600. doi:10.1523/JNEUROSCI.3864-09.2009 pmid:20007483
    OpenUrlAbstract/FREE Full Text
  54. ↵
    1. Shew WL,
    2. Yang H,
    3. Yu S,
    4. Roy R,
    5. Plenz D
    (2011) Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. J Neurosci 31:55–63. doi:10.1523/JNEUROSCI.4637-10.2011 pmid:21209189
    OpenUrlAbstract/FREE Full Text
  55. ↵
    1. Simmonite M,
    2. Carp J,
    3. Foerster BR,
    4. Ossher L,
    5. Petrou M,
    6. Weissman DH,
    7. Polk TA
    (2019) Age-related declines in occipital GABA are associated with reduced fluid processing ability. Acad Radiol 26:1053–1061. doi:10.1016/j.acra.2018.07.024 pmid:30327163
    OpenUrlCrossRefPubMed
  56. ↵
    1. Vakorin VA,
    2. Lippé S,
    3. McIntosh AR
    (2011) Variability of brain signals processed locally transforms into higher connectivity with brain development. J Neurosci 31:6405–6413. doi:10.1523/JNEUROSCI.3153-10.2011 pmid:21525281
    OpenUrlAbstract/FREE Full Text
  57. ↵
    1. Waschke L,
    2. Kloosterman NA,
    3. Obleser J,
    4. Garrett DD
    (2021) Behavior needs neural variability. Neuron 109:751–766.
    OpenUrl
  58. ↵
    1. Weintraub S,
    2. Dikmen SS,
    3. Heaton RK,
    4. Tulsky DS,
    5. Zelazo PD,
    6. Slotkin J,
    7. Carlozzi NE,
    8. Bauer PJ,
    9. Wallner-Allen K,
    10. Fox N,
    11. Havlik R,
    12. Beaumont JL,
    13. Mungas D,
    14. Manly JJ,
    15. Moy C,
    16. Conway K,
    17. Edwards E,
    18. Nowinski CJ,
    19. Gershon R
    (2014) The cognition battery of the NIH toolbox for assessment of neurological and behavioral function: validation in an adult sample. J Int Neuropsychol Soc 20:567–578. doi:10.1017/S1355617714000320 pmid:24959840
    OpenUrlCrossRefPubMed
  59. ↵
    1. Wickham H
    (2016) ggplot2: Elegant graphics for data analysis. Cham, Switzerland: Springer.
  60. ↵
    1. Woolrich MW,
    2. Ripley BD,
    3. Brady M,
    4. Smith SM
    (2001) Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 14:1370–1386. doi:10.1006/nimg.2001.0931 pmid:11707093
    OpenUrlCrossRefPubMed
  61. ↵
    1. Zhou S,
    2. Yu Y
    (2018) Synaptic EI balance underlies efficient neural coding. Front Neurosci 12:46. doi:10.3389/fnins.2018.00046 pmid:29456491
    OpenUrlCrossRefPubMed
Back to top

In this issue

The Journal of Neuroscience: 41 (45)
Journal of Neuroscience
Vol. 41, Issue 45
10 Nov 2021
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Ed Board (PDF)
Email

Thank you for sharing this Journal of Neuroscience article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Dynamic Recovery: GABA Agonism Restores Neural Variability in Older, Poorer Performing Adults
(Your Name) has forwarded a page to you from Journal of Neuroscience
(Your Name) thought you would be interested in this article in Journal of Neuroscience.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Print
View Full Page PDF
Citation Tools
Dynamic Recovery: GABA Agonism Restores Neural Variability in Older, Poorer Performing Adults
Poortata Lalwani, Douglas D. Garrett, Thad A. Polk
Journal of Neuroscience 10 November 2021, 41 (45) 9350-9360; DOI: 10.1523/JNEUROSCI.0335-21.2021

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Respond to this article
Request Permissions
Share
Dynamic Recovery: GABA Agonism Restores Neural Variability in Older, Poorer Performing Adults
Poortata Lalwani, Douglas D. Garrett, Thad A. Polk
Journal of Neuroscience 10 November 2021, 41 (45) 9350-9360; DOI: 10.1523/JNEUROSCI.0335-21.2021
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF

Keywords

  • aging
  • brain signal variability
  • lorazepam
  • pharmacological intervention
  • resting-state fMRI
  • SDBOLD

Responses to this article

Respond to this article

Jump to comment:

No eLetters have been published for this article.

Related Articles

Cited By...

More in this TOC Section

Research Articles

  • Increased neuronal expression of the early endosomal adaptor APPL1 replicates Alzheimer’s Disease-related endosomal and synaptic dysfunction with cholinergic neurodegeneration.
  • Presynaptic mu opioid receptors suppress the functional connectivity of ventral tegmental area dopaminergic neurons with aversion-related brain regions
  • Change of spiny neuron structure in the basal ganglia song circuit and its regulation by miR-9 during song development
Show more Research Articles

Behavioral/Cognitive

  • Increased neuronal expression of the early endosomal adaptor APPL1 replicates Alzheimer’s Disease-related endosomal and synaptic dysfunction with cholinergic neurodegeneration.
  • Presynaptic mu opioid receptors suppress the functional connectivity of ventral tegmental area dopaminergic neurons with aversion-related brain regions
  • Change of spiny neuron structure in the basal ganglia song circuit and its regulation by miR-9 during song development
Show more Behavioral/Cognitive
  • Home
  • Alerts
  • Follow SFN on BlueSky
  • Visit Society for Neuroscience on Facebook
  • Follow Society for Neuroscience on Twitter
  • Follow Society for Neuroscience on LinkedIn
  • Visit Society for Neuroscience on Youtube
  • Follow our RSS feeds

Content

  • Early Release
  • Current Issue
  • Issue Archive
  • Collections

Information

  • For Authors
  • For Advertisers
  • For the Media
  • For Subscribers

About

  • About the Journal
  • Editorial Board
  • Privacy Notice
  • Contact
  • Accessibility
(JNeurosci logo)
(SfN logo)

Copyright © 2025 by the Society for Neuroscience.
JNeurosci Online ISSN: 1529-2401

The ideas and opinions expressed in JNeurosci do not necessarily reflect those of SfN or the JNeurosci Editorial Board. Publication of an advertisement or other product mention in JNeurosci should not be construed as an endorsement of the manufacturer’s claims. SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in JNeurosci.