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. Author manuscript; available in PMC: 2023 May 7.
Published in final edited form as: Neuroimage. 2022 Oct 4;263:119651. doi: 10.1016/j.neuroimage.2022.119651

Spontaneous sensorimotor beta power and cortical thickness uniquely predict motor function in healthy aging

Maggie P Rempe a,b, Brandon J Lew a,b, Christine M Embury a,c, Nicholas J Christopher-Hayes a,d, Mikki Schantell a,b, Tony W Wilson a,b,e,*
PMCID: PMC10071137  NIHMSID: NIHMS1848624  PMID: 36206940

Abstract

Background:

Spontaneous beta activity in the primary motor cortices has been shown to increase in amplitude with advancing age, and that such increases are tightly coupled to stronger motor-related beta oscillations during movement planning. However, the relationship between these age-related changes in spontaneous beta in the motor cortices, local cortical thickness, and overall motor function remains unclear.

Methods:

We collected resting-state magnetoencephalography (MEG), high-resolution structural MRI, and motor function scores using a neuropsychological battery from 126 healthy adults (56 female; age range = 22–72 years). MEG data were source-imaged and a whole-brain vertex-wise regression model was used to assess age-related differences in spontaneous beta power across the cortex. Cortical thickness was computed from the structural MRI data and local beta power and cortical thickness values were extracted from the sensorimotor cortices. To determine the unique contribution of age, spontaneous beta power, and cortical thickness to the prediction of motor function, a hierarchical regression approach was used.

Results:

There was an increase in spontaneous beta power with age across the cortex, with the strongest increase being centered on the sensorimotor cortices. Sensorimotor cortical thickness was not related to spontaneous beta power, above and beyond age. Interestingly, both cortical thickness and spontaneous beta power in sensorimotor regions each uniquely contributed to the prediction of motor function when controlling for age.

Discussion:

This multimodal study showed that cortical thickness and spontaneous beta activity in the sensorimotor cortices have dissociable contributions to motor function across the adult lifespan. These findings highlight the complexity of interactions between structure and function and the importance of understanding these interactions in order to advance our understanding of healthy aging and disease.

Keywords: Magnetoencephalography, Multimodal, Sensorimotor, Resting-state, Oscillations

1. Introduction

Resting-state brain activity has traditionally been captured using functional MRI (fMRI) examining blood oxygenation-level dependent (BOLD) signals. More recently, however, there has been a push to examine the local oscillatory dynamics of resting state activity (i.e., spontaneous cortical activity) utilizing methods such as electroencephalography (EEG) or magnetoencephalography (MEG). Thus far, many of these studies have focused on aberrant activity in pathological conditions such as Alzheimer’s and Parkinson’s disease (Bosboom et al., 2006, 2009; Engels et al., 2017; Dubbelink et al., 2013; Wiesman et al., 2021, 2022). While it is important to study these conditions, it is also critical to identify age-related changes in healthy samples as a base to better understand the emergence of such diseases, as these conditions are frequently associated with advancing age and knowledge of normative changes across the lifespan may be useful to define diagnostic markers of early pathology. In healthy aging, changes in resting-state activity and cortical thickness have been widely described (Barry and De Blasio, 2017; Frangou et al., 2021; Gómez et al., 2013; Hoshi and Shigihara, 2020; Ishii et al., 2017; McGinnis et al., 2011; Shaw et al., 2016; Shaw et al., 2008; Vysata et al., 2012). In addition, aging populations often experience cognitive decline and difficulty with activities of daily living, even in the absence of pathological conditions. However, the intersection of these brain-related metrics and daily functioning are not fully understood.

Previous EEG and MEG studies in healthy adults have shown increases in power in slower frequencies (i.e., delta, theta, alpha) with advancing age, as well as an increase in power in higher frequencies, the most consistent finding being the increase in power in the beta frequency band (Barry and De Blasio, 2017; Gómez et al., 2013; Hoshi and Shigihara, 2020; Ishii et al., 2017; Vysata et al., 2012). However, these studies have stopped short of high-resolution source reconstruction. The few studies that have performed source-level analyzes have focused on gross regions of interest or collapsed across regions and used whole-brain values (Gómez et al., 2013; Hoshi and Shigihara, 2020). Although several studies have demonstrated such increases in the primary motor cortex during the baseline period preceding movement performance (Heinrichs-Graham et al., 2018; Heinrichs-Graham and Wilson, 2016; Rossiter et al., 2014), literature showing age-related increases in spontaneous beta power in specific cortical regions during the resting state is lacking.

Furthermore, age-related changes in cortical thickness are also well-documented in the literature. These studies generally show a period of significant thinning in childhood and a second period of gradual cortical thinning in older adulthood (Frangou et al., 2021). One normative study showed relatively little change in the sensorimotor cortex in older adulthood (Shaw et al., 2016), while others have shown significant and consistent thinning in pre- and post-central gyri with aging (Fjell et al., 2009; Fjell and Walhovd, 2010; McGinnis et al., 2011; Salat, 2004). Further, several studies have examined the relationship between cortical thickness and cortical oscillatory activity. For example, task-based studies have shown a relationship between gamma oscillations and local cortical thickness primarily in sensory paradigms, including visual (Gaetz et al., 2012; Muthukumaraswamy et al., 2010; van Pelt et al., 2018), auditory (Edgar et al., 2014), and somatosensory (Proskovec et al., 2019). Fewer studies have assessed the relationship between spontaneous activity and cortical thickness. Although one study showed an inverse relationship between cortical thickness and peak frequency along the poster-anterior axis in resting-state MEG (Mahjoory et al., 2020). Nonetheless, given the oscillatory findings described above and the relationship between specific oscillatory responses and spontaneous activity in the same tissue (i.e., beta in the motor cortex; Heinrichs-Graham and Wilson 2016), it seems possible that age-related changes in cortical spontaneous activity may relate to structural changes in cortical thickness, although to our knowledge such a relationship has not been reported.

In the current study, we examined resting-state MEG recordings in 126 healthy adults. Our primary goal was to characterize spatially-specific age-related changes in spontaneous beta activity in the sensorimotor cortices. We focused on beta activity in this region because of its well-known role in human movement and previous associations with aging. Briefly, strong beta oscillations are commonly observed just before and during movement, termed peri-movement beta event-related desynchronizations (ERD; Engel and Fries 2010; Gaetz et al. 2010; Heinrichs-Graham et al. 2018; Heinrichs-Graham and Wilson 2016, 2015; Jurkiewicz et al., 2006; Pfurtscheller and da Silva, 1999; Wilson et al., 2010, 2011, 2014), and these reliable patterns have been shown to change throughout the lifespan (Gehringer et al., 2019; Heinrichs-Graham et al., 2018; Heinrichs-Graham and Wilson, 2016; Rossiter et al., 2014). In fact, the beta ERD response along with baseline (i.e., spontaneous) beta immediately before the response show linear and quadratic increases, respectively, with age (Heinrichs-Graham et al., 2018; Heinrichs-Graham and Wilson, 2016; Rossiter et al., 2014). Additionally, three previous studies from our laboratory showed that spontaneous beta power was directly related to peri-movement beta ERD power (Heinrichs-Graham et al., 2018; Heinrichs-Graham and Wilson, 2016; Wilson et al., 2014), and that together these two responses predicted movement duration, such that stronger spontaneous beta and stronger peri-movement beta ERD predicted longer movement duration (Heinrichs-Graham et al., 2018). In addition to spontaneous beta, we quantified cortical thickness in the sensorimotor cortices and assessed overall motor function. We hypothesized that, in line with previous literature, resting beta power would increase with age and that this effect would be strongest in the sensorimotor cortices. Secondarily, we hypothesized that spontaneous beta power would be related to cortical thickness in these same regions and that cortical thickness and spontaneous beta would each be related to overall motor function.

2. Methods

2.1. Participants

One-hundred thirty-three (133) healthy adults (60 females) between the ages of 22–72 years-old (Mage = 44.4, SD = 15.4) were enrolled in this study. Exclusionary criteria included any medical illness affecting the CNS, any neurological or psychiatric disorder, history of head trauma, current substance use disorder, and standard MEG exclusion criteria (e.g., ferromagnetic implants). The local Institutional Review Board (IRB) approved the study protocol. Written informed consent was obtained from each participant after a full description of the study.

2.2. Neuropsychological testing

All participants underwent an assessment of motor function using the Grooved Pegboard (Kløve, 1963). Specifically, participants completed the task with both their dominant and non-dominant hands and their time was recorded in seconds. This time was reverse coded, such that higher values related to better motor function, and then averaged across runs to calculate an individual score. Individual scores were then transformed into z-scores using the sample mean and standard deviation, and averaged across the right and left hand to derive a motor composite z-score.

2.3. MEG data acquisition

MEG recordings took place in a one-layer magnetically shielded room (MSR) with active shielding engaged for environmental noise compensation. Participants were seated in a nonmagnetic chair within the MSR, with their head positioned within the sensor array. A 306-sensor MEGIN MEG system (Helsinki, Finland), equipped with 204 planar gradiometers and 102 magnetometers, was used to sample neuromagnetic responses continuously at 1 kHz with an acquisition bandwidth of 0.1–330 Hz. The same instrument was used across all recordings. Participants were instructed to rest with their eyes closed for 6 min and were monitored by a real-time audio-video feed from inside the shielded room throughout MEG data acquisition.

2.4. Structural MRI acquisition and MEG-MRI coregistration

Individual structural MRI (sMRI) data were obtained with a Philips Achieva 3T X-series scanner using an eight-channel head coil and a 3D fast field echo sequence with the following parameters: TR: 8.09 ms; TE: 3.7 ms; field of view: 24 cm; matrix: 256 × 256; slice thickness: 1 mm with no gap; in-plane resolution: 0.9375 × 0.9375 mm; sense factor: 1.5. All T1-weighted sMRI data were acquired and segmented with the computational anatomy toolbox (CAT12 v12.6; Gaser and Dahnke 2016). Details of this processing are described in Section 2.7.

Prior to MEG acquisition, four coils were attached to the participants’ heads and localized, together with the three fiducial points and scalp surface, using a 3-D digitizer (Fastrak 3SF0002, Polhemus Navigator Sciences, Colchester, VT, USA). Once the participant was positioned for MEG recording, an electrical current with a unique frequency label (e.g., 322 Hz) was fed to each of the coils. This induced a measurable magnetic field and allowed each coil to be localized in reference to the sensors throughout the recording session. Since coil locations were also known in head coordinates, all MEG measurements could be transformed into a common coordinate system. With this coordinate system (including the scalp surface points), each participant’s MEG data were co-registered with their structural MRI prior to source space analyzes using Brainstorm supplemented with visual inspection. For the six participants who did not have available T1 MRI data, the MNI ICBM152 brain template (Fonov et al., 2011) was used and warped to fit each participants’ digitized head points.

2.5. MEG data pre-processing

Each MEG dataset was individually corrected for head motion and subjected to noise reduction using the signal space separation method with a temporal extension (MaxFilter v2.2; correlation limit: 0.950; correlation window duration: 6 s; Taulu and Simola 2006). Noise-reduced MEG data underwent standard data preprocessing procedures using the Brainstorm software (Tadel et al., 2011). A high pass filter of 0.3 Hz and notch filters at 60 Hz and its harmonics were applied. Cardiac artifacts were identified in the raw MEG data and removed using an adaptive signal-space projection approach, which was subsequently accounted for during source reconstruction (Ille et al., 2002; Uusitalo and Ilmoniemi, 1997). Data were then divided into four second epochs for detection and rejection of bad segments of data. Using custom lab software (https://github.com/nichrishayes/ArtifactScanTool), amplitude and gradient metrics for each epoch were computed, and epochs containing outlier values were rejected using an individualized fixed threshold method, supplemented with visual inspection. To account for variance across participants in distance between the brain and the MEG sensor array, as well as other sources of variance, we used an individually-determined threshold based on the signal distribution for both amplitude and gradient to reject artifacts. To account for environmental noise, we utilized empty room data to compute a noise covariance matrix for source imaging.

2.6. MEG source imaging and frequency power maps

Source imaging methods and processing largely followed the analysis pipeline outlined in Wiesman et al. (2021). Source analysis of neuromagnetic fields used an overlapping-spheres forward model, unconstrained to the cortical surface. This approach models a single sphere per each sensor, which in this study included 204 gradiometers (minus any channels previously marked as “bad”) for each participant. A linearly constrained minimum variance (LCMV) beamformer implemented in Brainstorm was used to spatially filter the epoched data based on the data covariance computed from the resting-state recording and the noise covariance computed from recordings of the empty room.’

Using these source estimates, we then estimated the power of cortical activity in the beta frequency band (15–30 Hz). We used Welch’s method for estimating power spectrum densities (PSD) per four second epoch across each MEG recording, with one second sliding Hamming windows overlapping at 50%. We then standardized the PSD values at each frequency bin to the total power across the frequency spectrum. For each participant, we then averaged PSD maps across epochs to obtain one PSD map per participant. Finally, the norm of the three unconstrained orientations per location were then projected onto a common MNI ICBM152 brain template (Fonov et al., 2011). Ultimately, it is this normalized source map per participant that was used for further statistical analysis.

2.7. Cortical thickness processing

To examine cortical thickness, T1-weighted MRI data were further processed using additional surface-based morphometry calculations in the computational anatomy toolbox (CAT12 v12.6; Gaser and Dahnke 2016) at a resolution of 1 mm3 . This method uses a projection-based thickness approach to estimate cortical thickness and reconstruct the central surface in one step (Dahnke et al., 2013). Briefly, following the previous tissue segmentation, the white matter (WM) distance is estimated, and the local maxima are projected onto other gray matter voxels using a neighboring relationship described by the WM distance. This method accounts for partial volume correction, sulcal blurring, and sulcal asymmetries. Topological defects are corrected based on spherical harmonics (Yotter et al., 2011a), and the cortical surface mesh was reparameterized into a common coordinate system via an algorithm that reduces area distortion (Yotter et al., 2011b). Finally, the resulting maps were resampled and smoothed using a 15 mm FWHM Gaussian kernel. These maps of cortical thickness were ultimately utilized in subsequent statistical analysis.

2.8. Statistical analysis and visualization

To determine the spatially specific effects of aging on spontaneous beta power, we ran a regression model on the whole-brain beta power maps. Further, we tested the effects of sex by performing an ANCOVA with sex as a categorical predictor and age as a continuous predictor. To account for non-uniform spatial autocorrelation in the data, avoid assumptions of parametric modeling, and avoid selecting arbitrary cluster-forming thresholds, threshold-free cluster enhancement (TFCE; E = 0.6, H = 2.0; 5000 permutations; Smith and Nichols 2009) was performed, with multiple comparisons correction set to cluster-wise pFWE < .05. All whole-brain statistical analyzes were performed using SPM12.

Cortical thickness and spontaneous beta power values were extracted using an ROI of the sensorimotor strip (i.e., precentral and postcentral gyri) using the DK atlas (Desikan et al., 2006). Correlations were performed between age, motor function, cortical thickness, and spontaneous beta power. Additionally, partial correlations were computed between cortical thickness and spontaneous beta power controlling for age. Subsequent hierarchical regression analyzes were performed to determine the relative contribution of age, spontaneous beta power, and cortical thickness on motor function. Beta power and cortical thickness were mean centered and any identified outliers in all variables were winsorized. All correlations and regressions were computed in SPSS Statistics (version 25).

3. Results

Of the 133 healthy adults enrolled, 126 participants successfully completed the MEG protocol. The results presented here include these remaining 126 participants (56 females) with an age range of 22–72 years (Mage = 45.23, SD = 15.37). Note that the six participants without individual MRIs were excluded from the analyzes involving cortical thickness and one participant who was not able to complete the behavioral motor tests was excluded from the analyses involving motor function scores.

3.1. Beta power changes with age

Average beta relative power values across all participants are presented in Fig. 1. Statistical parametric mapping identified significant main effects of age, which survived multiple comparisons correction. Beta power increased with advancing age across the entire cortex, with peaks in bilateral sensorimotor cortices (Fig. 2; Right: Fpeak = 31.00, ppeak < .001; Left: Fpeak = 36.39, ppeak < .001). Average power values across the pre- and postcentral gyri in the left and right hemispheres were extracted separately and these are plotted in Fig. 2 to visualize directionality of the age effect. Significant effects of sex, above and beyond age, were not identified. Additionally, inclusion of sex in the model did not change age-related effects, therefore models showing the main effect of age do not include sex.

Fig. 1.

Fig. 1.

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Average beta power. Surface maps displaying average power values in the beta frequency band. Color bar shows relative beta power values.

Fig. 2.

Fig. 2.

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Beta power increases with age. Surface maps to the right indicate statistical outputs of whole-brain models relating spontaneous beta power and age, correcting for multiple comparisons using a stringent threshold-free cluster enhancement approach (pFWE < .05). Average power values across the pre- and postcentral gyri in the left and right hemispheres were extracted separately and these are plotted to the left to visualize directionality of the effect. Shaded bands indicate 95% confidence intervals.

To ensure these aging effects were not an artifact of age-related changes in aperiodic neural activity (Thuwal et al., 2021), we used the FOOOF toolbox (Donoghue et al., 2020) to model both the aperiodic and periodic (i.e., aperiodic corrected) components using the time series of each sensorimotor strip. We then reran these analyzes using the periodic beta power and the results were virtually identical. This suggests that the age-related changes reported above are robust to any possible age-related alterations in the aperiodic signal.

3.2. Cortical thickness does not relate to beta power

Average cortical thickness values across all participants are presented in Fig. 3. The strongest age effects were centered over the pre- and postcentral gyri, so cortical thickness and beta power values were extracted from these regions using the DK atlas (Desikan et al., 2006). We then averaged the spontaneous beta power and cortical thickness values across these regions to create a “sensorimotor” value for each metric and these were used in all subsequent analyses. Of note, all analyses were also reran using the peak spontaneous beta power value and the findings were identical. Correlations between age, motor function, cortical thickness, and spontaneous beta power values are presented in Table 1. In contrast to spontaneous beta power (see above), both motor function (r = −.55, p < .001) and sensorimotor cortical thickness (Right: r = −.57, p < .001; Left: r = −0.58, p < .001) were negatively correlated with age (Fig. 4). Follow-up partial correlation analysis showed that, when controlling for age, there was not a significant correlation between sensorimotor cortical thickness and spontaneous beta power.

Fig. 3.

Fig. 3.

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Average cortical thickness. Surface maps displaying average cortical thickness values across the cortex. Color bar shows cortical thickness values in millimeters.

Table 1.

Correlations between age, beta power, cortical thickness, and motor function.

Variables 1 2 3 4 5 6
1. Age – – – – – –
2. R sensorimotor spontaneous beta power .44*** – – – – –
3. R sensorimotor cortical thickness −.57*** −.26** – – – –
4. L sensorimotor spontaneous beta power .46*** .98*** −.23* – – –
5. L sensorimotor cortical thickness −.58*** −.31** .92*** .28** – –
6. Motor function −.55*** −.10 .45*** −.10 .44*** –
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Note. N = 119.

***

p < .001.

**

p < .01.

*

p < .05.

Fig. 4.

Fig. 4.

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Motor function and sensorimotor cortical thickness decrease with age. Left: Scatterplot displaying the negative correlation between age (in years) and motor function (Z score). Right: Scatterplot showing the negative correlation between age (in years) and sensorimotor cortical thickness (mm) in both right and left hemispheres. Shaded bands represent 95% confidence intervals.

3.3. Motor cortical thickness and beta power predict motor function

Considering the significant relationships between age and motor function, spontaneous beta power, and cortical thickness, a hierarchical regression analysis was used to determine the relative contributions of age, beta power, and cortical thickness in predicting motor function. This hierarchical regression was performed in three steps (Fig. 5). First, age was entered into the model, followed by sensorimotor spontaneous beta power, and finally sensorimotor cortical thickness. These analyses were performed on the right and left hemispheres separately. In the left hemisphere, the overall model significantly accounted for 35% of the variance in motor function (F(115) = 21.02, p < .001; Table 2), with left sensorimotor beta power significantly contributing to the variance explained above and beyond the effects of age (ΔR2 = .03, ΔF = 4.93, p = .028, β = .19, b = 4.36). Further, above and beyond the effects of age and beta power, left sensorimotor cortical thickness significantly contributed to the variance explained in motor function (ΔR = .02, ΔF = 4.19, p = .043, β = .19, b = 1.33).

Fig. 5.

Fig. 5.

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Hierarchical multiple regression model predicting motor function. Graphical representation of hierarchical regression model. Age, sensorimotor spontaneous beta power, and sensorimotor cortical thickness are each added to the model in their own step, predicting motor function. Model was run for left and right hemispheres separately.

Table 2.

Hierarchical multiple regression results for motor function.

Model: Left Sensorimotor Cortex b SE t β F R 2 ΔF ΔR2 95% CI
First Step: (Constant) 1.58 0.23 6.75*** 50.99*** .30 (1.11, 2.04)
Age −0.35 0.01 −7.12*** −.55 (−0.05, −0.03)
Second Step: (Constant) 1.83 0.26 7.14*** 28.65*** .33 4.93* .03 (1.32, 2.33)
Age −0.04 0.01 −7.45*** −.64 (−0.05, −0.03)
L Beta Power 4.36 1.96 2.22* .19 (0.47, 8.22)
Third Step: (Constant) 1.52 0.29 5.18*** 21.02*** .35 4.19* .02 (0.94, 2.10)
Age −0.03 0.01 −5.34*** −.53 (−0.05, −0.03)
L Beta Power 4.43 1.93 2.29* .19 (0.60, 8.25)
L Cortical Thickness 1.33 0.65 2.05* .19 (0.04, 2.61)
Model: Right Sensorimotor Cortex b SE t β F R 2 ΔF ΔR2 95% CI
First Step: (Constant) 1.58 0.23 6.75*** 50.99*** .30 (1.11, 2.04)
Age −0.35 0.01 −7.12*** −.55 (−0.05, −0.03)
Second Step: (Constant) 1.80 0.23 6.75*** 28.30*** .33 4.45* .03 (1.30, 2.31)
Age −0.04 0.01 −7.41*** −.63 (−0.05, −0.03)
R Beta Power 4.10 1.94 2.11* .18 (0.25, 7.95)
Third Step: (Constant) 1.48 0.29 5.08*** 21.04*** 0.35 4.70* .03 (0.90, 2.06)
Age −0.03 0.01 −5.24*** −.52 (−0.05, −0.02)
R Beta Power 4.15 1.91 2.17* .18 (0.36, 7.94)
R Cortical Tdickness 1.35 0.62 2.17* .20 (0.12, 2.59)
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Note. N = 119.

***

p < .001.

**

p < .01.

*

p < .05.

Likewise, in the right hemisphere, the overall model significantly accounted for 35% of the variance in motor function (F(115) = 21.04, p < .001; Table 2), with right sensorimotor beta power significantly contributing to the variance explained when controlling for the effects of age (ΔR2 = .03, ΔF = 4.45, p = .037, β = .18, b = 4.10). Additionally, above and beyond the effects of age and beta power, right sensorimotor cortical thickness significantly contributed to the variance explained in motor function (ΔR2 = .03, ΔF = 4.19, p = .032, β = .20, b = 1.35). Overall, in both models, increased age predicted worse motor performance, increased sensorimotor beta power predicted better motor function when controlling for age, and increased sensorimotor cortical thickness predicted better motor performance when controlling for both age and beta power (Fig. 6).

Fig. 6.

Fig. 6.

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Increased beta power and cortical thickness predict motor function in left and right sensorimotor cortices. Top: Scatterplot displaying the positive relationship between spontaneous sensorimotor beta power and motor function, above and beyond age, in left and right hemispheres. Bottom: Scatterplot displaying the positive relationship between sensorimotor cortical thickness and motor function, above and beyond age, in each hemisphere. All values plotted are residuals from the final model (third step) presented in Table 2. Shaded bars represent 95% confidence intervals.

4. Discussion

In the current study, we investigated age-related changes in spontaneous beta activity in healthy adults and how such changes are related to the local thickness of cortical tissue. Further, we characterized how these metrics are related to motor function, above and beyond the effects of age. Consistent with previous literature, we showed widespread age-related increases in beta power. Interestingly, sensorimotor cortical thickness was not related to spontaneous beta power, above and beyond age. Critically, adding to existing literature, we showed both cortical thickness and spontaneous beta levels in sensorimotor regions each uniquely contributed to the prediction of motor function when controlling for age. Below, we discuss these findings and their implications in more detail.

In agreement with our current findings, age-related increases in beta activity have been widely reported in both the task-based and resting state literature (Barry and De Blasio, 2017; Gaetz et al., 2010; Gehringer et al., 2019; Gómez et al., 2013; Heinrichs-Graham et al., 2018; Heinrichs-Graham and Wilson, 2016; Ishii et al., 2017; Rossiter et al., 2014; Vysata et al., 2012). In the resting state literature, previous aging studies have largely focused on sensor-level analyzes and those that have performed source-level analyzes have primarily used grossly defined regions of interest or whole-brain power values (Barry and De Blasio, 2017; Gómez et al., 2013; Hoshi and Shigihara, 2020; Ishii et al., 2017; Vysata et al., 2012). Here we have taken a whole-brain vertex-wise approach, adding valuable spatial-specificity to the literature showing that the increase in spontaneous beta power is strongest in the primary motor cortex, but also observed across the entire cortical mantle with regional variations in the magnitude of the age-related change.

Cortical beta oscillations are known to be modulated by γ-aminobutyric acid (GABA) activity. Studies involving pharmacologic manipulations of GABA in humans have shown that administration of benzodiazepines (i.e., GABA-A agonists) increases the amplitude of spontaneous beta, and these increases appear to be specific to the sensorimotor cortex (Hall et al., 2010, 2011; Jensen et al., 2005). In another study, a GABA reuptake inhibitor showed similar effects of increased spontaneous beta during the baseline period (Muthukumaraswamy et al., 2013). Alongside these increases in baseline beta power, these studies also showed an increase in the peri-movement beta ERD following drug administration (Hall et al., 2011; Muthukumaraswamy et al., 2013). These findings closely over-lap with the age-related changes in spontaneous beta power discussed above, suggesting that increased GABA activity in older adults could be driving the aging effects seen in the current study. In fact, studies utilizing GABA magnetic resonance spectroscopy (MRS) and focusing on the sensorimotor cortices of healthy adults have shown that increased GABA concentration is related to worse tactile discrimination thresholds (Puts et al., 2011), increased peak beta frequency (Baumgarten et al., 2016), and stronger motor-related beta oscillations (Gaetz et al., 2011). Decreases in GABA concentrations in the motor cortices of older adults are also thought to be crucial for synaptic plasticity and motor learning (King et al., 2020; Stagg, 2014), lending support to the theory that the increased spontaneous beta seen in aging may reflect decreased local GABAergic inhibitory activity.

Interestingly, we found that stronger spontaneous beta power predicted better motor performance, above and beyond the effects of age and cortical thickness. Two previous studies from our laboratory have shown a relationship between beta activity in the precentral gyrus and motor function (Heinrichs-Graham et al., 2018; Heinrichs-Graham and Wilson, 2016). Interestingly, both of these studies showed that increased spontaneous beta power during the baseline period was related to stronger beta ERD responses during a motor task, but the specifics of their neurobehavioral findings sightly differed. One study showed that the absolute beta level during movement was strongly correlated with both movement duration and reaction time (Heinrichs-Graham et al., 2018), while the other study showed that the strength of the beta ERD was negatively correlated with movement duration (i.e., more negative ERD responses correlated with worse performance; Heinrichs-Graham and Wilson 2016). Both studies corrected for the effect of age. These studies suggest that increased spontaneous beta power in aging requires even stronger beta oscillations (i.e., decreases in beta power/stronger beta ERD) in order to optimally perform motor actions, supporting the theory that changes in sensorimotor beta dynamics may be related to the decline in motor function seen in aging. These studies, together with our current findings, suggest that the suppression mechanism of beta power in preparation for movement is critical, such that sufficient suppression of beta activity, regardless of the strength of spontaneous beta activity, is required in order to optimally perform motor actions. Future aging studies should evaluate the key parameters of this oscillatory suppression mechanism, including whether it predicts performance on both real-time task-related movements and more complex motor function tasks (e.g., Grooved Pegboard), whether its trial-to-trial variability increases with aging, and whether it is universally affected by aging (i.e., are the strength of occipital alpha oscillations during visual processing linked to the strength of the sensorimotor beta ERD in individual participants.)

Recent literature has suggested that beta activity, rather than being oscillatory in nature, may have a more transient, burst-like quality when examined on the level of single trials/responses (Bonaiuto et al., 2021; Brady et al., 2020; Brady and Bardouille, 2022; Little et al., 2019; Sherman et al., 2016). Specifically, these studies have shown that characteristics of the bursting activity in the motor cortex, such as bursting rate, event duration, and power, change with age and may be related to motor function (Brady et al., 2020). On a single trial level, the timing of burst activity has been shown to predict single trial behavior (i.e., reaction time), and is associated with timing of motor initiation (Little et al., 2019). While the current study examined the dynamics of beta during the resting state, these recent studies suggest that bursting activity may in part explain the findings reported here regarding age-related changes in beta activity, and further, the relationships between beta activity, cortical thickness, and complex motor functioning. The specific influence of burst-like beta activity on these associations should be assessed in future task-based studies.

Finally, in this study we showed that cortical thickness was not directly related to spontaneous beta power, above and beyond the effects of age. Though the age-related findings of spontaneous beta power and cortical thickness are largely overlapping (Barry and De Blasio, 2017; Frangou et al., 2021; Gómez et al., 2013; Heinrichs-Graham et al., 2018; Heinrichs-Graham and Wilson, 2016; Hoshi and Shigihara, 2020; Ishii et al., 2017; McGinnis et al., 2011; Rossiter et al., 2014; Shaw et al., 2016; Shaw et al., 2008), these findings suggest that structural changes in aging may not be closely related to the associated changes in spontaneous beta activity. Further, we showed that increased sensorimotor cortical thickness predicted better motor function, above and beyond the effects of age and spontaneous beta power in the sensorimotor cortices. Previous studies have shown changes in cortical thickness to be related to improved motor function due to rehabilitation following stroke (Arachchige et al., 2021; Ueda et al., 2019). In one such study focusing on post-stroke patients with motor paralysis, cortical thickness of the postcentral and supramarginal gyri in the affected hemisphere were positively correlated with motor improvement following transcranial magnetic stimulation and occupational therapy sessions (Ueda et al., 2019). These studies show a link between sensorimotor cortical thickness and motor function, however, to our knowledge, we are the first to show this association in a healthy aging sample. Our findings suggest that greater cortical thickness in adulthood is associated with better motor performance.

Before closing, it is important to point out some limitations of this study and identify future directions. First, this study is cross-sectional, therefore only differences between ages can be detected rather than changes with aging, which can be quantified in longitudinal studies. Future studies should consider taking a longitudinal approach to fully characterize changes with aging. Second, we focused this analysis on aging in adulthood, however, it is well-known that there are both cortical thickness and beta activity changes across the full lifespan (Frangou et al., 2021; Heinrichs-Graham et al., 2018; Heinrichs-Graham and Wilson, 2016; Ott et al., 2021; Rossiter et al., 2014; Shaw et al., 2008; Trevarrow et al., 2019). Future studies should aim to characterize these relationships across the entire healthy lifespan.

In sum, the current study investigated age-related changes in spontaneous beta activity and their relationship to cortical thickness and motor function. We found, utilizing the high spatial and temporal precision of MEG, that there were widespread increases in spontaneous beta power with advancing age, with the strongest effects being in the sensorimotor cortices. Interestingly, we found that this increase in spontaneous beta power in sensorimotor cortices was not associated with cortical thickness, above and beyond the effects of age. Finally, we showed that sensorimotor spontaneous beta power and cortical thickness each uniquely contributed to the prediction of motor function as measured through the Grooved Pegboard task. Thus, this novel study adds to the literature by showing that cortical thickness and spontaneous beta activity in sensorimotor cortices have dissociable contributions to motor function. More broadly, these findings highlight the complexity of the interactions between structure and function and the importance of understanding these interactions in order to advance our understanding of healthy and pathological aging.

Supplementary Material

1

Acknowledgments

This research was supported by grants R01-MH116782, R01-MH118013, and P20-GM144641 from the National Institutes of Health. The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation. We want to thank the participants for volunteering to participate in the study and our staff and local collaborators for contributing to the work. We would also like to specifically thank Nichole Knott for extensive help with the MEG recordings.

Footnotes

Data and code availability

The data used in this article will be made publicly available through the COINS framework at the completion of the study (https://coins.trendscenter.org/).

Financial disclosures

The authors report no biomedical financial interests or potential conflicts of interest.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2022.119651.

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