The role of oxidative stress in brain aging has been studied since the emergence of the 42 free radical theory of aging. This theory posits that the cumulative result of a lifetime of oxidative 43 insult is diminished tissue functioning and the aging phenotype (Harman, 1955) . While evidence 44 exists both for and against the free radical theory of aging, the literature largely agrees that 45 markers of brain oxidative damage increase with advancing age (Chakrabarti et al., 2011) . The 46 brain is a highly oxidative organ that consumes 20% of the body's total oxygen uptake despite 47 stress increases brain GSH levels; this upregulation of GSH is thought to provide protection 119 against more severe oxidative stress (for review, see (Maher, 2005) ). Thus, it is possible that 120 normal aging could be associated with higher brain GSH levels as a compensatory response to 121 generalized aging processes. 122
The aims of the present study included: 1) to determine whether there are age 123 differences in in vivo MRS-measured brain GSH levels in the frontal and sensorimotor cortices; 124 2) to characterize regional differences in brain GSH levels; and 3) to characterize the 125 relationships between brain GSH levels and cognitive and motor function. 126
Results 127 37 young and 23 older adults completed cognitive and motor testing, as well as 128 collection of MRS data from voxels placed in the frontal and sensorimotor cortices. Of note, we 129 applied the Benjamini-Hochberg false discovery rate (FDR) correction (Benjamini & Hochberg, 130 1995) to all p-values reported below; aside from two cases where the corrected p-values were p 131 < 0.10, all results remained significant (p < 0.05) after applying this correction for multiple 132
Demographics 134
There were no significant age differences for most demographic variables, including sex, 135 alcohol use, handedness, or footedness. Importantly, there were also no age differences in the 136 number of days elapsed between the two testing sessions or in the difference in start time for 137 the two sessions. See Table A1 for complete demographic information. 138
The older adult group exhibited cortical atrophy, with both voxels showing a lower gray 140 matter fraction and higher cerebrospinal fluid (CSF) fraction compared to the younger adults 141 (Table A2 ; Fig. 1 ). Older adults also had less white matter within the frontal voxel compared to 142 younger adults. Older adults had significantly higher CSF-corrected GSH levels in both voxels 143 ( Fig. 2 ; although age differences in frontal GSH levels remained only at trend-level significance, 144 p = 0.066, after FDR correction for multiple comparisons). This difference in CSF-corrected 145 GSH levels implies that there is an age-related increase in cortical GSH concentration within the 146 tissue that remains in the voxel after accounting for age-related atrophy. Importantly, there was 147 no age difference in GSH fit error or water full width at half maximum (FWHM). 148 
Frontal GSH levels positively correlated with sensorimotor GSH levels for both the young 158 and older adults (Table A3; Fig. 3 ). This relationship was in the same direction for both groups, 159 and the correlation strength did not significantly differ by age. Within subjects, both groups also 160 had higher GSH levels in the sensorimotor voxel compared to the frontal voxel. The magnitude 161 of this regional effect did not differ between the age groups. For young adults, the gray matter 162 and CSF fraction was higher and the white matter fraction was lower in the sensorimotor 163 compared to the frontal voxel. However, for older adults, there were no significant differences in 164 tissue composition between the two voxels. 165 sensorimotor, but not the frontal voxel, were associated with poorer performance on multiple 177 motor measures for the older adults only. 178
Greater medial/lateral (M/L) sway speed and variability (i.e., greater postural instability) 179 was correlated with higher GSH levels only for the older adults (Table A5 ; Fig. 4 ). The young 180 adults had a weak but non-significant positive association between M/L sway speed and 181 variability and GSH levels; there was a trend for an age difference in the partial correlation 182 strength (p = 0.064). 183 
Greater gait variability was correlated with higher sensorimotor GSH levels for the older 191 adults only (Table A5 ; Fig. 4 ). No relationship emerged between gait variability and GSH levels 192 for the young adults; the partial correlation strength was significantly different between young 193 and older adults. As there is some evidence that walking speed contributes to gait variability 194 (e.g., (Jordan et al., 2007) ), we reran these models also including gait speed as a covariate; the 195 relationship between sensorimotor GSH and gait variability for older adults remained significant 196 (p = 0.004). 197
Poorer pegboard composite scores were associated with higher GSH levels only for the 198 older adults (Table A5 ; Fig. 4) , and there was a significant age difference in the partial 199 coordination. It was not the case that the pegboard composite score correlated with balance and 202 gait (r = -0.04 and -0.08 for young adults; r = -0.23 and -0.31 for older adults; p > 0.05 in all 203 cases); thus pegboard scores specifically index manual function. 204
To further test the specificity of the identified relationships between GSH and motor 205 function for older adults, and not global shifts in metabolite concentrations, we reran the 206 significant models above including as predictors the two other neurometabolites edited by 207 HERMES: the excitatory neurochemicals glutamate + glutamine (Glx) and the primary inhibitory 208 neurotransmitter within the brain, γ-aminobutyric acid (GABA). All relationships between 209 physical function and GSH remained when including Glx and GABA as additional predictors; for 210 older adults, the relationships remained significant between sensorimotor GSH levels and M/L 211 sway speed/variability (p = 0.007), gait variability (p = 0.003), and manual dexterity (p = 0.008). 212
There were no significant relationships between Glx or GABA levels and these motor metrics. 213
We identified higher CSF-corrected frontal and sensorimotor GSH levels for older 215 compared to younger adults when accounting for age-related cortical atrophy. For both age 216 groups, we identified a positive correlation between frontal and sensorimotor GSH levels, as 217 well as higher GSH levels for the sensorimotor compared to the frontal voxel. For the older 218 adults only, we identified multiple relationships between higher sensorimotor GSH levels and 219 poorer motor performance. 220
One potential explanation for higher brain GSH levels for older adults is that higher 221 levels of GSH occur as a compensatory response in an attempt to mitigate age-related 222 increases in oxidative stress and maintain regional redox homeostasis within the brain. That is, 223 perhaps in normal aging, in some regions of the brain, GSH antioxidant levels increase in 224 response to increasing oxidative stress that occurs during aging. Past in vivo human studies 225 have found higher MRS-measured GSH levels in MCI compared to age-matched controls (Duffy 226 et al., 2014) , but lower GSH levels in AD compared to controls (Mandal et al., 2015) . Given the 227 association between cognitive impairment and ROS production (Brawek et al., 2010) , these 228 findings could be interpreted as a ROS-induced compensatory upregulation of GSH in the early 229 stages of cognitive decline. Similarly, past evidence suggests that MRS-measured GSH levels 230 are higher in early schizophrenia (Wood et al., 2009 ), but lower after full symptoms emerge 231 (Matsuzawa et al., 2008) . Higher MRS-measured GSH levels have also been reported in post-232 traumatic stress disorder (Michels et al., 2014) and early psychosis (Godlewska et al., 2014) . 233
Further, pharmacologically-induced GSH depletion in the brain has been shown to result in 234 cognitive decline in rodents (González-Fraguela et al., 2018). Therefore, high GSH levels could 235 be associated with high levels of underlying cellular stress (e.g., ROS emissions) until reaching 236 a threshold that exceeds the hormetic response capabilities of the cell. 237
The precise mechanisms that dictate GSH regulation in the aging brain remain unknown. It could also be that the observed GSH changes relate to changes in cell type 265 abundance within the aging brain. As GSH is present in higher concentrations in glia compared 266 to neurons (Rice & Russo-Menna, 1997), increasing GSH levels could be associated with the 267 increased gliosis that occurs with brain aging (Tong et al., 2011) . Stereological cell counting in 268 post-mortem human brain suggests that the abundance of astrocytes, one of the predominant 269 producers of brain GSH, remains constant throughout aging while the abundance of other cell 270 types (e.g., oligodendrocytes) decreases (Pelvig et al., 2008) . This may be particularly important 271
given that astrocytes are also more capable of inducing the antioxidant defense response via 272
Nrf2 signaling compared to other neuronal cell types (Baxter & Hardingham, 2016) . 273
This finding of higher CSF-corrected GSH levels for older compared to younger adults is 274 in line with the results of Tong and colleagues (Tong et al., 2016) . This group identified GSH 275 increases across the lifespan (i.e., 1 day to 99 years old) in post-mortem frontal cortex. 276
However, this finding is in contrast to the results of Emir and colleagues (2011) who reported lower occipital cortex GSH levels for older compared to younger adults using edited MRS at 4T. 278 There are several key differences between our work and this study. Emir and colleagues 279 examined a different brain region, and their elderly sample (76.6 ± 6.1 years) was older than 280 ours; these factors likely contributed to their differing results. More recent work by this group 281 using non-edited MRS at 7T found no age differences in posterior cingulate or occipital cortex 282 GSH levels (Marjańska et al., 2017) ; however, again, this study tested different brain regions 283 and an older sample compared to our work. The lack of GSH age differences in posterior brain 284 regions (but not frontal or sensorimotor cortex) could also be due in part to the well-established 285 posterior to anterior shift of brain activity with aging (Davis et al., 2008; Jockwitz et al., 2019) . It 286 could be that reduced neural signaling within posterior brain areas leads to lower regional GSH 287 levels; that is, compensatory upregulation of GSH has ceased in these posterior regions as the 288 hormetic response capabilities of these cells have been exceeded. 289
Given the limited age range in the present study, it is unknown whether the apparent 290 age-related increase in brain GSH presented here may abate in extreme conditions of oxidative 291 stress, such as neurological disease or very old age, as the compensatory response is 292 overwhelmed (e.g., as recycling or de novo synthesis mechanisms are compromised). Future 293 longitudinal studies and enrollment of much older adults would clarify this. 294 Some work has reported regional differences in cortical GSH levels (Nezhad et and sensorimotor CSF-corrected GSH levels for both young and older adults, as well as higher 297 GSH levels in the sensorimotor compared to the frontal voxel for both age groups. However, we 298 identified tissue composition differences between the two voxels for the younger adults only. 299
Young adults had higher gray matter and CSF and lower white matter fractions in the 300 sensorimotor compared to the frontal voxel. For older adults, there were no tissue composition 301 differences between voxels. These young adult findings fit with one past study reporting higher 302 GSH concentrations in voxels with more gray matter than white matter (Srinivasan et al., 2010) . 303 However, another study (Nezhad et al., 2017) reported conflicting findings of higher GSH 304 concentrations in the cortical region with less gray matter (i.e., anterior cingulate versus occipital 305 cortex). Thus, as we did not find voxel composition differences for the older adults, but we did 306 find higher sensorimotor versus frontal GSH levels, we suspect that (as discussed by Rae and 307 Williams (2017)), GSH levels likely vary across brain region, but in a more complex manner than 308 that which reflects only gray and white matter differences. This notion is further supported by 309 recent work suggesting that human primary motor and somatosensory cortices show 310
proportionally steeper trajectories of volume, myelin, and iron declines with advancing age 311 compared to other brain regions (Taubert et al., 2020) . It could be that the sensorimotor cortex 312 structure and neurochemical composition is affected more or earlier by oxidative stress 313 compared to other brain regions. 314
There were no associations between GSH levels and cognitive performance (i.e., MoCA 315 scores). Our past work suggests that MoCA scores are 316 sensitive enough to identify associations between MRS-measured neurometabolites and 317 cognitive status. In contrast to our previous work (n = 93 older adults; mean age = 73.2 ± 9.9), 318 here we included fewer participants, although our older adult ages were similar. Additionally, 319 participants in the present sample had higher MoCA scores compared to our previous work 320 (mean = 25.5 ± 2.5). It could be that, among this higher-functioning older adult cohort, we did 321 not have enough variation in MoCA scores to identify a significant association. Furthermore, the 322 limited past work in normal aging has failed to find any relationships between GSH levels and 323 indicate that GSH is providing a compensatory response to increasing oxidative stress and 339 related tissue damage in the normally aging brain. Higher sensorimotor versus frontal cortex 340 GSH levels (discussed above) could suggest that the sensorimotor cortex is disproportionately 341 affected by oxidative stress in older age (and thus requires the largest GSH antioxidant 342 response). This regionally heightened oxidative stress may then be contributing to these age-343 related declines in motor function. 344 Importantly, we identified relationships between motor performance and sensorimotor 345 but not frontal GSH levels. This suggests regional specificity for these GSH relationships, rather 346 than poorer motor performance being a consequence of increased oxidative stress throughout 347 the brain. Moreover, we found no relationship between cortical Glx or GABA levels and these 348 motor performance metrics, again supporting the specificity of this GSH relationship with motor 349 behavior for older adults and not result of more general age related shifts in metabolite 350
Although we measured sensorimotor GSH levels over the lower limb cortical 352 representation, we still identified a GSH relationship with upper limb motor coordination. The 353 pegboard composite score did not correlate with gait or balance performance, suggesting that it 354 represents a unique motor measure, which independently associates with sensorimotor GSH. It 355 could be that lower limb sensorimotor GSH levels are related to upper limb sensorimotor GSH 356 levels; this is probable given that we did not find any associations between motor performance 357 and frontal GSH levels, and that we found positive correlations between GSH levels across the 358 two voxels. However, this remains to be examined in future studies. 359
There are several limitations to the present work. We included fewer older adults than 360 originally anticipated due to the COVID-19 global pandemic; however, based on our power 361 analysis, we were still adequately powered to test age group differences in GSH. In addition, our 362 cross-sectional approach precluded us from assessing how GSH levels alter with aging or how 363 changes in GSH levels across the lifespan relate to declines in motor performance. There is 364 some evidence that diet may influence MRS-measured GSH levels. One study (Choi et al., 365 2015) found an association between dairy consumption and brain GSH levels among older 366 adults. In the present work, we did not record food intake or restrict diet prior to the MRI scan; 367 future studies should characterize any effects of diet on brain GSH levels. Finally, other general 368 limitations of MRS, such as the large voxel size required, are currently unavoidable with this 369 methodology. 370
These results provide insight into the association between brain aging and oxidative 371 stress. We demonstrate higher CSF-corrected GSH levels with normal aging, suggesting a GSH 372 compensatory response to increased oxidative stress with older age. We report higher GSH 373 levels in the sensorimotor cortex compared to the frontal cortex for both age groups, as well as 374 multiple associations between sensorimotor CSF-corrected GSH levels (but not GABA or Glx 375 levels) and poorer balance, gait, and manual dexterity. Together, these results suggest that 376 MRS-measured GSH could be a marker of neural compensation for increased oxidative stress 377 with brain aging and also a marker of poorer motor performance. These results could stem from 378 greater or earlier effects of oxidative stress on the sensorimotor compared to the frontal cortex. 379
The University of Florida's Institutional Review Board provided ethical approval for the 381 study, and all participants provided their written informed consent at the first testing session. 382
We recruited 37 young and 23 older adults from the Gainesville, FL community. 384
Exclusion criteria included: history of any neurologic condition (e.g., stroke, Parkinson's disease, 385 seizures, or a concussion in the last six months) or psychiatric condition (e.g., active depression 386 or bipolar disorder). We also excluded those who self-reported smoking, consuming more than 387 two alcoholic drinks per day on average or a history of treatment for alcoholism. All subjects 388 were screened for magnetic resonance imaging (MRI) eligibility; we excluded those with any 389 contraindications (e.g., implanted metal, claustrophobia, or pregnancy). All subjects were right-390 handed and self-reported an ability to walk unassisted for at least 10 minutes and to stand for at 391 least 30 seconds with their eyes closed. Participants disclosed all current prescribed and over- 
Due to the COVID-19 global pandemic, data collection was terminated before we 402 completed the recruitment of older adult subjects. However, based on a power analysis, 37 403 young and 23 older adults is more than sufficient for detecting an age difference in MRS-404 measured GSH levels. We calculated the minimum necessary sample size using G*Power 3.1 405 (Erdfelder et al., 1996) . We based this calculation on the only past study testing age differences 406 in MRS-measured GSH (Emir et al., 2011) ; this study reported an effect size of d = 1.65 for age 407 differences in occipital cortex GSH levels (Emir et al., 2011) . With power = 0.80 and ⍺ = 0.05, a 408 two-sample independent t-test (i.e., to characterize group age differences in GSH levels) would 409 require only six subjects per group. 410
Prior to the first session, we collected basic demographic information, including age, sex, 412 years of education, and medical history, as well as information regarding self-reported exercise, 413 handedness, and footedness. We also collected basic anthropometric information, such as 414 height, weight, and leg length. 415 
Participants then completed behavioral testing, followed by an MRI session 425 approximately one week later (Fig. 5) Questionnaire, which asks for the number of hours slept the previous night and for a rating of 429 current sleepiness (Hoddes et al., 1972) . 430
Participants first completed the MoCA (Nasreddine et al., 2005) . We added one point to 433 the scores of participants with ≤12 years of education (Nasreddine et al., 2005) . Lafayette, IN, USA). For the bimanual task, participants had 30 seconds to place as many pegs 475 as possible into the slots; in this case, participants used both hands at the same time to place 476 peg pairs (Fig. 5) . Scores were based on the number of completed peg pairs. For the assembly 477 task, participants had one minute to complete as many "assemblies" as possible (Fig. 5 ). An 478 assembly consisted of using both hands to piece together metal pins, collars, and washers.
Scores were based on the number of completed assemblies. These tasks were selected as they 480 each require complex coordination of both hands and performance declines with age (Agnew et 481 al., 1988; Vasylenko et al., 2018) . For further analysis, we created a composite score of 482 pegboard performance by converting the bimanual and assembly scores to standardized Z-483 scores and then taking the sum of these two Z-scores. 
In the following sections and in Table A2, ms, 20-ms editing pulse duration, averages = 320, 2048 data points, 2 kHz spectral width, and 505 variable power and optimized relaxation delays (VAPOR) water suppression. Shimming was 506 performed using the Siemens interactive shim tool and FAST(EST)MAP (Gruetter, 1993) . 507
We collected data from two 30 × 30 × 30 mm 3 voxels in the medial frontal cortex and 508 bilateral sensorimotor cortex (Fig. 6) . We placed the frontal voxel superior to the genu of the 509 corpus callosum on the mid-sagittal slice. We placed the sensorimotor voxel to align with the 510 lower limb primary sensorimotor cortex. We aligned the center of this voxel with the posterior 511 portion of the motor hand knob in the axial view, then centered the voxel on the midline of the 512 brain, and placed the voxel as superior as possible while still remaining on brain tissue. 513 
We analyzed MRS data using Gannet (version 3.1.5) (Edden et al., 2014) in MATLAB 529 (R2019b). First, we ran the GannetLoad.m and GannetFit.m functions, which include: 1) coil 530 combination using generalized least squares ; 2) estimation of the B 0 drift using 531 the creatine (Cr) signal at 3 ppm; 3) robust spectral registration to minimize subtraction artifacts 532 (Mikkelsen et al., 2018) ; 4) Hadamard-combination of the fully processed HERMES sub-spectra 533 to generate GSH-and GABA+-edited difference spectra; 5) application of the Hankel singular 534 decomposition water filtering method to remove the residual water signal (Barkhuijsen et al., 535 1987) ; and 6) implementation of a weighted nonlinear regression to model the two difference-536 edited signals; here, the neighboring co-edited signals were downweighted to reduce their 537 impact on modeling errors. The GSH-edited spectrum was modeled between 2.25 and 3.5 ppm 538
using a Gaussian to model the GSH signal at 2.95 ppm, four Gaussians to model the coedited 539 aspartyl signals at 2.55 ppm, and a nonlinear baseline. 540
We used GannetCoRegister.m to create a binary mask of the MRS voxels and register 541 these masks to the T 1 -weighted structural image. We then used the Computational Anatomy 542
Toolbox 12 (CAT12, version 1450) (Gaser & Dahnke, 2016) to segment each subject's T 1 -543 weighted image. We implemented GannetSegment.m, which uses segmentation results to 544 determine voxel tissue fractions (i.e., fractions of gray matter, white matter, and CSF) and to 545 correct GSH estimates for tissue composition (Harris et al., 2015) . Correcting for tissue 546 composition enhances the interpretation of MRS data. Metabolite levels, as well as reference 547 signals, differ between gray matter, white matter, and CSF (Harris et al., 2015) . Tissue 548 correction is particularly relevant for aging populations (Porges, Woods, Lamb, et al., 2017) . For 549 instance, if older adults have less gray matter due to age-related atrophy in a voxel compared to 550 young adults, the older adults will also present with less metabolite concentration in that voxel. 551
Correcting for tissue composition thus permits assessment of whether there are age differences 552 in neurometabolite levels in the tissue that remains in the voxel. Throughout the present work, 553 we report CSF-corrected GSH levels referenced to water. 554
See Table E1 for details on exclusions of MRS datasets. We excluded MRS datasets if 556 the GSH fit error (i.e., GSH.FitError_W) was greater than 20% or if robust spectral registration 557 failed for that dataset. We selected 20% for several reasons: 1) datasets with fit errors <20% 558 passed acceptable visual inspection and 2) fit errors ≥20% were >2.5 standard deviations above 559 the group mean (i.e., >97th percentile). Thus, similar to (Saleh et al., 2020), we selected a 560 threshold value for data rejection. Of note, we did not exclude one older adult for whom we used 561 a 20-channel head coil instead of a 64-channel coil due to his large head size. The uncorrected 562 and CSF-corrected GSH levels for this individual fell within the range of that of the other older 563 subjects. See Fig. C1 for details. 564
We conducted all statistical analyses using R (version 4.0.0) (R Core Team, 2013). 566
For each analysis involving comparisons between the age groups, we first tested the 568 parametric t-test assumptions of normality within each group (using shapiro.test) and 569 homogeneity of variances between the groups (using leveneTest in the car package (Fox & 570 Weisberg, 2018)). We then tested age group differences, as described below. 571 Parametric Tests. The majority of MRS variables met the required assumptions, so we 572 used t.test to conduct parametric, independent-samples, two-sided t-tests. For each MRS 573 variable, we report t-test results, in addition to group means, standard deviations, and Cohen's d 574 as a measure of effect size. 575
Nonparametric Tests. In several cases (i.e., age differences in demographic information 576 and cognitive/motor performance), the majority of variables did not meet parametric t-test 577 assumptions, so we instead used wilcox.test to conduct nonparametric, independent-578 samples, two-sided Wilcoxon rank-sum tests for group differences. In these cases, we report 579 the group medians and interquartile ranges for each demographic variable. We also report 580 nonparametric effect sizes (Field et al., 2012; Rosenthal et al., 1994) ; see Appendix F for details 581 on this calculation. To test for differences in the sex distribution within each age group, we 582 conducted a Pearson chi-square test using chisq.test. 583
To examine within-subject differences in MRS variables between the frontal and 585 sensorimotor voxels, within each age group, we implemented parametric, paired-samples t-586
tests. These variables met the normality assumption required for parametric paired t-tests. 587
We conducted Pearson correlations using cor.test to assess the relationship between 589 frontal and sensorimotor GSH levels. These metrics met the assumptions of linear covariation 590 and normality. Here we also performed a Fisher r-to-Z transformation on the correlation 591 coefficient and then tested for a difference in correlation strength between the age groups using 592 a one-sided r.test in the psych package (Revelle, 2014) . 593
For each behavioral metric, we used lm to test the relationship between CSF-corrected 595 GSH levels and performance for both voxels and age groups. For the MoCA score models, we 596 controlled for sex and years of education (Malek-Ahmadi et al., 2015) . For the balance and gait 597 models, we controlled for sex and leg length, as these each affect postural sway (e.g., (Kim et 598 al., 2010) ) and gait (e.g., (Ko et al., 2011; Kobayashi et al., 2016; Samson et al., 2001) ). For the 599 manual dexterity model, we controlled for sex, as there is some evidence of sex differences in 600
We also computed the partial correlation for each GSH-performance relationship (i.e., 602 the correlation controlling for the covariates listed above) by correlating the residuals from 1) 603 regressing each of the covariates (but not GSH concentration) onto the performance variable, 604
and 2) regressing each of the covariates onto GSH concentration. Finally, as described above, 605
we used a Fisher r-to-Z transformation to test for age differences in the strength of the partial 606 correlation. As several variables did not meet the linear regression assumptions of 607 heteroscedasticity and normality, for each model that yielded a significant GSH-performance 608 relationship, we also ran a nonparametric version of that model using npreg and npsigtest in 609 the np package (Hayfield & Racine, 2008) . 610
As noted in the Results section, we reran the gait variability model including gait speed 611 as an additional covariate because there is some evidence that walking speed contributes to 612 gait variability (e.g., (Jordan et al., 2007) ). Further, for each model that indicated a significant 613 relationship between GSH levels and behavior, we reran the model also including GABA and 614
Glx as covariates. This was to provide further support for the specificity of the relationship 615 between GSH levels and motor performance; that is, we hypothesized that these 616 neurometabolites would not relate to behavior, and that including these would not influence the 617 significant relationship between GSH levels and motor performance. 618
We corrected p-values within each results table using p.adjust with method = "bh" 620 to apply the Benjamini-Hochberg FDR correction (Benjamini & Hochberg, 1995 The authors wish to thank Aakash Anandjiwala, Justin Geraghty, and Alexis Jennings-637
Coulibaly for their help in subject recruitment and data collection. The authors also wish to thank 638 all of the participants who volunteered their time, as well as the McKnight Brain Institute MRI 639
technologists, without whom this project would not have been possible. 640
KH participated in initial study design, collected all data, processed the MRS data, 642 conducted the statistical analyses, created the figures, and wrote the manuscript. HH 643 contributed to manuscript writing and results interpretation. PAJ assisted with data collection, 644 data processing, and manuscript preparation. MM advised on MRS data processing and 645 methods, in addition to contributing to manuscript preparation. CH consulted on the design and 646 analysis of the motor performance tests. RE advised on MRS data acquisition, processing, 647 interpretation, and manuscript preparation. RS and EP oversaw study design and led the 648 interpretation and discussion of results. All authors participated in revision of the manuscript. 649
The authors declare no competing interests.