Background
Methods
Inclusion criteria
Population
Interventions/Comparators
Outcomes
Brain structure
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Whole brain volume (WBV) or total brain volume (TBV)
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Volume or cross sectional area of regions within the brain (e.g. hippocampus, frontal lobes)
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White matter integrity (e.g. White matter hyperintensities (WMH) or white matter signal abnormalities (WMSA)
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Histological findings about brain structure on autopsy
Brain function
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Any recognised measure of cognitive function including: memory, attention, executive function, language and processing speed
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Reaction time will not be used as this is dependent on aspects of brain and muscle structure and function
Muscle structure
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Muscle cross sectional area on CT, MRI or USS
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Muscle volume (using CT or MRI)
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Whole body lean tissue mass using DEXA, giving: total lean mass (TLM) or appendicular lean mass (ALM)
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Bioimpedance analysis (BIA)
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Histological findings on muscle biopsy or on autopsy
Muscle function
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Any recognised test of muscle strength, including isometric, isotonic, isokinetic tests
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Any recognised test of muscle power
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Functional tests of muscle function (e.g. usual or maximum gait speed)
Study design
Search strategy
Study selection
Data extraction
Contacting authors
Quality assessment and risk of bias
Data analysis
Results
Reason | Number |
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Selected subjects (e.g. all had hip fracture, all had dementia etc.) | 73 |
No measure brain or muscle structure | 57 |
Review article, no relevant references | 56 |
No measure muscle structure or function | 47 |
Abstract, no published results within timeframe or irrelevant | 34 |
No measure brain structure or function | 19 |
Protocol paper, no published results within timeframe | 12 |
Anthropometry only measure of structure | 10 |
Letter or editorial, no results | 7 |
Technique or theory paper | 5 |
Case Report | 2 |
No full text available | 1 |
Total
|
323
|
Authors | Year | Country and dataset | n | Study design | Mean age (sd) | Male (%) | Brain structure/Function | Muscle structure | Associations* |
---|---|---|---|---|---|---|---|---|---|
1. Heymsfield et al. [26] | 2012 | Germany | 260 | Cross-sectional study | M 45.1 (14.9), F 38.6 (13.7) | 43.1 | Structure: Brain volume transformed into mass using 1.036 g/cm3
| DEXA for FFM | Study: Linear regression models found that after adjusting for age and fat mass, FFM predicted brain mass in men (beta 0.023, R2 5%, p = 0.01) and women (beta 0.003, R2 6%, p = <0.0001). |
Function: not measured | |||||||||
2. Kilgour et al. [27] | 2013 | UK, MacLullich Healthy Elderly Men Study | 51 | Longitudinal ageing study | 73.8 (1.5) | 100 | Structure: Whole brain, hippocampal, ventricular, cerebellar volumes and ICV | Neck muscle CSA on MR head scan | Study: Total neck muscle CSA was found to predict 17% of the variance in whole brain volume (t = 2.86, p = 0.01). However, total neck muscle CSA did not significantly predict the variance in ventricular, hippocampal or cerebellar volumes (p > 0.05). Total neck muscle CSA did not significantly predict variance in either the memory factor or the cognitive processing factor (p > 0.05), however, it did predict 10% of the variance in the NART score (t = −2.12, p < 0.05). Adjusting for age, sex, ICV and NART where appropriate. |
Function: 9 tests of cognitive function reduced to 2 factors (cognitive processing factor and memory factor) | |||||||||
3. Wetmore et al. [28] | 2011 | USA, Kansas, Brain Aging Project | 60 | 2 year observational case–control study (Alzheimer’s dementia vs. non-dementia) | 73.0 (7.2) | 43.4 | Structure: MRI for WM, GM, CSF, WBV and ICV | DEXA for lean mass and ASM (just arms and legs) | Study: none |
Function: Logical Memory I & II, Free & Cued Selective Reminding Task, Boston naming test, Verbal fluency, Digit span forward and backward, Letter-number sequencing, Trail making A & B, Stroop color-word test and Block design, MMSE | Calculated: Non-demented group only. WBV, GM volume and hippocampal volume not predicted by TLM adjusting for age, sex and ICV +/− education. WM volume was predicted by TLM (t 3.12, p = 0.003, partial eta squared 14%) adjusting for age, sex and ICV. TLM did not significantly predict global cognitive score or MMSE, adjusting for age and sex. Adjusting for height and education did not affect this. | ||||||||
4. Burns et al. [29] | 2010 | USA, Kansas, Brain Aging Project | 70 | Cross-sectional case–control study (Alzheimer’s dementia (AD) vs. non-dementia) | 73.3 (7.3) | 42.9 | Structure: MRI for WM, GM, CSF, WBV and ICV | DEXA for total lean mass | Study: Positive relationship between WBV and TLM when control and AD subjects grouped together (beta = 0.20, p < 0.001), adjusting for ICV, age and sex. This appears to be driven by WM (beta 0.19, p < 0.001) rather than GM (beta 0.06, p = 0.27) States this persists in just the control group but doesn’t give any statistics for this. Positive relationship between MMSE and global cognitive score (composite of the cognitive tests) and lean mass when grouping AD and control subjects together. States that controlling for dementia status attenuates these results, but no specific statistics given. Calculated: See Wetmore et al. (2011) for Kansas Brain Aging Project data analysis. |
Function: Logical Memory I & II, Free & Cued Selective Reminding Task, Boston naming test, Verbal fluency, Digit span forward and backward, Letter-number sequencing, Trail making A & B, Stroop color-word test and Block design, MMSE | |||||||||
5. Honea et al. [30] | 2009 | USA, Kansas, Brain Aging Project | 56 healthy controls | Cross-sectional case–control study (Alzheimer’s dementia vs. non-dementia) | 73.3 (6.2) | 41.1 | Structure: MRI for GM, WM, CSF, WBV, hippocampal and parahippocampal volumes | DEXA for total lean mass | Study: none |
Function: MMSE | Calculated: See Wetmore et al. (2011) for Kansas Brain Aging Project data analysis. | ||||||||
6. Weise et al. [31] | 2013 | USA, Phoenix | 76 | Cross-sectional study | 32.1 (8.8) | 31.6 | Structure: MRI brain volumes (GM, WM, CSF, regional GMV) | DEXA, FFMI (FFM/height2) | Study: Fat-free mass index (FFMI) was negatively associated with GMV of the bilateral temporal lobes, ventromedial prefrontal cortex (vmPFC) (mainly subgenual portion of the ACC) and caudolateral orbitofrontal cortex and unilaterally with the left insular cortex (all p < 0.01). After adjusting for percentage body fat and fat mass, negative associations of FFM with GMV of the right temporal pole and bilateral vmPFC remained. All models adjusted for age, sex and handedness. |
Function: not measured |
Author | Year | Country and dataset | n | Study design | Mean age (sd) | Male (%) | Brain structure#
| Muscle function | Associations* |
---|---|---|---|---|---|---|---|---|---|
Brain structure and grip strength
| |||||||||
1. Sachdev et al. [32] | 2009 | Australia, PATH through life project | 432 | Observational cohort study | M 62.61 (1.42) F 62.62 (1.44) | 52.8 | Volumes of GM, WM and CSF, ICV and TBV (GM plus WM). Brain atrophy and subcortical atrophy, WMH | Grip strength in writing hand | Study: Total brain WMH volume predicted grip strength in men (beta −0.140, delta R2 0.019, p < 0.05) but not in women (beta −0.140, delta R2 0.018, p > 0.05). |
2. Anstey et al. [33] | 2007 | Australia, PATH through life project | 432 | Observational cohort study | 62.63 (1.43) | 51.6 | Total, anterior, midbody and posterior corpus callosum (CC) area | Grip strength in writing hand | Study: Grip strength adjusted for sex and ICV was found to correlate with CC midbody area (r = 0.103, p < 0.05), however CC total area and anterior and posterior CC areas did not significantly correlate with grip strength (p > 0.05). |
3. Sachdev et al. [34] | 2006 | Australia, PATH through life project | 469 | Observational cohort study | M 62.56 (1.44) F 62.53 (1.47) | 51.8 | Volumes of GM, WM and CSF, ICV and TBV (GM plus WM). Brain atrophy and subcortical atrophy, WMH | Grip strength in writing hand | Study: None, see other articles from the PATH through life project for analysis using this dataset. |
4. Sachdev et al. [35] | 2005 | Australia, PATH through life project | 478 | Observational cohort study | M 62.56 (1.44) F 62.54 (1.47) | 52.3 | WMH, ICV | Grip strength in writing hand | Study: Total brain WMH significantly predicted grip strength (beta −0.09, p = 0.002) adjusted for age, sex and depression. Correcting for comorbidity, cognition and brain atrophy did not attenuate the results (beta −0.13, p =0.001). |
5. Doi et al. [36] | 2012 | Japan | 110 | Cross-sectional study | 75.4 (7.1) | 50 | GM, WM, CSF, brain atrophy (measured using healthy volunteers) | Grip strength | Study: A MLR model found that grip strength is not related to brain atrophy (beta −0.082 (SE 0.005) p = 0.54). Adjusting for age, gender, BMI, education, MMSE, Tokyo Metropolitan Institute of Gerontology Index of Competence, geriatric depression scale and change in walking whilst dual tasking. No other associations given. |
6. Hardan et al. [37] | 2003 | USA, Philadelphia | 41 controls | Case–control study | 18.6 (8.6) | Not given | Caudate, putamen and total brain volume | Grip strength | Study: Non-significant trends showed a negative correlation between right grip strength and total caudate volume (r = −0.303, p = 0.05) and left grip strength (r = −0.28, p = 0.07) in the control group. Not corrected for age or sex. No relationships given for other measures. |
Brain structure and gait speed
| |||||||||
7. Piguet et al. [38] | 2006 | Australia, Sydney Older Person's Study | 111 | Longitudinal observational cohort study | M 85.29 (2.89) F 85.72 (3.41) | 54.5 | Cerebellar vermis area, (V1, V2 and V3 and total), Cerebellar volume, cerebral volume and ICV | Timed walk over 5 m, adjusted for lower limb arthritis | Study: None of the brain size measures (cerebellar vermis area, cerebellar volume or cerebral volume) significantly predicted timed walk (p > 0.05) after adjustment for age (but not sex, as was not deemed to be a significant contributor after univariate analyses). |
8. Callisaya et al. [39] | 2013 | Australia, Tasmanian Study of Cognition and Gait (TASCOG) | 225 | Longitudinal cohort study | 71.4 (6.8) | 56.4 | ICV, GM, WM-lesion free, hippocampal volume, WML | 4.6 metre GaitRite computerized walkway (preferred speed) | Study: MLR were performed to investigate the relationship of longitudinal change in brain volumes and gait speed. They found that white matter atrophy (beta 0.25 (CI 0.09-0.40) p = 0.001), greater WML progression (beta −0.89 (CI −1.75- -0.02) p = 0.045), grey matter atrophy (beta 0.25 (CI 0.00-0.19) p = 0.06) and hippocampal atrophy (beta 0.01 (CI 0.00-0.02) p = 0.006) were all associated with a greater decline in gait speed. |
9. Srikanth et al. [40] | 2010 | Australia, TASCOG | 385 | Longitudinal cohort study | 72.2 (7.1) | 56 | WMLV, TBV | Gait speed using 4.2 m GAITRite system | Study: none, see Callisaya et al. (2013) for analysis using the TASCOG dataset. |
10. Srikanth et al. [41] | 2009 | Australia, TASCOG | 294 | Longitudinal cohort study | 72.3(7.0) | 55.4 | WMLV, TBV | Gait speed using 4.2 m GAITRite system | Study: none, see Callisaya et al. (2013) for analysis using the TASCOG dataset. |
11. Elbaz et al. [42] | 2013 | France, Three-city study | 4010 | Cohort study | 73.4 (4.6) | 38.4 | WML volumes | 6 metre walk speed (usual and maximum) | Study: Logistic regression stratified by education found that high WML volumes were not associated with slow walking speed among highly educated participants (OR = 0.72), but were associated with a 2-fold-increased risk of slow walking speed among those with low education (OR = 3.19/1.61 = 1.99) (p interaction = 0.026), adjusted for sex, age and total WM volume. Results remained unchanged after adjustment for height, BMI, and MMSE score. |
Given: WM volume did not predict walking speed at baseline, adjusted for age, gender and ICV in a MLR (p > 0.05, n = 1510), or decline in walking speed over 7 years, adjusted for age, gender, ICV and baseline walking speed, (p > 0.05, n = 928). A logistic regression found that WM volume was not significantly associated with an increased risk of being in the quartile with the highest walking speed decline (p > 0.05). | |||||||||
12. Dumurgier et al. [43] | 2012 | France, Three-city study | 1623 | Cohort study | 73.3 (4.1) | 39.5 | Regional grey matter volumes (sensorimotor cortex; frontal, parietal, temporal, occipital, and limbic lobes; insula; cerebellum; thalamus; basal ganglia nuclei, including the caudate nucleus, putamen and pallidum) and WMLs | Maximum walking speed over 6 metres | Study: A linear regression found that only basal ganglia volume (beta 0.075 (SE 0.025) p = 0.003) was significantly associated with walking speed; driven by caudate nucleus volume (beta 0.114 (SE 0.024) p < 0.001). All other regional GM volumes were not significantly associated with walking speed. A semi-bayes model found again only the basal ganglia volume (beta 0.061 (SE 0.028) p = 0.03) was significantly associated with walking speed; driven by caudate nucleus volume (beta 0.050 (se 0.019) p = 0.007). There was found to be a linear relationship between quartiles of caudate nucleus volume and faster walking speed (p for linear trend (0.001). These relationships were attenuated slightly for total basal ganglia volume by adjusting for MMSE and comorbidity plus smoking but not for caudate nucleus volume. All models adjusted for; age, sex, BMI, education level, ICV, volume of WMLs and silent infarcts. Given: See Elbaz et al. (2013) for Three-City Study data analysis. |
13. Dumurgier et al. [44] | 2010 | France, Three-city study | Baseline 3604, f/u at 4y 1774 | Cohort study | Baseline 73.4 (4.6) f/u 71.5 (3.6) | Baseline 38.1%, f/u 38.4% | WMH volume | Maximum walking speed over 6 metres, 1st and 4th follow up, mean 7 years | Study: none |
Given: See Elbaz et al. (2013) for Three-City Study data analysis. | |||||||||
14. Soumare et al. [45] | 2009 | France, Three-city study | 1702 | Cohort study | 72.4 (4.1) | 39.4 | PVH, deep WMH and total WMH and total WM and ICV | Maximum walking speed over 6 metres, 1st and 4th follow up, mean 7 years | Study: A significantly lower mean walking speed was found in those with a total WMH volume above the 75th percentile compared to those below the 25th (Beta −0.026, p = 0.0003). A similar relationship was found for both deep WMH and PVH. A WMH volume greater than the 90th percentile more than doubled the risk of decline in walking speed compared with subjects with lower volumes of WMH (OR 2.6 (1.5-4.5), p = 0.001). This finding was replicated when looking at PVH but not for deep WMH volume. Given: See Elbaz et al. (2013) for Three-City Study data analysis. |
15. Starr et al. [46] | 2003 | UK, ABC1921 cohort study | 97 | Longitudinal cohort study | 78-79years | 59.8 | WMH in deep/subcortical, PVH and brain stem, Fazekas score | Self-paced time to walk 6metres | Study: A slower 6metre walk test was associated with increased brain stem lesions (F 7.11, p = 0.009, partial eta2 0.070), but not with WMH (deep) (F 3.33, p = 0.071) or PVH (F 2.47, p = 0.12). Doesn’t state if age and sex are adjusted for in these models. If HADS score and Raven’s score are adjusted for, brainstem lesions are no longer significantly associated with walking time. |
16. Manor et al. [47] | 2012 | USA, Boston, | 89 in control group | Case–control study | 65.3 (8.2) | 48.3 | GM, WM, CSF, regional GM volumes; precentral and postcentral gyri, basal ganglia, cerebellum, and dorsolateral prefrontal cortex | 75 metre walk test at preferred pace | Study: Within linear regression models, global GM volume and all of the regional GM volumes were not associated with walking speed in the control group (p > 0.005, Bonferroni adjusted). Adjusted for age, sex and body mass. |
17. Hajjar et al. [48] | 2010 | USA, Boston, BP in stroke study (?overlap with Novak et al.) | Non-stroke group 43 | Case–control observational study | 68 (1) | 44 | WM, GM (global and regional), CSF normalized for ICV | Gait speed over 12mins at usual pace | Study: Gait speed was not significantly associated with GM volume (p = 0.85), but was significantly associated with WM volume (B = 1.30, p = 0.03) adjusting for age, gender, BMI and antihypertensive use. |
18. Novak et al. [49] | 2009 | USA, Boston (?overlap with Hajjar et al.) | 76 | Observational study | 64.7 (7.2) | 47.4 | GM, WM, CSF, WMH all as % brain tissue volume. WMH using Wahlund scale | Gait speed over 12mins at normal walking pace | Study: Gait speed was significantly associated with frontal WM normalized for brain tissue volume (R = 0.4, p = .003). Gait speed was significantly associated with frontal GM normalized for brain tissue volume (R = 0.3, p = .01). Adjusted for age and BMI (but not gender). Doesn’t say about other regional brain volumes, ie temporal etc. WMH volumes and PVH and punctuate scores were not associated with gait speed (p > 0.05). |
19. Moscufo et al. [50] | 2012 | USA, Boston, Moscufo study – 2 year f/u | 77 | Longitudinal cohort study | 84 (3.9) | 40 | WMH volume as % of ICV and regional WMH burden expressed as % of ROI volume. At baseline and 2y f/u. | Gait speed over 2.5 metres, maximum velocity and usual walking speed At baseline and 2y f/u. | Study: Total WMH burden was significantly associated with usual walking speed at baseline but not at follow-up, and maximum walking speed was not associated with total WMH at baseline or follow up. At baseline, regional WMH burden in the splenium of corpus callosum and anterior and superior corona radiata, was significantly associated with both walking measures (p < 0.05) and in addition the body of the corpus callosum was also associated with usual walking speed (p < 0.05). At follow-up, WMH burden in the splenium was significantly associated with both walking measures (p < 0.05) and in the body with maximum walking speed. Change in WMH burden, either total or in any of the 7 regional areas, over 2 years was not associated with a decline in usual walking speed (p > 0.1). |
Given: WMH burden is significantly associated with lower gait speed after adjustment for age, sex and BMI (rho = −0.327, p = 0.0008). WM/ICV is not significantly associated with gait speed with or without adjustment (p > 0.05). GM/ICV is significantly associated with gait speed with adjustment for age, gender and BMI (rho = 0.232, p < 0.05). CSF/ICV is significantly associated with gait speed with adjustment for age, sex, BMI (rho = −0.285, p = 0.004). | |||||||||
20. Moscufo et al. [51] | 2011 | USA, Boston, Moscufo study - baseline | 99 | Cross-sectional observational study | 83(4) | 42.4 | WM, GM, WMH and CSF volumes all corrected for ICC. Brain atrophy. Regional WMH burden expressed as % of ROI volume. | Gait speed over 2.5metres (done as part of SPPB) | Study: Total WMH burden (i.e. % of ICV) correlates with gait speed (rho = −0.288, p = 0.004). Also all 9x regional burden measurements correlate with gait speed score too except sup. longitudinal fasciculus. No adjustment. |
Given: See Moscufo et al. (2012) for analysis using this dataset. | |||||||||
21. Wolfson et al. [52] | 2005 | USA, Boston, WML and mobility | 28 at baseline, 14 at follow up | Prospective longitudinal observational study | SPPB 11or12 mean 81(1.7), SPPB = <8 mean 84(3.4) | 64.3 | GM, WM, WMSA, CSF, ICCV volumes | Gait velocity over 8metres | Study: Slower baseline gait velocity predicted more WMSA at visit 1 (p < 0.05), but not change in WMSA volume between visit 1 and 2 (p < 0.07). Significant negative relationship of between-visit change in gait velocity to CSF volume (r = 0.733, p < 0.005) and a positive relationship of between-visit change in gait velocity to WM volume (r = 0.558, p < 0.05). Betas not given. Brain volumes normalized for ICCV according to image processing section. |
22. Guttmann et al. [53] | 2000 | USA, Boston, WML and mobility | 28 (12 with SPPB score >10 and 16 < 9) | Observational cross-sectional study | SPPB > 10 79(5) SPPB < 9 83(6) | 42.9 | WM, WMSA, GM, CSF (normalized for ICCV) | Gait velocity over 8metres | Study: Gait velocity was not significantly predicted by age nor WMSA volume (no figures given or p value) adjusted with and without MMSE score. |
23. Rosano et al. [54] | 2012 | USA, Cardiovascular health study | 214 | Longitudinal observational study | 72.3 (3.8) | 35.5 | Brain volumes (GM, WMH, Prefrontal area, WM, CSF) | Timed 15 ft walk at usual pace | Study: Prefrontal area volume significantly predicted time to walk in a stepwise forward model (beta −0.15, p = 0.02). |
24. Barnes et al. [55] | 2009 | USA, Cardiovascular Health Cognition Study, nested within the CVS Health Study | 3375 | Prospective, population-based, longitudinal study | 75 (no sd) | 41 | White matter disease and ventricular enlargement | Gait speed over 15 ft | Study: none, see Rosano (2012), Rosano (2006), Rosano (2005) and Longstreth (1996) for analysis using the Cardiovascular Health study dataset |
25. Rosano et al. [56] | 2006 | USA, Cardiovascular health study | 321 | Longitudinal observational study mean f/u 4 years | 78.3 (no sd) | 39.3 | WMAs, ventricular enlargement | Gait speed at usual pace over 4 metres using GaitMat II | Study: Gait speed was significantly correlated to total WMAs (r = −.18, p < 0.0001) and white matter lesions in the brainstem (r = −.18, p = 0.01). After adjusting for age, slower gait speed was still significantly associated with white matter grade (p = 0.02). Logistic regression found that those in the lowest two quartiles of gait speed (ie < 1.02 m/s) had double the likelihood of having WMH graded 3 or above (p = 0.03), after adjustment for age, race, gender, and prevalent clinical CVD. VE graded >4 was not found to be significantly predicted by gait speed, however VE graded > 5 was, independent of age, gender, race and presence of CVD (OR = 2.91 for 1st vs. 4th quartile, OR 3.82 for 2nd vs 4th quartile) |
26. Rosano et al. [57] | 2005 | USA, Cardiovascular health study | 2450 | Longitudinal observational study mean f/u 4 years | 74.4 (4.7) | 43 | WMH and ventricular enlargement (graded as minimal, moderate and severe) | Gait speed over 15 ft at usual pace, starting from standing still | Study: Grade of ventricular enlargement was associated with baseline gait speed and mean change in gait speed/year. Gait speed decline was 2.5x that for those with severe VE than minimal VE. (p < 0.001). Grade of WMH was associated with baseline gait speed and mean change in gait speed/year (p = 0.003). In both analyses adjustment had been made for age, sex, race and education and CV risk factors (BMI, systolic BP, antihypertensive meds, internal carotid wall thickness, and ETOH intake) and prevalent CV disease. |
27. Silbert et al. [58] | 2008 | USA, Oregon Brain Aging Study | 104 | Longitudinal cross-sectional study | 85.1 (5.6) | 38.5 | PV WMH and s/c WMH, total WMH, brain volume, CSF volume, hippocampal volume, ICV | Gait speed over 9 m. Self-selected pace. | Study: Adjusted for age and ICV, higher baseline total WMH vol. was associated with increased rate of change in timed walking in seconds (r2 = 0.08, p = 0.0052). This relationship became non-significant after adjustment for multiple comparisons to threshold p value. PVH volume is associated with increased rate of change in timed walk in seconds (r2 = 0.12, p = .0039). However, baseline subcortical WMH vol. was not related to change in gait performance over time. Higher rate of PVH accumulation is associated with increased rate of change of time to walk 9 m (r2 = 0.15, p = .0453). Adjusted for age, ICV and baseline WMH volume: |
Calculated: In an unadjusted GLM, gait speed was predicted by total brain, WMH and hippocampal volume (p < 0.001). The relationship remained significant after adjusting for sex, age, ICV and height, for total brain volume (t = 3.61, p = .004, partial eta squared 4.3%) and WMH (t = −2.80, p = 0.006, partial eta squared 4.4%) but not for hippocampal volume. | |||||||||
28. Marquis et al. [59] | 2002 | USA, Oregon Brain Aging Study | 108 | Longitudinal cross-sectional study | 83.2 (7.9) | 37 | Total brain volume, hippocampal volume, ICV | Gait speed over 9 m. Self-selected pace. | Study: Negative correlation between hippocampal volume and time to walk 30 ft (r = −.12). No p value given. |
Calculated: See Silbert et al. (2008) for Oregon Brain Aging Study data analysis. | |||||||||
Brain structure and gait speed plus grip strength or isometric knee extension strength (IKES)
| |||||||||
29. Rosano et al. [60] | 2010 | Iceland, AGES-Reykjavik study | 795 | Longitudinal cohort study | M 75.6 (5.4) F 75.6 (5.7) | 41.1 | MTR, ICV, brain parenchyma volume, semiquantitative subcortical WMH and PVH and total WMH volume, brain atrophy index | Gait speed over 6 m usual speed and maximal isometric knee extension strength | Study: In men: Time to walk 6metres predicted by WMH volume (beta 0.13, p = 0.02) but not brain atrophy or peak height MTR (adjusted for age and brain size as includes measure of brain atrophy). In women: Usual walking speed predicted by lower MTR height (i.e. indicating abnormal brain tissue) (beta −0.14 (p = 0.01), increased WMH (beta 0.12, p = 0.003) and greater brain atrophy (beta 0.15, p = 0.01) (adjusted for age and brain size). Lower muscle strength associated with peak height MTR (p < 0.005, beta not given). |
30. Aribisala et al. [61] | 2013 | UK, LBC 1936 study | 694 | Longitudinal cohort study | 69.5 (0.7) wave 1 and 72.5 (0.7) wave 2 | 52.9 | TBV, ventricular volume, GM, NAWM and WML at wave 2 | 6 metre walk (normal walking pace) and grip strength at wave 1 and 2 | Study: Grip strength at wave 1 significantly predicts ventricular volume at wave 2 (standardized beta −0.10), however there was no significant association with other brain volumes. 6metre walk at wave 1 predicted TBV (−0.07), ventricular volume (0.09), NAWM (−0.07) and WML (0.11) all p < 0.05. Grip strength at wave 2 was associated with ventricular volume (−0.11) and NAWM (0.08). 6 MW at wave 2 was associated with TBV (−0.07), NAWM (−0.09) and WML (0.11) all p < 0.05. Change in physical function between wave 1 and 2 (i.e. decrease in grip strength or increase in 6 MW) was not significantly associated with any brain volume measure. GM volume did not significantly associate with any of the physical function variables at wave 1 or 2. All analyses were adjusted for age, ICV, age 11 IQ, years of education, social class, comorbidity and smoking status. Corrected for false discovery rate. |
31. Rosano et al. [62] | 2011 | USA, Cardiovascular health study | 643 | Longitudinal observational study | 72.1-72.6 broken down by BP diagnosis | 31-42.7 broken down by BP diagnosis | WMH scale 0-9 | Gait speed over 15 ft, starting from standstill. Grip strength of dominant hand. | Study: none, see Rosano (2012), Rosano (2006), Rosano (2005) and Longstreth (1996) for analysis using the Cardiovascular Health study dataset. |
32. Rosano et al. [63] | 2008 | USA, Cardiovascular health study | 3156 | Longitudinal observational study mean f/u 4 years | 74 (4.6) | 43.2 | White matter disease score, brain atrophy score (ventricular enlargement) | Gait speed over 15 ft and grip strength in dominant hand | Study: none, see Rosano (2012), Rosano (2006), Rosano (2005) and Longstreth (1996) for analysis using the Cardiovascular Health study dataset. |
33. Longstreth et al. [64] | 1996 | USA, Cardiovascular health study | 3658 | Longitudinal observational study | 70.7 (no sd) | 41.7 | MR WMSA graded 0-9 | Time to walk 15feet, grip strength in dom and non-dom hand | Study: Time to walk 15 ft correlated with white matter grade (0–9) (r = 0.153, p < 0.001), with adjustment for age, sex and presence of clinically silent stroke on MRI. Same model showed no significant associated between grip strength in dom hand or non-dom hand and white matter grade (p > 0.05). |
Author | Year | Country and dataset | n | Study design | Mean age (sd) | Male (%) | Brain function | Muscle structure | Associations* |
---|---|---|---|---|---|---|---|---|---|
1. Berryman et al. [65] | 2013 | Canada, Training Intervention Study | 48 | Baseline characteristics from a large physical training intervention study | 70.8 (5.4) | 41.67 | MMSE & modified Stroop test | LBM (DEXA) | Study: none |
Calculated: A GLM showed no association between LBM and MMSE, Stroop naming, reading or inhibition tasks, adjusted for sex and age. However there was an association between the Stroop flexibility task and LBM (t 2.126, p = 0.039, partial eta squared 9.3%), however after adjusting for education and height the effect was attenuated (p > 0.05). | |||||||||
2. Bites et al. [66] | 2013 | Chile | 306 | Retrospective study | M 74.9 (61–91), F 75.5 (69–90) | 24.5 | MMSE | TLM, Arm LM and Legs LM (DEXA) | Study: none |
Calculated: Authors sent one data sheet for this study and Bunout et al., as there is a large amount of overlap between the studies. N = 401, mean age 75.3 (sd 4.8), males 28.7%. GLM performed adjusting for sex and gender. Total LM (t 2.38, p = 0.018, partial eta squared 1.4%) and Leg LM (t 3.53, p < 0.001, partial eta squared 3.1%) were both associated with MMSE score but Arm LM is not. After adjusting for height the relationship between total LM and MMSE is non-significant and between leg LM and MMSE is attenuated (t 2.09, p = 0.038, partial eta squared 1.1%). | |||||||||
3. Bunout et al. [67] | 2005 | Chile | 298 | RCT | M 75.4 (4.8) F 75.8 (4.7) | 29.2 | MMSE | TLM, Arm LM and Legs LM (DEXA) | Study: none |
Calculated: See Bites et al. 2013 for analysis using this dataset | |||||||||
4. Auyeung et al. [68] | 2013 | Chinese University of Hong Kong - 4y f/u | 3153 | Prospective observational study | M 71.76 (4.67) F 72.03 (5.07) | 49.7 | CSI-D and MMSE | ASM, LLMM, FFM (DEXA) | Study: none |
Given: CS-CSID did not predict TLM or ASM at baseline or at 4 years (all p > 0.05). However baseline MMSE was associated with baseline TLM (rho = 0.058, p = 0.002) and ASM (rho = 0.061, p = 0.001) and at follow-up (TLM rho = 0.058, p = 0.002, ASM rho = 0.054, p = 0.005). MMSE at follow up was not associated with TLM or ASM at baseline or follow-up (p > 0.05). | |||||||||
5. Auyeung et al. [69] | 2011 | Chinese University of Hong Kong - 4y f/u | 2737 | Prospective observational study | M 71.6 (4.58) F 71.5 (4.85) | 55.3 | CSI-D and MMSE | ASM (DEXA) | Study: In men, low baseline ASM predicted lower MMSE score after 4 years (B = 0.246, p < 0.01) however after adjustment for age, years of education and baseline MMSE it no longer did (p > 0.05). In women, ASM did not significantly predict MMSE after 4 years, either before adjustment or after (p > 0.05). |
Given: see Auyeung et al. (2013) for analysis using this dataset | |||||||||
6. Lee et al. [70] | 2011 | Chinese University of Hong Kong | 4000 | Prospective observational study | M 72.3 (5.0) F 72.5 (5.3) | 50 | CSI-D and MMSE | ASM, LLMM, FFM (DEXA) | Study: none |
Given: see Auyeung et al. (2013) for analysis using this dataset | |||||||||
7. Auyeung et al. [71] | 2008 | Chinese University of Hong Kong - baseline | 4000 | Prospective observational study | M 72.3 (5.0) F 72.5 (5.3) | 50 | CSI-D and MMSE | ASM (DEXA) | Study: none |
Given: see Auyeung et al. (2013) for analysis using this dataset | |||||||||
8. Pedersen et al. [72] | 2012 | Denmark | 72 controls | Cross-sectional study | Median 53 (48–60 inter quartile range) | 46 | DART, WAIS-III information subtest, TMT-A&B, Rey Auditory Verbal Learning Test (RAVLT), Symbol Digit Modalities Test (SDMT), and fluency tests | FFM (DEXA) | Study: None |
Calculated: FFM did not predict the cognitive z score with or without adjusting for BMI and childhood intelligence (Danish Adult Reading Test, DART). The six individual cognitive tests were then analysed: FFM did not predict RAVLT, SDMT, category fluency (using animals) or TMT-b test, with or without adjusting for BMI and childhood intelligence (DART). Unadjusted, there was no significant association between the letter fluency test (using “s”) and FFM (P > 0.05), however after adjustment for BMI and DART, letter fluency was significantly associated with FFM (t 2.34, p = 0.02, partial eta squared 7.7%). TMT-a test did significantly predict FFM (t 3.08, p = 0.003, partial eta squared 12.3%). After adjusting for BMI and DART the relationship became non-significant. | |||||||||
9. Magri et al. [73] | 2006 | Italy | 27 controls | Cross-sectional case–control study | Controls 33.3 (7.15) | 0 | MMSE | FFM (BIA) | Study: none |
Calculated: FFM did not significantly predict MMSE (p > 0.05), adjusting for age. Adjustments for BMI and educational level did not significantly affect the results. | |||||||||
10. Lasaite et al. [74] | 2009 | Lithuania | 29 healthy controls | Observational case–control study | 66.2(6.3) | 0 | TMT-A and B and digit span | FFM (BIA) | Study: none |
Calculated: FFM does not significantly predict TMT-A or B adjusting for age +/− height (p > 0.05). Trend with FFM predicting digit span (t 1.96, p = 0.06, partial eta squared 13%) but attenuated when adjusted for height in addition to age (p = 0.37). | |||||||||
11. Liu et al. [75] | 2014 | Taiwan, I-Lan Longitudinal Aging Study | 983 | Population based ageing cohort study | 65.2 (9.3) | 50.6 | MMSE | LBM and Relative ASM (=ASM/ height2) (DEXA) | Study: A t test comparing mean MMSE in those with normal RASM and those within the lowest 20 % of RASM found a significant difference in men and women of all ages (p < 0.05). |
Given: In a MLR, RASM did not predict MMSE after adjusting for age and sex (beta −0.003, p = 0.940). Adjusting for education in addition did not affect the results. | |||||||||
12. Moore et al. [76] | 2012 | USA, Baltimore Longitudinal Study of Aging | 786 | Longitudinal cohort study | 66.3 (range 26–96) | 51.9 | California Verbal Learning Test (CVLT), digit-span test, TMT A & B | Mid-femur thigh CSA (CT) | Study: none |
Given: In a linear regression, none of the cognitive tests predicted thigh CSA, adjusting for age and gender. After adjusting for age, gender and height, the digit-span backward test became significantly associated with thigh CSA (beta −1.55, p = 0.024). | |||||||||
13. Kamijo et al. [77] | 2014 | USA, FITKids Study | 37 (healthy weight) | Cross-sectional study (case–control substudy comparing obese and healthy weight children) | 8.8 (0.6) | 46 | Kaufman Brief Intelligence Test (K-BIT) | TLM (DEXA) | Study: none |
Calculated: Authors sent one data sheet for the FITKids study as there is considerable overlap in subjects between the two Kamijo et al. papers [77, 78], (n = 139, mean age 8.8 (sd 0.6), male 51.1%). A GLM found that TLM did not predict K-BIT after adjustment for age and gender (p > 0.05). Adjusting for BMI in addition did not alter the results. | |||||||||
14. Kamijo et al. [78] | 2012 | USA, FITKids Study | 126 | Cross-sectional study | 8.9 (0.5) | 50 | Kaufman Brief Intelligence Test (K-BIT) | TLM (DEXA) | Study: none |
Calculated: as per Kamijo et al. [77] |
Authors | Year | Country and dataset | n | Study design | Mean age (sd) | Male (%) | Brain structure or function | Muscle structure or function |
---|---|---|---|---|---|---|---|---|
1. Chowdhury et al. [79] | 1994 | Sweden | 8 | Methodology paper | 35 (8) | 100 | Brain volume (CT) | Calculated skeletal muscle volume (CT) |
2. Liu-Ambrose et al. [80] | 2010 | Canada, Exercise RCT in Vancouver | 155 | RCT, prospective over 52 weeks | 69.6 (2.9) | 0 | Whole brain volume (MRI) | Gait speed, quads strength and muscle power |
3. Nadkarni et al. [81] | 2012 | Canada, Sunnybrook Dementia Study | 20 controls | Cross-sectional substudy of longitudinal study | 75 (9) | 40 | Score on Age-Related White Matter Change Scale (MRI) | Self-selected speed on a treadmill |
4. Sullivan et al. [82] | 2005 | USA, California, Stanford | 51 | Case–control study | 45.2 (13.9) | 100 | Caudate, putamen, nucleus accumbens and medial septal / diagonal band volumes and ICV (MRI) | Bilateral grip strength |
5. Guthrie et al. [83] | 2004 | Australia, The Melbourne Women's Midlife Health Project | 1897 | 9 year prospective, observational population based sample | Median 50 | 0 | Episodic verbal memory using a 10 word recall task (CERAD) | Body composition (DEXA) |
6. Ellis et al. [84] | 2009 | Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging | 768 healthy controls | Longitudinal case control study (AD vs MCI vs normal) | 70.0 (7.0) | 43 | CVLT-II, Logical memory, RCFT, digit span, digit symbol coding, D-KEFS verbal fluency, BNT, clock, WTAR, Stroop. | Body composition (DEXA) in subgroup in Perth |
7. Dao et al. [85] | 2013 | Canada, Exercise RCT in Vancouver | 114 | Secondary analysis of RCT data | 69.4 (2.9) | 0 | Stroop test, MMSE | Sub-total lean mass (DEXA) |
8. Schwartz et al. [86] | 2013 | Canada, Saguenay Youth Study | 983 | Longitudinal cohort study | M 14.9 (1.8), F 15.1 (1.9) | 48.8 | Executive function and Memory | FFM (BIA) |
9. Bagger et al. [87] | 2004 | Denmark, PERF study | 5607 | Prospective, observational cohort study | 71.1 (6.6) | 0 | Short Blessed Test | TLM (DEXA) |
10. Abellan van Kan [88] | 2013 | France, EPIDOS study | 3025 | Prospective multi-centre cohort study | 80.51(3.9) | 0 | SPMSQ | Lean mass and ALM (DEXA) |
11. Nourhashemi et al. [89] | 2002 | France, EPIDOS study | 7105 | Cross-sectional study | 80.3 (3.65) (SPMSQ > =8) | 0 | SPSMQ for orientation, concentration and memory | FFM (DEXA) |
12. Nourhashemi et al. [90] | 2001 | France, EPIDOS study | 7364 | Prospective multicentre study | Broken down by ADLs; means 79.9-82.7 years | 0 | Pfeiffer’s test (aka SPMSQ) | Body composition (DEXA) |
13. Paolisso et al. [91] | 1997 | Italy, Naples | 30 (>50y), 30 (75-99y) 19 (>99y) | Observational study | 44.5(1.8), 78(0.7), 102(0.8) | 46.8 | MMSE | FFM (BIA) |
14. Malaguarnera et al. [92] | 2007 | Italy, Sicily | 66 | Placebo controlled, randomized, double-blind, 2-phase study | 101(1.3) treatment, 101(1.4) placebo | 31.8 | MMSE | Total muscle mass (BIA) |
15. Jacobsen et al. [93] | 2012 | Netherlands | 318 | RCT | Mean for each arm given range 73.4-74.0 | 0 | 15 words test and Trails B test | BIA and DEXA |
16. Genton et al. [94] | 2011 | Switzerland | 213 in 1999 and 112 in 2008 | Cross-sectional study with 9 year f/u visit | 1999 M 71.7(5.2) | 1999 49.3 | MMSE | FFM (BIA), ASMM (DEXA) and BCM (total body potassium) |
2008 M 80.3(5.2) | 2008 49.1 | |||||||
1999 F 73.2(5.5) | ||||||||
2008 F 82.2(5.6) | ||||||||
17. Donaldson et al. [95] | 1996 | USA, Baltimore | 73 | Cross-sectional study | 68.8 (7.2) | 31.5 | MMSE | FFM (DEXA) |
18. Bove et al. [96] | 2013 | USA, Boston, Harvard | 12 | Cross-sectional study | 31.6 (6.4) | 0 | Multiple tests broken down to 5 cognitive domains | Cross sectional area of mid-thigh (CT) |
19. Papadakis et al. [97] | 1995 | USA, California, San Francisco | 104 | Cross-sectional study | 75.5(4.9) | 100 | MMSE, Trails B and DSST | Lean tissue mass (DEXA) |
20. Janssen [98] | 2006 | USA, Cardiovascular health study | Baseline 5036 | Longitudinal observational study (over 8 years) | 65-70 (42.7%), 71–76 (32.7%), 83–89 (18.2%), ≥90 (6.4%) | 43.6 | MMSE | Whole body muscle mass (BIA) and normalized for height to the skeletal muscle index (SMI, kg/m2) |
21. Masley et al. [99] | 2008 | USA, Florida | 56 | RCT | Controls 43.5 (11.2), Intervention 47.1 (9.4) | Control 39.3, Intervention 53.6 | CNS vital signs battery | FFM (BIA) |
22. Houston et al. [100] | 2012 | USA, Health, Aging, and Body Composition study | 2641 | Longitudinal cohort study | 74.7 (2.9) | 48.9 | MMSE | Lean mass (DEXA) |
23. Middleton et al. [101] | 2011 | USA, Health, Aging, and Body Composition study | 197 | Cross-sectional study from a 9 year longitudinal cohort study | Separated into tertile of activity, means range from 73.9-75.8 | Not given | 3MS | FFM (DEXA) |
24. Koster et al. [102] | 2010 | USA, Health, Aging, and Body Composition study | 2949 | Cross-sectional study from a 9 year longitudinal cohort study | Age 70–79 at baseline | 48.5 | 3MS | Total bone-free lean mass, trunk lean mass, appendicular lean mass (DEXA) |
25. de Rekeneire et al. [103] | 2003 | USA, Health, Aging, and Body Composition study | 2926 | Baseline data from a 9 year longitudinal cohort study | Diabetes mellitis (DM) 73.6 (2.9) and non-DM 73.6 (2.9) | DM 55.9 Non-DM 46.9 | MMSE and DSST | Lean mass and lean soft tissue mass (i.e. lean mass minus bone) (DEXA) |
26. de Rekeneire et al. [104] | 2003 | USA, Health, Aging, and Body Composition study | Fallers 652, non-fallers 2398 | Baseline data from a 9 year longitudinal cohort study | Range 70-79 | Fallers 41.7, non-fallers 50.3 | Teng Mini-mental State Examination and DSST | Total muscle mass and skeletal muscle mass in the legs (DEXA) |
27. Watts et al. [105] | 2013 | USA, Kansas, Brain Aging Project | 74 healthy controls | Longitudinal case–control study (Alzheimer’s dementia vs. controls) | 74.0 (7.2) | 43 | MMSE | Lean mass (DEXA) |
28. Canon et al. [106] | 2011 | USA, National Health and Nutrition Examination Survey (NHANES) | 867 | Cross-sectional longitudinal study | Range 60-85 | 44.8 | Digit-symbol coding test | Lean tissue mass (DEXA) |
29. Garry et al. [107] | 2007 | USA, New Mexico Aging Process Study | 809 rolling participants (average 302 seen per year) | Longitudinal Aging study (1979–2003) | 60+ Varied between years | 40 | 3MS (annual), WAIS R digit span, Fuld object memory evaluation, Color Trails 1 and 2, clock drawing (all less than annual) | Annual skeletal tissue mass (DEXA) |
30. Haren et al. [108] | 2008 | USA, St Louis, African-American Health Study | 124 | Population based longitudinal study | 56.1(4.4) | 100 | MMSE, TMT A&B | TLM and ASM (DEXA) |
31. Dvorak et al. [109] | 1998 | USA, Vermont | 30 | Case–control study | 73(7) | 43.3 | MMSE | ASM and FFM (DEXA) |
Association of brain structure and muscle structure
Association of brain structure and muscle function
Brain structure and grip strength
Gait speed and brain structure
Association of brain function and muscle structure
Discussion
Brain volumes and muscle mass
Negative association (n)
| No association (n)
| Positive association (n)
| References | |
---|---|---|---|---|
Whole brain size and muscle size
| - | 1 (70)
| 2 (311)
| |
White matter volume and muscle size
| - | - | 1 (70)
| |
Grey matter volume and muscle size
| 1 (76)* | 2 (146)* | - | |
Hippocampal volume and muscle size
| - | 2 (121)
| - | |
Cerebellar volume and muscle size
| - | 1 (51)
| - | [27] |
Ventricular volume and muscle size
| - | 1 (51)
| - | [27] |
Brain structure and muscle function
Negative association (n)
| No association (n)
| Positive association (n)
| References | |
---|---|---|---|---|
Grip strength and whole brain volume
| - | 1 (694)
| - | [61] |
Grip strength and WM volume
| - | 1 (694)
a
| 1 (694)
a
| [61] |
Grip strength and GM volume
| - | 1 (694)
| - | [61] |
Grip strength and caudate volume
| - | 1 (41)
| - | [37] |
Grip strength and ventricular volume/brain atrophy
| 2 (804)
| - | - | |
Grip strength and WMH
| 1 (478)
| 2 (4352)
| - | |
Change in grip strength over f/u and whole brain volume
| - | 1 (694)
| - | [61] |
Change in grip strength over f/u and WM volume
| - | 1 (694)
| - | [61] |
Change in grip strength over f/u and GM volume
| - | 1 (694)
| - | [61] |
Change in grip strength over f/u and ventricular volume
| - | 1 (694)
| - | [61] |
Change in grip strength over f/u and WMH volume
| - | 1 (694)
| - | [61] |
Negative association (n)
| No association (n)
| Positive association (n)
| References | |
---|---|---|---|---|
Gait speed and whole brain volume
| - | - | 2 (885)
| |
Gait speed and WM volume
| - | 2 (1587)
| 3 (813)
b
| |
Gait speed and GM volume
| - | 4 (2449)
c
| 3 (367)
b,d
| |
Gait speed and hippocampal volume
| - | 1 (191)
| - | |
Gait speed and cerebellar volume
| - | 1 (111)
| - | [38] |
Gait speed and WMH volume
| 7 (7145)
e
| 4 (278)
e,f
| - | |
Gait speed and CSF volume/ventricular volume/brain atrophy
| 3 (3221)
g
| 2 (1489)
g
| - | |
Gait speed and WMH progression over f/u
| - | 1 (14)
| - | [52] |
Change in gait speed over f/u and whole brain volume
| - | 1 (694)
| - | [61] |
Change in gait speed over f/u and WM volume
| - | 2 (1622)
| - | |
Change in gait speed over f/u and GM volume
| - | 1 (694)
| - | [61] |
Change in gait speed over f/u and CSF/ventricular volume
| - | 1 (694)
| 1 (2450)
| |
Change in gait speed over f/u and WMH volume
| - | 1 (694)
| 2 (4152)
| |
Change in gait speed over f/u and WM atrophy
| - | - | 1 (225)
| [39] |
Change in gait speed over f/u and GM atrophy
| - | 1 (225)
| - | [39] |
Change in gait speed over f/u and hippocampal atrophy
| - | - | 1 (225)
| [39] |
Change in gait speed over f/u and WMH progression
| - | 1 (77)
| 1 (225)
|
Brain structure and grip strength
Brain structure and gait speed
Cognitive function and muscle mass
Negative association (n)
| No association (n)
| Positive association (n)
| References | |
---|---|---|---|---|
Muscle size and global cognitive score
| - | 3 (193) | - | |
Muscle size and MMSE
| - | 5 (1434)h,i
| 2 (3459)h
| |
Muscle size and California Verbal Learning Test
| - | 1 (786) | - | [76] |
Muscle size and CSI-D
| - | 1 (3153) | - | [68] |
Muscle size and digit span
| - | 2 (815)j
| - | |
Muscle size and Kaufman Brief Intelligence Test
| - | 1 (139) | - | |
Muscle size and modified Stroop test
| - | 1 (48) | - | [65] |
Muscle size and NART
| 1 (51) | - | - | [27] |
Muscle size and TMT-a
| - | 3 (887) | - | |
Muscle size and TMT-b
| - | 3 (887) | - |