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Background: Repetitive head impacts (RHI) from contact sports have been associated with cognitive and neuropsychiatric disorders. However, not all individuals exposed to RHI develop such disorders. This may be explained by the reserve hypothesis. It remains unclear if the reserve hypothesis accounts for the heterogenous symptom presentation in RHI-exposed individuals. Moreover, optimal measurement of reserve in this population is unclear and likely unique from non-athlete populations.Objective: We examined the association between metrics of reserve and cognitive and neuropsychiatric functioning in 89 symptomatic former National Football League players.Methods: Individual-level proxies (e.g., education) defined reserve. We additionally quantified reserve as remaining residual variance in 1) episodic memory and 2) executive functioning performance, after accounting for demographics and brain pathology. Associations between reserve metrics and cognitive and neuropsychiatric functioning were examined.Results: Higher reading ability was associated with better attention/information processing (β=0.25; 95% CI, 0.05-0.46), episodic memory (β=0.27; 95% CI, 0.06-0.48), semantic and phonemic fluency (β=0.24; 95% CI, 0.02-0.46; β=0.38; 95% CI, 0.17-0.59), and behavioral regulation (β=-0.26; 95% CI, -0.48, -0.03) performance. There were no effects for other individual-level proxies. Residual episodic memory variance was associated with better attention/information processing (β=0.45; 95% CI, 0.25, 0.65), executive functioning (β=0.36; 95% CI, 0.15, 0.57), and semantic fluency (β=0.38; 95% CI, 0.17, 0.59) performance. Residual executive functioning variance was associated with better attention/information processing (β=0.44; 95% CI, 0.24, 0.64) and episodic memory (β=0.37; 95% CI, 0.16, 0.58) performance.Conclusion: Traditional reserve proxies (e.g., years of education, occupational attainment) have limitations and may be unsuitable for use in elite athlete samples. Alternative approaches of reserve quantification may prove more suitable for this population. [ABSTRACT FROM AUTHOR]

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions. [ABSTRACT FROM AUTHOR]