Table 1.
Focal white matter signal abnormalities, and global and regional atrophy brain outcomes in the study groups.
| Controls (n=21) |
Athletes (n=21) |
95% CI | p-value | Effect size* | |
|---|---|---|---|---|---|
| WM-SA presence, n (%) (Stage 1)† | 11 (52.4) | 12 (57.1) | NA | .532 | 0.05 |
| WM-SA volume (Stage 2)† | 2.4 (4.5) | 1.1 (1.6) | −1.6 – 4.1 | .530 | 0.38 |
| WM-SA volume (total) | 1.2 (3.4) | .6 (1.3) | −1.0 – 2.2 | .693 | 0.23 |
| WM-SA number‡ | 10.0 (17.5) | 6.3 (11.5) | −5.6 – 12.9 | .586 | 0.25 |
| NBV | 1504.4 (72.7) | 1483.6 (80.7) | −28.6 – 69.6 | .516 | 0.27 |
| NGMV | 728.4 (34.2) | 720.1 (40.5) | −15.6 – 32.2 | .664 | 0.22 |
| NWMV | 775.7 (45.5) | 763.5 (46.9) | −17.4 – 41.8 | .221 | 0.26 |
| NLVV | 46.4 (16.3) | 39.2 (15.2) | −2.9 – 17.3 | .452 | 0.46 |
| NCV | 587.9 (30.5) | 578.8 (35.7) | −12.1 – 30.3 | .323 | 0.27 |
| Total DGM | 60.1 (4.3) | 59.4 (4.7) | −2.2 – 3.5 | .555 | 0.16 |
| Thalamus | 20.2 (1.6) | 20.5 (1.7) | −1.3 – 0.8 | .256 | 0.18 |
| Caudate | 9.1 (.8) | 8.7 (1.1) | −0.2 – 1.0 | .631 | 0.42 |
| Putamen | 12.8 (1.1) | 12.7 (1.3) | −0.7 – 0.9 | .645 | 0.08 |
| Globus pallidus | 4.6 (.5) | 4.6 (.4) | −0.3 – 0.3 | .058 | 0.03 |
| Hippocampus | 9.1 (1.0) | 8.8 (.9) | −0.3 – 1.0 | .598 | 0.32 |
| Amygdala | 3.2 (.7) | 3.1 (.5) | −0.3 – 0.5 | .705 | 0.16 |
| Accumbens | 1.1 (.3) | 1.1 (.3) | −0.3 – 0.1 | .459 | 0.12 |
WM-SA – white matter signal abnormality; NBV – normalized brain volume; NGMV – normalized gray matter volume; NWMV – normalized white matter volume; NLVV – normalized lateral ventricle volume; NCV – normalized cortical volume; DGM – deep gray matter; NA – not available.
The values are presented as mean (SD), if not specified otherwise.
The differences between the groups were tested using analysis of co-variance adjusted for age, body-mass-index and education (p-value adjusted). 95% confidence interval (CI) are reported for the mean group difference.
Effect size estimates calculated using Cohen’s d and Cramer’s V.
WM-SA volume was modeled in a two-stage fashion. First for presence/absence of WM-SA (binary logistic regression), secondly as a log-transformed general linear model of the remaining positive values.
WM-SA number was calculated using a negative binomial model.