Multimodal MR Imaging Signatures of Cognitive Impairment in Active Professional Fighters

Abstract

Purpose To investigate whether combining multiple magnetic resonance (MR) imaging modalities such as T1-weighted and diffusion-weighted MR imaging could reveal imaging biomarkers associated with cognition in active professional fighters. Materials and Methods Active professional fighters (n = 297; 24 women and 273 men) were recruited at one center. Sixty-two fighters (six women and 56 men) returned for a follow-up examination. Only men were included in the main analysis of the study. On the basis of computerized testing, fighters were separated into the cognitively impaired and nonimpaired groups on the basis of computerized testing. T1-weighted and diffusion-weighted imaging were performed, and volume and cortical thickness, along with diffusion-derived metrics of 20 major white matter tracts were extracted for every subject. A classifier was designed to identify imaging biomarkers related to cognitive impairment and was tested in the follow-up dataset. Results The classifier allowed identification of seven imaging biomarkers related to cognitive impairment in the cohort of active professional fighters. Areas under the curve of 0.76 and 0.69 were obtained at baseline and at follow-up, respectively, with the optimized classifier. The number of years of fighting had a significant (P = 8.8 × 10^-7) negative association with fractional anisotropy of the forceps major (effect size [d] = 0.34) and the inferior longitudinal fasciculus (P = .03; d = 0.17). A significant difference was observed between the impaired and nonimpaired groups in the association of fractional anisotropy in the forceps major with number of fights (P = .03, d = 0.38) and years of fighting (P = 6 × 10^-8, d = 0.63). Fractional anisotropy of the inferior longitudinal fasciculus was positively associated with psychomotor speed (P = .04, d = 0.16) in nonimpaired fighters but no association was observed in impaired fighters. Conclusion Without enforcement of any a priori assumptions on the MR imaging-derived measurements and with a multivariate approach, the study revealed a set of seven imaging biomarkers that were associated with cognition in active male professional fighters. ^© RSNA, 2017 Online supplemental material is available for this article.

Figure 1a:

(a) Flowchart shows total number of subjects who participated in study and breakdown of these subjects into impaired fighters, nonimpaired fighters, and control subject groups. Breakdown of the subjects according to sex at baseline and follow-up for each group is also shown. Of note, only male fighters were included to design the classifier for separation of impaired and nonimpaired fighters. (b) Bar graph shows distribution of male cohort at baseline and follow-up and classification into impaired and nonimpaired fighter groups based on standardized clinical scores is shown. Numbers in bar graph represent total number of subjects in each group at baseline and follow-up.

Figure 1b:

(a) Flowchart shows total number of subjects who participated in study and breakdown of these subjects into impaired fighters, nonimpaired fighters, and control subject groups. Breakdown of the subjects according to sex at baseline and follow-up for each group is also shown. Of note, only male fighters were included to design the classifier for separation of impaired and nonimpaired fighters. (b) Bar graph shows distribution of male cohort at baseline and follow-up and classification into impaired and nonimpaired fighter groups based on standardized clinical scores is shown. Numbers in bar graph represent total number of subjects in each group at baseline and follow-up.

Figure 2a:

(a) Bar graph shows total impaired and nonimpaired fighters who were included for classification. Subjects in each fighter population were divided into training (60%; 110 of 182 nonimpaired and 55 of 91 impaired fighters), validation (20%; 36 of 182 nonimpaired and 18 of 91 impaired fighters), and testing (20%; 36 of 182 nonimpaired and 18 of 91 impaired fighters) sets. Training and validation sample set were shuffled 10 times to estimate best training parameters. Numbers represent actual number of subjects in each set. (b) Pipeline used to generate features from diffusion-weighted and T1-weighted MR imaging is shown. Brief description of classifier for which least absolute shrinkage and selection operator (LASSO) and radial basis functional networks (RBFNs) were used, and imaging features associated with cognition in our cohort of active professional fighters, overlaid on Montreal Neurological Institute 152 template, is also shown. AAL = anatomical atlas labeling, MD = mean diffusivity, DTI = diffusion-tensor imaging.

Figure 2b:

(a) Bar graph shows total impaired and nonimpaired fighters who were included for classification. Subjects in each fighter population were divided into training (60%; 110 of 182 nonimpaired and 55 of 91 impaired fighters), validation (20%; 36 of 182 nonimpaired and 18 of 91 impaired fighters), and testing (20%; 36 of 182 nonimpaired and 18 of 91 impaired fighters) sets. Training and validation sample set were shuffled 10 times to estimate best training parameters. Numbers represent actual number of subjects in each set. (b) Pipeline used to generate features from diffusion-weighted and T1-weighted MR imaging is shown. Brief description of classifier for which least absolute shrinkage and selection operator (LASSO) and radial basis functional networks (RBFNs) were used, and imaging features associated with cognition in our cohort of active professional fighters, overlaid on Montreal Neurological Institute 152 template, is also shown. AAL = anatomical atlas labeling, MD = mean diffusivity, DTI = diffusion-tensor imaging.

Figure 3:

Boxplots of features identified by using our classifier is shown for every group. Black central dot represents mean, and radius of red circle represents standard deviation of each feature. All standard deviations were scaled to same number throughout groups to reflect between-group differences. ** = Between-group statistical differences for each feature over respective groups. Imaging biomarkers were overlaid on Montreal Neurological Institute 152 template brain and shown at bottom. Name of biomarker is color coordinated with overlaid colors on Montreal Neurological Institute 152 template. Data in boxplots for feature 3 are × 10⁴, and data in the feature 4 boxplot are × 10⁵. ** = corrected P < .05 indicates a significant difference.

Figure 4:

Scatterplots show average values for each feature that were extracted for every subject and plotted against processing speed, psychomotor speed, and number of professional fights. Impaired fighters are represented by green circles, nonimpaired fighters are represented by blue cross, and control subjects are represented by red squares. Regression lines are shown in the same colors as those in the scatterplot for the group. Statistically significant regression lines (correlation P < .05) are shown as solid line for every feature. Dashed regression lines were nonsignificant but are shown for those features that have a significant differential association among groups.