Diffusion tensor analysis of white matter tracts is prognostic of persisting post-concussion symptoms in collegiate athletes

Abstract

Background and objectives: After a concussion diagnosis, the most important issue for patients and loved ones is how long it will take them to recover. The main objective of this study is to develop a prognostic model of concussion recovery. This model would benefit many patients worldwide, allowing for early treatment intervention.

Methods: The Concussion Assessment, Research and Education (CARE) consortium study enrolled collegiate athletes from 30 sites (NCAA athletic departments and US Department of Defense service academies), 4 of which participated in the Advanced Research Core, which included diffusion-weighted MRI (dMRI) data collection. We analyzed the dMRI data of 51 injuries of concussed athletes scanned within 48 h of injury. All athletes were cleared to return-to-play by the local medical staff following a standardized, graduated protocol. The primary outcome measure is days to clearance of unrestricted return-to-play. Injuries were divided into early (return-to-play < 28 days) and late (return-to-play >= 28 days) recovery based on the return-to-play clinical records. The late recovery group meets the standard definition of Persisting Post-Concussion Symptoms (PPCS). Data were processed using automated, state-of-the-art, rigorous methods for reproducible data processing using brainlife.io. All processed data derivatives are made available at https://brainlife.io/project/63b2ecb0daffe2c2407ee3c5/dataset. The microstructural properties of 47 major white matter tracts, 5 callosal, 15 subcortical, and 148 cortical structures were mapped. Fractional Anisotropy (FA) and Mean Diffusivity (MD) were estimated for each tract and structure. Correlation analysis and Receiver Operator Characteristic (ROC) analysis were then performed to assess the association between the microstructural properties and return-to-play. Finally, a Logistic Regression binary classifier (LR-BC) was used to classify the injuries between the two recovery groups.

Results: The mean FA across all white matter volume was negatively correlated with return-to-play (r = -0.38, p = 0.00001). No significant association between mean MD and return-to-play was found, neither for FA nor MD for any other structure. The mean FA of 47 white matter tracts was negatively correlated with return-to-play (rμ = -0.27; rσ = 0.08; r_min = -0.1; r_max = -0.43). Across all tracts, a large mean ROC Area Under the Curve (AUC_FA) of 0.71 ± 0.09 SD was found. The top classification performance of the LR-BC was AUC = 0.90 obtained using the 16 statistically significant white matter tracts.

Discussion: Utilizing a free, open-source, and automated cloud-based neuroimaging pipeline and app (https://brainlife.io/docs/tutorial/using-clairvoy/), a prognostic model has been developed, which predicts athletes at risk for slow recovery (PPCS) with an AUC=0.90, balanced accuracy = 0.89, sensitivity = 1.0, and specificity = 0.79. The small number of participants in this study (51 injuries) is a significant limitation and supports the need for future large concussion dMRI studies and focused on recovery.

Keywords: Collegiate athletes; Diffusion tensor analysis; Mild Traumatic Brain Injury (mTBI); Persisting post-concussion symptoms (PPCS); Prognostic model; White matter.

Fig. 1

Consort diagram of injury selection.

Fig. 2

Correlation between brain microstructure and time to return-to-play (return-to-play). a. Deep white matter. Distribution of mean Fractional Anisotropy (FA) across the deep white matter voxels versus return-to-play. b. Subcortical nuclei. Distribution of mean FA across 14 subcortical nuclei versus return-to-play. c. Cortex. Distribution of mean FA across 148 cortical parcels versus return-to-play. Left and right hemisphere structures are plotted separately, resulting in two data points per injury. Blue is early recovery and orange is late recovery. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

White matter tract profilometry. a. The core tract is computed for each tract (inner tube). An example is provided for the left IFOF in the top panel. The corresponding Fractional Anisotropy (FA) profile is extracted by taking a weighted average of the FA measurements of each individual fiber (see bottom panel). b. Visualization of the anatomy of some of the white matter tracts of interest. From left to right: the left inferior fronto-occipital fasciculus (left IFOF), the Cross-callosal tract (anterior Frontal CC), the right corticospinal tract (right CST), and the right vertical occipital fasciculus (right VOF). c. FA profile analysis. FA profile mean ± sd are plotted for each tract and for the early vs. late recovery groups (blue and orange, respectively). To minimize partial volume effects when computing the mean by averaging the FA measurements along the tract, only nodes from 10 to 90 were taken into account, disregarding the extremities of the tracts. The two tracts on the left have a high effect size (d > 1), while the two tracts on the right have a poor effect size (d < 1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Receiver-Operating Characteristics (ROC) Area Under the Curve (AUC) for each of the 47 white matter tracts. Mean ± SD ROC AUC scores estimated to discriminate between the early- and late-recovery groups. AUC scores for each of the 47 tracts were ranked and plotted in decreasing order. Red: Statistically significant tracts at p < 0.001 (FDR). Gray: non significant tracts. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Classification of late and early recovery using a Logistic Regression classifier. A representative classification plot reporting the Fractional Anisotropy (FA) of the two top-performing white matter tracts (from Fig. 4). Each symbol indicates a study participant. Individual symbols are color-coded by indicating whether a participant was part of the late- or early-recovery group. The decision boundary (black dotted line) divides the plane into the two classes. There are no false negatives, at the expenses of a few false positives.