Diffusion and functional MRI findings and their relationship to behaviour in postconcussion syndrome: a scoping review

Association between sports-related concussions and the risk of self-injury thoughts and behaviors: Who, and under what circumstances?

BACKGROUND

Understanding the association between sport-related concussions and the risk of suicidal and non-suicidal self-injury thoughts and behaviors (SITBs), including non-suicidal self-injury (NSSI), suicidal ideation (SI), suicidal plan (SP), and suicidal attempt (SA), is crucial for suicide prevention. We aimed to identify the circumstances in which individuals with or without a concussion are vulnerable to SITBs.

METHODS

The cross-sectional study included 85,469 students from 63 Chinese university with a mean age of 19.6 years. Firstly, propensity score matching, and inverse probability of treatment weighting (IPTW) were used to match the concussion and non-concussion group based on a range of biological, social, and psychological factors. Subsequently, multivariable logistic regression and a decision tree algorithm were employed to evaluate the interaction and cumulative impact of these risk factors and concussion on the probability of SITBs.

RESULTS

In the unmatched sample, concussion exposures were associated with all SITBs, with NSSI (OR, 1.41), SI (OR, 1.10), SP (OR, 1.23), and SA (OR, 1.28). However, the matched and weighted sample only had a significant association with NSSI and SI. The decision tree model revealed that, in the unmatched sample, among individuals without depressive symptoms or childhood emotional abuse, the risk of concussion on SITBs increased from 45.5 % to 65.2 % (χ2, 9.370; adjusted P = .002) after experiencing sexual abuse and verbal bullying. In the matched sample, the risk increased from 46.2 % to 64.6 % (χ2, 6.848; adjusted P = .009).

CONCLUSION

Our study revealed that individuals who suffered concussions exhibited a significantly higher risk of SITBs.

Clinical performance of a multiparametric MRI-based post concussive syndrome index

Introduction

Diffusion Tensor Imaging (DTI) has revealed measurable changes in the brains of patients with persistent post-concussive syndrome (PCS). Because of inconsistent results in univariate DTI metrics among patients with mild traumatic brain injury (mTBI), there is currently no single objective and reliable MRI index for clinical decision-making in patients with PCS.

Purpose

This study aimed to evaluate the performance of a newly developed PCS Index (PCSI) derived from machine learning of multiparametric magnetic resonance imaging (MRI) data to classify and differentiate subjects with mTBI and PCS history from those without a history of mTBI.

Materials and methods

Data were retrospectively extracted from 139 patients aged between 18 and 60 years with PCS who underwent MRI examinations at 2 weeks to 1-year post-mTBI, as well as from 336 subjects without a history of head trauma. The performance of the PCS Index was assessed by comparing 69 patients with a clinical diagnosis of PCS with 264 control subjects. The PCSI values for patients with PCS were compared based on the mechanism of injury, time interval from injury to MRI examination, sex, history of prior concussion, loss of consciousness, and reported symptoms.

Results

Injured patients had a mean PCSI value of 0.57, compared to the control group, which had a mean PCSI value of 0.12 (p = 8.42e-23) with accuracy of 88%, sensitivity of 64%, and specificity of 95%, respectively. No statistically significant differences were found in the PCSI values when comparing the mechanism of injury, sex, or loss of consciousness.

Conclusion

The PCSI for individuals aged between 18 and 60 years was able to accurately identify patients with post-concussive injuries from 2 weeks to 1-year post-mTBI and differentiate them from the controls. The results of this study suggest that multiparametric MRI-based PCSI has great potential as an objective clinical tool to support the diagnosis, treatment, and follow-up care of patients with post-concussive syndrome. Further research is required to investigate the replicability of this method using other types of clinical MRI scanners.

Microstructural Alterations in Tract Development in College Football and Volleyball Players: A Longitudinal Diffusion MRI Study

BACKGROUND AND OBJECTIVES

Repeated impacts in high-contact sports such as American football can affect the brain's microstructure, which can be studied using diffusion MRI. Most imaging studies are cross-sectional, do not include low-contact players as controls, or lack advanced tract-specific microstructural metrics. We aimed to investigate longitudinal changes in high-contact collegiate athletes compared with low-contact controls using advanced diffusion MRI and automated fiber quantification.

METHODS

We examined brain microstructure in high-contact (football) and low-contact (volleyball) collegiate athletes with up to 4 years of follow-up. Inclusion criteria included university and team enrollment. Exclusion criteria included history of neurosurgery, severe brain injury, and major neurologic or substance abuse disorder. We investigated diffusion metrics along the length of tracts using nested linear mixed-effects models to ascertain the acute and chronic effects of subconcussive and concussive impacts, and associations between diffusion changes with clinical, behavioral, and sports-related measures.

RESULTS

Forty-nine football and 24 volleyball players (271 total scans) were included. Football players had significantly divergent trajectories in multiple microstructural metrics and tracts. Longitudinal increases in fractional anisotropy and axonal water fraction, and decreases in radial/mean diffusivity and orientation dispersion index, were present in volleyball but absent in football players (all findings |T-statistic|> 3.5, p value <0.0001). This pattern was present in the callosum forceps minor, superior longitudinal fasciculus, thalamic radiation, and cingulum hippocampus. Longitudinal differences were more prominent and observed in more tracts in concussed football players (n = 24, |T|> 3.6, p < 0.0001). An analysis of immediate postconcussion scans (n = 12) demonstrated a transient localized increase in axial diffusivity and mean/radial kurtosis in the uncinate and cingulum hippocampus (|T| > 3.7, p < 0.0001). Finally, within football players, those with high position-based impact risk demonstrated increased intracellular volume fraction longitudinally (T = 3.6, p < 0.0001).

DISCUSSION

The observed longitudinal changes seen in football, and especially concussed athletes, could reveal diminished myelination, altered axonal calibers, or depressed pruning processes leading to a static, nondecreasing axonal dispersion. This prospective longitudinal study demonstrates divergent tract-specific trajectories of brain microstructure, possibly reflecting a concussive and repeated subconcussive impact-related alteration of white matter development in football athletes.

Brain Connectivity Changes in Post-Concussion Syndrome as the Neural Substrate of a Heterogeneous Syndrome

BACKGROUND

Post-concussion syndrome (PCS) or persistent symptoms of concussion refers to a constellation of symptoms that persist for weeks and months after a concussion. To better capture the heterogeneity of the symptoms of patients with post-concussion syndrome, we aimed to separate patients into clinical subtypes based on brain connectivity changes.

METHODS

Subject-specific structural and functional connectomes were created based on Diffusion Weighted and Resting State Functional Magnetic Resonance Imaging, respectively. Following an informed dimensionality reduction, a gaussian mixture model was used on patient specific structural and functional connectivity matrices to find potential patient clusters. For validation, the resulting patient subtypes were compared in terms of cognitive, neuropsychiatric, and post-concussive symptom differences.

RESULTS

Multimodal analyses of brain connectivity were predictive of behavioural outcomes. Our modelling revealed 2 patient subtypes; mild and severe. The severe group showed significantly higher levels of depression, anxiety, aggression, and a greater number of symptoms than the mild patient subgroup.

CONCLUSION

This study suggests that structural and functional connectivity changes together can help us better understand the symptom severity and neuropsychiatric profiles of patients with post-concussion syndrome. This work allows us to move towards precision medicine in concussions and provides a novel machine learning approach that can be applicable to other heterogeneous conditions.