Advanced mapping of the human white matter microstructure better separates elite sports participation

Abstract

Collision-sport athletes, especially football players, are exposed to a higher number of repetitive head impacts. Little is known, however, regarding the effects of long-term exposure to repetitive head impacts on brain tissue structure and the locations (i.e. superficial or deep tissue structures) affected. On top of this, little is known about the effects of highly competitive athletics on brain tissue structure. We investigated this relationship, including the baseline effect of collegiate athletic participation, by mapping measures of microstructure to cortical and subcortical white matter parcels and major white matter tracts of varsity IU football players, cross-country runners, and non-athlete students using advanced microstructural mapping techniques and machine-learning. We tested a series of hypotheses on the differences between the subject groups. To do so, we implemented an innovative approach to statistical hypothesis testing combining advanced white matter tissue properties and machine-learning classifiers. We used cross-validation to select the best subject group model and best machine-learning classifier. Wide-spread differences in brain tissue microstructure across cortical, subcortical, and major white matter structures were documented. The tissue properties of the major white matter tracts were found to best predict collision-sport participation and sports participation in general, however, cortical and subcortical white matter parcels were also found to predict collision-sports participation. The biggest differences in brain tissue microstructure is between the athletes (football and cross country combined) and the non-athletes students. The rewards and risks of playing competitive sports at the highest collegiate level may account for the differences in brain microstructure. This was the first investigation into the effects of repetitive head impacts to use an open-source data processing platform brainlife.io. The data and code for these analyses are available via brainlife.io.

White Matter Microstructural Alteration in Type 2 Diabetes: A Combined UK Biobank Study of Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging

“…Furthermore, the decreased neurite density in participants with T2DM may be accompanied by axonal disorganization, myelin loss, and loss of fibre orientation coherence that may lead to increased ODI in response to alterations in WM 10,40–42 . On the contrary, increased ICVF in a few WM tracts (e.g., right cerebral peduncle and left tapetum) may indicate an early cellular swelling such as astrocyte/axonal swelling or activation of adaptive axons as they correlate with shorter disease duration and lower HbA1c levels 43–46 . Interestingly, the result shows decreased ODI is observed in a few WM tracts, such as bilateral posterior limbs of internal capsules, which may reflect the loss of neuronal/crossing fibres, reorganisation of fibres, or reduced axonal dispersion in T2DM 39,47,48 .…”

White Matter Alterations in Military Service Members With Remote Mild Traumatic Brain Injury

“…Fraction of tissue water restricted within neuritis, amount of neurites Reduced ICVF, axonal loss or demyelination [22][23][24][25] ; increased ICVF, axonal swelling in acute phase and regeneration in recovery phase 14,26,27 ODI Variability of neurite orientations, neurite spatial arrangement from 0 (parallel) to 1 (random)…”

Collegiate athlete brain data for white matter mapping and network neuroscience

“…Data outputs can be conveniently downloaded from brainlife.io using the BIDS standard 92 . The data outputs described below can be downloaded at 10.25663/brainlife.pub.14 93 . The standard does not yet provide a specification for processed dMRI, tractograms, white matter tracts, and connectivity matrices.…”