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Guchait, A.

Publications and source records attributed to Guchait, A..

2 recordsLinked to original sources

Multiscale structural MRI differences among collegiate American football players, cross-country runners, and non-athletes

Structural differences observed in collision-sport athletes may reflect repetitive head-impact exposure, broader effects of athletic training, or pre-existing individual variation, making inclusion of a non-collision athlete group important for interpretation of any differences seen. Here, structural T1-weighted MRI data from 17 collegiate American football players, 16 cross-country runners, and 9 non-athletes were analyzed using FSL voxel-based morphometry (VBM), FIRST subcortical volumetry and vertex-wise shape analysis, and FAST whole-brain tissue segmentation. Spatial inference used permutation testing with threshold-free cluster enhancement and family-wise error correction, while scalar analyses used multiplicity-corrected procedures. FAST-derived absolute tissue volumes were also examined in a sensitivity analysis adjusting for FreeSurfer estimated total intracranial volume (eTIV). VBM showed recurrent brainstem-centered differences, with non-athletes showing greater local gray matter measures than both athlete groups, whereas football players showed greater brainstem measures than runners. FIRST vertex-wise analysis identified corrected differences involving the brainstem and selected subcortical structures, while no total FIRST structure volume survived false discovery rate correction. Unadjusted FAST analysis identified a CSF group effect, F(2, 24.38) = 11.46, p < .001, q = .001. After eTIV adjustment, the CSF group effect remained, F(2, 38) = 14.30, q < .001, and a white matter group effect was also identified, F(2, 38) = 9.59, q < .001; gray matter and total segmented tissue did not show corrected adjusted effects. These findings indicate that structural variation depends on anatomical scale, comparison group, and head-size adjustment and cannot be attributed uniquely to collision-sport exposure.

neuroscience↗

Resolution-aware spectral parameterization reveals robust context-dependent aperiodic EEG dynamics during real-world table tennis

Mobile EEG can link neuroimaging to natural behavior, but effects recorded during movement, and sport in particular, are difficult to interpret because neural activity overlaps with signals from movement, muscle activity, and cable movement artifacts. We analyzed the OpenNeuro Real World Table Tennis dataset to test whether peri-hit spectral activity could be analyzed and how it differed between human-opponent and ball-machine play. Data from 25 participants comprising EEG, noise-layers, EMG and motion recordings was analyzed with pre-hit, impact, and post-hit windows. EEG power bands were residualized using trial-level root mean square from noise-layer, EMG, and motion/miscellaneous channels. Spectral parameterization modeled spectra as an aperiodic component plus periodic peaks. The principal short-window model was resolution-aware; sensitivity analyses reduced model complexity, used a 2.0-s peri-hit subwindow (-1.0 to +1.0 s), or disabled periodic fitting. Results showed residualized alpha and beta power was lower during ball-machine play, including pre-hit posterior alpha power. The clearest context difference was a higher ball-machine aperiodic exponent over posterior and parietal sensors, corresponding to a steeper broadband spectral slope. Posterior post-hit means were 0.451 and 0.606 for human-opponent and ball-machine play, respectively. A focal parietal effect persisted across specifications. The results support artifact-informed, resolution-aware spectral analysis as an effective approach for detecting robust spectral differences between naturalistic visuomotor contexts and suggest that EEG may provide useful information about the processes engaged by different practice environments, without establishing a single cognitive or physiological mechanism or demonstrating a training outcome.

neuroscience↗