2026-10-13T19:30:46Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33957384 2026-09-21T17:42:30Z category_7 category_13 category_15 category_45 category_734 category_106 portal_5 item_type_3 month_year_09_2026

<p>Study data.</p> Gary B. Wilkerson (11510026) Mohammad Joghataee (25084764) Ashish Gupta (465372) Medicine Genetics Neuroscience Sociology Biological Sciences not elsewhere classified Science Policy might increase understanding diffusion computational model body segment movement 202 healthy adolescents statistical modeling reveal div >< p supervised machine learning directed neck rotation impaired perceptual decision sc ), nc multiple concussion history making </ p machine learning p </ statistical regression multiple concussions young adults year period virtual reality versus right trial inconsistency theoretical relationship subtle post sequential presentations rtv ). response completion persisting deficiency perceptual latency perceptual decision metrics believed lunge responses insufficiently sensitive identify metrics different studies concussion impairments concussion history combination may cognitive function best metric best discriminated behavior relationships arm reach arm movements altered brain accuracy tradeoff 86 females 116 males 001 ). <div><p>Current clinical assessment methods are insufficiently sensitive for detection of subtle post-concussion impairments in perceptual-cognitive function. The purpose of this study was to search for any perceptual response metrics associated with concussion history that might increase understanding of altered brain-behavior relationships. Immersive virtual reality test data were aggregated for 202 healthy adolescents and young adults (116 males, 86 females) who participated in different studies over a 2-year period. Left- versus right-directed neck rotation, arm reach, and step-lunge responses to sequential presentations of 2 types of horizontally moving visual stimuli were measured in terms of time to initiation of body segment movement (perceptual latency [PL]), as well as response completion (response time [RT]). Speed-accuracy tradeoff was represented by rate correct per second for PL (RCS-PL) and RT (RCS-RT) of neck and arm movements, and across-trial inconsistency was represented by PL variability (PLV) and RT variability (RTV). Both supervised machine learning and theory-based statistical regression methods were used to identify metrics that best discriminated between participants who reported a history of no concussion (NC), single concussion (SC), NC + SC, or multiple concussions (MC). Additionally, statistical regression was used to assess a theoretical relationship between metrics believed to align with components of the drift-diffusion computational model of decision-making. The best metric for discrimination between NC + SC and MC was Neck RCS-PL. Neck PLV values demonstrated a strong inverse logarithmic correlation with Neck RCS-PL (r = –0.796, <i>P</i> < 0.001). The Neck RCS-PL and Neck PLV behavioral metrics may have relevance to the two components of the drift-diffusion computational model of perceptual decision-making, and their combination may be associated with a cumulative and persisting deficiency after having sustained more than one lifetime concussion.</p></div> 2026-09-21T17:42:29Z Dataset Dataset 10.1371/journal.pone.0358042.s001 https://figshare.com/articles/dataset/_p_Study_data_p_/33957384 CC BY 4.0