Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Traumatic Brain Injury l: Introduction

Traumatic Brain Injury l: Introduction

DefinitionTraumatic brain injury, or TBI, is a disturbance of normal brain function induced by an external mechanical force, such as a direct blow to the head or a penetrating injury. It can affect both brain structure and function, producing a wide range of clinical outcomes. TBI is a heterogeneous condition, meaning its effects may differ based on the type, location, and severity of the injury.Basis of ClassificationTBI is classified based on severity, injury mechanism, or pathophysiology. In...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Contextualizing racial differences in baseline neuropsychological assessment of collegiate athletes.

Journal of the International Neuropsychological Society : JINS·2026

Same author

Symptom clusters and cognitive changes: predictors of recovery timelines following concussion in college athletes.

Journal of clinical and experimental neuropsychology·2026

Same author

Quality of Life, Self-Reported Cognitive Difficulties, and Performance-Based Cognitive Problems in Multiple Sclerosis: What's Sleep Got to Do With It?

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026

Same author

Depressive Symptoms, Sleep Complaints, and Cognitive Performance: Examining the Mediating Role of Sleep in College Athletes.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2025

Same author

Coping is a moderator of relationships between cognitive fatigue and cognitive variability in multiple sclerosis.

Journal of the International Neuropsychological Society : JINS·2025

Same author

Improving Early Detection of Cognitive Impairment in Older Adults in Primary Care Clinics: Recommendations From an Interdisciplinary Geriatrics Summit.

Annals of family medicine·2024

Same journal

Safety-critical odor identification failure and cognitive performance in older U.S. adults: a cross-sectional NHANES analysis.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026

Same journal

Does performance invalidity undermine replication? Evidence from an undergraduate student participant sample.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026

Same journal

The relationship between subjective cognitive complaints and objective cognitive assessment in severe mental disorders: a systematic review and meta-analysis.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026

Same journal

Cognitive rehabilitation in contemporary neuropsychological practice: an exploratory survey of service delivery, reimbursement, and perceived barriers.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026

Same journal

The Short Executive Function Scale: Psychometric properties and normative data in a U.S. population sample.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026

Same journal

Distinguishing depression from healthy controls using brain network features: A fNIRS and machine learning approach.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion

A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion

Published on: September 25, 2014

Get Your Brain in the Game: Using Machine Learning to Predict Recovery Timelines Following Sports-Related Concussion.

Garrett A Thomas^1,2, Peter A Arnett¹

¹Department of Psychology, The Pennsylvania State University, University Park, PA, USA.

Archives of Clinical Neuropsychology : the Official Journal of the National Academy of Neuropsychologists

|July 21, 2025

Summary

This summary is machine-generated.

Machine learning models can predict concussion recovery timelines in athletes. These models, using symptom and cognitive data, show promise for improving concussion management and return-to-play decisions.

Keywords:

Athletes Concussion Head injury Sports Statistical methods Traumatic brain injury

More Related Videos

A Neuroscientific Approach to the Examination of Concussions in Student-Athletes

A Neuroscientific Approach to the Examination of Concussions in Student-Athletes

Published on: December 8, 2014

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

Published on: January 20, 2019

Related Experiment Videos

Last Updated: Jun 20, 2026

A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion

A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion

Published on: September 25, 2014

A Neuroscientific Approach to the Examination of Concussions in Student-Athletes

A Neuroscientific Approach to the Examination of Concussions in Student-Athletes

Published on: December 8, 2014

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

Published on: January 20, 2019

Area of Science:

Sports Medicine
Neurology
Data Science

Background:

Sports-related concussions pose challenges for determining athlete recovery timelines.
Accurate prediction of return-to-play (RTP) is crucial for athlete safety and management.

Purpose of the Study:

To develop and evaluate machine learning models for predicting RTP timelines after sports-related concussion.
To identify key factors influencing concussion recovery duration.

Main Methods:

Utilized data from 971 college athletes via FITBIR and CARE Consortium.
Employed Random Forest (RF) regression and classification modeling.
Recursive Feature Elimination (RFE) identified predictive features from symptom and cognitive data.

Main Results:

RF regression models explained 17-21% of variance in RTP timelines.
RF classification achieved 89.04% accuracy (F1 score 0.56) on the testing set for recovery type.
AUC of 0.85 indicates good predictive performance for prolonged recovery.

Conclusions:

Machine learning models demonstrate potential for concussion management.
Predictive modeling can aid in estimating concussion recovery and RTP timelines.