Oculomotor, vestibular, reaction time and cognitive eye-tracking mild traumatic brain injury assessment

Neurology ◽  
2020 ◽  
Vol 95 (20 Supplement 1) ◽  
pp. S2.1-S2
Author(s):  
Alex Kiderman ◽  
Michael Hoffer ◽  
Mikhaylo Szczupak ◽  
Hillary Snapp ◽  
Sara Murphy ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Reaction Time ◽  
Eye Tracking ◽  
Regression Model ◽  
Mild Traumatic Brain Injury ◽  
Data Set ◽  
Retest Reliability ◽  
Test Retest Reliability ◽  
Tracking Technology

ObjectiveCan oculomotor, vestibular, reaction time and cognitive eye-tracking tests (OVRT-C) assess mild traumatic brain injury?BackgroundOVRT-C tests using eye tracking technology have been employed in our previous studies for assessing mild traumatic brain injury (mTBI). Here we present a composite Concussion Assessment algorithm that incorporates these findings.Design/MethodsConcussion Assessment algorithm was based on a data analysis from 406 males and females 18–45 years old. The subjects included 106 patients diagnosed with mTBI and 300 healthy controls. Diagnosis of mTBI was made using accepted medical practice. The participants were tested with a battery of OVRT-C tests delivered on the I-Portal Neuro Otologic Test Center (Dx NOTC) device (Neurolign Technology). A logistic regression model was used to derive the algorithm using a random sample of 70% of the data-set and validated on the remaining 30% of the data-set. Device test-retest reliability and inter-rater variability were assessed in a separate study in healthy control volunteers, ages 19–43 (n = 30). Subjects were tested with OVRT-C tests using the Dx100 which is equivalent to the NOTC. Test-retest reliability was assessed using Intraclass Correlation Coefficient (ICC) and Cronbach's alpha; testers and devices influence were assessed using a random effect regression model.ResultsTest-retest reliability of OVRT-C tests using eye tracking technology was acceptable (ICC >0.6 for all variables). The Concussion assessment algorithm was based on six OVRT-C tests. In the validation data Concussion Assessment algorithm was able to separate concussed versus controls with a sensitivity of 78.6% and specificity of 72.3%.ConclusionsOVRT-C tests delivered on I-Portal devices are repeatable and reliable. The assessment can identify mTBI subjects within an acute time post-injury with high sensitivity and specificity. The results support the use of this eye tracking device as well as the assessment to aid in the diagnosis of mTBI for patients 18–45 year old.

scholarly journals Symptom-Dependent Changes in MEG-Derived Neuroelectric Brain Activity in Traumatic Brain Injury Patients with Chronic Symptoms

2021 ◽  
Vol 9 (2) ◽  
pp. 20
Author(s):  
Don Krieger ◽  
Paul Shepard ◽  
Ryan Soose ◽  
Ava M. Puccio ◽  
Sue Beers ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Brain Activity ◽  
Severity Index ◽  
Brief Symptom Inventory ◽  
Good Test ◽  
Retest Reliability ◽  
Test Retest Reliability

Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort, Cambridge Centre for Ageing and Neuroscience (CamCAN), baseline: n = 619; mean 16-month follow-up: n = 253) and a chronic symptomatic TBI cohort, Targeted Evaluation, Action and Monitoring of Traumatic Brain Injury (TEAM-TBI), baseline: n = 64; mean 6-month follow-up: n = 39). For the CamCAN cohort, MEG-derived neuroelectric measures showed good long-term test-retest reliability for most of the 103 automatically identified stereotypic regions. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index. Linear classifiers constructed from the 103 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, with p < 0.01 for each—i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful—i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.

Test-Retest Reliability of Traumatic Brain Injury Outcome Measures

2017 ◽  
Vol 32 (5) ◽  
pp. E1-E16 ◽  
Author(s):  
Jennifer A. Bogner ◽  
Gale G. Whiteneck ◽  
Jessica MacDonald ◽  
Shannon B. Juengst ◽  
Allen W. Brown ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Outcome Measures ◽  
Retest Reliability ◽  
Test Retest Reliability

The test–retest reliability of motor performance measures after traumatic brain injury

2006 ◽  
Vol 8 (2) ◽  
pp. 50-59 ◽  
Author(s):  
Matti V. Vartiainen ◽  
Marjo B. Rinne ◽  
Tommi M. Lehto ◽  
Matti E. Pasanen ◽  
Jaana M. Sarajuuri ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Performance Measures ◽  
Motor Performance ◽  
Retest Reliability ◽  
Test Retest Reliability

scholarly journals Detection of mild traumatic brain injury in pediatric populations using BrainCheck, a tablet-based cognitive testing software: a preliminary study

2020 ◽  
Vol 1 (6) ◽  
pp. 396-405
Author(s):  
Siao Ye ◽  
Brian Ko ◽  
Huy Q. Phi ◽  
Kevin Sun ◽  
David M. Eagleman ◽  
...  
Keyword(s):  
High School ◽  
Traumatic Brain Injury ◽  
Logistic Regression ◽  
Brain Injury ◽  
Regression Model ◽  
Mild Traumatic Brain Injury ◽  
Logistic Regression Model ◽  
Control Group ◽  
Pediatric Populations ◽  
Normal Controls

Aim: Despite its high frequency of occurrence, mild traumatic brain injury (mTBI), or concussion, is difficult to recognize and diagnose, particularly in pediatric populations. Conventional methods to diagnose mTBI primarily rely on clinical questionnaires and sometimes include neuroimaging or pencil and paper neuropsychological testing. However, these methods are time consuming, require administration/interpretation from health professionals, and lack adequate test sensitivity and specificity. This study explores the use of BrainCheck Sport, a computerized neurocognitive test that is available on iPad, iPhone, or computer desktop, for mTBI assessment. The BrainCheck Sport Battery consists of 6 gamified traditional neurocognitive tests that assess areas of cognition vulnerable to mTBI such as attention, processing speed, executing functioning, and coordination. Methods: We administered BrainCheck Sport to 10 participants diagnosed with mTBI at the emergency department of Children’s hospital or local high school within 96 hours of injury, and 115 normal controls at a local high school. Statistical analysis included Mann-Whitney U test, chi-square tests, and Hochberg tests to examine differences between the mTBI group and control group on each assessment in the battery. Significant metrics from these assessments were used to build a logistic regression model that distinguishes mTBI from control participants. Results: BrainCheck Sport was able to detect significant differences in Coordination, Stroop, Immediate/Delayed Recognition between normal controls and mTBI patients. Receiver operating characteristic (ROC) analysis of our logistic regression model found a sensitivity of 84% and specificity of 81%, with an area under the curve of 0.884. Conclusions: BrainCheck Sport has potential in distinguishing mTBI from control participants, by providing a shorter, gamified test battery to assess cognitive function after brain injury, while also providing a method for tracking recovery with the opportunity to do so remotely from a patient’s home.

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