Longitudinal Study on the Detection and Evaluation of Onset Mild
Traumatic Brain Injury during Dual Motor and Cognitive Tasks
Hung Nguyen
1
, Fouaz Ayachi
1
, Etienne Goubault
1
, Catherine Lavigne-Pelletier
1
,
Bradford J. McFadyen
2,3
and Christian Duval
1,4
1
Département des Sciences des I’activité physique, Université du Québec à Montréal, Montréal, Canada
2
Département de Réadaptation, Faculté de Médecine, Université laval, Québec, Canada
3
Centre interdisciplinaire de recherche en réadaptation et intégration sociale, Québec, Canada
4
Centre de Recherche Institut Universitaire de Gériatrie de Montréal, Montreal, Canada
Keywords: Concussion, Youth, Athletes, Markerless, Football, Walking, Obstacle, Performance, Speed, Step Width,
Preventive, Sensors, Head Injuries.
Abstract: Currently, concussions are detected by observing physical and cognitive symptoms such as dizziness,
disorientation and loss of consciousness that are often associated with mild traumatic brain injury (mTBI).
Evaluation methods such as neurocognitive tests and neuroimaging are often performed post-concussion.
However, these methods can be expensive and cumbersome to use. In this study, we developed a new
testing protocol using a markerless motion capture system to quickly monitor the cognitive and motor
dysfunction of football players over the course of the season. This protocol utilized a dual-task paradigm to
identify kinematic measures that could detect the subtle changes in the motor and cognitive function of
players due to mTBI. Four high school football players (2 healthy and 2 with history of concussion)
volunteered to participate in the study. Participants were asked to navigate a staged obstacle course with and
without an N-Back (N-2) cognitive task. Positional data of 23 limb segment nodes were recorded using
markerless motion tracking system. Data collection lasted less than 5 minutes, with minimal preparation
time. The results showed that walking speed, median frequency of sacrum in the vertical direction and step
width variability during straightway walking were strongly associated with the presence of mTBI.
1 INTRODUCTION
Among high school athletes in the U.S, football
players have the highest rate of concussion at 15%
per season (Rosenthal et al., 2014). However, the
true incidence is estimated to be much higher due to
the difficulty of detecting concussion as well as
under reporting. Recently, concussion in sports has
garnered tremendous media attention due to the
catastrophic effects on the health of retired
professional football players (Pilon and Belson,
2013) as they deal with consequences of repetitive
impact on their body throughout their football
career. While the attention to concussion has
encouraged team and medical professionals to be
more cautious when administering post-concussion
protocols, there are still elements of subjective
diagnoses involving concussion. Furthermore, the
reactive diagnosis of concussion ignores the
progressive aging of the brain due to repetitive
impacts (Bowen, 2003, Bey and Ostick, 2009, Cantu
and Gean, 2010). These repeated impacts often
induced more long-term problems due to our
inability to quantify the effect of concussion. In
youth athletics, where resources are often
unavailable to monitor the impact of these hits on
their developing brain (Field et al., 2003, Grady,
2010), a quick and accurate testing mechanism is
needed to arm healthcare professionals with
quantifiable metrics to evaluate the cognitive and
motor dysfunction of players who are at high risk of
concussion. If we can longitudinally monitor the
players and detect subtle changes in their behaviour
that correlate to traumatic brain injury, it is possible
to prescribe preventive care that could minimize the
long-term effects of mild traumatic brain injury
(mTBI).
Currently, concussion is diagnosed by visually
Nguyen, H., Ayachi, F., Goubault, E., Lavigne-Pelletier, C., McFadyen, B. and Duval, C..
Longitudinal Study on the Detection and Evaluation of Onset Mild Traumatic Brain Injury during Dual Motor and Cognitive Tasks.
In Proceedings of the 3rd International Congress on Sport Sciences Research and Technology Support (icSPORTS 2015), pages 77-83
ISBN: 978-989-758-159-5
Copyright
c
2015 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
77
observing signs that often associate with mTBI such
as headache, disorientation, dizziness and more
severely, loss of consciousness. However, loss of
consciousness only happens in less than 5% of
reported concussion (Castile et al., 2012). These
symptoms make concussion difficult to objectively
quantify. The diagnostics and follow-up of clinical
concussion are normally done by administering
neurocognitive and balance tests such as the Sports
Concussion Assessment Tool (SCAT3) that
subjectively scores the cognitive and motor
performance of an individual based on
questionnaires and the results of simple motor and
cognitive tasks. More advance methods of
diagnosing concussion such as Functional Magnetic
Resonance Imaging (fMRI) has also been used to
evaluate cranial fracture, bleeding (Khurana and
Kaye, 2012) and monitor of the alteration in the
brain (Talavage et al., 2014). However, fMRI are
expensive, time consuming and not widely available.
Accordingly, it would be impossible to administer
these tests chronically to monitor the state of the
player during the season. The diagnostic value of
such tests could also be challenged since most
concussions do not show enough structural damage
to be detected by neuroimaging.
Recently, more efforts are focused on embedding
sensors, such as accelerometers, into helmets to
longitudinally monitor the cranial movement during
exposure to impacts. The aim is to use these sensors
to develop performance metrics to determine if an
impact experienced by a player could lead to clinical
concussion. However, the issue is complex and it
had been shown that acceleration alone could not
predict concussion (Greenwald et al., 2008, Post and
Hoshizaki, 2015). For example, Talavage (2014)
used a combination of hit events (6 accelerometers
in the helmet), neurocognitive test and neuroimaging
to monitor the neurological and mental health of
varsity and junior varsity football players from pre
to post-season. The data showed that there was no
correlation between the peak helmet acceleration
and cognitive deficit. Auerbach (2015) used multiple
cranial accelerometers attached to the player’s
helmet to measure the pulsation of the cerebral
blood flow and its impact at different locations of
the skull. The results showed that there was a shift in
the high harmonic frequency of these sensors in the
concussed group. However, using a system of
sensors only monitor the physical impact to the head
of the individual while the cognitive performance is
ignored during process. Furthermore, the cause of
this shift in harmonic frequency remains
unexplained.
In this study, we proposed a quick and non-
invasive testing protocol using a dual-task paradigm
to extract relevant kinematics measurements that
could be used to longitudinally monitor the cognitive
and motor dysfunction of players who are at high
risk of concussion. Dual tasks that involved both
cognitive and motor tasks have demonstrated
promising results in differentiating the kinematic
behaviour between concussed and non-concussed
group. Cossette (2014) showed that gait speed and
dual-task cost during stepping over obstacle were
sensitive to the executive dysfunction of people with
mTBI. Parker (2005) showed gait and center of mass
measurement were significantly different between
the two groups during walking with a cognitive task.
A systematic review of the dual-task paradigm in
detecting concussion also concluded that gait
velocity and the sway in the medial lateral direction
could be used for long-term monitoring of sport-
related concussion (Lee et al., 2013). These results
in dual-task paradigm provide a viable platform to
develop a novel testing protocol that could quickly
and reliably monitor the cognitive and motor
behaviour of these high-risk players. Interestingly,
dual-tasking has a clear advantage compared to other
diagnostic methods; it replicates situations
encountered during play. Indeed, players are
systematically required to move their body onto the
playing field while making quick decisions. Their
ability to maintain their concentration, which is
normally associated with high cognitive load, is vital
in order to play the game. In fact, a reduction in
dual-tasking ability could put the player at risk of
further unnecessary impacts due to loss of situational
awareness on the field (Fait et al., 2013).
Contrary to previous studies where optical
systems requiring complicated setup and lengthy
testing protocol, the present study used a markerless
motion tracking system that is simple to setup. The
protocol was designed to minimize the disruption to
the player’s life while monitoring their cognitive and
motor performance throughout the season. Such a
system may eventually provide a platform to follow
players during an entire season, so as to help athletic
trainers and healthcare professionals detect and
manage concussions.
2 METHODS
2.1 Participants
Four male football players volunteered from a local
high school for the study (15.5 ± 0.6 years old,
icSPORTS 2015 - International Congress on Sport Sciences Research and Technology Support
78
height =1.82 ± 0.07 m, weight = 79.3 ± 13.0 kg).
Participants were excluded if they suffered physical
injury during the season that compromised their
normal gait pattern. Two participants had a history
of concussion while two had never been diagnosed
with concussion. During the course of the season,
one participant was clinically diagnosed with a
concussion. The institutional research ethics review
board of the Centre de Recherche de l’Institut
Universitaire de Gériatrie de Montreal (CRIUGM)
approved this research and each participant and their
guardians read and signed an informed consent
form.
2.2 Data Acquisition
A markerless motion capturing system from Organic
Motion (New York, NY) was used to monitor and
measure the performance of the football players
throughout the playing season. The system contains
16 cameras to capture the motion of the participants
within the staged course. The system generates a full
body skeletal model by tracking 23 nodes on the
body. The (x,y,z) coordinates of these nodes are
tracked by the system at 60 Hz. The data were
filtered using a zero-phase fourth-order digital filter
with a 6 Hz cut-off frequency. An external data
acquisition system controlled by DasyLab (Norton,
MA, USA) was also programed to randomly
generate numbers between 1 and 9 for the cognitive
tests (see below). The acquisition system also
monitored sensors attached to the turn markers and
the hanging obstacle on the testing stage to monitor
errors during turning (contact). The sensors data
were sampled at 100 Hz. All data were exported to
Matlab® (Natick, MA, USA) for analysis.
2.3 Protocol
Participants were asked to navigate a staged obstacle
course at higher than normal walking speed (without
running) for 45 seconds (Figure 1). The obstacle
course included tasks such as straightway walking,
turning and stepping under and over obstacles.
The testing stage had two obstacles: a ground
obstacle and a hanging obstacle. These obstacles
allowed us to measure the motor function in
stepping over and ducking under an obstacle. The
testing stage also had three turns. At each turn, there
was a strain gauge sensor attached to a string
marking the centroid of the turn. These sensors
recorded if the participants touched the string while
turning. Similarly, the hanging obstacle was also
equipped with a strain gauge to measure the
disturbance if participants touched the bar.
Figure 1: A) Top view schematic of the different
challenging segments in the testing protocol. B) 3-D view
of the testing stage.
An N-back (N-2) cognitive task (Kirchner, 1958)
was used to challenge the cognitive and motor
functions of the players. The test aims at evaluating
the working memory, concentration and their impact
on motor functions during the single and dual-task
environments. The dual-task was optimized to
challenge the players during the navigation of the
obstacle course. Such dual-task is ideal since it
replicates game situations where players are asked to
move on the field (motor aspect) while make
decisions according to their environment (cognitive
aspect). Participants were monitored throughout the
football season and tested at least once a week.
Participants were asked to perform two trials of the
cognitive task only, two trials of the motor task only,
one in each direction (clockwise and counter
clockwise) and, similarly, two trials of the motor and
cognitive tasks combined. It is important to note that
each testing session lasted on average less than 15
minutes from the time the participant entered the
laboratory and the time the test was completed. The
actual data collection was only 4.5 minutes.
2.4 Performance Analysis
2.4.1 Task Segmentation
The obstacle course was separated into different
sections for analysis. The segmentation was done by
specifying the x-y boundary on the stage to mark
different regions. Each segment provided specific
metrics for performance analysis corresponding to
the activities within each segment. For instance,
during the hanging obstacle segment, the distance
between the participant and the obstacle (clearance)
was used as a possible marker sensitive to change
over the course of the season. Similarly walking
speed and step width were only calculated during
straightway walking segment.
Longitudinal Study on the Detection and Evaluation of Onset Mild Traumatic Brain Injury during Dual Motor and Cognitive Tasks
79
2.4.2 Kinematics Analysis
While many kinematic performance parameters
could be used to determine whether they were viable
factors in discriminating the symptoms of mild
traumatic brain injury, this paper focused on
segment task time (of each segment was defined by
tracking when the sacrum entered and exited the x-y
boundary of each segment), median frequency (the
spectral analysis of the oscillation of the sacrum in
the vertical direction) and step width variability
(calculated by tracking the (x,y) coordinates of the
left and right foot) during the free walking segments.
The perpendicular distance between the left and
right foot along the walking path were used to
estimate the step width. Cognitive scores were
recorded separately for each condition. The average
and standard deviation across all conditions were
calculated. The scores were calculated based on the
correct responses given by the participants divided
by the total number of possible correct responses.
3 RESULTS
Four participants volunteered for the project.
Participant 3 and 4 had no history of concussion
while participant 1 had a mild concussion in the last
year. Participant 2 had suffered three concussions
and two within the last four years. The latest one, in
2012, occurred during a football game.
There were no significant differences in the
cognitive scores of the participants when it was
performed alone or when it was performed in
conjunction with a motor task given that they are
within one standard deviation of each other. The
average cognitive scores for the participants for the
N-2 cognitive task was 89.6% (std +/- 7.1%)
accuracy. Furthermore, the player who suffered a
concussion (participant 4) during the season did not
register a significant decrease in cognitive task
score. Similarly, there were no significant
differences in the motor performance of each
participant across the different segments of the
obstacle course in the motor-only condition given
that their trends where relatively constant and within
one standard deviation of each other. However,
when cognitive task and motor function were
simultaneously active during the obstacle course,
several kinematic performance variables emerge as
markers of concussion. Specifically, one participant
experienced a hard hit(s) during practice (Tuesday,
Fig.2). We proceeded to test him on the following
day. Subsequently, he played a regular season game
on the following Friday where he reported being
light headed, with headache, and felt nauseous. The
player was pulled from the game and was diagnosed
with a concussion by the athletic trainer. The
concussed participant was evaluated using a SCAT3
test a day after the game. He exhibited 16 of 22
possible symptoms and his Standardized Assessment
Concussion score dropped 9 points from his baseline
(out of a possible 25). He underwent concussion
protocol before returning to football activities two
weeks later.
The players who were not diagnosed with a
concussion showed a steady walking time during
free walking (Figure 2). While there were
differences between the non-concussed participants,
their relative trends remained flat throughout the
season. Furthermore, their values were close to the
99% confidence interval. However, the concussed
participant showed more variable walking time
throughout the season. For the test one day after
impact during practice, there was an increase
walking time during the free walking segment.
While the concussed participant showed a return to
baseline after the post-concussion protocol, he still
showed a variable trend post-concussion.
Figure 2: Walking time during free walking segment and
the 99% confidence interval band for each testing week
during cognitive + motor dual task.
Similar kinematic behaviours were also observed
in the step width variability (Figure 3). The step
width variability was normalized to height of each
participant for comparison. The non-concussed
group showed a relative steady trend over the course
of the season while the concussed participant who
showed an increasing trend in step width variability
after the impact during practice and the subsequent
concussion diagnosed during the game. However,
this increase in the relative trend of step width
variability was only observed when the participant
walk in the counter clockwise direction.
icSPORTS 2015 - International Congress on Sport Sciences Research and Technology Support
80
Figure 3: Step width variability during free walking
segment normalized with the height of each participant
and the 99% confidence interval band for each testing
week during cognitive + motor dual task.
Spectral analysis of the sacrum during free
walking also showed a shift in the median frequency
of the concussed participants after a head impact
during practice (Figure 4). Following the prescribed
post-concussion protocol, the median frequency
returned to baseline. However, the median frequency
of participant 2 deviated from the baseline behaviour
during the last week of testing while the other non-
concussed participants remained relatively steady
throughout the season.
Figure 4: Median frequency of the sacrum in the z-
direction during free walking segment and the 99%
confidence interval band for each testing week.
4 DISCUSSION
Concussion is difficult to objectively quantify.
Current testing mechanisms can be lengthy,
cumbersome and expensive. Concussions are often
not diagnosed until after gross cognitive and motor
dysfunctions are visibly observed. We demonstrated
here that our automated motor and cognitive test was
able to detect changes in performance before the
health staff of the football team detected the problem
in one player. The majority of concussions do not
exhibit any of these gross motor dysfunctions. This
is why there is a need for long-term monitoring of
youth athletes who are at risk of concussion. In this
paper, we proposed a kinematic-based testing
protocol using markerless motion capture system to
monitor the cognitive and motor performance of
football players throughout the playing season. This
system would reduce the amount of time required to
test each participant. Furthermore, it has the
potential to reveal cognitive and motor deficits that
are otherwise not visually observable.
Evidence suggests that youth athletes cognitively
recover more slowly than a college-age group (Field
et al., 2003, Covassin et al., 2012) and their
symptoms tend to last longer (Castile et al., 2012).
Furthermore, prolonged exposure to repetitive hits
could expedite the aging of the brain that could
result in more serious neurodegenerative diseases
such as chronic traumatic encephalopathy (CTE)
(Stern et al., 2011). Therefore, it is imperative that
preventive care is provided at any sign of motor and
cognitive deficiency to minimize the long-term
effects of these impacts. Objectively quantifying
these motor behaviours will remove the subjective
nature of concussion detection and management.
Castile (2012) found that 15.2% of youth athletes
who suffered concussion returned to play while still
symptomatic. This increases their exposure to
recurrent concussion and catastrophic effects of
second impact syndrome (Bowen, 2003, Bey and
Ostick, 2009, Cantu and Gean, 2010, McCrory et al.,
2012). Our approach will provide trainers with
continuous monitoring of their behaviour to ensure
that they are fully recovered from the effects of
concussion before return to play.
Under reporting is a major problem in the
detection and management of concussion. This could
be attributed to many factors such as the effect of
wanting more playing time and a perceived
weakness when not playing through injury. Talavage
(2014) longitudinal study of football player from
pre- to post-season found a high number of players
who were not diagnosed with concussion yet showed
measurable cognitive impairments. This
demonstrated that traditional cognitive testing might
not be sufficient to detect athletes who exhibit signs
of neurocognitive damage, yet never clinically
diagnosed with concussion. For example, participant
2 was never diagnosed with concussion during the
season; however, the median frequency was shifted
toward a lower frequency with a larger variation.
Relative to his baseline behaviour over the course of
the season, this player could be experiencing an
mTBI during the last week of the season. While
more investigative work is needed to determine the
reasons for the shift in the median frequency, the
Longitudinal Study on the Detection and Evaluation of Onset Mild Traumatic Brain Injury during Dual Motor and Cognitive Tasks
81
results should at least raise warning flags to medical
professionals to implement preventive measures to
ensure that the player is physically and cognitively
healthy given his history of concussion. The aim of
these preventive measures is to reduce the amount of
undetected and unreported incidences of concussion.
For youth athletes, these undetected injuries could
have a detrimental impact on their cognitive
development.
Long-term monitoring of these players is crucial
in identifying players who might exhibit signs of
cognitive and motor deficits without being clinically
diagnosed with concussion. Coaches and medical
professionals at the youth level often lack the
resources and training to deal with the complexity of
concussion detection since symptoms do not
manifest themselves unless major injury occurs.
Therefore, it is essential to provide these
professionals with simple metrics to easily monitor
the change in the motor and cognitive dysfunction of
these players throughout the season. Multi-tasking is
an essential element of sports; therefore, it is
imperative to monitor players’ performance when
subjecting to these types of tasks. In this study, we
aim to develop a testing protocol using a kinematic-
based dual-task paradigm to quickly and non-
invasively monitor high-risk players throughout the
season to track their motor and cognitive functions.
The ultimate goal would be to build a cognitive and
biomechanical passport for each player using data
collected longitudinally with a protocol such as the
one presented here. Then, the performance of each
athlete could be compared with that of his/her peers,
but also with that of him/herself. Any abrupt
changes could be considered as tell-tale signs that
mTBI has occurred. Since the test is also performed
without the cognitive tasks, the motor development
or alterations could be taken into consideration in
the detection of possible mTBI.
While the sample size is a limiting factor in this
study, we hope to test and monitor more players pre
and in-season to develop more comprehensive
metrics to measure the motor and cognitive function
of these at-risk athletes in future studies. This could
include simple performance metrics, such as the
ones presented here, but also full-body pattern
recognitions using more sophisticated techniques
such as machine learning, neural or Bayesian
network. Furthermore, as we are developing the
algorithms to detect mTBI, we are also exploring
hardware alternatives that could capture full-body
kinematics in a markerless fashion easily, and with
lower costs, so that such a system could eventually
become accessible to teams. Still, the present results
are encouraging, and are in line with previous
research using more conventional (yet more
complicated and time consuming) approaches
(Cossette et al., 2014). We hope to provide coaches
and medical professionals with a more simple, fast
and fully automated tool to objectively identify at-
risk players, and provide preventive care so as to
avoid the unhealthy consequences of multiple
undetected concussions and their impact long-term
health.
5 CONCLUSIONS
An automated kinematic-based dual-task paradigm
provided a promising testing mechanism to detect
the onset of concussion. This novel approach allows
for the monitoring of players over the entire season
and provides faster assessment of concussion effects
than traditional neurocognitive tests.
REFERENCES
Auerbach, P. S., Baine, J. G., Schott, M. L., Greenhaw, A.,
Acharya, M. G. & Smith, W. S. (2015). Detection of
concussion using cranial accelerometry. Clin J Sport
Med, 25, 126-32.
Bey, T. & Ostick, B. (2009). Second impact syndrome.
West J Emerg Med, 10, 6-10.
Bowen, A. P. (2003). Second impact syndrome: a rare,
catastrophic, preventable complication of concussion
in young athletes. J Emerg Nurs, 29, 287-9.
Cantu, R. C. & Gean, A. D. (2010). Second-impact
syndrome and a small subdural hematoma: an
uncommon catastrophic result of repetitive head injury
with a characteristic imaging appearance. J
Neurotrauma, 27, 1557-64.
Castile, L., Collins, C. L., McIlvain, N. M. & Comstock,
R. D. (2012). The epidemiology of new versus
recurrent sports concussions among high school
athletes, 2005-2010. Br J Sports Med, 46, 603-10.
Cossette, I., Ouellet, M. C. & McFadyen, B. J. (2014). A
preliminary study to identify locomotor-cognitive dual
tasks that reveal persistent executive dysfunction after
mild traumatic brain injury. Arch Phys Med Rehabil,
95, 1594-7.
Covassin, T., Elbin, R. J., Harris, W., Parker, T. & Kontos,
A. (2012). The role of age and sex in symptoms,
neurocognitive performance, and postural stability in
athletes after concussion. Am J Sports Med, 40, 1303-
12.
Fait, P., Swaine, B., Cantin, J. F., Leblond, J. &
McFadyen, B. J. (2013). Altered integrated locomotor
and cognitive function in elite athletes 30 days
postconcussion: a preliminary study. J Head Trauma
Rehabil, 28, 293-301.
icSPORTS 2015 - International Congress on Sport Sciences Research and Technology Support
82
Field, M., Collins, M. W., Lovell, M. R. & Maroon, J.
(2003). Does age play a role in recovery from sports-
related concussion? A comparison of high school and
collegiate athletes. J Pediatr, 142, 546-53.
Grady, M. F. (2010). Concussion in the adolescent athlete.
Curr Probl Pediatr Adolesc Health Care, 40, 154-69.
Greenwald, R. M., Gwin, J. T., Chu, J. J. & Crisco, J. J.
(2008). Head impact severity measures for evaluating
mild traumatic brain injury risk exposure.
Neurosurgery, 62, 789-98; discussion 798.
Khurana, V. G. & Kaye, A. H. (2012). An overview of
concussion in sport. J Clin Neurosci, 19, 1-11.
Kirchner, W. K. (1958). Age differences in short-term
retention of rapidly changing information. J Exp
Psychol, 55, 352-8.
Lee, H., Sullivan, S. J. & Schneiders, A. G. (2013). The
use of the dual-task paradigm in detecting gait
performance deficits following a sports-related
concussion: a systematic review and meta-analysis. J
Sci Med Sport, 16, 2-7.
McCrory, P., Davis, G. & Makdissi, M. (2012). Second
impact syndrome or cerebral swelling after sporting
head injury. Curr Sports Med Rep, 11, 21-3.
Parker, T. M., Osternig, L. R., Lee, H. J., Donkelaar, P. &
Chou, L. S. (2005). The effect of divided attention on
gait stability following concussion. Clin Biomech
(Bristol, Avon), 20, 389-95.
Pilon, M. & Belson, K. 2013. Seau Had Brain Disease
Found in Other Ex-Players. New York Times, January
11, 2013.
Post, A. & Blaine Hoshizaki, T. (2015). Rotational
acceleration, brain tissue strain, and the relationship to
concussion. J Biomech Eng, 137.
Rosenthal, J. A., Foraker, R. E., Collins, C. L. &
Comstock, R. D. (2014). National High School Athlete
Concussion Rates From 2005-2006 to 2011-2012. Am
J Sports Med, 42, 1710-1715.
Stern, R. A., Riley, D. O., Daneshvar, D. H., Nowinski, C.
J., Cantu, R. C. & McKee, A. C. (2011). Long-term
consequences of repetitive brain trauma: chronic
traumatic encephalopathy. PM R, 3, S460-7.
Talavage, T. M., Nauman, E. A., Breedlove, E. L., Yoruk,
U., Dye, A. E., Morigaki, K. E., Feuer, H. & Leverenz,
L. J. (2014). Functionally-detected cognitive
impairment in high school football players without
clinically-diagnosed concussion. J Neurotrauma, 31,
327-38.
Longitudinal Study on the Detection and Evaluation of Onset Mild Traumatic Brain Injury during Dual Motor and Cognitive Tasks
83