1. Emory Sports Performance and Research Center (SPARC), Flowery Branch, GA, USA
2. Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
3. Ohio Musculoskeletal & Neurological Institute; College of Health Sciences and Professions, Ohio University, Athens, OH, USA
1. Emory Sports Performance and Research Center (SPARC), Flowery Branch, GA, USA
2. Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
1. Emory Sports Performance and Research Center (SPARC), Flowery Branch, GA, USA
2. Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
1. Emory Sports Performance and Research Center (SPARC), Flowery Branch, GA, USA

2. Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
4. Department of Neuroscience, University of Cincinnati School of Medicine, Cincinnati, OH, USA
1. Emory Sports Performance and Research Center (SPARC), Flowery Branch, GA, USA
2. Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
3. Ohio Musculoskeletal & Neurological Institute; College of Health Sciences and Professions, Ohio University, Athens, OH, USA
1. Emory Sports Performance and Research Center (SPARC), Flowery Branch, GA, USA
2. Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
5. Department of Diagnostic Radiology and Imaging Sciences, Division of Musculoskeletal Imaging, Emory University School of Medicine, Atlanta, GA, USA
1. Emory Sports Performance and Research Center (SPARC), Flowery Branch, GA, USA
2. Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA

6. Emory Sports Medicine Center, Atlanta, GA, USA
7. The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
1. Hewett TE, Myer GD, Ford KR, et al. Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: a prospective study. Am J Sports Med. 2005 Apr;33(4):492-501. doi: 10.1177/0363546504269591.
2. Grooms DR, Diekfuss JA, Ellis JD, Yuan W, Dudley JA, Barber Foss KD, Thomas S, Altaye M, Haas L, Williams B, Lanier JM, Bridgewater K, Myer GD. A novel approach to evaluate brain activation for lower extremity motor control. J Neuroimaging. 2019;29(5):580-588.
Figure 1.
High injury-risk landing movement (left). High injury risk movement data prepared in Visual 3D for post-processing (right).
3. Diekfuss JA, Grooms DR, Nissen KS, Schneider DK, Barber Foss KD, Thomas S, Dudley JA, Yuan W, Reddington Danielle L, Ellis JD, Leach J, Gordon M, Lindsey C, Rushford K, Shafer C, Myer GD. Alterations in knee sensorimotor brain functional connectivity contributes to ACL injury in male high-school football players: A prospective neuroimaging analysis. Brazilian Journal of Physical Therapy. 2020;24(5):415-423.
Figure 2.
Knee position control task (left); knee force control task (right).
Position control
Figure 3.
Neural activity profile for those at high-injury risk.
Force control
Figure 3.
Neural activity profile for those at high-injury risk.
4. Anand M, Diekfuss JA, Slutsky-Ganesh AB, Grooms DR, Bonnette S, Barber Foss KD, DiCesare CA, Hunnicutt JL, Myer GD. Novel brain mechanisms regulating anterior cruciate ligament injury risk biomechanics utilizing a motion analysis system integrated with functional magnetic resonance imaging during lower extremity movement National Athletic Trainers’ Association; June 17-20, 2020.
5. Diekfuss JA, Bonnette S, Hogg JA, Riehm C, Grooms DR, Singh H, Anand M, Slutsky AB, Wilkerson G, Myer GD. Practical training strategies to apply neuro-mechanistic motor learning principles to facilitate adaptations towards injury-resistant movement in youth. Journal of Science in Sport and Exercise. 2021;[Epub ahead of print].
6. Diekfuss JA, Grooms DR, Hogg JA, Singh H, Slutsky AB, Bonnette S, Riehm C, Anand M, Nissen KS, Wilkerson G, Myer GD. Targeted application of motor learning theory to leverage youth neuroplasticity for enhanced injury-resistance and exercise performance: OPTIMAL PREP. Journal of Science in Sport and Exercise. 2021;[Epub ahead of print].
7. Grooms DR, Kiefer AW, Riley MA, Ellis JD, Thomas S, Kitchen K, DiCesare CA, Bonnette S, Gadd B, Barber Foss KD, Yuan W, Silva P, Galloway R, Diekfuss JA, Leach J, Berz K, Myer GD. Brain-Behavior Mechanisms for the Transfer of Neuromuscular Training Adaptions to Simulated Sport: Initial Findings From the Train the Brain Project. J Sport Rehabil. 2018;27(5):1-5.
Figure 4.
Brain activation during the force control fMRI motor task (green) and neural correlates of frontal plane range of motion (positive = red, negative = blue) shown in the axial view with Z-coordinate slices. All results significant at p <0.05, z >3.1. Data published in the Journal of Neuroscience Methods8. Figure reprinted with design modifications from the Journal of Neuroscience Methods, published online May 2021, Anand et al., Integrated 3D motion analysis with functional magnetic resonance neuroimaging to identify neural correlates of lower extremity movement, Copyright 2021, with permission from Elsevier.
8. Anand M, Diekfuss JA, Slutsky-Ganesh AB, Grooms DR, Bonnette S, Barber Foss KD, DiCesare CA, Hunnicutt JL, Myer GD. Integrated 3D motion analysis with functional magnetic resonance neuroimaging to identify neural correlates of lower extremity movement. J Neurosci Methods. 2021 May 1;355:109108. doi: 10.1016/j.jneumeth.2021.109108.
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Anand_headshot_BW_c.jpg
Manish Anand, PhD
Grooms_headshot_BW.jpg
Dustin R. Grooms, PhD
Diekfuss_headshot_BW_c.jpg
Jed A. Diekfuss, PhD
Ganesh_headshot_BW_c.jpg
Alexis B. Slutsky-Ganesh, PhD
Zuleger_headshot_BW_c.jpg
Taylor Zuleger
Warren_headshot_BW_c.jpg
Shayla Warren
Kim_headshot_BW_c.jpg
HoWon Kim, MS
Schlink_headshot_BW_c.jpg
Bryan R. Schlink, PhD
Wong_Headshot_BW_c.jpg
Philip K. Wong, MD
Myer_headshot_BW.jpg
Gregory D. Myer, PhD


SPOTLIGHT

Multimodal neuroimaging of neuromuscular control — technology propelled development of strategies to identify neural signatures of injury risk

by Manish Anand, PhD, Research Associate1,2, Dustin R. Grooms, PhD, Associate Professor3, Jed A. Diekfuss, PhD, Assistant Professor1,2, Alexis B. Slutsky-Ganesh, PhD, Post-doctoral Research Fellow1,2, Taylor Zuleger, BA/BS, doctoral student1,2,4, Shayla Warren, BS, Clinical Research Coordinator II1,2, HoWon Kim, MS, doctoral student3, Bryan R. Schlink, PhD, Post-doctoral Research Fellow1,2, Philip K. Wong, MD, radiologist5, and Gregory D. Myer, PhD, Professor, Director of Emory SPARC1,2,6,7
Anand_headshot_BW_c.jpg
Manish Anand, PhD
Grooms_headshot_BW.jpg
Dustin R. Grooms, PhD
Diekfuss_headshot_BW_c.jpg
Jed A. Diekfuss, PhD
Ganesh_headshot_BW_c.jpg
Alexis B. Slutsky-Ganesh, PhD
Zuleger_headshot_BW_c.jpg
Taylor Zuleger
Warren_headshot_BW_c.jpg
Shayla Warren
Kim_headshot_BW_c.jpg
HoWon Kim, MS
Schlink_headshot_BW_c.jpg
Bryan R. Schlink, PhD
Wong_Headshot_BW_c.jpg
Philip K. Wong, MD
Myer_headshot_BW.jpg
Gregory D. Myer, PhD
Knee anterior cruciate ligament (ACL) injuries are a common occurrence in sports that involve rapid cutting, pivoting and landing maneuvers. The mechanism of injury is primarily non-contact, secondary to motor coordination errors that result in aberrant knee loads that exceed tensile strength of the ligament1.
The nature of the injury event implicates the central nervous system as a key contributor to injury risk identification and prevention efforts. However, due to limitations in technological integration of movement tasks with neuroimaging, the potential neural biomarkers of lower extremity neuromuscular control have remained elusive.
Our research team at the Emory Sports Performance And Research Center (SPARC) along with collaborators have employed traditional (e.g., resting-state functional magnetic resonance imaging [rs-fMRI]) and novel fMRI paradigms (lower extremity movement concurrent with fMRI2) to discover neural signatures related to ACL injury and ACL injury risk biomechanics. Herein we summarize multiple integrated experiments ranging from 1) prospective neuroimaging to ACL injury events, 2) injury-risk assessments and 3) concurrent 3D motion analysis with task-based fMRI.
The first experiment (cohort) included prospective neuroimaging of 62 male high school football players and 72 female high school soccer players with fMRI prior to their respective season.
Five athletes (3 males and 2 females) went on to experience a non-contact ACL injury and were matched to four teammates who did not sustain an ACL injury based on school, age, height, weight and year in school. A second cohort of 31 female high school soccer players (16.10 ±0.87 years, 165.10 ±4.64 cm, 63.43 ±8.80 kg) were evaluated with 3D biomechanics during a standardized drop vertical jump from a 30 cm box (Figure 1) and completed knee joint position control and multi-joint force control fMRI paradigms (Figure 2) to better understand neural correlates of injury risk. Athletes were classified into a high- or low-risk motor control landing strategy based on prior literature thresholds of landing biomechanics during the drop vertical jump test. A total of nine athletes fit the highrisk classification (≥21.74 Nm knee abduction moment) and 11 fit low risk (≤10.6 Nm knee abduction moment). Eleven were between thresholds and were excluded. Out of the 20 participants assigned to a risk group, five had excessive head motion during neuroimaging (>0.75 mm of absolute or >0.20 mm of relative head motion during either motor task), three in the high injury risk group, resulting in six potential matches. One athlete could not be matched across groups due to activity level/sport participation status differences. Final analyses thus yielded five pairs for neuroimaging assessment (n=10), based on age (±1 year), sport (all soccer), activity level/sport participation (starter vs. reserve) and suitability for MR (no metal, no claustrophobia, etc.) with <0.75 mm of absolute and <0.20 mm of relative head motion during either motor task. A third cohort of 17 high school female athletes (14.5 ±1.4 years, 168.1 ±6.9 cm, 62.4 ±19.5 kg) were imaged with integrated 3D biomechanics of right leg unilateral multi-joint force control against resistance during fMRI. Injury risk behavior was characterized by increased out-of-plane movement in the frontal plane and neural correlates of this biomechanical variable were identified.
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Figure 1. High injury-risk landing movement (left). High injury risk movement data prepared in Visual 3D for post-processing (right).
In the first cohort, male athletes who sustained an ACL injury had significantly lower resting-state functional connectivity compared to the matched controls between the left secondary somatosensory cortex and the left supplementary motor area, right pre-motor cortex, right supplementary motor area, left primary somatosensory cortex and left primary motor cortex (all regions important for sensorimotor control)3. Females in this same cohort who sustained a subsequent ACL injury also demonstrated significantly lower Jed A. Diekfuss, PhD Alexis B. Slutsky-Ganesh, PhD Figure 1. High injury-risk landing movement (left). High injury risk movement data prepared in Visual 3D for post-processing (right). Figure 2. Knee position control task (left); knee force control task (right). resting-state functional connectivity relative to matched controls between the left primary somatosensory cortex and the right posterior lobe of the cerebellum (both regions also implicated in sensorimotor control).
IS_MultiModal Figure 2.jpg
A
Figure 2. Knee position control task (left); knee force control task (right).
In the injury-risk assessment (second) cohort that utilized the fMRI motor control tasks, those with high injury risk biomechanics had lower neural activity for knee position control in the precuneus (area integrating sensorimotor coordination and spatial attention) and the posterior cingulate gyrus (area processing spatial awareness and attention for motor control) compared to those with low injury risk biomechanics (Figure 3). The identified brain regions in the injury risk assessment analysis provide the motor cortex with vital sensory, attentional and preparatory information to fine-tune motor action. The high-risk group’s relative lower activity in regions that integrate sensory, spatial and motor information may impair their ability to maintain a safe knee position during more dynamic maneuvers, such as landing. In the force control paradigm, those with high injury-risk biomechanics movement had worse sensory-cognitive efficiency (increased activity) when controlling hip and knee load, indicating a loss of knee force control expertise in the injury (Figure 3).
IS_MultiModal Figure 3 Position Control.jpg
Position control
IS_MultiModal Figure 3 Force Control.jpg
Force control
Figure 3. Neural activity profile for those at high-injury risk.
In the integrated 3D motion analysis and fMRI leg press (third) cohort, poorer frontal plane control (greater out-of-plane range of motion) was bidirectionally related to activity in sensorimotor control (precentral gyrus, cerebellum), sensorimotor integration (precuneus, postcentral gyrus) and cognitive (middle frontal gyrus, posterior cingulate cortex) regions4. The latter, integrated experiment provided further support that regulating knee positions for safer loads is intricately linked with sensory-cognitive brain activity.
Prospectively identified alterations in cognitive- and sensorimotor-related regions identified via rs-fMRI connectivity and task-based fMRI implicate neural deficiencies that compromise the ability to fine-tune knee motor control that may be exacerbated during dynamic situations. For instance, increased sensorimotor neural activity (i.e., less neural efficiency), as seen in the high-injury-risk group, may provoke a more rapid saturation of motor coordination capacity during high-demand sports activity leading to a breakdown in neuromuscular control. Similarly, both the neural correlates of injury risk mechanics and the high-risk group’s relative decreased activity in regions important for sensory integration may be indicative of reduced efficiency to navigate a sport environment with increased sensory processing demand.
Collectively, our research data indicate that ACL injury and biomechanics that contribute to the incident may likely be due, in part, to a manifestation of maladapted spatial awareness and attention to knee motor control that contributes to increased abduction loading during more demanding activity. Though the present fMRI findings are not to be used for diagnostic purposes (these data from Emory SPARC are analyzed in a "blinded" fashion and used for research purposes only), these preliminary data represent a pathway towards identifying neural signatures of ACL injury and ACL injury risk biomechanics to guide future clinical application. Specifically, we expect our research findings to accelerate the development of novel neurotherapies for ACL injury risk reduction by promoting adaptive neuroplasticity that supports the rapid acquisition, retention and transfer of injury-resist biomechanics5-7.
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Figure 4. Figure 4. Brain activation during the force control fMRI motor task (green) and neural correlates of frontal plane range of motion (positive = red, negative = blue) shown in the axial view with Z-coordinate slices. All results significant at p <0.05, z >3.1. Data published in the Journal of Neuroscience Methods8. Figure reprinted with design modifications from the Journal of Neuroscience Methods, published online May 2021, Anand et al., Integrated 3D motion analysis with functional magnetic resonance neuroimaging to identify neural correlates of lower extremity movement, Copyright 2021, with permission from Elsevier.
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