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The Evolution of Neuroimaging Technologies to Evaluate Neural Activity Related to Knee Pain and Injury Risk

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Abstract

Purpose of Review

In this review, we present recent findings and advancements in the use of neuroimaging to evaluate neural activity relative to ACL injury risk and patellofemoral pain. In particular, we describe prior work using fMRI and EEG that demonstrate the value of these techniques as well as the necessity of continued development in this area. Our goal is to support future work by providing guidance for the successful application of neuroimaging techniques that most effectively expose pain and injury mechanisms.

Recent Findings

Recent studies that utilized both fMRI and EEG indicate that athletes who are at risk for future ACL injury exhibit divergent brain activity both during active lower extremity movement and at rest. Such activity patterns are likely due to alterations to cognitive, visual, and attentional processes that manifest as coordination deficits during naturalistic movement that may result in higher risk of injury. Similarly, in individuals with PFP altered brain activity in a number of key regions is related to subjective pain judgements as well as measures of fear of movement. Although these findings may begin to allow objective pain assessment and identification, continued refinement is needed. One key limitation across both ACL and PFP related work is the restriction of movement during fMRI and EEG data collection, which drastically limits ecological validity.

Summary

Given the lack of sufficient research using EEG and fMRI within a naturalistic setting, our recommendation is that researchers target the use of mobile, source localized EEG as a primary methodology for exposing neural mechanisms of ACL injury risk and PFP. Our contention is that this method provides an optimal balance of spatial and temporal resolution with ecological validity via naturalistic movement.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Mall NA, Chalmers PN, Moric M, Tanaka MJ, Cole BJ, Bach BR Jr, et al. Incidence and trends of anterior cruciate ligament reconstruction in the United States. American J Sports Med. 2014;42(10):2363–70.

    Article  Google Scholar 

  2. Lohmander L, Östenberg A, Englund M, Roos H. High prevalence of knee osteoarthritis, pain, and functional limitations in female soccer players twelve years after anterior cruciate ligament injury. Arthritis Rheumatism: Official J Ame College Rheumatol. 2004;50(10):3145–52.

    Article  CAS  Google Scholar 

  3. Luc B, Gribble PA, Pietrosimone BG. Osteoarthritis prevalence following anterior cruciate ligament reconstruction: a systematic review and numbers-needed-to-treat analysis. J Athletic Train. 2014;49(6):806–19.

    Article  Google Scholar 

  4. Heintjes EM, Berger M, Bierma‐Zeinstra SM, Bernsen RM, Verhaar JA, Koes BW, et al. Exercise therapy for patellofemoral pain syndrome. Cochrane Database of Systematic Reviews. 1996;2010(1).

  5. Smith BE, Selfe J, Thacker D, Hendrick P, Bateman M, Moffatt F, et al. Incidence and prevalence of patellofemoral pain: a systematic review and meta-analysis. PloS one. 2018;13(1):e0190892.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Willy RW, Hoglund LT, Barton CJ, Bolgla LA, Scalzitti DA, Logerstedt DS, et al. Patellofemoral pain: clinical practice guidelines linked to the international classification of functioning, disability and health from the academy of orthopaedic physical therapy of the American physical therapy association. J Orthopaedic Sports Phys Therapy. 2019;49(9):CPG1–95.

    Article  Google Scholar 

  7. Myer GD, Ford KR, Di Stasi SL, Foss KDB, Micheli LJ, Hewett TE. High knee abduction moments are common risk factors for patellofemoral pain (PFP) and anterior cruciate ligament (ACL) injury in girls: is PFP itself a predictor for subsequent ACL injury? British J Sports Med. 2015;49(2):118–22.

    Article  Google Scholar 

  8. Thomas MJ, Wood L, Selfe J, Peat G. Anterior knee pain in younger adults as a precursor to subsequent patellofemoral osteoarthritis: a systematic review. BMC Musculoskeletal Dis. 2010;11(1):1–8.

    Article  Google Scholar 

  9. Lohmander LS, Englund PM, Dahl LL, Roos EM. The long-term consequence of anterior cruciate ligament and meniscus injuries: osteoarthritis. Ame J Sports Med. 2007;35(10):1756–69.

    Article  Google Scholar 

  10. Cowan SM, Bennell KL, Crossley KM, Hodges PW, McConnell J. Physical therapy alters recruitment of the vasti in patellofemoral pain syndrome. Med Sci Sports Exercise. 2002;34(12):1879–85.

    Article  Google Scholar 

  11. Neto T, Sayer T, Theisen D, Mierau A. Functional brain plasticity associated with ACL injury: a scoping review of current evidence. Neural Plast. 2019;2019:3480512. https://doi.org/10.1155/2019/3480512.

  12. Diekfuss JA, Grooms DR, Nissen KS, Coghill RC, Bonnette S, Barber Foss KD, et al. Does central nervous system dysfunction underlie patellofemoral pain in young females? Examining brain functional connectivity in association with patient‐reported outcomes. Journal of Orthopaedic Research®. 2022;40(5):1083-96.

  13. López-Solà M, Pujol J, Monfort J, Deus J, Blanco-Hinojo L, Harrison BJ, et al. The neurologic pain signature responds to nonsteroidal anti-inflammatory treatment vs placebo in knee osteoarthritis. Pain Reports. 2022;7(2).

  14. Mansfield CJ, Culiver A, Briggs M, Schmitt LC, Grooms DR, Onate J. The effects of knee osteoarthritis on neural activity during a motor task: a scoping systematic review. Gait & Posture. 2022.

  15. Klug M, Gramann K. Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments. Eur J Neurosci. 2021;54(12):8406–20.

    Article  PubMed  Google Scholar 

  16. Newton JM, Dong Y, Hidler J, Plummer-D’Amato P, Marehbian J, Albistegui-DuBois RM, et al. Reliable assessment of lower limb motor representations with fMRI: use of a novel MR compatible device for real-time monitoring of ankle, knee and hip torques. Neuroimage. 2008;43(1):136–46.

    Article  PubMed  Google Scholar 

  17. Corbett DB, Simon CB, Manini TM, George SZ, Riley JL III, Fillingim RB. Movement-evoked pain: transforming the way we understand and measure pain. Pain. 2019;160(4):757.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Chaput M, Onate JA, Simon JE, Criss CR, Jamison S, McNally M, et al. Visual cognition associated with knee proprioception, time to stability, and sensory integration neural activity after ACL reconstruction. J Orthopaedic Res®. 2022;40(1):95–104.

    Article  Google Scholar 

  19. Criss CR, Onate JA, Grooms DR. Neural activity for hip-knee control in those with anterior cruciate ligament reconstruction: a task-based functional connectivity analysis. Neuroscience Lett. 2020;730: 134985.

    Article  CAS  Google Scholar 

  20. •• Grooms DR, Diekfuss JA, Criss CR, Anand M, Slutsky-Ganesh AB, DiCesare CA, et al. Preliminary brain-behavioral neural correlates of anterior cruciate ligament injury risk landing biomechanics using a novel bilateral leg press neuroimaging paradigm. Plos one. 2022;17(8): e0272578. This citation is the first study to link altered neural activation measured with fMRI to injury risk landing mechanics collected using standardised biomechanical tests.

  21. Grooms DR, Diekfuss JA, Slutsky-Ganesh AB, DiCesare CA, Bonnette S, Riley MA, et al. Preliminary Report on the Train the Brain Project, Part II: Neuroplasticity of Augmented Neuromuscular Training and Improved Injury-Risk Biomechanics. J Athletic Training. 2022;57(9–10):911–20.

    Article  Google Scholar 

  22. Diekfuss JA, Grooms DR, Nissen KS, Schneider DK, Foss KDB, Thomas S, et al. Alterations in knee sensorimotor brain functional connectivity contributes to ACL injury in male high-school football players: a prospective neuroimaging analysis. Brazilian J Phys Therapy. 2020;24(5):415–23.

    Article  Google Scholar 

  23. Diekfuss JA, Grooms DR, Yuan W, Dudley J, Foss KDB, Thomas S, et al. Does brain functional connectivity contribute to musculoskeletal injury? A preliminary prospective analysis of a neural biomarker of ACL injury risk. J Sci Med Sport. 2019;22(2):169–74.

    Article  PubMed  Google Scholar 

  24. •• Grooms DR, Diekfuss JA, Ellis JD, Yuan W, Dudley J, Foss KDB, et al. A novel approach to evaluate brain activation for lower extremity motor control. Journal of Neuroimaging. 2019;29(5):580–8. This citation demonstrates the test-retest reliability of neural activation for knee motor control during fMRI.

  25. Anand M, Diekfuss JA, Bonnette S, Short I, Hurn M, Grooms DR, et al. Validity of an MRI-compatible motion capture system for use with lower extremity neuroimaging paradigms. Int J Sports Phys Therapy. 2020;15(6):936.

    Article  Google Scholar 

  26. Grooms DR, Page SJ, Onate JA. Brain activation for knee movement measured days before second anterior cruciate ligament injury: neuroimaging in musculoskeletal medicine. J Athletic Training. 2015;50(10):1005–10.

    Article  Google Scholar 

  27. Grooms DR, Page SJ, Nichols-Larsen DS, Chaudhari AM, White SE, Onate JA. Neuroplasticity associated with anterior cruciate ligament reconstruction. J Orthopaedic Sports Phys Therapy. 2017;47(3):180–9.

    Article  Google Scholar 

  28. Slutsky-Ganesh AB, Anand M, Diekfuss JA, Myer GD, Grooms DR. Lower extremity Interlimb coordination associated brain activity in young female athletes: a biomechanically instrumented neuroimaging study. Psychophysiology. 2023;60(4):e14221.

    Article  PubMed  Google Scholar 

  29. Anand M, Diekfuss JA, Slutsky-Ganesh AB, Grooms DR, Bonnette S, Foss KDB, et al. Integrated 3D motion analysis with functional magnetic resonance neuroimaging to identify neural correlates of lower extremity movement. J Neurosci Methods. 2021;355:109108.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sugimoto D, Myer GD, Barber Foss KD, Hewett TE. Dosage effects of neuromuscular training intervention to reduce anterior cruciate ligament injuries in female athletes: meta-and sub-group analyses. Sports Med. 2014;44:551–62.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Grooms D, Appelbaum G, Onate J. Neuroplasticity following anterior cruciate ligament injury: a framework for visual-motor training approaches in rehabilitation. J Orthopaed Sports Phys Therapy. 2015;45(5):381–93.

    Article  Google Scholar 

  32. Foss KDB, Slutsky-Ganesh AB, Diekfuss JA, Grooms DR, Simon JE, Schneider DK, et al. Brain activity during experimental knee pain and its relationship with kinesiophobia in patients with patellofemoral pain: a preliminary functional magnetic resonance imaging investigation. J Sport Rehab. 2022;31(5):589–98.

    Google Scholar 

  33. Diekfuss JA, Saltman AJ, Grooms DR, Bonnette S, Foss KB, Berz K, et al. Neural correlates of knee motor control for young females with patellofemoral pain. Orthopaedic J Sports Med. 2019;7(3_suppl):2325967119S00012.

    Article  Google Scholar 

  34. Diekfuss JA, Grooms DR, Coghill RC, Nissen KS, Saltman AJ, Berz K, et al. Kinesiophobia is related to brain activity for knee motor control in pediatric patients with patellofemoral pain. Orthopaedic J Sports Med. 2020;8(4_suppl3):2325967120S000187.

    Article  Google Scholar 

  35. Cohen MX. Where does EEG come from and what does it mean? Trends Neurosci. 2017;40(4):208–18.

    Article  PubMed  CAS  Google Scholar 

  36. Ismail LE, Karwowski W. Applications of EEG indices for the quantification of human cognitive performance: a systematic review and bibliometric analysis. Plos one. 2020;15(12):e0242857.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Hussain I, Hossain MA, Jany R, Bari MA, Uddin M, Kamal ARM, et al. Quantitative evaluation of EEG-biomarkers for prediction of sleep stages. Sensors. 2022;22(8):3079.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Buzsaki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304(5679):1926–9.

    Article  PubMed  CAS  Google Scholar 

  39. Gutmann B, Mierau A, Hülsdünker T, Hildebrand C, Przyklenk A, Hollmann W, et al. Effects of physical exercise on individual resting state EEG alpha peak frequency. Neural Plasticity. 2015;2015.

  40. Baumeister J, Reinecke K, Weiss M. Changed cortical activity after anterior cruciate ligament reconstruction in a joint position paradigm: an EEG study. Scandinavian J Med Sci Sports. 2008;18(4):473–84.

    Article  CAS  Google Scholar 

  41. Baumeister J, Reinecke K, Schubert M, Weiss M. Altered electrocortical brain activity after ACL reconstruction during force control. J Ortho Res. 2011;29(9):1383–9.

    Article  Google Scholar 

  42. •• Sherman DA, Baumeister J, Stock MS, Murray AM, Bazett-Jones DM, Norte GE. Weaker quadriceps corticomuscular coherence in individuals after ACL reconstruction during force tracing. Med Sci Sports Exerc. 2023;55(4):625-32. This work is very important for the current topic because, in our opinion, it is the most representative example of the source localized EEG methodology that is reccomended in the latter section of this review.

  43. Mima T, Hallett M. Corticomuscular coherence: a review. J Clin Neurophysiol. 1999;16(6):501.

    Article  PubMed  CAS  Google Scholar 

  44. Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clinical Neurophysiol. 2005;116(10):2266–301.

    Article  CAS  Google Scholar 

  45. Webber CL, Marwan N. Recurrence quantification analysis. Theory and Best Practices. 2015:426.

  46. Bonnette S, Diekfuss JA, Grooms DR, Kiefer AW, Riley MA, Riehm C, et al. Electrocortical dynamics differentiate athletes exhibiting low-and high-ACL injury risk biomechanics. Psychophysiology. 2020;57(4):e13530.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ebrahimi N, Rojhani-Shirazi Z, Yoosefinejad AK, Nami M. The effects of virtual reality training on clinical indices and brain mapping of women with patellofemoral pain: a randomized clinical trial. BMC Musculoskeletal Disorders. 2021;22(1):1–10.

    Article  Google Scholar 

  48. Rocha HA, Marks J, Woods AJ, Staud R, Sibille K, Keil A. Re-test reliability and internal consistency of EEG alpha-band oscillations in older adults with chronic knee pain. Clin Neurophysiol. 2020;131(11):2630–40.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Michel CM, He B. EEG source localization. Handbook Clin Neuro. 2019;160:85–101.

    Article  Google Scholar 

  50. Sherman DA, Baumeister J, Stock MS, Murray AM, Bazett-Jones DM, Norte GE. Brain activation and single-limb balance following anterior cruciate ligament reconstruction. Clin Neurophysiolog. 2023;149:88–99.

    Article  Google Scholar 

  51. Næss S, Halnes G, Hagen E, Hagler DJ Jr, Dale AM, Einevoll GT, et al. Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. NeuroImage. 2021;225:117467.

    Article  PubMed  Google Scholar 

  52. Liao X-H, Xia M-R, Xu T, Dai Z-J, Cao X-Y, Niu H-J, et al. Functional brain hubs and their test–retest reliability: a multiband resting-state functional MRI study. Neuroimage. 2013;83:969–82.

    Article  PubMed  Google Scholar 

  53. Marcu S, Pegolo E, Ívarsson E, Jónasson AD, Jónasson VD, Aubonnet R, et al. Using high density EEG to assess TMS treatment in patients with schizophrenia. Eur J Translational Myology. 2020;30(1).

  54. Oliveira AS, Schlink BR, Hairston WD, König P, Ferris DP. Proposing metrics for benchmarking novel EEG technologies towards real-world measurements. Front Human Neurosci. 2016;10:188.

    Article  Google Scholar 

  55. Homölle S, Oostenveld R. Using a structured-light 3D scanner to improve EEG source modeling with more accurate electrode positions. J Neurosci Methods. 2019;326:108378.

    Article  PubMed  Google Scholar 

  56. Richer N, Downey RJ, Hairston WD, Ferris DP, Nordin AD. Motion and muscle artifact removal validation using an electrical head phantom, robotic motion platform, and dual layer mobile EEG. IEEE Trans Neural Syst Rehab Eng. 2020;28(8):1825–35.

    Article  Google Scholar 

  57. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21.

    Article  PubMed  Google Scholar 

  58. Pion-Tonachini L, Kreutz-Delgado K, Makeig S. ICLabel: an automated electroencephalographic independent component classifier, dataset, and website. NeuroImage. 2019;198:181–97.

    Article  PubMed  Google Scholar 

  59. • Klug M, Jeung S, Wunderlich A, Gehrke L, Protzak J, Djebbara Z, et al. The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data. bioRxiv. 2022:2022.09. 29.510051. This citation is marked as important because it represents a very useful tool for those looking to apply mobile EEG methodologies, which are of key importance for the current topic.

  60. Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Int Neurosci. 2011;2011:1–13.

    Article  Google Scholar 

  61. Schrader S, Westhoff A, Piastra MC, Miinalainen T, Pursiainen S, Vorwerk J, et al. DUNEuro—a software toolbox for forward modeling in bioelectromagnetism. PloS one. 2021;16(6):e0252431.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 2002;24(Suppl D):5–12.

    PubMed  Google Scholar 

  63. Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage. 2013;80:360–78.

    Article  PubMed  Google Scholar 

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Funding

We would like to acknowledge funding support from the University of Cincinnati Neuroscience Graduate Program (T32 NS007453-20).

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Authors and Affiliations

  1. Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA

    Christopher D. Riehm, Taylor Zuleger, Jed A. Diekfuss & Gregory D. Myer

  2. Emory Sports Medicine Center, Atlanta, GA, USA

    Christopher D. Riehm, Taylor Zuleger, Jed A. Diekfuss & Gregory D. Myer

  3. Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA

    Christopher D. Riehm, Taylor Zuleger, Jed A. Diekfuss, Emilio Arellano & Gregory D. Myer

  4. Neuroscience Graduate Program, University of Cincinnati School of Medicine, Cincinnati, OH, USA

    Taylor Zuleger

  5. Youth Physical Development Centre, Cardiff Metropolitan University, Wales, UK

    Gregory D. Myer

  6. The Micheli Center for Sports Injury Prevention, Waltham, MA, USA

    Gregory D. Myer

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  1. Christopher D. Riehm
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  2. Taylor Zuleger
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Correspondence to Christopher D. Riehm.

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Conflict of Interest

Gregory D. Myer consults with commercial entities to support commercialization strategies and applications to the US Food and Drug Administration but has no direct financial interest in the commercialization of the products. Dr. Myer’s institution receives current and ongoing grant funding from National Institutes of Health/NIAMS Grants U01AR067997, R01 AR070474, R01AR055563, R01AR076153, R01 AR077248, R61AT012421, the Department of Defense W81XWH22C0062 and Arthritis Foundation OACTN. Dr. Myer has received industry sponsored research funding to his institutions related to injury prevention, sport performance and has current ongoing funding from Arthrex, Inc. to evaluate ACL surgical treatment optimization strategies. Dr. Myer receives author royalties from Human Kinetics and Wolters Kluwer. Dr. Myer is an inventor of biofeedback technologies (Patent No: US11350854B2, Augmented and Virtual reality for Sport Performance and Injury Prevention Application, Approval Date: 06/07/2022, Software Copyrighted) designed to enhance rehabilitation and prevent injuries that receives licensing royalties.

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Riehm, C.D., Zuleger, T., Diekfuss, J.A. et al. The Evolution of Neuroimaging Technologies to Evaluate Neural Activity Related to Knee Pain and Injury Risk. Curr Rev Musculoskelet Med 17, 14–22 (2024). https://doi.org/10.1007/s12178-023-09877-5

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