Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-27T09:40:02.210Z Has data issue: false hasContentIssue false
  • English
  • Français

Theta-Band Functional Connectivity and Single-Trial Cognitive Control in Sports-Related Concussion: Demonstration of Proof-of-Concept for a Potential Biomarker of Concussion

Published online by Cambridge University Press:  25 January 2019

Ezra E. Smith  and
John J.B. Allen
Ezra E. Smith*
Affiliation:
University of Arizona, Department of Psychology, Tucson, Arizona
John J.B. Allen
Affiliation:
University of Arizona, Department of Psychology, Tucson, Arizona
*
Correspondence and reprint requests to: Ezra E. Smith, University of Arizona, Department of Psychology, 1503 E. University Blvd., Tucson, AZ 85721. E-mail: ezrasmith@email.arizona.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: This report examined theta-band neurodynamics for potential biomarkers of brain health in athletes with concussion. Methods: Participants included college-age contact/collision athletes with (N=24) and without a history of concussion (N=16) in Study 1. Study 2 (N=10) examined changes over time in contact/collision athletes. There were two primary dependent variables: (1) theta-band phase-synchronization (e.g., functional connectivity) between medial and right-lateral electrodes; and (2) the within-subject correlation between synchronization strength on error trials and post-error reaction time (i.e., operationalization of cognitive control). Results: Head injury history was inversely related with medial-lateral connectivity. Head injury was also related to declines in a neurobehavioral measure of cognitive control (i.e., the single-trial relationship between connectivity and post-error slowing). Conclusions: Results align with a theory of connectivity-mediated cognitive control. Mild injuries undetectable by behavioral measures may still be apparent on direct measures of neural functioning. This report demonstrates that connectivity and cognitive control measures may be useful for tracking recovery from concussion. Theoretically relevant neuroscientific findings in healthy adults may have applications in patient populations, especially with regard to monitoring brain health. (JINS 2019, 25, 314–323)

Keywords

EEGCognitive functionHead injuriesBiomarkersHumansAthletes
Type
Special Section: Traumatic Brain Injury
Information
Journal of the International Neuropsychological Society , Volume 25 , Issue 3 , March 2019 , pp. 314 - 323
Copyright
Copyright © The International Neuropsychological Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aron, A.R., Robbins, T.W., & Poldrack, R.A. (2014). Inhibition and the right inferior frontal cortex: one decade on. Trends in cognitive sciences, 18(4), 177–185.Google Scholar
Bailey, N.W., Rogasch, N.C., Hoy, K.E., Maller, J.J., Segrave, R.A., Sullivan, C.M., & Fitzgerald, P.B. (2017). Increased gamma connectivity during working memory retention following traumatic brain injury. Brain Injury, 31(3), 379–389.Google Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society . Series B, Statistical Methodology, 57, 289–300.Google Scholar
Bigler, E.D. (2007). Anterior and middle cranial fossa in traumatic brain injury: Relevant neuroanatomy and neuropathology in the study of neuropsychological outcome. Neuropsychology, 21(5), 515.Google Scholar
Broglio, S.P., Moore, R.D., & Hillman, C.H. (2011). A history of sport-related concussion on event-related brain potential correlates of cognition. International Journal of Psychophysiology, 82(1), 16–23.Google Scholar
Broglio, S.P., Pontifex, M.B., O'Connor, P., & Hillman, C.H. (2009). The persistent effects of concussion on neuroelectric indices of attention. Journal of neurotrauma, 26(9), 1463–1470.Google Scholar
Buzsáki, G. (2006). Rhythms of the brain. New York: Oxford University Press.Google Scholar
Buzzell, G.A., Barker, T.V., Troller-Renfree, S.V., Bernat, E.M., Bowers, M.E., MoralesS.,. S.,. & Fox, N.A. (2018). Adolescent cognitive control, theta oscillations, and social motivation. bioRxiv, doi: https://doi.org/10.1101/366831 Google Scholar
Cavanagh, J.F., Cohen, M.X., & Allen, J.J. (2009). Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. The Journal of Neuroscience, 29(1), 98–105.Google Scholar
Cavanagh, J.F., & Frank, M.J. (2014). Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences, 18(8), 414–421.Google Scholar
Cavanagh, J.F., Frank, M.J., Klein, T.J., & Allen, J.J. (2010). Frontal theta links prediction errors to behavioral adaptation in reinforcement learning. Neuroimage, 49(4), 3198–3209.Google Scholar
Cavanagh, J.F., Meyer, A., & Hajcak, G. (2017). Error-specific cognitive control alterations in generalized anxiety disorder. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 2(5), 413–420.Google Scholar
Cavanagh, J.F., & Shackman, A.J. (2015). Frontal midline theta reflects anxiety and cognitive control: Meta-analytic evidence. Journal of Physiology, Paris, 109(1), 3–15.Google Scholar
Cavanagh, J.F., Zambrano‐Vazquez, L., & Allen, J.J. (2012). Theta lingua franca: A common mid‐frontal substrate for action monitoring processes. Psychophysiology, 49(2), 220–238.Google Scholar
Cohen, M.X. (2011a). Error-related medial frontal theta activity predicts cingulate-related structural connectivity. Neuroimage, 55(3), 1373–1383.Google Scholar
Cohen, M.X. (2011b). It’s about time. Frontiers in Human Neuroscience, 5, 2.Google Scholar
Cohen, M.X. (2014). Analyzing neural time series data: Theory and practice. Cambridge: MIT Press.Google Scholar
Cohen, M.X., & Cavanagh, J.F. (2011). Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict. Frontiers in Psychology, 2, 30.Google Scholar
Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah NJ: Erlbaum.Google Scholar
Cohen, M.X., & van Gaal, S. (2013). Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors. Cerebral Cortex, 23(5), 1061–1072.Google Scholar
Cook, E.W., & Miller, G.A. (1992). Digital filtering: Background and tutorial for psychophysiologists. Psychophysiology, 29(3), 350–362.Google Scholar
Coronado, V.G., McGuire, L.C., Sarmiento, K., Bell, J., Lionbarger, M.R., Jones, C.D., . . . Xu, L. (2012). Trends in traumatic brain injury in the U.S. and the public health response: 1995–2009. Journal of Safety Research, 43(4), 299–307.Google Scholar
De Beaumont, L., Beauchemin, M., Beaulieu, C., & Jolicoeur, P. (2013). Long-term attenuated electrophysiological response to errors following multiple sports concussions. Journal of Clinical and Experimental Neuropsychology, 35(6), 596–607.Google Scholar
Debener, S., Ullsperger, M., Siegel, M., Fiehler, K., Von Cramon, D.Y., & Engel, A.K. (2005). Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring. The Journal of Neuroscience, 25(50), 11730–11737.Google Scholar
Duncan, C.C., Kosmidis, M.H., & Mirsky, A.F. (2003). Event-related potential assessment of information processing after closed head injury. Psychophysiology, 40(1), 45–59.Google Scholar
Eierud, C., Craddock, R.C., Fletcher, S., Aulakh, M., King-Casas, B., Kuehl, D., & LaConte, S.M. (2014). Neuroimaging after mild traumatic brain injury: Review and meta-analysis. NeuroImage. Clinical, 4, 283–294.Google Scholar
Faul, M., Xu, L., Wald, M.M., & Coronado, V.G. (2010). Traumatic brain injury in the United States: Emergency department visits, hospitalizations, and deaths. Atlanta: Centers for Disease Control and Prevention. National Center for Injury Prevention and Control.Google Scholar
Fries, P. (2005). A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence. Trends in Cognitive Sciences, 9(10), 474–480.Google Scholar
Gehring, W.J., Goss, B., Coles, M.G., Meyer, D.E., & Donchin, E. (1993). A neural system for error detection and compensation. Psychological Science, 4(6), 385–390.Google Scholar
Gehring, W.J., & Knight, R.T. (2000). Prefrontal-cingulate interactions in action monitoring. Nature Neuroscience, 3(5), 516.Google Scholar
Hajcak, G., McDonald, N., & Simons, R.F. (2003). To err is autonomic: Error‐related brain potentials, ANS activity, and post‐error compensatory behavior. Psychophysiology, 40(6), 895–903.Google Scholar
Hill, K.E., Samuel, D.B., & Foti, D. (2016). Contextualizing individual differences in error monitoring: Links with impulsivity, negative affect, and conscientiousness. Psychophysiology, 53(8), 1143–1153.Google Scholar
Hogan, A.M., Vargha-Khadem, F., Saunders, D.E., Kirkham, F.J., & Baldeweg, T. (2006). Impact of frontal white matter lesions on performance monitoring: ERP evidence for cortical disconnection. Brain, 129(8), 2177–2188.Google Scholar
Iverson, G.L., Wojtowicz, M., Brooks, B.L., Maxwell, B.A., Atkins, J.E., Zafonte, R., & Berkner, P.D. (2016). High school athletes with ADHD and learning difficulties have a greater lifetime concussion history. Journal of Attention Disorders, 1, 1–7.Google Scholar
Karr, J.E., Areshenkoff, C.N., & Garcia-Barrera, M.A. (2014). The neuropsychological outcomes of concussion: A systematic review of meta-analyses on the cognitive sequelae of mild traumatic brain injury. Neuropsychology, 28(3), 321.Google Scholar
Kerns, J.G., Cohen, J.D., MacDonald, A.W., Cho, R.Y., Stenger, V.A., & Carter, C.S. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science, 303(5660), 1023–1026.Google Scholar
Kumar, S., Rao, S.L., Chandramouli, B.A., & Pillai, S.V. (2009). Reduction of functional brain connectivity in mild traumatic brain injury during working memory. Journal of Neurotrauma, 26(5), 665–675.Google Scholar
Langlois, J.A., Rutland-Brown, W., & Wald, M.M. (2006). The epidemiology and impact of traumatic brain injury: A brief overview. Journal of Head Trauma and Rehabilitation, 21(5), 375–378.Google Scholar
Larson, M.J., Clayson, P.E., & Farrer, T.J. (2012). Performance monitoring and cognitive control in individuals with mild traumatic brain injury. Journal of the International Neuropsychological Society, 18(2), 323.Google Scholar
Larson, M.J., Farrer, T.J., & Clayson, P.E. (2011). Cognitive control in mild traumatic brain injury: Conflict monitoring and conflict adaptation. International Journal of Psychophysiology, 82(1), 69–78.Google Scholar
McCrory, P., Meeuwisse, W., Johnston, K., Dvorak, J., Aubry, M., Molloy, M., & Cantu, R. (2009). Consensus statement on concussion in sport 3rd International Conference on Concussion in Sport held in Zurich, November 2008. British Journal of Sports Medicine, 19(3), 185–200.Google Scholar
Miller, D.R., Hayes, J.P., Lafleche, G., Salat, D.H., & Verfaellie, M. (2017). White matter abnormalities are associated with overall cognitive status in blast-related mTBI. Brain Imaging and Behavior, 11(4), 1129–1138.Google Scholar
Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2011). ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology, 48(2), 229–240.Google Scholar
Moore, R.D., Pindus, D.M., Drolette, E.S., Scudder, M.R., Raine, L.B., & Hillman, C.H. (2015). The persistent influence of pediatric concussion on attention and cognitive control during flanker performance. Biological Psychology 109, 93–102.Google Scholar
Narayanan, N.S., Cavanagh, J.F., Frank, M.J., & Laubach, M. (2013). Common medial frontal mechanisms of adaptive control in humans and rodents. Nature. Neuroscience, 16(12), 1888.Google Scholar
Nieuwenhuis, S., Ridderinkhof, K.R., Blom, J., Band, G.P., & Kok, A. (2001). Error‐related brain potentials are differentially related to awareness of response errors: Evidence from an antisaccade task. Psychophysiology, 38(5), 752–760.Google Scholar
Olvet, D.M., & Hajcak, G. (2009). Reliability of error-related brain activity. Brain Research, 1284, 89–99.Google Scholar
Olvet, D.M., Klein, D.N., & Hajcak, G. (2010). Depression symptom severity and error-related brain activity. Psychiatry Research, 179(1), 30–37.Google Scholar
Orr, C.A., Albaugh, M.D., Watts, R., Garavan, H., Andrews, T., Nickerson, J.P., . . . Hudziak, J.J. (2016). Neuroimaging biomarkers of a history of concussion observed in asymptomatic young athletes. Journal of Neurotrauma, 33(9), 803.Google Scholar
Pang, E.W., Dunkley, B.T., Doesburg, S.M., da Costa, L., & Taylor, M.J. (2016). Reduced brain connectivity and mental flexibility in mild traumatic brain injury. Annals of Clinical and Translational Neurology, 3(2), 124–131.Google Scholar
Perrin, F., Pernier, J., Bertrand, O., Echallier, J.F., 1989. Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72, 184–187.Google Scholar
Perrin, F., Pernier, J., Bertrand, O., & Echallier, J.F. 1990. Corrigenda. Electroencephalography and Clinical Neurophysiology, 76, 565–566.Google Scholar
Pontifex, M.B., O’Connor, P.M., Broglio, S.P., & Hillman, C.H. (2009). The association between mild traumatic brain injury history and cognitive control. Neuropsychologia, 47(14), 3210–3216.Google Scholar
Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology, 118(10), 2128–2148.Google Scholar
Riesel, A., Weinberg, A., Endrass, T., Meyer, A., & Hajcak, G. (2013). The ERN is the ERN is the ERN? Convergent validity of error-related brain activity across different tasks. Biological Psychology, 93(3), 377–385.Google Scholar
Reches, A., Kutcher, J., Elbin, R.J., Or-Ly, H., Sadeh, B., Greer, J., . . . Kontos, A.P. (2017). Preliminary investigation of Brain Network Activation (BNA) and its clinical utility in sport-related concussion. Brain Injury, 31(2), 237–246.Google Scholar
Rutland-Brown, W., Langlois, J.A., Thomas, K.E., & Xi, Y.L. (2006). Incidence of traumatic brain injury in the United States, 2003. The Journal of Head Trauma Rehabilitation, 21(6), 544–548.Google Scholar
Smith, E.E., Reznik, S.J., Stewart, J.L., & Allen, J.J. (2017). Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry. International Journal of Psychophysiology, 111, 98–114.Google Scholar
Sponheim, S.R., McGuire, K.A., Kang, S.S., Davenport, N.D., Aviyente, S., Bernat, E.M., & Lim, K.O. (2011). Evidence of disrupted functional connectivity in the brain after combat-related blast injury. Neuroimage, 54, S21–S29.Google Scholar
Stemmer, B., Segalowitz, S.J., Witzke, W., & Schönle, P.W. (2004). Error detection in patients with lesions to the medial prefrontal cortex: An ERP study. Neuropsychologia, 42(1), 118–130.Google Scholar
Tsirka, V., Simos, P.G., Vakis, A., Kanatsouli, K., Vourkas, M., Erimaki, S., . . . Micheloyannis, S. (2011). Mild traumatic brain injury: Graph-model characterization of brain networks for episodic memory. International Journal of Psychophysiology, 79(2), 89–96.Google Scholar
Ullsperger, M., & von Cramon, D.Y. (2006). The role of intact frontostriatal circuits in error processing. Journal of Cognitive Neuroscience, 18(4), 651–664.Google Scholar
Van de Vijver, I., Ridderinkhof, K.R., & Cohen, M.X. (2011). Frontal oscillatory dynamics predict feedback learning and action adjustment. Journal of Cognitive Neuroscience, 23(12), 4106–4121.Google Scholar
Wessel, J.R., Klein, T.A., Ott, D.V., & Ullsperger, M. (2014). Lesions to the prefrontal performance-monitoring network disrupt neural processing and adaptive behaviors after both errors and novelty. Cortex, 50, 45–54.Google Scholar
Wessel, J.R., Ullsperger, M., Obrig, H., Villringer, A., Quinque, E., Schroeter, M.L., . . . & Klein, T.A. (2016). Neural synchrony indexes impaired motor slowing after errors and novelty following white matter damage. Neurobiology of Aging, 38, 205–213.Google Scholar
Wessel, J.R. (2018). An adaptive orienting theory of error processing. Psychophysiology, 55(3). doi:10.1111/psyp.13041Google Scholar
Zambrano-Vazquez, L., & Allen, J.J. (2014). Differential contributions of worry, anxiety, and obsessive compulsive symptoms to ERN amplitudes in response monitoring and reinforcement learning tasks. Neuropsychologia, 61, 197–209.Google Scholar
Zelazo, P.D., Anderson, J.E., Richler, J., Wallner-Allen, K., Beaumont, J.L., Conway, K.P., . . . Weintraub, S. (2014). NIH Toolbox Cognition Battery (CB): Validation of executive function measures in adults. Journal of the International Neuropsychological Society, 20(6), 620–629.Google Scholar
Supplementary material: PDF

Smith and Allen supplementary material

Smith and Allen supplementary material 1

Download Smith and Allen supplementary material(PDF)
PDF 329.6 KB