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Associations between interhemispheric functional connectivity and the Automated Neuropsychological Assessment Metrics (ANAM) in civilian mild TBI

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An Erratum to this article was published on 02 July 2014

Abstract

This study investigates cognitive deficits and alterations in resting state functional connectivity in civilian mild traumatic brain injury (mTBI) participants with high and low symptoms. Forty-one mTBI participants completed a resting state fMRI scan and the Automated Neuropsychological Assessment Metrics (ANAM) during initial testing (<10 days of injury) and a 1 month follow up. Data were compared to 30 healthy control subjects. Results from the ANAM demonstrate that mTBI participants performed significantly worse than controls on the code substitution delayed subtest (p = 0.032) and weighted throughput score (p = 0.001). Among the mTBI patients, high symptom mTBI participants performed worse than those with low symptoms on the code substitution delayed (p = 0.017), code substitution (p = 0.012), repeated simple reaction time (p = 0.031), and weighted throughput score (p = 0.009). Imaging results reveal that during the initial visit, low symptom mTBI participants had reduced interhemispheric functional connectivity (IH-FC) within the lateral parietal lobe (p = 0.020); however, during follow up, high symptom mTBI participants showed reduced IH-FC compared to the control group within the dorsolateral prefrontal cortex (DLPFC) (p = 0.013). Reduced IH-FC within the DLPFC during the follow-up was associated with reduced cognitive performance. Together, these findings suggest that reduced rs-FC may contribute to the subtle cognitive deficits noted in high symptom mTBI participants compared to control subjects and low symptom mTBI participants.

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Abbreviations

ANAM:

Automated Neuropsychological Assessment Metrics

DAI:

Diffuse axonal injury

DLPFC:

Dorsolateral prefrontal cortex

DMN:

Default Mode Network

DRS:

Disability Rating Scale

IH-FC:

Interhemspheric functional connectivity

GOSE:

Glasgow Outcome Scale Extended

MACE:

Military Initial Concussion Evaluation

MMSE:

Mini Mental Status Exam

mTBI:

Mild traumatic brain injury

PCS:

Post concussive syndrome

RPQ:

Rivermead Post Concussion Symptoms Questionnairre

rs-FC:

Resting state functional connectivity

rs-fMRI:

Resting state functional MRI

TPN:

Task Positive Network

References

  • Aoki, Y., Inokuchi, R., Gunshin, M., Yahagi, N., & Suwa, H. (2012). Diffusion tensor imaging of mild traumatic brain injury: a meta-analysis. Journal of Neurology, Neurosurgery, and Psychiatry, 83, 870–876.

    Article  PubMed Central  PubMed  Google Scholar 

  • Arfanakis, K., Haughton, V. M., Carew, J. D., Rogers, B. P., Dempsey, R. J., & Meyerand, M. E. (2002). Diffusion tensor MR imaging in diffuse axonal injury. AJNR - American Journal of Neuroradiology, 23, 794–802.

    PubMed  Google Scholar 

  • Bazarian, J. J., Zhong, J., Blyth, B., Zhu, T., Kavcic, V., & Peterson, D. (2007). Diffusion tensor imaging detects clinically important axonal damage after mild traumatic brain injury: a pilot study. Journal of Neurotrauma, 24, 1447–1459.

    Article  PubMed  Google Scholar 

  • Bleiberg, J., Garmoe, W. S., Halpern, E. L., Reeves, D. L., & Nadler, J. D. (1997). Consistency of within-day and across-day performance after mild brain injury. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 10, 247–253.

    CAS  PubMed  Google Scholar 

  • Brown, C. N., Guskiewicz, K. M., & Bleiberg, J. (2007). Athlete characteristics and outcome scores for computerized neuropsychological assessment: a preliminary analysis. Journal of Athletic Training, 42, 515–523.

    PubMed Central  PubMed  Google Scholar 

  • Centers for Disease Control and Prevention (CDC) (2003). National Center for Injury Prevention and Control. Report to Congress on mild traumatic brain injury in the United States: steps to prevent a serious public health problem. In: Atlanta, GA: Centers for Disease Control and Prevention.

  • Coldren, R. L., Russell, M. L., Parish, R. V., Dretsch, M., & Kelly, M. P. (2012). The ANAM lacks utility as a diagnostic or screening tool for concussion more than 10 days following injury. Military Medicine, 177, 179–183.

    Article  PubMed  Google Scholar 

  • Dischinger, P. C., Ryb, G. E., Kufera, J. A., & Auman, K. M. (2009). Early predictors of postconcussive syndrome in a population of trauma patients with mild traumatic brain injury. Journal of Trauma, 66, 289–296. discussion 296–7.

    Article  PubMed  Google Scholar 

  • Faul, M., Xu, L., Wald, M., Coronado, V. (2010). Traumatic brain injury in the United States: emergency department visits, hospitalizations, and deaths. Center of Disease Control.

  • Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.

    Article  CAS  PubMed  Google Scholar 

  • Gouvier, W. D., Blanton, P. D., LaPorte, K. K., & Nepomuceno, C. (1987). Reliability and validity of the disability rating scale and the levels of cognitive functioning scale in monitoring recovery from severe head injury. Archives of Physical Medicine and Rehabilitation, 68, 94–97.

    CAS  PubMed  Google Scholar 

  • Gysland, S. M., Mihalik, J. P., Register-Mihalik, J. K., Trulock, S. C., Shields, E. W., & Guskiewicz, K. M. (2012). The relationship between subconcussive impacts and concussion history on clinical measures of neurologic function in collegiate football players. Annals of Biomedical Engineering, 40, 14–22.

    Article  PubMed  Google Scholar 

  • Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006). Brain connectivity related to working memory performance. Journal of Neuroscience, 26, 13338–13343.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Hawkins, K. A., Jennings, D., Vincent, A. S., Gilliland, K., West, A., & Marek, K. (2012). Traditional neuropsychological correlates and reliability of the automated neuropsychological assessment metrics-4 battery for Parkinson’s disease. Parkinsonism & Related Disorders, 18, 864–870.

    Article  Google Scholar 

  • Hillary, F. G., Genova, H. M., Medaglia, J. D., Fitzpatrick, N. M., Chiou, K. S., Wardecker, B. M., et al. (2010). The nature of processing speed deficits in traumatic brain injury: is less brain more? Brain Imaging and Behavior, 4, 141–154.

    Article  PubMed  Google Scholar 

  • Iverson, G. L., Lovell, M. R., Smith, S., & Franzen, M. D. (2000). Prevalence of abnormal CT-scans following mild head injury. Brain Injury, 14, 1057–1061.

    Article  CAS  PubMed  Google Scholar 

  • Ivins, B. J., Kane, R., & Schwab, K. A. (2009). Performance on the automated neuropsychological assessment metrics in a nonclinical sample of soldiers screened for mild TBI after returning from Iraq and Afghanistan: a descriptive analysis. The Journal of Head Trauma Rehabilitation, 24, 24–31.

    Article  PubMed  Google Scholar 

  • Johansson, B., Berglund, P., & Ronnback, L. (2009). Mental fatigue and impaired information processing after mild and moderate traumatic brain injury. Brain Injury, 23, 1027–1040.

    Article  PubMed  Google Scholar 

  • Johnson, B., Zhang, K., Gay, M., Neuberger, T., Horovitz, S., Hallett, M., et al. (2012a). Metabolic alterations in corpus callosum may compromise brain functional < br/>connectivity in MTBI patients: an 1H-MRS study. Neuroscience Letters, 509, 5–8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Johnson, B., Zhang, K., Gay, M., Horovitz, S., Hallett, M., Sebastianelli, W., et al. (2012b). Alteration of brain default network in subacute phase of injury in concussed individuals: resting-state fMRI study. NeuroImage, 59, 511–518.

    Article  PubMed Central  PubMed  Google Scholar 

  • Kabat, M. H., Kane, R. L., Jefferson, A. L., & DiPino, R. K. (2001). Construct validity of selected automated neuropsychological assessment metrics (ANAM) battery measures. Clinical Neuropsychology, 15, 498–507.

    Article  CAS  Google Scholar 

  • Kane, R. L., Roebuck-Spencer, T., Short, P., Kabat, M., & Wilken, J. (2007). Identifying and monitoring cognitive deficits in clinical populations using Automated Neuropsychological Assessment Metrics (ANAM) tests. Archives of Clinical Neuropsychology, 22(Suppl 1), S115–S126.

    Article  PubMed  Google Scholar 

  • King, N. S., Crawford, S., Wenden, F. J., Moss, N. E., & Wade, D. T. (1995). The Rivermead post concussion symptoms questionnaire: a measure of symptoms commonly experienced after head injury and its reliability. Journal of Neurology, 242, 587–592.

    Article  CAS  PubMed  Google Scholar 

  • Kumar, R., Husain, M., Gupta, R. K., Hasan, K. M., Haris, M., Agarwal, A. K., et al. (2009). Serial changes in the white matter diffusion tensor imaging metrics in moderate traumatic brain injury and correlation with neuro-cognitive function. Journal of Neurotrauma, 26, 481–495.

    Article  PubMed  Google Scholar 

  • Lee, H., Wintermark, M., Gean, A., Ghajar, J., Manley, G., & Mukherjee, P. (2008). Focal lesions in acute mild traumatic brain injury and neurocognitive outcome: CT versus 3T MRI. Journal of Neurotrauma, 25, 1049–1056.

    Article  PubMed  Google Scholar 

  • Levinson, D., Reeves, D., Watson, J., & Harrison, M. (2005). Automated neuropsychological assessment metrics (ANAM) measures of cognitive effects of Alzheimer’s disease. Archives of Clinical Neuropsychology, 20, 403–408.

    Article  CAS  PubMed  Google Scholar 

  • Mac Donald, C. L., Johnson, A. M., Cooper, D., Nelson, E. C., Werner, N. J., Shimony, J. S., et al. (2011). Detection of blast-related traumatic brain injury in U.S. Military personnel. New England Journal of Medicine, 364, 2091–2100.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Marquez de la Plata, C. D., Garces, J., Shokri Kojori, E., Grinnan, J., Krishnan, K., Pidikiti, R., et al. (2011). Deficits in functional connectivity of hippocampal and frontal lobe circuits after traumatic axonal injury. Archives of Neurology, 68, 74–84.

    Article  PubMed Central  PubMed  Google Scholar 

  • Maruishi, M., Miyatani, M., Nakao, T., & Muranaka, H. (2007). Compensatory cortical activation during performance of an attention task by patients with diffuse axonal injury: a functional magnetic resonance imaging study. Journal of Neurology, Neurosurgery, and Psychiatry, 78, 168–173.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Mayer, A. R., Ling, J., Mannell, M. V., Gasparovic, C., Phillips, J. P., Doezema, D., et al. (2010). A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology, 74, 643–650.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Mayer, A. R., Mannell, M. V., Ling, J., Gasparovic, C., & Yeo, R. A. (2011). Functional connectivity in mild traumatic brain injury. Human Brain Mapping, 32, 1825–1835.

    Article  PubMed Central  PubMed  Google Scholar 

  • McAllister, T. W., Sparling, M. B., Flashman, L. A., Guerin, S. J., Mamourian, A. C., & Saykin, A. J. (2001). Differential working memory load effects after mild traumatic brain injury. NeuroImage, 14, 1004–1012.

    Article  CAS  PubMed  Google Scholar 

  • McAllister, T. W., Flashman, L. A., McDonald, B. C., & Saykin, A. J. (2006). Mechanisms of working memory dysfunction after mild and moderate TBI: evidence from functional MRI and neurogenetics. Journal of Neurotrauma, 23, 1450–1467.

    Article  PubMed  Google Scholar 

  • McCrea, M., Kelly, J., Randolph, C. (2000). Standardized Assessment of Concussion (SAC): Manual for Adminstration, Scoring, and Interpretation, Comprehensive Neuropsychological Services 2nd Eddition.

  • McDowell, S., Whyte, J., & D’Esposito, M. (1997). Working memory impairments in traumatic brain injury: evidence from a dual-task paradigm. Neuropsychologia, 35, 1341–1353.

    Article  CAS  PubMed  Google Scholar 

  • Miotto, E. C., Cinalli, F. Z., Serrao, V. T., Benute, G. G., Lucia, M. C., & Scaff, M. (2010). Cognitive deficits in patients with mild to moderate traumatic brain injury. Arquivos de Neuro-Psiquiatria, 68, 862–868.

    Article  PubMed  Google Scholar 

  • Pellicano, C., Kane, R. L., Gallo, A., Xiaobai, L., Stern, S. K., Ikonomidou, V. N., et al. (2013). Cognitive impairment and its relation to imaging measures in multiple sclerosis: a study using a computerized battery. Journal of Neuroimaging, 23, 445–452.

    Article  PubMed  Google Scholar 

  • Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59, 2142–2154.

    Article  PubMed Central  PubMed  Google Scholar 

  • Reeves, D. L., Bleiberg, J., Roebuck-Spencer, T., Cernich, A. N., Schwab, K., Ivins, B., et al. (2006). Reference values for performance on the automated neuropsychological assessment metrics V3.0 in an active duty military sample. Military Medicine, 171, 982–994.

    PubMed  Google Scholar 

  • Reeves, D. L., Winter, K. P., Bleiberg, J., & Kane, R. L. (2007). ANAM genogram: historical perspectives, description, and current endeavors. Archives of Clinical Neuropsychology, 22(Suppl 1), S15–S37.

    Article  PubMed  Google Scholar 

  • Reich, S., Short, P., Kane, R., Weiner, W., Shulman, L., Anderson, K. (2005). Validation of the ANAM Test Battery in Parkinson’s Disease. Ft. Belvoir: Defense Technical Information Center.

  • Roebuck-Spencer, T. M., Vincent, A. S., Twillie, D. A., Logan, B. W., Lopez, M., Friedl, K. E., et al. (2012). Cognitive change associated with self-reported mild traumatic brain injury sustained during the OEF/OIF conflicts. Clinical Neuropsychology, 26, 473–489.

    Article  Google Scholar 

  • Shumskaya, E., Andriessen, T. M. J. C., Norris, D. G., & Vos, P. E. (2012). Abnormal whole-brain functional networks in homogeneous acute mild traumatic brain injury. Neurology, 79, 176–182.

    Article  Google Scholar 

  • Slobounov, S. M., Gay, M., Zhang, K., Johnson, B., Pennell, D., Sebastianelli, W., et al. (2011). Alteration of brain functional network at rest and in response to YMCA physical stress test in concussed athletes: RsFMRI study. NeuroImage, 55, 1716–1727.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Sours, C., Zhuo, J., Janowich, J., Aarabi, B., Shanmuganathan, K., & Gullapalli, R. P. (2013). Default mode network interference in mild traumatic brain injury - a pilot resting state study. Brain Research, 1537, 201–215.

    Article  CAS  PubMed  Google Scholar 

  • Tang, L., Ge, Y., Sodickson, D. K., Miles, L., Zhou, Y., Reaume, J., et al. (2011). Thalamic resting-state functional networks: disruption in patients with mild traumatic brain injury. Radiology, 260, 831–840.

    Article  PubMed Central  PubMed  Google Scholar 

  • Teasdale, G. M., Pettigrew, L. E., Wilson, J. T., Murray, G., & Jennett, B. (1998). Analyzing outcome of treatment of severe head injury: a review and update on advancing the use of the Glasgow outcome scale. Journal of Neurotrauma, 15, 587–597.

    Article  CAS  PubMed  Google Scholar 

  • Thorne, D. R. (2006). Throughput: a simple performance index with desirable characteristics. Behavioural Brain Methods, 38, 569–573.

    Google Scholar 

  • van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: a review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20, 519–534.

    Article  PubMed  Google Scholar 

  • Vincent, A. S., Bleiberg, J., Yan, S., Ivins, B., Reeves, D. L., Schwab, K., et al. (2008). Reference data from the automated neuropsychological assessment metrics for use in traumatic brain injury in an active duty military sample. Military Medicine, 173, 836–852.

    Article  PubMed  Google Scholar 

  • Vincent, A. S., Roebuck-Spencer, T., Gilliland, K., & Schlegel, R. (2012). Automated neuropsychological assessment metrics (v4) traumatic brain injury battery: military normative data. Military Medicine, 177, 256–269.

    Article  PubMed  Google Scholar 

  • Warner, M. A., Marquez de la Plata, C., Spence, J., Wang, J. Y., Harper, C., Moore, C., et al. (2010). Assessing spatial relationships between axonal integrity, regional brain volumes, and neuropsychological outcomes after traumatic axonal injury. Journal of Neurotrauma, 27, 2121–2130.

    Article  PubMed Central  PubMed  Google Scholar 

  • Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect, 2, 125–141.

    Article  PubMed  Google Scholar 

  • World Health Organization. (2010). International Classification of Disease, 10th Review. In: World Health Organization.

  • Yuh, E. L., Mukherjee, P., Lingsma, H. F., Yue, J. K., Ferguson, A. R., Gordon, W. A., et al. (2013). Magnetic resonance imaging improves 3-month outcome prediction in mild traumatic brain injury. Annals of Neurology, 73, 224–235.

    Article  PubMed Central  PubMed  Google Scholar 

  • Zhou, Y., Milham, M. P., Lui, Y. W., Miles, L., Reaume, J., Sodickson, D. K., et al. (2012). Default-mode network disruption in mild traumatic brain injury. Radiology, 265, 882–892.

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank Joshua Betz, Jacqueline Janowich and Teodora Stoica for their help with participant recruitment and George Makris for his help with acquiring and processing the data. This study was supported by DOD award W81XWH-08-1-0725 and W81XWH- 12-1-0098. Chandler Sours was partly supported by an NRSA Grant from the National Institute of Neurological Disorders and Stroke (1F31NS081984).

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Authors and Affiliations

  1. Magnetic Resonance Research Center, University of Maryland School of Medicine, Baltimore, MD, 21201, USA

    Chandler Sours, Joseph Rosenberg, Steve Roys, Jiachen Zhuo & Rao P. Gullapalli

  2. Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, 22 South Greene Street, Baltimore, MD, 21201, USA

    Chandler Sours, Joseph Rosenberg, Steve Roys, Jiachen Zhuo, Kathirkamanthan Shanmuganathan & Rao P. Gullapalli

  3. Program in Neuroscience, University of Maryland School of Medicine, Baltimore, MD, 21201, USA

    Chandler Sours & Rao P. Gullapalli

  4. Georgetown University School of Medicine, Washington, DC, 20057, USA

    Robert Kane

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  1. Chandler Sours
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Correspondence to Rao P. Gullapalli.

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Supplemental Figure 1

Comparison of mean motion parameters for the three groups. Bar graphs of the mean of the 6 motion parameters for the three groups (control, low symptom mTBI, high symptom mTBI) for (A) the initial visit and (B) the 1 month follow up. Significance assessed using separate One-Way ANOVA between groups at each time point to assess group differences between the three groups. * p <0.05. (JPEG 34 kb)

High resolution Image (TIFF 1454 kb)

Supplemental Figure 2

Scatter plot of the mean translation in the y direction versus IH-FC within the dorsolateral prefrontal cortex (DLPFC), thalamus, lateral parietal lobe, and medial temporal lobe (MTL). Data is shown for all rs-fMRI scans (control at one visit and high symptom mTBI and low symptom mTBI at the initial and 1 month follow up). Significance assessed Pearson’s bivariate correlations. (JPEG 39 kb)

High resolution Image (TIFF 1544 kb)

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Sours, C., Rosenberg, J., Kane, R. et al. Associations between interhemispheric functional connectivity and the Automated Neuropsychological Assessment Metrics (ANAM) in civilian mild TBI. Brain Imaging and Behavior 9, 190–203 (2015). https://doi.org/10.1007/s11682-014-9295-y

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