Changes in functional connectivity following intensive attention training in patients with traumatic brain injury. A pilot study

Authors

  • Hanna Persson Division of Rehabilitation Medicine, Danderyd University Hospital, Stockholm, Sweden
  • Tie-Qiang Li Department of Clinical Science, Intervention, and Technology, Karolinska Institutet, Stockholm, Sweden; Department of Medical Radiation and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden https://orcid.org/0000-0002-4866-5904
  • Gabriela Markovic Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden, Division of Rehabilitation Medicine, Danderyd University Hospital, Stockholm, Sweden https://orcid.org/0000-0002-7500-9531

DOI:

https://doi.org/10.2340/jrmcc.v7.12436

Keywords:

attention process training (APT), language capacity, traumatic brain injury (TBI), fMRI, functional connectivity metrics

Abstract

Objective: To explore functional connectivity after intensive attention training in the chronic phase after traumatic brain injury as clinical evidence indicates that intensive attention training improves attention dysfunction in persons with traumatic brain injury.

Design and subjects: A case series study. Two young adults, 13- and 18-months post traumatic brain injury, with traumatic brain injury induced attention deficits were assigned to 20 h of intensive attention training and neuroimaging.

Methods: Functional magnetic resonance imaging during a psychomotor vigilance test was conducted pre- and post-intervention.

Results: The neuroimaging indicated both increased and decreased connectivity density in frontal, posterior and subcortical brain regions, for some regions with separate change patterns for left and right hemisphere respectively, and an overall reduction in variability in functional connectivity.

Conclusion: The changed and decreased variability of functional connectivity in various brain regions, captured by fMRI during a psychomotor vigilance test after direct attention training in a small sample of persons with traumatic brain injury, suggests further studies of functional connectivity changes in neural networks.

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References

Markovic G, Schult ML, Elg M, Bartfai A. Beneficial effects of early attention process training after acquired brain injury: a randomized controlled trial. J Rehabil Med 2019; 52: jrm00011.

https://doi.org/10.2340/16501977-2628 DOI: https://doi.org/10.2340/16501977-2628

Blank IA, Kiran S, Fedorenko E. Can neuroimaging help aphasia researchers? Addressing generalizability, variability, and interpretability. Cogn Neuropsychol 2017; 34: 377–393.

https://doi.org/10.1080/02643294.2017.1402756 DOI: https://doi.org/10.1080/02643294.2017.1402756

Chand GB, Dhamala M. Interactions among the brain default-mode, salience, and central-executive networks during perceptual decision-making of moving dots. Brain Connect 2016; 6: 249–254.

https://doi.org/10.1089/brain.2015.0379 DOI: https://doi.org/10.1089/brain.2015.0379

Goulden N, Khusnulina A, Davis NJ, Bracewell RM, Bokde AL, McNulty JP, et al. The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM. NeuroImage 2014; 99: 180–190.

https://doi.org/10.1016/j.neuroimage.2014.05.052 DOI: https://doi.org/10.1016/j.neuroimage.2014.05.052

Li D, Christ SE, Cowan N. Domain-general and domain-specific functional networks in working memory. NeuroImage 2014; 102: 646–656.

https://doi.org/10.1016/j.neuroimage.2014.08.028 DOI: https://doi.org/10.1016/j.neuroimage.2014.08.028

Nyberg L. Kognitiv neurovetenskap: studier av sambandet mellan hjärnaktivitet och mentala processer. Lund: Studentlitteratur; 2009.

Petersen SE, Posner MI. The attention system of the human brain: 20 years after. Ann Rev Neurosci 2012; 35: 73–89.

https://doi.org/10.1146/annurev-neuro-062111-150525 DOI: https://doi.org/10.1146/annurev-neuro-062111-150525

Rosenberg MD, Hsu WT, Scheinost D, Todd Constable R, Chun MM. Connectome-based models predict separable components of attention in novel individuals. J Cogn Neurosci 2018; 30: 160–173.

https://doi.org/10.1162/jocn_a_01197 DOI: https://doi.org/10.1162/jocn_a_01197

Kim YH, Yoo WK, Ko MH, Park CH, Kim ST, Na DL. Plasticity of the attentional network after brain injury and cognitive rehabilitation. Neurorehabil Neural Repair 2009; 23: 468–477.

https://doi.org/10.1177/1545968308328728 DOI: https://doi.org/10.1177/1545968308328728

Sohlberg MM, Mateer CA. Cognitive rehabilitation: an integrative neuropsychological approach. Guilford Press. 2001.

Sohlberg MM, Johnson L, Paule L, Raskin SA, Mateer CA. Attention process training-II: a program to address attentional deficits for persons with mild cognitive dysfunction. Wake Forest, NC: Lash & Associates; 1989.

Sohlberg MM, Mateer CA. Effectiveness of an attention-training program, J Clin Exp Neuropsychol 1987; 9: 117–130. DOI: 10.1080/01688638708405352

Sohlberg MM, Mateer CA. Effectiveness of an attention-training program. J Clin Exp Neuropsychol 1987; 9: 117–130.

https://doi.org/10.1080/01688638708405352 DOI: https://doi.org/10.1080/01688638708405352

Rohling ML, Faust ME, Beverly B, Demakis G. Effectiveness of cognitive rehabilitation following acquired brain injury: a meta-analytic re-examination of Cicerone et al.’s (2000, 2005) systematic reviews. Neuropsychology 2009; 23: 20–39.

https://doi.org/10.1037/a0013659 DOI: https://doi.org/10.1037/a0013659

Eberlee R, Shapiro-Rosenbaum A, editors. The ACRM cognitive rehabilitation manual & textbook-translating evidence based recommendations into practice. 2022. Reston: ACRM Publishing; 2022.

Cicerone KD, Goldin Y, Ganci K, Rosenbaum A, Wethe JV, Langenbahn DM, et al. Evidence-based vognitive rehabilitation: systematic review of the literature from 2009 through 2014. Arch Phys Med Rehabili 2019; 100: 1515–1533.

https://doi.org/10.1016/j.apmr.2019.02.011 DOI: https://doi.org/10.1016/j.apmr.2019.02.011

Barker-Collo SL, Feigin VL, Lawes CM, Parag V, Senior H, Rodgers A. Reducing attention deficits after stroke using attention process training: a randomized controlled trial. Stroke 2009; 40: 3293–3298.

https://doi.org/10.1161/STROKEAHA.109.558239 DOI: https://doi.org/10.1161/STROKEAHA.109.558239

Sohlberg MM, McLaughlin KA, Pavese A, Heidrich A, Posner MI. Evaluation of attention process training and brain injury education in persons with acquired brain injury. J Clin Exp Neuropsychol 2000; 22: 656–676.

https://doi.org/10.1076/1380-3395(200010)22:5;1-9;FT656 DOI: https://doi.org/10.1076/1380-3395(200010)22:5;1-9;FT656

Tiersky LA, Anselmi V, Johnston MV, Kurtyka J, Roosen E, Schwartz T, et al. A trial of neuropsychologic rehabilitation in mild-spectrum traumatic brain injury. Arch Phys Med Rehabil 2005; 86: 1565–1574.

https://doi.org/10.1016/j.apmr.2005.03.013 DOI: https://doi.org/10.1016/j.apmr.2005.03.013

Bartfai A, Markovic G, Sargenius Landahl K, Schult ML. The protocol and design of a randomised controlled study on training of attention within the first year after acquired brain injury. BMC Neurol 2014; 14: 102.

https://doi.org/10.1186/1471-2377-14-102 DOI: https://doi.org/10.1186/1471-2377-14-102

Pantoni L, Poggesi A, Diciotti S, Valenti R, Orsolini S, Della Rocca E, et al. Effect of attention training in mild cognitive impairment patients with subcortical vascular changes: The RehAtt study. J Alzheimer’s Dis 2017; 60: 615–624.

https://doi.org/10.3233/JAD-170428 DOI: https://doi.org/10.3233/JAD-170428

Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological assessment. Oxford: Oxford University Press; 2012.

Wilson L, Boase K, Nelson LD, Temkin NR, Giacino JT, Markowitz AJ, et al. A manual for the Glasgow Outcome Scale-Extended interview. J Neurotrauma 2021; 38: 2435–2446.

https://doi.org/10.1089/neu.2020.7527 DOI: https://doi.org/10.1089/neu.2020.7527

Moller MC, Nordin LE, Bartfai A, Julin P, Li TQ. Fatigue and cognitive fatigability in mild traumatic brain injury are correlated with altered neural activity during vigilance test performance. Front Neurol 2017; 8: 496.

https://doi.org/10.3389/fneur.2017.00496 DOI: https://doi.org/10.3389/fneur.2017.00496

Nordin LE, Moller MC, Julin P, Bartfai A, Hashim F, Li TQ. Post mTBI fatigue is associated with abnormal brain functional connectivity. Sci Rep 2016; 6: 21183.

https://doi.org/10.1038/srep21183 DOI: https://doi.org/10.1038/srep21183

Thompson BJ, Shugart C, Dennison K, Louder TJ. Test-retest reliability of the 5-minute psychomotor vigilance task in working-aged females. J Neurosci Methods 2022; 365: 109379.

https://doi.org/10.1016/j.jneumeth.2021.109379 DOI: https://doi.org/10.1016/j.jneumeth.2021.109379

Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34: 537–541.

https://doi.org/10.1002/mrm.1910340409 DOI: https://doi.org/10.1002/mrm.1910340409

Li TQ, Wang Y, Hallin R, Juto JE. Resting-state fMRI study of acute migraine treatment with kinetic oscillation stimulation in nasal cavity. NeuroImage Clin 2016; 12: 451–459.

https://doi.org/10.1016/j.nicl.2016.08.014 DOI: https://doi.org/10.1016/j.nicl.2016.08.014

Wang Y, Berglund IS, Uppman M, Li TQ. Juvenile myoclonic epilepsy has hyper dynamic functional connectivity in the dorsolateral frontal cortex. Neuroimage Clin 2019; 21: 101604.

https://doi.org/10.1016/j.nicl.2018.11.014 DOI: https://doi.org/10.1016/j.nicl.2018.11.014

Li X, Fischer H, Manzouri A, Månsson KNT, Li TQ. A quantitative data-driven analysis framework for resting-state functional magnetic resonance imaging: a study of the impact of adult age. Front Neurosci 2021; 15: 768418.

https://doi.org/10.3389/fnins.2021.768418

Li X, Fischer H, Manzouri A, Månsson KNT, Li TQ. Dataset of whole-brain resting-state fMRI of 227 young and elderly adults acquired at 3T. Data Brief 2021; 38: 107333.

https://doi.org/10.1016/j.dib.2021.107333 DOI: https://doi.org/10.1016/j.dib.2021.107333

Hindriks R, Adhikari MH, Murayama Y, Ganzetti M, Mantini D, Logothetis NK, et al. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage 2016; 127: 242–256.

https://doi.org/10.1016/j.neuroimage.2015.11.055 DOI: https://doi.org/10.1016/j.neuroimage.2015.11.055

Mokhtari F, Akhlaghi MI, Simpson SL, Wu G, Laurienti PJ. Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state. NeuroImage 2019; 189: 655–666.

https://doi.org/10.1016/j.neuroimage.2019.02.001 DOI: https://doi.org/10.1016/j.neuroimage.2019.02.001

Tjernström N, Li TQ, Holst S, Roman E. Functional connectivity in reward-related networks is associated with individual differences in gambling strategies in male Lister hooded rats. Addict Biol 2022; 27: e13131.

https://doi.org/10.1111/adb.13131 DOI: https://doi.org/10.1111/adb.13131

Li X, Fischer H, Manzouri A, Månssonb K, Li TQ. A quantitative data-driven analysis (QDA) framework for resting-state fMRI: a study of the impact of adult age. Front Neurosci 2021; 15: 768418 DOI: https://doi.org/10.3389/fnins.2021.768418

Rolls ET, Huang CC, Lin CP, Feng J, Joliot M. Automated anatomical labelling atlas 3. Neuroimage 2020; 206: 116189.

https://doi.org/10.1016/j.neuroimage.2019.116189 DOI: https://doi.org/10.1016/j.neuroimage.2019.116189

Bellner J, Jensen SM, Lexell J, Romner B. Diagnostic criteria and the use of ICD-10 codes to define and classify minor head injury. J Neurol NeurosurgPsychiatry 2003; 74: 351–352.

https://doi.org/10.1136/jnnp.74.3.351 DOI: https://doi.org/10.1136/jnnp.74.3.351

Teasdale G, Maas A, Lecky F, Manley G, Stocchetti N, Murray G. The Glasgow Coma Scale at 40 years: standing the test of time. Lancet Neurol 2014; 13: 844–854.

https://doi.org/10.1016/S1474-4422(14)70120-6 DOI: https://doi.org/10.1016/S1474-4422(14)70120-6

Van Dongen HP, Maislin G, Mullington JM, Dinges DF. The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep 2003; 26: 117–126.

https://doi.org/10.1093/sleep/26.2.117 DOI: https://doi.org/10.1093/sleep/26.2.117

Drummond SP, Bischoff-Grethe A, Dinges DF, Ayalon L, Mednick SC, Meloy MJ. The neural basis of the psychomotor vigilance task. Sleep 2005; 28: 1059–1068.

Lim J, Dinges DF. A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychol Bull 2010; 136: 375–389.

https://doi.org/10.1037/a0018883 DOI: https://doi.org/10.1037/a0018883

Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain J Neurol 2014; 137: 12–32.

https://doi.org/10.1093/brain/awt162 DOI: https://doi.org/10.1093/brain/awt162

Markovic G, Schult ML, Bartfai A, Elg M. Statistical process control: a feasibility study of the application of time-series measurement in early neurorehabilitation after acquired brain injury. J Rehabil Med 2017; 49: 128–135.

https://doi.org/10.2340/16501977-2172 DOI: https://doi.org/10.2340/16501977-2172

Gronwall DM. Paced auditory serial-addition task: a measure of recovery from concussion. Percept Motor Skills 1977; 44: 367–373.

https://doi.org/10.2466/pms.1977.44.2.367 DOI: https://doi.org/10.2466/pms.1977.44.2.367

Wig GS. Segregated systems of human brain networks. Trends Cogn Sci. 2017; 21: 981–996.

https://doi.org/10.1016/j.tics.2017.09.006 DOI: https://doi.org/10.1016/j.tics.2017.09.006

Published

2024-01-16

How to Cite

Persson, H., Li, T.-Q., & Markovic, G. (2024). Changes in functional connectivity following intensive attention training in patients with traumatic brain injury. A pilot study. Journal of Rehabilitation Medicine - Clinical Communications, 7, jrmcc12436. https://doi.org/10.2340/jrmcc.v7.12436

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