Project 3

Verbal communication is a highly complex process. Language recruits a set of brain regions, some of which are specific for speech and language, while others are also involved in memory, attention, and other functions. Stroke can damage any part of this delicate network, as well as disrupt the communication between parts of the network. This damage often leads to aphasia, the clinical term for impairment of speech and language, which is the focus of the research of the C-STAR team.

Project 3, led by Dr. Chris Rorden, aims to provide the methodology which helps to better understand the link between the damage to the brain, the resulting impairment of speech and language, and the potential to recover when the patient undergoes therapy. This project utilizes many types of brain scanning (evaluation of grey and white matter integrity, brain activity, and blood supply) to get a rich and detailed picture of post-stroke brain damage. This information is analyzed in relation to the data that we get from our team of speech-language pathologists, who evaluate the stroke patients on a wide range of tasks (speech fluency, comprehension, articulation, repetition, and many others). We are developing and validating methods that can combine brain imaging and clinical tests to predict which individuals are most likely to recover, and identify individuals who will benefit from specific forms of therapy. We hope the end result will be treatment tailored to optimize each individual’s recovery.

The team that is working on Project 3 has been active in developing and distributing the tools for data analysis and visualization of brain imaging data:
http://www.mccauslandcenter.sc.edu/crnl/tools

Selected academic publications:

Teghipco, A. et al. (2024) ‘Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity’, Communications medicine. Springer Science and Business Media LLC, 4(1), p. 115. doi: 10.1038/s43856-024-00541-8.

Hannan, J. et al. (2024) ‘Under pressure: the interplay of hypertension and white matter hyperintensities with cognition in chronic stroke aphasia’, Brain communications. Oxford University Press (OUP), 6(3), p. fcae200. doi: 10.1093/braincomms/fcae200.

Busby, N. et al. (2024) ‘Regional brain aging: premature aging of the domain general system predicts aphasia severity’, Communications biology. Springer Science and Business Media LLC, 7(1), p. 718. doi: 10.1038/s42003-024-06211-8.

Matchin, W. et al. (2024) ‘Verbal working memory and syntactic comprehension segregate into the dorsal and ventral streams’, bioRxiv.org: the preprint server for biology. doi: 10.1101/2024.05.05.592577.

Harrington, R. M. et al. (2024) ‘Dissociating reading and auditory comprehension in persons with aphasia’, Brain communications, 6(2), p. fcae102. doi: 10.1093/braincomms/fcae102.

Bonilha, L. et al. (2024) ‘Improved naming in patients with Broca’s aphasia with tDCS’, Journal of neurology, neurosurgery, and psychiatry. BMJ, 95(3), pp. 273–276. doi: 10.1136/jnnp-2023-331541.

Johnson, L. et al. (2024) ‘Progressive lesion necrosis is related to increasing aphasia severity in chronic stroke’, NeuroImage. Clinical. Elsevier BV, 41(103566), p. 103566. doi: 10.1016/j.nicl.2024.103566.

Busby, N., Newman-Norlund, R., Wilmskoetter, J., et al. (2023) ‘Longitudinal progression of white matter hyperintensity severity in chronic stroke aphasia’, Archives of rehabilitation research and clinical translation. Elsevier BV, 5(4), p. 100302. doi: 10.1016/j.arrct.2023.100302.

Busby, N., Wilson, S., Wilmskoetter, J., et al. (2023) ‘White matter hyperintensity load mediates the relationship between age and cognition’, Neurobiology of aging. Elsevier BV, 132, pp. 56–66. doi: 10.1016/j.neurobiolaging.2023.08.007.

Busby, N., Newman-Norlund, S., Sayers, S., et al. (2023) ‘Lower socioeconomic status is associated with premature brain aging’, Neurobiology of aging. Elsevier BV, 130, pp. 135–140. doi: 10.1016/j.neurobiolaging.2023.06.012.

Stockbridge, M. D. et al. (2023) ‘Subacute aphasia recovery is associated with resting-state connectivity within and beyond the language network’, Annals of clinical and translational neurology, 10(9), pp. 1525–1532. doi: 10.1002/acn3.51842.

Wilmskoetter, J. et al. (2023) ‘Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity’, Communications biology, 6(1), p. 727. doi: 10.1038/s42003-023-05119-z.

Teghipco, A. et al. (2023) ‘Distinct brain morphometry patterns revealed by deep learning improve prediction of aphasia severity’, Research square. doi: 10.21203/rs.3.rs-3126126/v1.

Roth, R. et al. (2023) ‘Diabetes, brain health, and treatment gains in post-stroke aphasia’, Cerebral cortex (New York, N.Y.: 1991), 33(13), pp. 8557–8564. doi: 10.1093/cercor/bhad140.

Zhu, H. et al. (2023) ‘How can graph theory inform the dual-stream model of speech processing? a resting-state fMRI study of post-stroke aphasia’, bioRxiv.org: the preprint server for biology. doi: 10.1101/2023.04.17.537216.

Busby, N., Hillis, A. E., Bunker, L., et al. (2023) ‘Comparing the brain-behaviour relationship in acute and chronic stroke aphasia’, Brain communications. Oxford University Press (OUP), 5(2), p. fcad014. doi: 10.1093/braincomms/fcad014.

Busby, N., Wilmskoetter, J., Gleichgerrcht, E., et al. (2023) ‘Advanced brain age and chronic poststroke aphasia severity’, Neurology. Ovid Technologies (Wolters Kluwer Health), 100(11), pp. e1166–e1176. doi: 10.1212/WNL.0000000000201693.

Kristinsson, S. et al. (2023) ‘Predicting Outcomes of Language Rehabilitation: Prognostic factors for immediate and long-term outcomes after aphasia therapy’, Journal of speech, language, and hearing research: JSLHR, 66(3), pp. 1068–1084. doi: 10.1044/2022_JSLHR-22-00347.

Yourganov, G., Fridriksson, J., Rorden, C., Gleichgerrcht, E., & Bonilha, L. (2016). Multivariate connectome-based symptom mapping in post-stroke patients: Networks supporting language and speech. The Journal of Neuroscience, 36(25), 6668-6679.

Yourganov, G., Smith, K. G., Fridriksson, J., & Rorden, C. (2015). Predicting aphasia type from brain damage measured with structural MRI. Cortex, 73, 203-215.

Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., & Karnath, H.O. (2012) Age-specific CT and MRI templates for spatial normalization. NeuroImage, 61, 957-65.

Rorden, C., Karnath, H., & Bonilha, L. (2007) Improving lesion-symptom mapping. Journal of Cognitive Neuroscience, 19, 1081-1088.

Rorden, C., Fridriksson, J., Karnath, H.O. (2009) An evaluation of traditional and novel tools for lesion behavior mapping. Neuroimage, 44, 1355-1362.