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The role of functional modularity in recovery from chronic aphasia

Yuan Tao1* and Brenda Rapp1
  • 1 Johns Hopkins University, United States

Graph theoretic analyses of functional network connectivity have revealed that the brain is organized into a non-random, modular structure consisting of densely inter-connected clusters of brain regions Specifically, modular structure relies on two types of “hubs”: “local hubs” that connect regions within one module, and “global hubs” that facilitate communication between modules. Functional modularity structure has been linked to cognitive performance in healthy populations (e.g. Bassett et al., 2011) and various neurological disorders (e.g. Bucker et al., 2009; Lynall et al., 2010; Duncan & Small, 2016). Understanding how lesions affect modularity structure can provide critical insights into neural plasticity. Computer simulations predict that lesions affecting hub areas will have the most widespread effects (Honey & Sporns, 2008). However, the consequences of actual lesions are less clear. We review current research on the use of modularity (Newman, 2006) and related graph-theoretic measures in understanding aphasia and language recovery and provide a detailed example from our own research. Methods and Results Participants were 15 individuals with acquired dysgraphia (n=15, 4 females, age 61+/-10) resulting from a left-hemisphere stroke (>1 year post-stroke). FMRI data were collected while participants performed a spelling task before and after behavioral intervention. We estimated the whole-brain functional connectivity for each participant (lesioned regions excluded) at each time-point, and calculated modularity and related measures based on the modular structure derived from an age-matched, healthy control group. We found a significant increase in modularity from pre- to post-treatment (p<0.01). Regression analysis demonstrated that higher modularity scores before treatment were related to less severe deficits (p<0.05) and greater treatment gains (p<0.1), suggesting that higher modularity scores index a system with higher functionality which might, in turn, retain greater re-learning capacity. The effects were similar in both the ipsilesional (LH) and contralesional hemispheres (RH). To investigate the underlying bases for the observed modularity increases, we identified the global and local hubs from the control data and examined their connectivity properties in the lesioned brains. The global hubs (i.e., inter-module connectors) exhibited no difference between the lesioned and the control group, neither at pre- nor post-treatment. In contrast, the local hubs (i.e., within-module connectors) exhibited lower within-module density in the lesioned participants and, furthermore, these values increased significantly from pre to post-treatment. These results indicate that the lesions primarily affected the within-module connectivity that was strengthened by the treatment, supporting the observed improvements in spelling. Conclusions We used modularity, a graph-theoretic measure, to assess the neural integrity of the functional connectivity of participants with chronic stroke-induced dysgraphia before and after behavioral treatment. We found that modularity indexed deficit severity and extent of re-learning such that an increase in modularity was associated with behavioral improvement and that treatment leads to greater normalization of within-module integration. Consistent with other network-based connectivity studies (e.g. Gratton et al., 2012), our findings demonstrate that modular structure is an important principle of functional organization of the brain and that modularity can be a useful tool for evaluating post-stroke neural re-organization.

Acknowledgements

The research reported here is part of a multi-site NIH-funded P50 project examining the neurobiology of language recovery in people with aphasia (DC 012283). We thank our participants for their effort and dedication, Jennifer Shea and Donna Gotsch for their work with the participants.

References

Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences, 108(18), 7641-7646.

Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., ... & Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. Journal of neuroscience, 29(6), 1860-1873.

Duncan, E. S., & Small, S. L. (2016). Increased modularity of resting state networks supports improved narrative production in aphasia recovery. Brain connectivity, 6(7), 524-529.

Gratton, C., Nomura, E. M., Pérez, F., & D'Esposito, M. (2012). Focal brain lesions to critical locations cause widespread disruption of the modular organization of the brain. Journal of cognitive neuroscience, 24(6), 1275-1285.

Honey, C. J., & Sporns, O. (2008). Dynamical consequences of lesions in cortical networks. Human brain mapping, 29(7), 802-809.

Lynall, M. E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Muller, U., & Bullmore, E. (2010). Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience, 30(28), 9477-9487.

Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577-8582.

Keywords: Network analysis, fMRI, Stroke, dysgraphia, Recovery of Function, Treatment

Conference: Academy of Aphasia 56th Annual Meeting, Montreal, Canada, 21 Oct - 23 Oct, 2018.

Presentation Type: symposium

Topic: not eligible for a student prize

Citation: Tao Y and Rapp B (2019). The role of functional modularity in recovery from chronic aphasia. Conference Abstract: Academy of Aphasia 56th Annual Meeting. doi: 10.3389/conf.fnhum.2018.228.00091

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Received: 30 Apr 2018; Published Online: 22 Jan 2019.

* Correspondence: Dr. Yuan Tao, Johns Hopkins University, Baltimore, United States, yuan.tao@jhu.edu