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Frontotemporal effective connectivity during semantic feature judgments in patients with aphasia versus healthy controls

Erin L. Meier1* and Swathi Kiran1
  • 1 Boston University, Speech, Language, and Hearing Sciences, United States

Background Semantic judgments are mediated by integrated functioning between anatomically-remote areas spanning frontotemporoparietal cortex, including regions implicated in heteromodal conceptual processing (e.g., mid-left middle temporal gyrus [LMTG]), semantic control (e.g., left inferior frontal gyrus [LIFG]), and domain-general cognitive control (e.g., left middle frontal gyrus [LMFG]) (e.g., Binder et al., 2009; Noonan et al., 2013). Persons with aphasia (PWA) activate similar regions (e.g., Turkeltaub et al., 2011; Sims et al., 2016), but little is known about how stroke impacts dynamic connectivity of such regions. Therefore, we used fMRI and Dynamic Causal Modeling (DCM; Friston, Harrison, & Penny, 2003) to investigate differences between PWA and controls in connectivity of a three-node semantic network. Methods Sixteen PWA (10 males; mean age=64.9 years) and 17 age-matched controls (10 males; mean age=60.4 years) completed an event-related fMRI task in which participants verified features of pictured items (experimental condition) or scrambled pictures (control condition). The fMRI and DCM methods used in Meier, Kapse, & Kiran (2016) were applied in the present study. In brief, the DCM model space was organized according to exogenous input to LIFG, LMFG, and LMTG and included different combinations of uni- and bi-directional connections across models. The resulting 72 models were partitioned into separate families (i.e., #1: input LIFG, #2: input LMFG; #3: input LMTG). Random-effects individual and group-level family-wise Bayesian Model Selection (BMS) determined which family best fit these data (Penny et al., 2010). Bayesian Model Averaging yielded parameter values reflecting the strength/direction of experimental condition effects on all regions (i.e., Ep.C) and connections (i.e., Ep.B). Two-way (group x family) ANOVA and MANOVA were employed to investigate group differences by family in Ep.C and Ep.B, respectively. Results fMRI task accuracy was significantly lower in PWA than controls (t(17)=-3.47, p=.001). Group-level family-wise BMS revealed models with driving input to LIFG (family #1) best fit control data (xp=.881). Models with input to LMTG (family #3) best explained PWA data (xp=.499) but single-subject BMS indicated family #3 best fit only 6 PWA’s data. In regions, PWA had significantly weaker task-induced perturbation (Ep.C) than controls (F(1,93)=16.02, p<.001) but task effects on connections (Ep.B) were significantly more positive (i.e., excitatory) for PWA compared to controls (F(6,88)=4.43, p=.001) (see Figure 1). Post-hoc comparisons revealed this effect occurred across all connections (p<.001 to p=.048). A significant group x family interaction (F(12,178)=3.11, p=.001) indicated group differences varied by family for all connections excluding LMTG→LIFG. Discussion Models with input to LIFG (implicated in semantic control) best fit control data whereas models with input to LMTG (implicated in low-level conceptual processing) best explained group PWA data although heterogeneity in PWA model fit was noted. Poorer PWA task accuracy may reflect these connectivity differences as successful feature judgements require semantic control (e.g., Thompson-Schill et al., 1997). Additionally, despite weaker task effects in each region, PWA had more excitatory connections across families than controls, signifying a patient network that is functionally distinct from that of healthy individuals.

Figure 1
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Acknowledgements

This work was supported by the National Institutes of Health/National Institute on Deafness and Other Communication Disorders through grant NIH/NIDCD 1P50DC012283.

References

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Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. Neuroimage, 19(4), 1273-1302.

Meier, E. L., Kapse, K. J., & Kiran, S. (2016). The relationship between frontotemporal effective connectivity during picture naming, behavior, and preserved cortical tissue in chronic aphasia. Front. Hum. Neurosci., 10, 109. doi:10.3389/fnhum.2016.00109 [doi]

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Penny, W. D., Stephan, K. E., Daunizeau, J., Rosa, M. J., Friston, K. J., Schofield, T. M., et al. (2010). Comparing families of dynamic causal models. PLoS Computational Biology, 6(3), e1000709. doi:10.1371/journal.pcbi.1000709 [doi]

Sims, J. A., Kapse, K., Glynn, P., Sandberg, C., Tripodis, Y., & Kiran, S. (2016). The Relationships between the amount of spared tissue, percent signal change, and accuracy in semantic processing in aphasia. Neuropsychologia, 84, 113-126. doi:10.1016/j.neuropsychologia.2015.10.019 [doi]

Thompson-Schill, S. L., D'Esposito, M., Aguirre, G. K., & Farah, M. J. (1997). Role of left inferior prefrontal cortex in retrieval of semantic knowledge: A reevaluation. Proceedings of the National Academy of Sciences of the United States of America, 94(26), 14792-14797.

Turkeltaub, P. E., Messing, S., Norise, C., & Hamilton, R. H. (2011). Are networks for residual language function and recovery consistent across aphasic patients? Neurology, 76(20), 1726-1734. doi:10.1212/WNL.0b013e31821a44c1 [doi]

Keywords: Aphasia, Semantic Processing, fMRI, effective connectivity, Dynamic causal modeling (DCM)

Conference: 54th Annual Academy of Aphasia Meeting, Llandudno, United Kingdom, 16 Oct - 18 Oct, 2016.

Presentation Type: Poster Sessions

Topic: Student Submissions

Citation: Meier EL and Kiran S (2016). Frontotemporal effective connectivity during semantic feature judgments in patients with aphasia versus healthy controls. Front. Psychol. Conference Abstract: 54th Annual Academy of Aphasia Meeting. doi: 10.3389/conf.fpsyg.2016.68.00101

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Received: 30 Apr 2016; Published Online: 15 Aug 2016.

* Correspondence: Ms. Erin L Meier, Boston University, Speech, Language, and Hearing Sciences, Boston, Massachusetts, 02215, United States, e.meier@northeastern.edu