Left frontotemporal effective connectivity during semantic feature judgments in patients with chronic aphasia and age-matched healthy controls

Abstract

Traditional models of neural reorganization of language skills in patients with chronic stroke-induced aphasia (PWA) propose activation of reperfused or spared left hemisphere tissue results in the most favorable language outcomes. However, these models do not fully explain variable behavioral recovery patterns observed in chronic patients. Instead, investigation of connectivity patterns of critical network nodes may elucidate better-informed recovery models. In the present study, we combined fMRI and dynamic causal modeling (DCM) to examine effective connectivity of a simple three-node left hemisphere network during a semantic feature decision task in 25 PWA and 18 age-matched neurologically intact healthy controls. The DCM model space utilized in Meier, Kapse, & Kiran (2016), which was organized according to exogenous input to one of three regions (i.e., left inferior frontal gyrus, pars triangularis [LIFGtri], left posterior middle temporal gyrus [LpMTG], or left middle frontal gyrus [LMFG]) implicated in various levels of lexical-semantic processing, was interrogated. This model space included all possible combinations of uni- and bidirectional task-modulated connections between LIFGtri, LMFG and LpMTG, resulting in 72 individual models that were partitioned into three separate families (i.e., Family #1: Input to LIFGtri, Family #2: Input to LMFG, Family #3: Input to LpMTG). Family-wise Bayesian model selection revealed Family #2: Input to LMFG best fit both patient and control data at a group level. Both groups relied heavily on LMFG's modulation of the other two model regions. By contrast, between-group differences in task-modulated coupling of LIFGtri and LpMTG were observed. Within the patient group, the strength of activity in LIFGtri and connectivity of LpMTG → LIFGtri were positively associated with lexical-semantic abilities inside and outside of the scanner, whereas greater recruitment of LpMTG was associated with poorer lexical-semantic skills.

Keywords: Aphasia; Connectivity; Dynamic causal modeling; Lexical-semantics.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Figures

Figure 1.

fMRI task. (A) Example experimental and control trials and comparison of (B) fMRI task accuracy and (C) reaction times between participant groups

Figure 2.

Definition of anatomically-constrained bounding masks. (A) Rendered results of the control group 2nd-level analysis for pictures – scrambled pictures, uncorrected p < 0.001 are shown and activation peaks within each ROI that were used as the center input in the anatomically-constrained bounding masks are circled. (B) Rectangular bounding boxes at a search depth of 12mm are shown as dark edges. The edges were trimmed to constrain the masks to the anatomical boundaries of the ROIs, resulting in the three lightly-shaded masks used in other analyses. (C) Overlay of lesion maps from the patient group included in the DCM analysis are shown in sagittal, coronal and axial slices. (D) Lesion masks (in which lesioned voxels were deleted) were manually drawn slice-by-slice on each patient’s T1 structural image in native space to create lesion maps (in which lesioned voxels were preserved). The normalized lesion map overlaid on the normalized T1 structural image for one participant (i.e., P13) is shown to illustrate this process. (E) Each patient’s lesion map was overlaid onto the anatomical bounding masks shown in (B) in order to create individually-tailored bounding masks reflecting the spared tissue within each mask. The percentage of residual tissue within each mask was determined by subtracting the patient’s lesion volume from the volume of the anatomically-constrained bounding mask, divided by the volume of the mask. The lesion map (in white) and individually-tailored bounding masks are shown for P13. (F) Visual inspection of overlaid t maps for pictures – scrambled pictures, lesion maps, and individually-constrained bounding masks for each patient ensured that the extracted VOIs fell outside the lesion and within (or approximate to) the bounding mask borders. The location of P13’s VOIs extracted for the connectivity analysis are denoted by the yellow circles.

Figure 3.

Activity within bounding masks. (A) Rendered t-maps from the one-sample t-tests (at p

Figure 4.

VOI location. Overlays shown of all regional peaks for all (A) controls and (B) patients. (C) The results of the MANOVA revealed the distance between each subject’s regional peak and the corresponding bounding mask peak did not differ between groups for LIFGtri and LMFG but approached significance for LpMTG. Note: for overlay of patients’ VOIs, only functional spheres are shown (i.e., n = 21 LIFGtri VOIs, 24 LMFG VOIs, and 20 LpMTG VOIs)

Figure 5.

Family-wise Bayesian model selection within each group

Figure 6.

Modulatory connections within the (A) controls and (B) PWA, with statistically significant and non-significant connections per one-sample t-tests denoted by solid and dashed lines, respectively. Purple = LMFG, blue = LpMTG, green = LIFGtri

Figure 7.

Neural metrics predicting lexical-semantics showing relationships between (A) fMRI task accuracy and the influence of task-modulation on the strength of LpMTG→LIFGtri per Ep.B values (B) between fMRI task accuracy and contrast estimates within the LIFGtri bounding mask (C) between accuracy on PALPA 51 and the strength of LpMTG→LIFGtri (D) and between accuracy on PALPA 51 and contrast estimates extracted from the LIFGtri and LpMTG bounding masks.