Structural Variability within Frontoparietal Networks and Individual Differences in Attentional Functions: An Approach Using the Theory of Visual Attention

Abstract

Visuospatial attention allows us to select and act upon a subset of behaviorally relevant visual stimuli while ignoring distraction. Bundesen's theory of visual attention (TVA) (Bundesen, 1990) offers a quantitative analysis of the different facets of attention within a unitary model and provides a powerful analytic framework for understanding individual differences in attentional functions. Visuospatial attention is contingent upon large networks, distributed across both hemispheres, consisting of several cortical areas interconnected by long-association frontoparietal pathways, including three branches of the superior longitudinal fasciculus (SLF I-III) and the inferior fronto-occipital fasciculus (IFOF). Here we examine whether structural variability within human frontoparietal networks mediates differences in attention abilities as assessed by the TVA. Structural measures were based on spherical deconvolution and tractography-derived indices of tract volume and hindrance-modulated orientational anisotropy (HMOA). Individual differences in visual short-term memory (VSTM) were linked to variability in the microstructure (HMOA) of SLF II, SLF III, and IFOF within the right hemisphere. Moreover, VSTM and speed of information processing were linked to hemispheric lateralization within the IFOF. Differences in spatial bias were mediated by both variability in microstructure and volume of the right SLF II. Our data indicate that the microstructural and macrostrucutral organization of white matter pathways differentially contributes to both the anatomical lateralization of frontoparietal attentional networks and to individual differences in attentional functions. We conclude that individual differences in VSTM capacity, processing speed, and spatial bias, as assessed by TVA, link to variability in structural organization within frontoparietal pathways.

Keywords: diffusion tractography; frontoparietal attention networks; individual differences; theory of visual attention; visual attention; visuospatial memory.

Figure 1.

CombiTVA paradigm: stimulus display conditions (whole report and partial report) and experimental design.

Figure 2.

The raw data plots from three representative participants illustrating the relationship between the raw data and the five calculated parameters. A, Whole report data when 6 targets are presented at the different exposure duration. In addition to the plotted raw scores/observations (black triangles), the scores predicted by the TVA model are plotted (solid black line) to indicate that the model is a good fit to the data and to illustrate how K, C, and t₀ parameters are related to these scores. B, The data when 2 targets are presented with and without distractors (2T4D = 2 targets and 4 distractors; 2T0D = 2 targets and 0 distractors). The difference between both conditions relates to α parameter. C, Average number of correctly reported letters in the left and right visual field (averaged across all conditions in the experiment). The difference between both conditions measures the laterality index (ω_index). To further assess the relationship between raw data and the calculated TVA parameters, we performed correlation analyses, which showed overall good correspondence between the raw and calculated measures. For example: (1) the raw scores for the whole report: 6 target letter condition (measuring the number of reported letters at the longest exposure duration, i.e., 200 ms) were strongly correlated with TVA-based K parameter (r = 0.91); (2) the difference in the raw scores between the number of correctly reported letters at 10 and 80 ms was strongly correlated with TVA-based parameter C (r = 0.85); (3) based on the raw scores for conditions 2T0D and 2T4D (2T4D = 2 targets and 4 distractors; 2T0D = 2 targets and 0 distractors), we calculated an attentional selection index 2T0D/(2T0D + 2T4D), which was strongly correlated with TVA-based α parameter (r = 0.88); and (4) based on the raw scores for the left and right visual fields, we calculated the following LI left/(left + right), which was strongly correlated with the TVA-based laterality index (ω_index; r = 0.99).

Figure 3.

Location and delineation of ROIs used in spherical deconvolution tractography. A, For the three branches of the SLF (I, II, and III), a multiple ROI approach was used (Thiebaut de Schotten et al., 2011). Frontal ROIs in the superior frontal gyrus (SFg), middle frontal gyrus (MFg), and precentral gyrus (Prg) were used for reconstruction of SLF I, SLF II, and SLF III, respectively, in the right and left hemisphere. These frontal ROIs were used in combination with a single parietal (Pa) ROI for all three branches of SLF in both hemispheres. A final “not” ROI was defined in temporal (Te) lobe of each hemisphere, to exclude fibers of the arcuate fasciculus. This “not” ROI was applied for the tractography of all three branches of SLF. B, For the IFOF connecting the frontal and occipital lobes, we used two ROIs for both right and left hemispheres: one within frontal cortex (Fr) at the level where frontal and temporal lobes are separated and the other one within occipital cortex (Oc) at the level of parieto-occipital sulcus (Mori et al., 2002).

Figure 4.

Examples of spherical deconvolution tractography reconstruction of the frontoparietal pathways in three participants. Three branches of the SLF, I (light blue and red), II (dark blue and yellow), and III (violet and green), and the IFOF (pink and purple) reconstructed within the right and the left hemispheres.

Figure 5.

Hemispheric lateralization of the frontoparietal pathways. Mean HMOA index (±SD) of (A) the IFOF and the three branches of superior longitudinal fasciculus: (B) SLF I, (C) SLF II, and (D) SLF III in the left and right hemispheres. *p < 0.01. **p < 0.00005. Left, Left hemisphere; Right, right hemisphere.

Figure 6.

Relationship between structural variability and individual differences in attentional abilities. A, Correlations between visual short-term memory capacity (parameter K) and HMOA measures within the right frontoparietal pathways (SLF II and SLF III and IFOF) and the HMOA-derived SLF II and IFOF lateralization indices. B, Correlations between speed of information processing (parameter C) and the HMOA-derived IFOF LI. C, Correlations between attentional spatial bias (ω_index) and HMOA measures within the right SLF II.