Joint impact on attention, alertness and inhibition of lesions at a frontal white matter crossroad

Abstract

In everyday life, information from different cognitive domains-such as visuospatial attention, alertness and inhibition-needs to be integrated between different brain regions. Early models suggested that completely segregated brain networks control these three cognitive domains. However, more recent accounts, mainly based on neuroimaging data in healthy participants, indicate that different tasks lead to specific patterns of activation within the same, higher-order and 'multiple-demand' network. If so, then a lesion to critical substrates of this common network should determine a concomitant impairment in all three cognitive domains. The aim of the present study was to critically investigate this hypothesis, i.e. to identify focal stroke lesions within the network that can concomitantly affect visuospatial attention, alertness and inhibition. We studied an unselected sample of 60 first-ever right-hemispheric, subacute stroke patients using a data-driven, bottom-up approach. Patients performed 12 standardized neuropsychological and oculomotor tests, four per cognitive domain. A principal component analysis revealed a strong relationship between all three cognitive domains: 10 of 12 tests loaded on a first, common component. Analysis of the neuroanatomical lesion correlates using different approaches (i.e. voxel-based and tractwise lesion-symptom mapping, disconnectome maps) provided convergent evidence on the association between severe impairment of this common component and lesions at the intersection of superior longitudinal fasciculus II and III, frontal aslant tract and, to a lesser extent, the putamen and inferior fronto-occipital fasciculus. Moreover, patients with a lesion involving this region were significantly more impaired in daily living cognition, which provides an ecological validation of our results. A probabilistic functional atlas of the multiple-demand network was performed to confirm the potential relationship between patients' lesion substrates and observed cognitive impairments as a function of the multiple-demand network connectivity disruption. These findings show, for the first time, that a lesion to a specific white matter crossroad can determine a concurrent breakdown in all three considered cognitive domains. Our results support the multiple-demand network model, proposing that different cognitive operations depend on specific collaborators and their interaction, within the same underlying neural network. Our findings also extend this hypothesis by showing (i) the contribution of superior longitudinal fasciculus and frontal aslant tract to the multiple-demand network; and (ii) a critical neuroanatomical intersection, crossed by a vast amount of long-range white matter tracts, many of which interconnect cortical areas of the multiple-demand network. The vulnerability of this crossroad to stroke has specific cognitive and clinical consequences; this has the potential to influence future rehabilitative approaches.

Keywords: alertness; inhibition; multiple-demand network; right-hemispheric stroke; visuospatial attention.

Figure 1

Consort flow diagram. Patients’ inclusion flow-chart based on the CONSORT 2010 guidelines: 99 patients were assessed for eligibility. Apart from a history of first-ever right-hemispheric stroke, the main inclusion criteria were age >18 years, normal or corrected-to-normal visual acuity, and being able to undergo an MRI scan. Exclusion criteria were other neurological diseases, major psychiatric diagnoses and alcohol/drug abuse. 63 patients were allocated to study participation, from which three patients withdrew for personal reasons. In the end, 60 patients completed the assessments and were included in the final analyses.

Figure 2

Behavioural analyses for the three cognitive domains. (A) The violin wrapping box-and-whisker plots of all z-transformed outcome variables included in the study. The width of the violins represents the proportion of patients with an equivalent z-value. The overall median z-values are indicated by the horizontal white line in each box-and-whisker plot. Each box represents the lower (Q1) to the upper (Q3) quartiles, with whiskers extending from the minimum to the maximum of 1.5 times the interquartile range. The number of available patient datasets for each variable is depicted at the bottom of each violin. Outliers are depicted by grey circles. Blue represents outcome variables typically measuring visuospatial attention (Letter Cancellation Tests = Letter CoC; Line Bisection Test = LB; CoC in the Five-Point Test = FPT CoC; mean gaze position during FVE), yellow represents outcome variables typically measuring alertness (TAP phasic alertness = phasic Alert; TAP tonic alertness = tonic Alert; mean fixation duration during FVE = FVE fix dur; peak saccade velocity during FVE = FVE peak vel), grey represents outcome variables typically measuring inhibition (percentage of perseverative errors in the Five-Point Test = FPT Persev; Go-NoGo paradigm of the FAB = Go-NoGo; errors in the Stroop Interference condition = STROOP; antisaccade errors = Antis). (B) The significant correlations between all 12 variables included in the PCA. The lines between variables represent significant correlations and their strength: the darker the shade, the stronger the correlation, as represented by the legend on the right-hand side of the panel. (C) The principal components extracted from outcome variables of the cognitive domains of visuospatial attention, alertness and inhibition, with factor loadings >0.40. The length of the bars represents the loading of each outcome variable onto the extracted factor components. The components were named as follows: Component 1 = common component; Component 2 = inhibition/alertness component; Component 3 = alertness component. The figure was illustrated using the R package ggplot2.^,

Figure 3

Neuroanatomical analysis. (A) The results of the VLSM analysis using the PCA factor values of the common component (i.e. PCA Component I) as predictive values. The results show two significant lesion clusters (red, with a total volume of 325 voxels). The first and larger cluster (top row) is located within the second branch of the SLF (SLF II, dark blue), the third branch of the SLF (SLF III, light blue) and the FAT (green). The second and smaller cluster (bottom row) is located within the SLF III, the anterior part of the putamen and the IFOF (yellow). Patients with right-hemispheric stroke presenting with a lesion within these clusters were significantly more likely to show an impairment in overall cognitive performance in all three considered cognitive domains, as reflected by the lower factor values in the common component (PCA Component I). (B) The results of the disconnectome map analysis for a ROI including all four white matter tracts identified as affected by the previous VLMS analysis, i.e. the SLF II, the SLF III, the FAT and the IFOF. Patients with right-hemispheric stroke presenting with a lesion within these clusters were significantly more likely to show an impairment in overall cognitive performance in all three considered cognitive domains, as reflected by the lower factor values in the common component. For both panels, lesion voxels that were a significant predictor for the common component factor values are depicted in red (significance level P < 0.05, based on the Brunner–Munzel test, false discovery rate-corrected, 4000 permutations). Lesion clusters and white matter tracts are displayed on the MNI152 template in MNI space, as available in MRIcroGL (https://www.nitrc.org/projects/mricrogl/). The axial slices are oriented according to the neurological convention. The position of each slice in MNI space is indicated by numbers at the top of the respective slices. White matter tracts are depicted according to published probabilistic diffusion tensor imaging atlases^, (the probability of voxels belonging to the SLF II (in dark blue), SLF III (in light blue), the FAT (in green) and the IFOF (in yellow) was set at ≥50%).

Figure 4

Clinical relevance. The relationship between factor values of the common component (i.e. PCA Component I), the number of tests with clinical significant impairments (top) and cognition during daily living (bottom) is shown. Top shows a clinically significant impaired behaviour in a larger number of tests measuring visuospatial attention (At, blue), Alertness (Al, yellow) and inhibition (I, grey) was accompanied by lower PCA values. Darker colours indicate a higher number of tests with clinically significant impairment (z < −1.5) in the respective cognitive domain. Bottom shows the individual PCA factor values on the common component (PCA Component I) significantly correlated with measures of cognitive performance in daily living (LIMOS cognition; P < 0.001, r = 0.575). Patients with a lesion involving the intersection of SLF II/III and FAT as well as Putamen/IFOF (red triangles) showed a more severe impairment in cognition during daily living than patients with a lesion not involving the aforementioned VLSM clusters [black circles; t(58) = −2.507, P = 0.015]. The probability of an individual brain lesion being in or outside the VLSM clusters is further depicted by the double-headed arrow. The figure was illustrated using the R package ggplot2.^,

Figure 5

A putative neuroanatomical model. The putative neuroanatomical model explains how a frontal lesion (red volume), located at the strategical intersection of fronto-frontal and fronto-parietal tracts, can disrupt multiple tracts interconnecting cortical areas within the MD network. In particular, the affected white matter fibre tracts are the SLF II (dark blue^,), the SLF III (light blue^,) and the FAT (green). The SLF II and III are generally known to connect parieto-temporal areas to frontal areas (SLF II connects the IPL with the frontal eye field^,, and SLF III connects the IPL and the STG with the IFG^,,,,). The FAT connects the posterior part of the IFG and the pre-SMA. The illustration was created using the HCP1065.2 mm template and the implemented automated fibre tracking tool, visualized on the respective T₁-image implemented in DSIstudio (v.2021.12.03; available at http://dsi-studio.labsolver.org/).