Differential white matter involvement associated with distinct visuospatial deficits after right hemisphere stroke

Abstract

Visuospatial attention depends on the integration of multiple processes, and people with right hemisphere lesions after a stroke may exhibit severe or no visuospatial deficits. The anatomy of core components of visuospatial attention is an area of intense interest. Here we examine the relationship between the disruption of core components of attention and lesion distribution in a heterogeneous group (N=70) of patients with right hemisphere strokes regardless of the presence of clinical neglect. Deficits of lateralized spatial orienting, measured as the difference in reaction times for responding to visual targets in the contralesional or ipsilesional visual field, and deficits in re-orienting attention, as measured by the difference in reaction times for invalidly vs. validly cued targets, were measured using a computerized spatial orienting task. Both measures were related through logistic regression and a novel ridge regression method to anatomical damage measured with magnetic resonance imaging. While many regions were common to both deficit maps, a deficit in lateralized spatial orienting was more associated with lesions in the white matter underlying the posterior parietal cortex, and middle and inferior frontal gyri. A deficit in re-orienting of attention toward unattended locations was associated with lesions in the white matter of the posterior parietal cortex, insular cortex and less so with white matter involvement of the anterior frontal lobe. An hodological analysis also supports this partial dissociation between the white matter tracts that are damaged in lateralized spatial biases versus impaired re-orienting. Our results underscore that the integrity of fronto-parietal white matter tracts is crucial for visuospatial attention and that different attention components are mediated by partially distinct neuronal substrates.

Structural disconnections explain brain network dysfunction after stroke

“…SDC measures were defined for each patient based on the intersection of their lesion with a structural connectome atlas constructed with data from healthy individuals (see Methods). Similar atlas-based approaches have been used by other recent lesion studies (Carter et al, 2017;Foulon et al, 2018a;Griffis et al, 2017bGriffis et al, , 2017aHope et al, 2018;Kuceyeski et al, 2013Kuceyeski et al, , 2014Kuceyeski et al, , 2015Kuceyeski et al, , 2016bPustina et al, 2017a;Ramsey et al, 2017), and analogous strategies are often employed to study SC-FC relationships in animal models (Adachi et al, 2011;Grandjean et al, 2017;Grayson et al, 2016;Shen et al, 2015b). While these approaches assume similar approximations of individual structural connectomes by the atlas and cannot account for interindividual variability in the properties of un-damaged fiber pathways (Forkel and Catani, 2018;Forkel et al, 2014), they also offer some protection against potential biases arising from interindividual differences in diffusion MRI data quality, lesion effects on data processing/reconstruction, etc.…”

Using machine learning-based lesion behavior mapping to identify anatomical networks of cognitive dysfunction: spatial neglect and attention

“…These might have contributed to the heterogeneous results of previous lesion-symptom mapping investigations in spatial neglect using mass-univariate VLBM (Karnath et al, 2004Committeri et al, 2007;Sarri et al, 2009;Chechlacz et al, 2010;Saj et al, 2012;Thiebaut De Schotten et al, 2014;Rousseaux et al, 2015) and DTI/white matter fiber analyses (Thiebaut De Schotten et al, 2005;Urbanski et al, 2008Urbanski et al, , 2011Karnath et al, 2009;Shinoura et al, 2009;Ciaraffa et al, 2013;Thiebaut De Schotten et al, 2014;Umarova et al, 2014;Lunven et al, 2015;Vaessen et al, 2016;Carter et al, 2017). While using meta-analytic approaches combining various VLBM results, one might be able to find all critical parts of the presumed network.…”

Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions

“…The severity of hemi-spatial neglect, as estimated by the attention visual field factor scores, was most strongly correlated with the severity of disconnections sustained by the right SLF. Hemi-spatial neglect severity also correlated with the severity of disconnections sustained by the right AF and right frontal aslant tract (FAT), which have also been previously implicated in post-stroke visuo-spatial neglect (Carter et al, 2017;. Left motor deficit severity was most strongly correlated with the severity of disconnections sustained by the right CST.…”