Anosognosia for hemiplegia as a tripartite disconnection syndrome

Abstract

The syndrome of Anosognosia for Hemiplegia (AHP) can provide unique insights into the neurocognitive processes of motor awareness. Yet, prior studies have only explored predominately discreet lesions. Using advanced structural neuroimaging methods in 174 patients with a right-hemisphere stroke, we were able to identify three neural systems that contribute to AHP, when disconnected or directly damaged: the (i) premotor loop (ii) limbic system, and (iii) ventral attentional network. Our results suggest that human motor awareness is contingent on the joint contribution of these three systems.

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

“…The white matter connections of the human brain are often described in terms of canonical macroscale white matter tracts (Catani et al, 2002;Yeh et al, 2018) that include association pathways such as the arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF), projection pathways such as the cortico-spinal tract (CST) and cortico-thalamic (CT) projections, and commissural pathways such as the corpus callosum (CC) and anterior commissure (AC). It is therefore common for researchers to measure white matter disconnections at the level of these macroscale white matter tracts (Forkel and Catani, 2018;Fridriksson et al, 2013;Griffis et al, 2019Griffis et al, , 2017aHope et al, 2015;Pacella et al, 2019;Thiebaut De Schotten et al, 2014). Accordingly, the toolkit includes functionality to output white matter tract disconnection severities.…”

“…typically < 100) of white matter structures (Rojkova et al, 2016;Yeh et al, 2018). Importantly, as noted in the previous section, tract disconnection measures can be combined with parcel lesion load measures to obtain a complete-but-compact anatomically-informed description of the grey matter damage and white matter disconnections caused by a given lesion (Fridriksson et al, 2013;Griffis et al, 2017a;Pacella et al, 2019;Pustina et al, 2017b;Smith et al, 2013). This allows the lesion data, typically expressed in terms of several hundred thousand voxels, to be represented in terms of several hundred (or fewer) anatomical structures.…”

“…In addition, it is worth noting that while measures of tract disconnection "probability" are also commonly used in the literature (Foulon et al, 2018;Pacella et al, 2019;Thiebaut De Schotten et al, 2014), they are also conceptually problematic. This is because they do not actually measure the probability that a specific tract will be disconnected given a specific lesion topography (Forkel and Catani, 2018).…”

“…Importantly, while binary voxel-wise lesion status representations are sufficient to characterize a lesion's immediate spatial topography, they cannot account for the fact that this spatial topography is embedded within the dense and highly distributed network of white matter connections that together comprise the brain's structural connectome. Accordingly, failure to account for the effects of lesions on white matter connections may obscure or distort the relationships of interest in group-level lesion analyses (Griffis et al, 2019;Pacella et al, 2019;Rudrauf et al, 2008;Thiebaut De Schotten et al, 2014).…”

“…Importantly, in addition to enabling large-scale lesion studies, modern neuroimaging techniques have also enabled researchers to create detailed population-scale brain atlases that delineate the boundaries of regional grey matter parcels and map the trajectories of inter-regional white matter connections (Power et al, 2011;Rojkova et al, 2016;Yeh et al, 2018;Yeo et al, 2011). These atlases can be integrated with empirical lesion data to quantify a lesion's expected impact on specific grey matter regions and white matter connections (Carter et al, 2012;Catani et al, 2012;Foulon et al, 2018;Fridriksson et al, 2013;Griffis et al, 2020Griffis et al, , 2019Griffis et al, , 2017aGriffis et al, , 2017bHope et al, 2015;Kuceyeski et al, 2015Kuceyeski et al, , 2016Pacella et al, 2019;Thiebaut De Schotten et al, 2014), enabling advanced, anatomically-informed lesion analyses that can account for both the focal damage and distributed disconnections caused by lesions. Atlas-based lesion quantification approaches typically involve first registering a patient's lesion to the same brain coordinate space as the desired brain atlas, and then embedding the lesion into the atlas to estimate the amount of damage/disconnection sustained by the structures of interest (Catani et al, 2012;De Schotten and Foulon, 2018;Forkel and Catani, 2018).…”