Dataset Information

Multiple Network Disconnection in Anosognosia for Hemiplegia.

ABSTRACT: Anosognosia for hemiplegia (AHP) is a complex syndrome whose neural correlates are still under investigation. One hypothesis, mainly based on lesion mapping studies, is that AHP reflects a breakdown of neural systems of the right hemisphere involved in motor function. However, more recent theories have suggested that AHP may represent a disorder of cognitive systems involved in belief updating, self-referential or body processing. Two recent studies, using a method to estimate the degree of white matter disconnection from lesions, have indeed shown that patients with AHP suffer from damage of several long-range white matter pathways in association cortex. Here, we use a similar indirect disconnection approach to study a group of patients with motor deficits without anosognosia (hemiparesis or hemiplegia, HP, n = 35), or motor deficits with AHP (n = 28). The HP lesions came from a database of stroke patients, while cases of AHP were selected from the published literature. Lesions were traced into an atlas from illustrations of the publications using a standard method. There was no region in the brain that was more damaged in AHP than HP. In terms of structural connectivity, AHP patients had a similar pattern of disconnection of motor pathways to HP patients. However, AHP patients also showed significant disconnection of the right temporo-parietal junction, right insula, right lateral and medial prefrontal cortex. These associative cortical regions were connected through several white matter tracts, including superior longitudinal fasciculus III, arcuate, fronto-insular, frontal inferior longitudinal, and frontal aslant. These tracts connected regions of different cognitive networks: default, ventral attention, and cingulo-opercular. These results were not controlled for clinical variables as concomitant symptoms and other disorders of body representation were not always available for co-variate analysis. In conclusion, we confirm recent studies of disconnection demonstrating that AHP is not limited to dysfunction of motor systems, but involves a much wider set of large-scale cortical networks.

SUBMITTER: Monai E

PROVIDER: S-EPMC7201993 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The syndrome of anosognosia for hemiplegia, or the lack of awareness for one's paralysis following right hemisphere stroke, can provide unique insights into the neurocognitive mechanisms of self-awareness. Yet it remains unclear whether anosognosia for hemiplegia is a modality-specific deficit of sensorimotor monitoring, or whether domain-general processes of attention and belief-updating converge to cause anosognosia for hemiplegia. Using a Bayesian learning framework, we formalized and empirically investigated the hypothesis that failures to update anosognosic beliefs can be explained by abnormalities in the relative uncertainty (i.e. precision) ascribed to prior beliefs versus sensory information in different contexts. We designed a new motor belief-updating task that manipulated both the temporal (prospective and retrospective) and spatial (hemispace most affected by inattention and hemispace less affected by inattention) conditions in which beliefs had to be updated, and we validated its sensitivity to anosognosia for hemiplegia in 26 patients with right hemisphere stroke. We then computed and empirically tested two different Bayesian predictors of prospective beliefs using two proxies for precision in anosognosia for hemiplegia patients: (i) standardized, neuropsychological measures of objective attention abilities, i.e. visuospatial neglect scores and (ii) subjective uncertainty reports, i.e. confidence ratings. Our results suggest that while neglect does not affect local, sensorimotor error monitoring, it does seem to affect the degree to which observed errors are used to update more general, prospective beliefs about counterfactual motor abilities in anosognosia for hemiplegia. Difficulties in such 'counterfactual' belief-updating were associated with disruptions in tracts of the ventral attentional network (i.e. superior longitudinal fasciculus connecting the temporo-parietal junction and ventral frontal cortex) and associated lesions to the insula, inferior parietal cortex and superior temporal regions. These results suggest that self-awareness extends beyond local, retrospective monitoring, requiring also salience-based, convergence of beliefs about the self that go beyond the 'here-and-now' of sensorimotor experience.

Project description:Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 0.25). These results were then replicated in a separate group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality, which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs, due to the impact of traumatic axonal injury on network connections. The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairment after traumatic brain injury results from the disconnection of network hubs by traumatic axonal injury.

Dataset Information

Multiple Network Disconnection in Anosognosia for Hemiplegia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets