Abstract
A cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes.We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n = 44) and matched controls (n = 100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tracking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load.MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes.
Highlights
Unlike the insidious onset and progressive neurological decline observed in most neurodegenerative diseases, a cerebral stroke is characterized by instant damage to brain tissue due to a compromise in cerebrovascular blood supply
Task based functional magnetic resonance imaging (fMRI) studies have shown that functional connections at rest are engaged during various cognitive tasks (Raichle, 2010; Smith et al, 2009) and the increase in joint blood-oxygen-level dependent (BOLD) activations in spatially dispersed brain regions has revealed networks engaged during attention (Alnæs et al, 2015; Szczepanski et al, 2013), working memory (Compte et al, 2000), language (Ferstl et al, 2008) and motor task (Hanakawa et al, 2008), as well as various other cognitive operations
Clinical assessment quantifying stroke severity was performed according to the National Institute of Health Stroke Scale (NIHSS) at the respective stroke units by an attending physician specialized in internal medicine, neurology or geriatric medicine at the time of discharge
Summary
Unlike the insidious onset and progressive neurological decline observed in most neurodegenerative diseases, a cerebral stroke is characterized by instant damage to brain tissue due to a compromise in cerebrovascular blood supply. FMRI has shown great promise in probing alterations in brain activity for a range of neurodegenerative (Cordova-Palomera et al, 2017; Dickerson et al, 2016; Paulsen et al, 2004) and neuropsychiatric (Kaufmann et al, 2015; Skåtun et al, 2016; Yu et al, 2015) conditions. These techniques may provide novel understanding of brain function, and potentially, clinical information used to predict patient recovery, outcome and aid in tailoring individual rehabilitation strategies. Task based fMRI studies have shown that functional connections at rest are engaged during various cognitive tasks (Raichle, 2010; Smith et al, 2009) and the increase in joint BOLD activations in spatially dispersed brain regions has revealed networks engaged during attention (Alnæs et al, 2015; Szczepanski et al, 2013), working memory (Compte et al, 2000), language (Ferstl et al, 2008) and motor task (Hanakawa et al, 2008), as well as various other cognitive operations
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