Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking. Issue 9 (September 2020)

• Record Type: Journal Article
• Authors: Dørum, Erlend S.; Kaufmann, Tobias; Alnæs, Dag; Richard, Geneviève; Kolskår, Knut K.; Engvig, Andreas; Sanders, Anne-Marthe; Ulrichsen, Kristine; Ihle-Hansen, Hege; Nordvik, Jan Egil; Westlye, Lars T.
• Is Part Of: Heliyon. Volume 6:Issue 9(2020)
• Publication Date: 2020-09
• Subjects: Cerebral stroke -- fMRI -- Brain network connectivity -- Machine learning -- Behavioral neuroscience -- Cognitive neuroscience -- Systems neuroscience -- Neurology -- Medical imaging; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2020.e04854
• Languages: English
• ISSNs: 2405-8440

Abstract

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. Abstract : Cerebral stroke, fMRI, Brain network connectivity, Machine learning, Behavioral Neuroscience, Cognitive Neuroscience, Systems Neuroscience, Neurology, Medical Imaging