Degeneration of structural brain networks is associated with cognitive decline after ischaemic stroke. Issue 2 (26th September 2020)

• Record Type: Journal Article
• Authors: Veldsman, Michele; Cheng, Hsiao-Ju; Ji, Fang; Werden, Emilio; Khlif, Mohamed Salah; Ng, Kwun Kei; Lim, Joseph K W; Qian, Xing; Yu, Haoyong; Zhou, Juan Helen; Brodtmann, Amy
• Is Part Of: Brain communications. Volume 2:Issue 2(2020)
• Publication Date: 2020-09-26
• Subjects: stroke -- structural networks -- cognition -- neurodegeneration; 616
• DOI: 10.1093/braincomms/fcaa155
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Over one-third of stroke patients has long-term cognitive impairment. The likelihood of cognitive dysfunction is poorly predicted by the location or size of the infarct. The macro-scale damage caused by ischaemic stroke is relatively localized, but the effects of stroke occur across the brain. Structural covariance networks represent voxelwise correlations in cortical morphometry. Atrophy and topographical changes within such distributed brain structural networks may contribute to cognitive decline after ischaemic stroke, but this has not been thoroughly investigated. We examined longitudinal changes in structural covariance networks in stroke patients and their relationship to domain-specific cognitive decline. Seventy-three patients (mean age, 67.41 years; SD = 12.13) were scanned with high-resolution magnetic resonance imaging at sub-acute (3 months) and chronic (1 year) timepoints after ischaemic stroke. Patients underwent a number of neuropsychological tests, assessing five cognitive domains including attention, executive function, language, memory and visuospatial function at each timepoint. Individual-level structural covariance network scores were derived from the sub-acute grey-matter probabilistic maps or changes in grey-matter probability maps from sub-acute to chronic using data-driven partial least squares method seeding at major nodes in six canonical high-order cognitive brain networks (i.e. dorsal attention, executive control, salience, default mode, language-related and memory networks). We then investigated co-varying patterns between structural covariance network scores within canonical distributed brain networks and domain-specific cognitive performance after ischaemic stroke, both cross-sectionally and longitudinally, using multivariate behavioural partial least squares correlation approach. We tested our models in an independent validation data set with matched imaging and behavioural testing and using split-half validation. We found that distributed degeneration in higher-order cognitive networks was associated with attention, executive function, language, memory and visuospatial function impairment in sub-acute stroke. From the sub-acute to the chronic timepoint, longitudinal structural co-varying patterns mirrored the baseline structural covariance networks, suggesting synchronized grey-matter volume decline occurred within established networks over time. The greatest changes, in terms of extent of distributed spatial co-varying patterns, were in the default mode and dorsal attention networks, whereas the rest were more focal. Importantly, faster degradation in these major cognitive structural covariance networks was associated with greater decline in attention, memory and language domains frequently impaired after stroke. Our findings suggest that sub-acute ischaemic stroke is associated with widespread degeneration of higher-order structural brain networks and degradation of these structural brain networks may contribute to longitudinal domain-specific cognitive dysfunction. Abstract : Examining network-based degeneration, Veldsman and Cheng et al. show that higher-order structural covariance networks are associated with cognition in sub-acute stroke and that greater degeneration of these networks is associated with longitudinal cognitive decline after ischaemic stroke. Graphical Abstract: