J Stroke.  2021 Sep;23(3):297-311. 10.5853/jos.2021.02376.

Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques

• Lim JS
• Lee JJ
• Woo CW

• KMID: 2520909
• DOI: http://doi.org/10.5853/jos.2021.02376

Abstract

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

Keyword

Stroke; Cognitive dysfunction; Dementia; Neuroimaging; Brain mapping; Connectome

Figure

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  Figure 1. Contribution of functional connections within a resting-state network and between resting-state network pairs for each functional connectivity-behavior prediction. Each circle refers to a representative resting-state network, and its size is proportional to the contribution of within-network connections. Line thickness is proportional to the weighting of between-network connectivity. Adapted from Siegel et al.,[32] with permission from National Academy of Sciences.

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  Figure 2. Integration and segregation in the brain as a complex system. (A) Schematic representation of functional segregation and integration. (B) Stroke lesions can cause different changes in segregation and integration depending on where they occur: upper panel, maintained segregation and reduced integration; bottom panel, decreased segregation while maintaining integration. (C) Stroke lesions can have widespread effects in the brain if they occur in the hubs (dashed arrow).

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  Figure 3. Dispersion caused by lesions and the subsequent compensation processes. The intra-network connectivity of the lesion-occurring module is weakened and dispersed; however, the compensation for this can enhance the between-network connectivity with functionally adjacent networks.

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  Figure 4. Hierarchy in brain connectivity. A hierarchy exists in functional brain connections. Figure 2A can be represented according to the connectivity hierarchy via a gradient analysis. In the lower left and right panels, the principal connectivity gradient explaining most of the variance in the connectivity data was represented as separate layers. It usually follows the well-known axis of unimodal (e.g., visual, somatomotor networks; periphery; green-colored circles) to heteromodal (e.g., default mode network; core; red-colored circles) hierarchy of the brain.

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  Figure 5. Effects of vulnerability factors on stroke-induced brain network changes. Prognosis after stroke can be affected by multiple risk factors, including amyloid decomposition, blood-brain barrier (BBB) disruption, and whiter matter hyperintensities. Left: Brain without vulnerability. Stroke lesion (black polygon) induces disconnection of brain regions (dashed skyblue line), followed by compensatory increase of brain network integration. Line thickness is proportional to connectivity strength. Circles indicate different brain regions. Right: Brain with vulnerability. Amyloid decomposition (brown speckles), white matter hyperintensities (gray polygon), BBB disruption (cyan polygon) disrupt compensatory brain network integration, indicated as gray line with hatch marks. Lacune are also marked as gray colored open circles.

Reference

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