Neuroimaging Biomarkers Predict Brain Structural Connectivity Change in a Mouse Model of Vascular Cognitive Impairment. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Boehm-Sturm, Philipp; Füchtemeier, Martina; Foddis, Marco; Mueller, Susanne; Trueman, Rebecca C.; Zille, Marietta; Rinnenthal, Jan Leo; Kypraios, Theodore; Shaw, Laurence; Dirnagl, Ulrich; Farr, Tracy D.
• Is Part Of: Stroke. Volume 48:Issue 2(2017)
• Publication Date: 2017-02
• Subjects: biomarkers -- diffusion tensor imaging -- hypoperfusion -- magnetic resonance imaging -- mouse -- neuroimaging -- vascular cognitive impairment; Cerebrovascular disease -- Periodicals; Cerebral circulation -- Periodicals; 616.81
• DOI: 10.1161/STROKEAHA.116.014394
• Languages: English
• ISSNs: 0039-2499

Abstract

Abstract : Background and Purpose—: Chronic hypoperfusion in the mouse brain has been suggested to mimic aspects of vascular cognitive impairment, such as white matter damage. Although this model has attracted attention, our group has struggled to generate a reliable cognitive and pathological phenotype. This study aimed to identify neuroimaging biomarkers of brain pathology in aged, more severely hypoperfused mice. Methods—: We used magnetic resonance imaging to characterize brain degeneration in mice hypoperfused by refining the surgical procedure to use the smallest reported diameter microcoils (160 μm). Results—: Acute cerebral blood flow decreases were observed in the hypoperfused group that recovered over 1 month and coincided with arterial remodeling. Increasing hypoperfusion resulted in a reduction in spatial learning abilities in the water maze that has not been previously reported. We were unable to observe severe white matter damage with histology, but a novel approach to analyze diffusion tensor imaging data, graph theory, revealed substantial reorganization of the hypoperfused brain network. A logistic regression model from the data revealed that 3 network parameters were particularly efficient at predicting group membership (global and local efficiency and degrees), and clustering coefficient was correlated with performance in the water maze. Conclusions—: Overall, these findings suggest that, despite the autoregulatory abilities of the mouse brain to compensate for a sudden decrease in blood flow, there is evidence of change in the brain networks that can be used as neuroimaging biomarkers to predict outcome. Abstract : Supplemental Digital Content is available in the text.