Cerebrovascular longitudinal atlas: Changes in cerebral arteries in unruptured intracranial aneurysm patients followed with MRA.

BACKGROUND

Patterns of change in cerebrovascular (CV) morphology associated with aging are highly relevant to the incidence and progression of CV disease, particularly stroke. Intracranial aneurysms (IA), a leading cause of hemorrhagic stroke, are linked with factors such as blood flow, arterial stiffness, and inflammation that may also drive other changes in CV morphology. We worked with a cohort of longitudinally-imaged IA patients to construct the first longitudinal atlas of CV morphology and studied its relationship with disease.

METHODS

110 IA patients, ranging from 19 to 84 years old at IA detection, were monitored using 3D magnetic resonance angiography (MRA) for a mean of 6.11 (2.60) years with 3.6 (1.3) scans per patient. Using 405 image studies, we applied a machine learning diffeomorphic shape analysis to construct a longitudinal atlas of the cerebral arteries which defined a general trajectory of CV morphological change vs. age. This was paired with a centerline analysis to verify changes in individual arteries.

RESULTS

Patient characteristics influenced the speed of CV shape change (e.g. diabetes mellitus, faster, p = 0.016), while other factors mapped to older CV age (e.g. hypertension, p = 0.0004). In parallel, we found that groups including autosomal dominant polycystic kidney disease (p = 0.0004), sex (p = 0.005), smoking (p = 0.046), and IA growth (p = 0.020) shared CV morphology characteristics. The centerline analysis validated changes consistent with the longitudinal atlas.

CONCLUSION

A general CV trajectory of increasing artery length and tortuosity over a period of several decades was found. Although specific IA characteristics were not found to significantly affect this trajectory, these changes in the CV may contribute to increases in IA risk with aging. While our longitudinal findings were consistent with previous cross-sectional studies of individuals without IA, it remains to be determined whether the pattern of morphological change we observed is representative of aging within the general population. The model we developed provides a basis for integrating CV morphological change into understanding of aging and disease.