Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA

Jensen, Paul N.; Rashid, Tanweer; Ware, Jeffrey B.; Cui, Yuhan; Sitlani, Colleen M.; Austin, Thomas R.; Longstreth, W. T.; Bertoni, Alain G.; Mamourian, Elizabeth; Bryan, R. Nick; Nasrallah, Ilya M.; Habes, Mohamad; Heckbert, Susan R.

doi:10.1002/alz.13346

Cited by 5 publications

References 42 publications

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Atherosclerotic burden and cerebral small vessel disease: exploring the link through microvascular aging and cerebral microhemorrhages

Csiszar,

Ungvari,

Patai

et al. 2024

GeroScience

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Cerebral microhemorrhages (CMHs, also known as cerebral microbleeds) are a critical but frequently underestimated aspect of cerebral small vessel disease (CSVD), bearing substantial clinical consequences. Detectable through sensitive neuroimaging techniques, CMHs reveal an extensive pathological landscape. They are prevalent in the aging population, with multiple CMHs often being observed in a given individual. CMHs are closely associated with accelerated cognitive decline and are increasingly recognized as key contributors to the pathogenesis of vascular cognitive impairment and dementia (VCID) and Alzheimer’s disease (AD). This review paper delves into the hypothesis that atherosclerosis, a prevalent age-related large vessel disease, extends its pathological influence into the cerebral microcirculation, thereby contributing to the development and progression of CSVD, with a specific focus on CMHs. We explore the concept of vascular aging as a continuum, bridging macrovascular pathologies like atherosclerosis with microvascular abnormalities characteristic of CSVD. We posit that the same risk factors precipitating accelerated aging in large vessels (i.e., atherogenesis), primarily through oxidative stress and inflammatory pathways, similarly instigate accelerated microvascular aging. Accelerated microvascular aging leads to increased microvascular fragility, which in turn predisposes to the formation of CMHs. The presence of hypertension and amyloid pathology further intensifies this process. We comprehensively overview the current body of evidence supporting this interconnected vascular hypothesis. Our review includes an examination of epidemiological data, which provides insights into the prevalence and impact of CMHs in the context of atherosclerosis and CSVD. Furthermore, we explore the shared mechanisms between large vessel aging, atherogenesis, microvascular aging, and CSVD, particularly focusing on how these intertwined processes contribute to the genesis of CMHs. By highlighting the role of vascular aging in the pathophysiology of CMHs, this review seeks to enhance the understanding of CSVD and its links to systemic vascular disorders. Our aim is to provide insights that could inform future therapeutic approaches and research directions in the realm of neurovascular health.

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Atherosclerotic burden and cerebral small vessel disease: exploring the link through microvascular aging and cerebral microhemorrhages

Csiszar,

Ungvari,

Patai

et al. 2024

GeroScience

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Comparison of brain imaging and physical health between research and clinical neuroimaging cohorts of ageing

Mossa-Basha,

Andre,

Yuh

et al. 2024

British Journal of Radiology

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Objectives To compare brain MRI measures between Adult Changes in Thought (ACT) participants who underwent research, clinical, or both MRI scans, and clinical health measures across the groups and non-MRI subjects. Methods Retrospective cohort study leveraging MRI, clinical, demographic, and medication data from ACT. Three neuroradiologists reviewed MRI scans using NIH Neuroimaging Common Data Elements (CDEs). Total brain and white matter hyperintensity (WMH) volumes, clinical characteristics and outcome measures of brain and overall health were compared between groups. 1166 MRIs were included (77 research, 1043 clinical, and 46 both) and an additional 3146 participants with no MRI were compared. Results Compared to the group with research MRI only, the clinical MRI group had higher prevalence of the following: acute infarcts, chronic hematoma, subarachnoid hemorrhage, subdural hemorrhage, hemorrhagic transformation, and hydrocephalus (each p < 0.001). Quantitative WMH burden was significantly lower (p < 0.001) and total brain volume significantly higher (p < 0.001) in research MRI participants compared to other MRI groups. Prevalence of hypertension, self-reported cerebrovascular disease, congestive heart failure, dementia, and recent hospitalization (all p < 0.001) and diabetes (p = 0.002) differed significantly across groups, with smaller proportions in the research MRI group. Conclusion In aging populations, significant differences were observed in MRI metrics between research MRI and clinical MRI groups, and clinical health metric differences between research MRI, clinical MRI and no MRI groups. Advances in Knowledge This questions whether research cohorts can adequately represent the greater aging population undergoing imaging. These findings may also be useful to radiologists when interpreting neuroimaging of aging.

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Deep learning applications in vascular dementia using neuroimaging

Dong,

Hayashi

2023

Current Opinion in Psychiatry

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Purpose of review Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning applications in VaD-related imaging biomarkers and diagnosis. Recent findings The main deep learning technology applied in VaD using neuroimaging data is convolutional neural networks (CNNs). CNN models have been widely used for lesion detection and segmentation, such as white matter hyperintensities (WMH), cerebral microbleeds (CMBs), perivascular spaces (PVS), lacunes, cortical superficial siderosis, and brain atrophy. Applications in VaD subtypes classification also showed excellent results. CNN-based deep learning models have potential for further diagnosis and prognosis of VaD. Summary Deep learning neural networks with neuroimaging data in VaD research represent significant promise for advancing early diagnosis and treatment strategies. Ongoing research and collaboration between clinicians, data scientists, and neuroimaging experts are essential to address challenges and unlock the full potential of deep learning in VaD diagnosis and management.

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Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA

Cited by 5 publications

References 42 publications

Atherosclerotic burden and cerebral small vessel disease: exploring the link through microvascular aging and cerebral microhemorrhages

Atherosclerotic burden and cerebral small vessel disease: exploring the link through microvascular aging and cerebral microhemorrhages

Comparison of brain imaging and physical health between research and clinical neuroimaging cohorts of ageing

Deep learning applications in vascular dementia using neuroimaging

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