Cerebral perivascular spaces as predictors of dementia risk and accelerated brain atrophy.
Giuseppe BarisanoMichael Ivnull nullJeiran ChoupanMelanie Hayden GephartPublished in: medRxiv : the preprint server for health sciences (2024)
Cerebral small vessel disease, an important risk factor for dementia, lacks robust, in vivo measurement methods. Perivascular spaces (PVS) on brain MRI are surrogates for small parenchymal blood vessels and their perivascular compartment, and may relate to brain health. We developed a novel, robust algorithm to automatically assess PVS count and size on MRI, and investigated their relationship with dementia risk and brain atrophy. We analyzed 46,478 clinical measurements of cognitive functioning and 20,845 brain MRI scans from 10,004 participants (71.1±9.7 years-old, 56.6% women). Fewer PVS and larger PVS diameter at baseline were associated with higher dementia risk and accelerated brain atrophy. Longitudinal trajectories of PVS markers were significantly different in non-demented individuals who converted to dementia compared with non-converters. In simulated placebo-controlled trials for treatments targeting cognitive decline, screening out participants less likely to develop dementia based on our PVS markers enhanced the power of the trial. These novel radiographic cerebrovascular markers may improve risk-stratification of individuals, potentially reducing cost and increasing throughput of clinical trials to combat dementia.
Keyphrases
- mild cognitive impairment
- cognitive decline
- cognitive impairment
- white matter
- resting state
- cerebral ischemia
- clinical trial
- magnetic resonance imaging
- healthcare
- contrast enhanced
- type diabetes
- subarachnoid hemorrhage
- depressive symptoms
- squamous cell carcinoma
- multiple sclerosis
- study protocol
- randomized controlled trial
- pregnant women
- machine learning
- radiation therapy
- phase ii
- magnetic resonance
- mass spectrometry
- placebo controlled
- mental health
- cancer therapy
- polycystic ovary syndrome
- high resolution
- brain injury
- deep learning
- metabolic syndrome
- atomic force microscopy