Mapping Dynamic Changes in Ventricular Volume onto Baseline Cortical Surfaces in Normal Aging, MCI, and Alzheimer's Disease.

Sarah K Madsen, Boris A Gutman, Shantanu H Joshi, Arthur W Toga, Clifford R Jack, Michael W Weiner, Paul M Thompson
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引用次数: 15

Abstract

Ventricular volume (VV) is a powerful global indicator of brain tissue loss on MRI in normal aging and dementia. VV is used by radiologists in clinical practice and has one of the highest obtainable effect sizes for tracking brain change in clinical trials, but it is crucial to relate VV to structural alterations underlying clinical symptoms. Here we identify patterns of thinner cortical gray matter (GM) associated with dynamic changes in lateral VV at 1-year (N=677) and 2-year (N=536) intervals, in the ADNI cohort. People with faster VV loss had thinner baseline cortical GM in temporal, inferior frontal, inferior parietal, and occipital regions (controlling for age, sex, diagnosis). These findings show the patterns of relative cortical atrophy that predict later ventricular enlargement, further validating the use of ventricular segmentations as biomarkers. We may also infer specific patterns of regional cortical degeneration (and perhaps functional changes) that relate to VV expansion.

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在正常衰老、轻度认知损伤和阿尔茨海默病的基线皮质表面上绘制心室容积的动态变化
心室容积(VV)是正常衰老和痴呆患者MRI上脑组织损失的一个强有力的全局指标。VV被放射科医生用于临床实践,在临床试验中追踪大脑变化具有最高的可获得效应量之一,但将VV与潜在临床症状的结构改变联系起来至关重要。在ADNI队列中,我们在1年(N=677)和2年(N=536)的间隔中确定了与侧位VV动态变化相关的薄皮质灰质(GM)模式。VV丧失较快的人在颞、额下、顶叶下和枕叶区域的基线皮质GM较薄(控制年龄、性别、诊断)。这些发现显示了相对皮质萎缩的模式,预测了后来的心室增大,进一步验证了心室分割作为生物标志物的使用。我们还可以推断出与VV扩张相关的区域皮质退化(可能还有功能变化)的特定模式。
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Mapping Dynamic Changes in Ventricular Volume onto Baseline Cortical Surfaces in Normal Aging, MCI, and Alzheimer's Disease. A Dynamical Clustering Model of Brain Connectivity Inspired by the N -Body Problem. Modeling 4D Changes in Pathological Anatomy using Domain Adaptation: Analysis of TBI Imaging using a Tumor Database. PARP1 gene variation and microglial activity on [11C]PBR28 PET in older adults at risk for Alzheimer's disease. A Graph-Based Integration of Multimodal Brain Imaging Data for the Detection of Early Mild Cognitive Impairment (E-MCI).
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