Neuroimaging statistical approaches for determining neural correlates of Alzheimer's disease via positron emission tomography imaging.

IF 4.4 2区 数学 Q1 STATISTICS & PROBABILITY
Daniel F Drake, Gordana Derado, Lijun Zhang, F DuBois Bowman
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引用次数: 0

Abstract

Alzheimer's disease (AD) is a degenerative disorder involving significant memory loss and other cognitive deficits, manifesting as a progression from normal cognitive functioning to mild cognitive impairment to AD. The sooner an accurate diagnosis of probable AD is made, the easier it is to manage symptoms and plan for future therapy. Functional neuroimaging stands to be a useful tool in achieving early diagnosis. Among the many neuroimaging modalities, positron emission tomography (PET) provides direct regional assessment of, among others, brain metabolism, cerebral blood flow, amyloid deposition-all quantities of interest in the characterization of AD. However, there are analytic challenges in identifying early indicators of AD from these high-dimensional imaging data sets, and it is unclear whether early indicators of AD are more likely to emerge in localized patterns of brain activity or in patterns of correlation between distinct brain regions. Early PET-based analyses of AD focused on alterations in metabolic activity at the voxel-level or in anatomically defined regions of interest. Other approaches, including seed-voxel and multivariate techniques, seek to characterize metabolic connectivity by identifying other regions in the brain with similar patterns of activity across subjects. We briefly review various neuroimaging statistical approaches applied to determine changes in metabolic activity or metabolic connectivity associated with AD. We then present an approach that provides a unified statistical framework for addressing both metabolic activity and connectivity. Specifically, we apply a Bayesian spatial hierarchical framework to longitudinal metabolic PET scans from the Alzheimer's Disease Neuroimaging Initiative.

通过正电子发射断层成像确定阿尔茨海默病神经相关因素的神经影像学统计方法
阿尔茨海默病(AD)是一种退行性疾病,涉及严重的记忆丧失和其他认知缺陷,表现为从正常认知功能到轻度认知障碍再到AD的发展。越早准确诊断出可能的AD,就越容易控制症状并计划未来的治疗。功能性神经影像学是实现早期诊断的有用工具。在许多神经成像模式中,正电子发射断层扫描(PET)提供了对大脑代谢、脑血流、淀粉样蛋白沉积等的直接区域评估,所有这些都是AD表征的重要内容。然而,从这些高维成像数据集中识别AD的早期指标存在分析挑战,目前尚不清楚AD的早期指标是更可能出现在大脑活动的局部模式中,还是出现在不同大脑区域之间的相关性模式中。早期基于PET的AD分析侧重于体素水平或解剖学定义的感兴趣区域的代谢活动变化。其他方法,包括种子体素和多元技术,试图通过识别受试者大脑中具有相似活动模式的其他区域来表征代谢连接。我们简要回顾了用于确定与AD相关的代谢活动或代谢连接性变化的各种神经影像学统计方法。然后,我们提出了一种方法,为解决代谢活动和连接性提供了统一的统计框架。具体来说,我们将贝叶斯空间层次框架应用于阿尔茨海默病神经成像倡议的纵向代谢PET扫描。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
31
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