A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data.
A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data.
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DOI：
10.1038/s41598-018-27997-8
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发表时间：
2018-06-27
影响因子：
4.6
通讯作者：
Wong-Lin K
中科院分区：
文献类型：
--
作者：
Ding X;Bucholc M;Wang H;Glass DH;Wang H;Clarke DH;Bjourson AJ;Dowey LRC;O'Kane M;Prasad G;Maguire L;Wong-Lin K
由于阿尔茨海默病（AD）的病因和发病机制复杂，目前缺乏一种有效、客观和系统的方法来对其进行分类。由于AD本质上是动态的，因此也不清楚AD指标之间的关系如何随时间变化。为了解决这些问题，我们提出了一种用于AD分类的混合计算方法，并在异构纵向AIBL数据集上对其进行了评估。具体来说，使用临床痴呆等级作为AD严重程度的指标，最重要的指标（迷你精神状态检查、逻辑记忆回忆、MRI的灰质和脑脊液体积、PiB-PET脑扫描的活动体素、ApoE和年龄）可以从并行数据挖掘算法中自动识别。在这项工作中，跨不同时间点的贝叶斯网络建模用于识别和可视化重要特征之间的时变关系，重要的是，仅使用粗粒度数据以一种有效的方式。至关重要的是，我们的方法提出了与AD严重程度分类相关的关键数据特征及其适当组合，具有很高的准确性。总的来说，我们的研究为阿尔茨海默病的发展提供了见解，并证明了我们的方法在支持有效的阿尔茨海默病诊断方面的潜力。
There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer’s disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.
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DOI：
10.1093/brain/awp123
发表时间：
2009-08
期刊：
Brain : a journal of neurology
影响因子：
--
作者：
Desikan RS;Cabral HJ;Hess CP;Dillon WP;Glastonbury CM;Weiner MW;Schmansky NJ;Greve DN;Salat DH;Buckner RL;Fischl B;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
4.8
作者：
Gomez-Ramirez J;Wu J
通讯作者：
Wu J
影响因子：
9.9
作者：
Kohler, Sebastian;Hamel, Renske;Ramakers, Inez
通讯作者：
Ramakers, Inez
DOI：
10.1523/jneurosci.3785-09.2010
发表时间：
2010-02-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Fjell AM;Walhovd KB;Fennema-Notestine C;McEvoy LK;Hagler DJ;Holland D;Brewer JB;Dale AM;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
--
作者：
BATTITI, R
通讯作者：
BATTITI, R