Son Seong-Jin, Kim Jonghoon, Park Hyunjin
Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea.
PLoS One. 2017 Mar 23;12(3):e0173426. doi: 10.1371/journal.pone.0173426. eCollection 2017.
Regional volume atrophy and functional degeneration are key imaging hallmarks of Alzheimer's disease (AD) in structural and functional magnetic resonance imaging (MRI), respectively. We jointly explored regional volume atrophy and functional connectivity to better characterize neuroimaging data of AD and mild cognitive impairment (MCI). All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compared regional volume atrophy and functional connectivity in 10 subcortical regions using structural MRI and resting-state functional MRI (rs-fMRI). Neuroimaging data of normal controls (NC) (n = 35), MCI (n = 40), and AD (n = 30) were compared. Significant differences of regional volumes and functional connectivity measures between groups were assessed using permutation tests in 10 regions. The regional volume atrophy and functional connectivity of identified regions were used as features for the random forest classifier to distinguish among three groups. The features of the identified regions were also regarded as connectional fingerprints that could distinctively separate a given group from the others. We identified a few regions with distinctive regional atrophy and functional connectivity patterns for NC, MCI, and AD groups. A three label classifier using the information of regional volume atrophy and functional connectivity of identified regions achieved classification accuracy of 53.33% to distinguish among NC, MCI, and AD. We identified distinctive regional atrophy and functional connectivity patterns that could be regarded as a connectional fingerprint.
在结构和功能磁共振成像（MRI）中，区域体积萎缩和功能退化分别是阿尔茨海默病（AD）的关键影像学特征。我们联合探讨了区域体积萎缩和功能连接性，以更好地表征AD和轻度认知障碍（MCI）的神经影像学数据。所有数据均来自阿尔茨海默病神经影像学倡议（ADNI）数据库。我们使用结构MRI和静息态功能MRI（rs-fMRI）比较了10个皮质下区域的区域体积萎缩和功能连接性。比较了正常对照组（NC）（n = 35）、MCI（n = 40）和AD（n = 30）的神经影像学数据。使用排列检验评估了10个区域中各组之间区域体积和功能连接性测量的显著差异。将识别区域的区域体积萎缩和功能连接性用作随机森林分类器的特征，以区分三组。识别区域的特征也被视为连接指纹，可以将给定组与其他组明显区分开来。我们为NC、MCI和AD组识别了一些具有独特区域萎缩和功能连接模式的区域。使用识别区域的区域体积萎缩和功能连接性信息的三标签分类器在区分NC、MCI和AD时的分类准确率达到了53.33%。我们识别出了可被视为连接指纹的独特区域萎缩和功能连接模式。
