机构:[1]School of Biological Science & Medical Engineering, Beihang University, Beijing 100191, China,[2]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China,神经内科首都医科大学宣武医院[3]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing 100053, China,[4]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China,放射科首都医科大学宣武医院[5]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
Previous studies have suggested that amnestic mild cognitive impairment (aMCI) is associated with changes in cortical morphological features, such as cortical thickness, sulcal depth, surface area, gray matter volume, metric distortion, and mean curvature. These features have been proven to have specific neuropathological and genetic underpinnings. However, most studies primarily focused on mass-univariate methods, and cortical features were generally explored in isolation. Here, we used a multivariate method to characterize the complex and subtle structural changing pattern of cortical anatomy in 24 aMCI human participants and 26 normal human controls. Six cortical features were extracted for each participant, and the spatial patterns of brain abnormities in aMCI were identified by high classification weights using a support vector machine method. The classification accuracy in discriminating the two groups was 76% in the left hemisphere and 80% in the right hemisphere when all six cortical features were used. Regions showing high weights were subtle, spatially complex, and predominately located in the left medial temporal lobe and the supramarginal and right inferior parietal lobes. In addition, we also found that the six morphological features had different contributions in discriminating the two groups even for the same region. Our results indicated that the neuroanatomical patterns that discriminated individuals with aMCI from controls were truly multidimensional and had different effects on the morphological features. Furthermore, the regions identified by our method could potentially be useful for clinical diagnosis.
基金:
National Science Foundation of China (81171403, 30970823, 31371007, and 81030028),
the Beijing Municipal Science & Technology Commission (Grant Z131100006813022),
the National Science Fund for Distinguished Young Scholars (81225012),
the National Key Department of Neurology funded by the Chinese Health and Family Planning Committee.
第一作者机构:[1]School of Biological Science & Medical Engineering, Beihang University, Beijing 100191, China,
通讯作者:
通讯机构:[1]School of Biological Science & Medical Engineering, Beihang University, Beijing 100191, China,[2]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China,
推荐引用方式(GB/T 7714):
Shuyu Li ,Xiankun Yuan ,Fang Pu ,et al.Abnormal Changes of Multidimensional Surface Features Using Multivariate Pattern Classification in Amnestic Mild Cognitive Impairment Patients[J].JOURNAL OF NEUROSCIENCE.2014,34(32):10541-10553.doi:10.1523/JNEUROSCI.4356-13.2014.
APA:
Shuyu Li,,Xiankun Yuan,,Fang Pu,,Deyu Li,,Yubo Fan,...&Ying Han.(2014).Abnormal Changes of Multidimensional Surface Features Using Multivariate Pattern Classification in Amnestic Mild Cognitive Impairment Patients.JOURNAL OF NEUROSCIENCE,34,(32)
MLA:
Shuyu Li,,et al."Abnormal Changes of Multidimensional Surface Features Using Multivariate Pattern Classification in Amnestic Mild Cognitive Impairment Patients".JOURNAL OF NEUROSCIENCE 34..32(2014):10541-10553