期刊
JOURNAL OF NEUROSCIENCE
卷 34, 期 32, 页码 10541-10553出版社
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.4356-13.2014
关键词
aMCI; cortical surface feature; entorhinal; MRI; multivariate classification
资金
- National Science Foundation of China [81171403, 30970823, 31371007, 81030028]
- Beijing Municipal Science & Technology Commission [Z131100006813022]
- National Science Fund for Distinguished Young Scholars [81225012]
- National Key Department of Neurology - Chinese Health and Family Planning Committee
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.
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