RT Journal Article
SR Electronic
T1 Abnormal Changes of Multidimensional Surface Features Using Multivariate Pattern Classification in Amnestic Mild Cognitive Impairment Patients
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 10541
OP 10553
DO 10.1523/JNEUROSCI.4356-13.2014
VO 34
IS 32
A1 Shuyu Li
A1 Xiankun Yuan
A1 Fang Pu
A1 Deyu Li
A1 Yubo Fan
A1 Liyong Wu
A1 Wang Chao
A1 Nan Chen
A1 Yong He
A1 Ying Han
YR 2014
UL http://www.jneurosci.org/content/34/32/10541.abstract
AB 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.