정신 질환, 그 중에서도 abnormal trajectory에 영향을 미치는 정신 질환과 연관된 Brain의 latent something을 Neural Net이 represent 할 수 있을까?
뇌-행동을 한번 mapping 해보고자 하는 것.
Brain에서 질병으로 가는 ‘latent something’을 알 수 있지 않을까?
이를 multi task learning을 통해서, 서로 heterogeneous한 것으로 보이는 정신질환들에서, ‘abnormal developmental trajectory’를 뇌의 수준에서 표상하는 structural feature를 뽑아낼 수 있을까?
ABCD release 2.0 T1w data (n_subject = 7088)
Background
Sex differences in human cognition and behaviors originate from the differences of brain structure. Conventional neuroscience studies rarely support a clear-cut sex difference in the human brain. On the stark contrast, recent computational neuroimaging (e.g., convolutional neural networks (CNN)) shows an accurate classification of sex based on the brain anatomical images (e.g., T1-weighted MRI), supporting a strong case for the brain sex differences. Since these studies are based on adults, an important question is the developmental window in which the sex differences in the brain emerge. I.e., can neural networks classify a sex of a preadolescent brain of which the organizational effect of sex steroids have yet to be full blown?
To test this, we trained multiple neural network architectures to learn sex differences of the brain with T1-weighted brain images in prepubertal children.
Methods
We used T1w images of 7,088 (male: 3,694, female: 3,356) children ages 9 to 10 from the Adolescent Brain Cognitive Development study. Each image was skull-stripped and spatially normalized to the ICBM pediatric asymmetrical template. The input size of images was resized for (96, 96, 96). We built simple CNN architecture as a baseline model and compared two different CNN architectures: VGGNet-19 and DenseNet-121.
Results
VGGNet-19 outperformed to predict sex (Accuracy=0.94; F1-score=0.95). Baseline model and DesNet-121 also show over 90% accuracy. Of note, our models show a higher accuracy with the smaller samples than the prior CNN studies with adults.
Conclusions
In summary, this study shows CNN can predict sex from T1w images. The results not only support sex differences in brain structure in preadolescent, but also show the capability of CNN to learn latent features from brain images. Considering the important role of sex differences in human, pre-trained CNN models with sex could be utilized for predicting more complex cognition and behavior by transfer learning.
With code (folder name "3DCNN_hardparameter_sharing") performing prediction task. (e.g., age, sex...)