基于皮质WM/GM对比度、皮质厚度和容量测定来预测智力
Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry
摘要：
我们提出了一个四层全连接神经网络（FNN）的T1加权MR图像的ABCD挑战预测流体智力分数。除了脑结构的体积之外，FNN还使用78个皮质区域的皮质WM/GM对比度和皮质厚度。这最后两个测量值来自T1加权MR图像，使用CIVET管道产生的皮质表面。受试者的年龄和性别以及扫描仪制造商也被用作学习算法的特征。这产生了提供给FNN的283个特征，具有20和15个节点的两个隐藏层。该方法适用于ABCD研究的数据。在3736名受试者的训练集上进行训练，所提出的方法在415名受试者的验证集中实现了71.596的MSE和0.151的相关性。对于最终提交，该模型使用3568名受试者进行训练，并且在由4383名受试者组成的测试集中实现了94.0270的MSE。
英文摘要：
We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes of brain structures, the FNN uses cortical WM/GM contrast and cortical thickness at 78 cortical regions. These last two measurements were derived from the T1-weighted MR images using cortical surfaces produced by the CIVET pipeline. The age and gender of the subjects and the scanner manufacturer are also used as features for the learning algorithm. This yielded 283 features provided to the FNN with two hidden layers of 20 and 15 nodes. The method was applied to the data from the ABCD study. Trained with a training set of 3736 subjects, the proposed method achieved a MSE of 71.596 and a correlation of 0.151 in the validation set of 415 subjects. For the final submission, the model was trained with 3568 subjects and it achieved a MSE of 94.0270 in the test set comprised of 4383 subjects.
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