[论文精读]Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network
论文网址：Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network | SpringerLink
英文是纯手打的！论文原文的summarizing and paraphrasing。可能会出现难以避免的拼写错误和语法错误，若有发现欢迎评论指正！文章偏向于笔记，谨慎食用！
目录
1. 省流版
1.1. 心得
1.2. 论文总结图
2. 论文逐段精读
2.1. Abstract
2.2. Introduction
2.3. Methodology
2.3.1. Pipeline
2.3.2. Proposed Network
2.4. Experimental Results
2.4.1. Experimental Settings
2.4.2. Performance Evaluation
2.4.3. Biomarker Detection
2.5. Conclusion
3. 知识补充
3.1. Poly learning rate
4. Reference List
1. 省流版
1.1. 心得
（1）用自闭症数据集来预测智商？？好吧（我真的想说这后面也没有圆回来！！！我还以为有什么惊为天人的发现呢！）
（2）从前面看是好简单的一个网络啊！后面看也没有很难
（3）这个随机选样本，hhhhh
（4）你有什么拓扑结构？？？我怎么没get到。提取的特征就是高级拓扑特征？
（5）宝宝你是一个...
1.2. 论文总结图
![[论文精读]Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network_第1张图片](http://img.e-com-net.com/image/info8/e57eeb9c5c5e4d649efcc06e67d9f23c.jpg)
2. 论文逐段精读
2.1. Abstract
①Due to the complexity of brain connections and the change of topological structure in brain, IQ predictions always get inaccurate results
②They put forward a model including attention branch and global branch
2.2. Introduction
①There is no relevant topic of IQ prediction in ASD datasets
②Machine learning separate feature selection and regression. However, it is hard to obrain accurate advanced topological features from brain networks
③They proposed a Graph Convolutional Regression Network (GCR-Net) for IQ prediction
engrain v.使根深蒂固，深透，确立 adj.根深蒂固的
2.3. Methodology
2.3.1. Pipeline
①The schematic of GCR-Net
![[论文精读]Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network_第2张图片](http://img.e-com-net.com/image/info8/6e831adc99ab49b9ae848e9a8ee71f56.jpg)
②Atlas: Automated Anatomical Labeling (AAL) with 116 ROIs
③Functional connectivity matrix : calculated by Pearson correlation 
④The brain graph 
, where 
are ROIs
2.3.2. Proposed Network
①Their overall framework:
![[论文精读]Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network_第3张图片](http://img.e-com-net.com/image/info8/8822d7973cb548498f605f13d9bc0963.jpg)
where 
denotes the node features
②The ConvNet includes a graph convolution (GC) layer and a dropout layer
（1）Attention branch
①The operation of GC:
where 
represents the degree matrix of 
and they set 
（等等，为什么我这里怎么也理解不了，这A是个啥玩意儿啊？）;
![\mathbf{X}^{(l)}=\left[x_1^{(l)},x_2^{(l)},\ldots,x_n^{(l)}\right]^\top](http://img.e-com-net.com/image/info8/57767f098fbf493293b715355bed0665.png){kind=small}
denotes node features
②In the self attention module, the attention score 
can be calculated by:
③The pooling module contains matrix multiplication 
and maximum pooling. After pooling, it generates prediction 
（2）Global branch
①The GC operation:
②Transposing 
and regrding it as a global (personal) feature
③MLP 
（3）Finally, fusing 
and 
with element-wise mean operation then giving the prediction
2.4. Experimental Results
2.4.1. Experimental Settings
（1）Settings
①Optimizer: Adam
②Learning rate: 0.001
③Weight decay: 0.0005
④⭐Poly learning rate: 0.9 power
⑤Maximum epoch: 100
⑥Loss function: mean square error (MSE)
⑦Hyper-parameters: 
（2）Dataset
①Autism Brain Imaging Data Exchange (ABIDE) adopted
②Preprocessing pipelines: Connectome Computation System (CCS), Configurable Pipeline for the Analysis of Connectomes (CPAC), Data Processing Assistant for Resting-State fMRI (DPARSF), Neuroimaging Analysis Kit (NIAK)
③⭐Samples: randomly choosing 226 NT and 202 ASD
（3）Evaluation Metrics
①Cross validation: 3 fold, 2 for training and 1 for test
②Runs: mean of 3
③mean absolute error (MAE): 
④root mean squared error (RMSE): 
where 
is the predicted answer and 
is the true label, 
denotes the number of patients
2.4.2. Performance Evaluation
①Regression comparison in ASD:
![[论文精读]Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network_第4张图片](http://img.e-com-net.com/image/info8/12f5e469e17b417686c88f6c84b38713.jpg)
②Regression comparison in NT:
![[论文精读]Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network_第5张图片](http://img.e-com-net.com/image/info8/9d5c7900d3894b7389a719d0ecca3f58.jpg)
2.4.3. Biomarker Detection
①5 most influential ROIs for IQ on ASD:
②5 most influential ROIs for IQ on NT:
![[论文精读]Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network_第6张图片](http://img.e-com-net.com/image/info8/8f14c24b5be04dfc971d4e30cd40873c.jpg)
③Analysing the similarity between predicted associated brain regions and medical research
episodic adj.不定期的;情节性的;偶尔发生的;有许多片段的;由松散片段组成的
2.5. Conclusion
No need for that.
3. 知识补充
3.1. Poly learning rate
参考学习1：Pytorch几种常用的学习率调整方式_pytorch poly-CSDN博客
参考学习2：pytorch动态调整学习率之Poly策略_poly学习率-CSDN博客
4. Reference List
Zhang H. et al. (2022) 'Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network', CAAI International Conference on Artificial Intelligence, pp. 477-488. doi: Intelligence Quotient Scores Prediction in rs-fMRI via Graph Convolutional Regression Network | SpringerLink