## 4 Results
### Classification Experiment of Training and Test on Individual Imaging Site Data  
Each of the five imaging sites (i.e., KKI, NI, NYU, OHSU, and Peking) had a training set and benchmark test set. The ADHD classification experiment of the training and test on individual imaging site data, individual site experiment for short, refers to the training data using the training set of a single imaging site and the test data using the benchmark test set of this same imaging site. During the training of the representation learning neural network, all of the parameters of the pre-trained neural network in the individual site experiment were frozen, which was mainly based on two considerations. On the one hand, it can alleviate the overfitting problem of the deep neural network with a small number of training samples; on the other hand, by freezing all of the parameters of the pre-trained neural network, verifying the applicability of the proposed method can be focused on. In the individual site ADHD classification experiments, the weight \(\alpha\) in the cross-loss function was set to 3.  
Table 2 shows the comparison of the ADHD classification results of different methods in the individual site experiments, where the last column is the average classification accuracies. It can be seen from Table 2 that our method achieved the best results with KKI, NYU, and OHSU, reaching a state-of-the-art performance with an average accuracy of 73.73%.  
To evaluate the proposed method more comprehensively, other performance metrics, such as specificity and sensitivity, had also been exploited on all five imaging sites. To compare with the existing methods, all the results with our proposed method in the individual site experiments are presented in Table 3. The fMRI data had the dual characteristics of imbalance and high dimension. In the high-dimensional feature space, the data distribution  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **KKI** & **NI** & **NYU** & **OHSU** & **Peking** & **Average** \\ \hline [PERSON] et al. (2014) [1] & 54.55 & 48 & - & 82.35 & 58.82 & 60.93 \\ Fusion of fMRI and non-imaging data & 81.8 & - & 60.9 & - & 64.7 & 69.1 \\ (2018) [7] & - & 67.9 & 73.1 & - & 62.7 & 67.9 \\ DeepFMRI (2020) [15] & 60 & 70 & 73.3 & 71 & 73,3 & 69.52 \\ Knowledge distillation-based (2021) [8] & 81.82 & 64 & 80.49 & 82.35 & 60 & 73.73 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification accuracies (%) comparison of different methods in the individual site experiments.  
was sparser and contained more redundant and irrelevant features. It was usually difficult to obtain effective information from the preprocessed fMRI data, which made it more difficult to identify ADHD. Therefore, the sensitivity of all the methods in Table 3 was generally not high. Compared with the other three methods, our proposed method achieved better performances on all five test sets, which means that our method has better generalization ability when applied to different data sets. Especially, the classification accuracy and sensitivity on NYU were significantly better in our method than in the other methods. The confusion matrices of the classification results in the individual site experiments are shown in Figure 4. It can be observed from Figure 4c that most samples of HC and ADHD in the NYU data set could be classified correctly, the reason for which was perhaps because the samples of ADHD in NYU were more than that of HC.