## 3 Materials and Methods
### Data Sets  
In this paper, the experimental data came from the published Neuro Bureau ADHD-200 data set (ADHD-200 sample) [17], where the data were collected from eight independent imaging sites, including 947 individuals, and the collected information includes structural MRI data, resting fMRI data, age, gender, IQ, hand habits, and other phenotypic information. According to the American Psychiatric Association 1994 standard, ADHD in these data sets is divided into three subtypes: inattention subtype, hyperactive impulse subtype, and complex subtype. In this paper, the three subtypes are uniformly called ADHD, and binary classification experiments for HC and ADHD were carried out. Meanwhile, ADHD-200 also provides benchmark test data of six imaging sites for competition.  
To facilitate comparison with current mainstream methods, this study only used five data sites: Kennedy Krieger Institute (KKI), NeuroImage Sample (NI), New York University Child Study Center (NYU), Oregon Health and Science University (OHSU), and Peking University (Peking). In these five imaging sites, both training data sets and benchmark test data sets are included. The statistical data of each imaging site used in our experiments are shown in Table 1. The training data set provided by the Peking imaging site included three subsets, Peking_1, Peking_2, and Peking_3, and the sample numbers for these three subsets were 85, 67, and 42, respectively. In addition, some imaging sites collect multiple operation data for one participant, while some imaging sites only collect one operation datum for a single participant; therefore, only data named run1 were used in our experiment. In the training set of the NYU imaging site, one participant was not marked by gender. Therefore, the sum of the female and male training samples from the NYU site is 215, which is 1 less than the total samples 216. It can be seen from Table 1 that the number of samples in the ADHD-200 dataset is small and the samples are imbalanced.  
The number of subjects, scanning parameters, and equipment used varied across these different imaging sites. Detailed information about fMRI data acquisition parameters and instructions is available in Table 3 in [18]. For example, when the fMRI instrument was  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Imaging Site**} & \multicolumn{4}{c}{**Training Set**} & \multicolumn{4}{c}{**Benchmark Test Set**} \\  & **Age** & **Female** & **Male** & **HC** & **ADHD** & **Subtotal** & **Age** & **Female** & **Male** & **HC** & **ADHD** & **Subtotal** \\ \hline KKI & 8–13 & 37 & 45 & 60 & 22 & 82 & 8–12 & 1 & 10 & 8 & 3 & 11 \\ NI & 11–22 & 17 & 31 & 23 & 25 & 48 & 13–26 & 13 & 12 & 14 & 11 & 25 \\ NYU & 7–18 & 75 & 140 & 98 & 118 & 216 & 7–17 & 13 & 28 & 12 & 29 & 41 \\ OHSU & 7–12 & 36 & 43 & 42 & 37 & 79 & 7–12 & 17 & 17 & 28 & 6 & 34 \\ Peking & 8–17 & 50 & 144 & 116 & 78 & 194 & 8–16 & 18 & 32 & 27 & 23 & 50 \\ Total & - & 215 & 403 & 339 & 280 & 619 & - & 62 & 99 & 89 & 72 & 161 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the data sets used in this study.  
Figure 1: Flow chart of the methodology: Firstly, by operating the fMRI data preprocessing module, the original four-dimensional fMRI data are converted into a two-dimensional time series of ROIs. Then, the data conversion module converts the two-dimensional time series into three-dimensional images. Finally, the sequence representation learning neural network accomplishes the representation learning and ADHD classification.  
scanning, the participants in the NI and NYU sites were asked to keep their eyes closed, the participants in the KKI and OHSU sites were asked to keep their eyes open and fixate on an image, and the participants in Peking site were asked to keep their eyes closed or fixate. The variations in these imaging sites, as described above, lead to the heterogeneity of fMRI data, which increases the difficulty of ADHD classification.