Identifying Methamphetamine Abstainers With Convolutional Neural Networks and Short-Time Fourier Transform - Lai, Xin; Huang, Qiuping; Xin, Jiang; Yu, Hufei; Wen, Jingxi; Huang, Shucai; Zhang, Hao; Shen, Hongxian; Tang, Yan (Frontiers in Psychology, 2021)
Few study report important insights into methamphetamine (MA) abstainers’ brain functional pattern. A better understanding of underlying functional mechanism in the brains of MA abstainers will help to explain abnormal behaviors. 42 male MA abstainers who were currently in long-term abstinence status (at least 14 months) and 32 normal males were recruited. All subjects underwent functional magnetic resonance imaging (fMRI) while responding to drug clues. In this study, a convolutional neural network (CNN) recognition model based on short-time Fourier transform (STFT) was proposed to identify MA abstainers and normal control group. STFT provided the time-localized frequency information and CNN was used to extract the structure features of the time-frequency spectrograms. The results showed that the classifier achieved satisfactory performance (98.9% accuracy) and could extract stabile brain voxels information. The highly discriminative power voxels mainly concentrated in the left inferior frontal gyrus of the orbit, the bilateral postcentral gyrus and the bilateral paracentral lobule. This study provides a new insight into the difference functional pattern between MA abstainers and normal, which elucidates the pathological mechanism of MA abstainers from time-frequency spectrograms integration viewpoint.