A schizophrenia study based on multi-frequency dynamic functional connectivity analysis of fMRI - Shi, Yuhu; Shen, Zehao; Zeng, Weiming; Luo, Sizhe; Zhou, Lili; Wang, Nizhuan (Frontiers in Human Neuroscience, 2023)
At present, the fMRI studies mainly focus on the entire low-frequency band (0.01–0.08 Hz). However, the neuronal activity is dynamic, and different frequency bands may contain different information. Therefore, a novel multi-frequency based dynamic functional connectivity (dFC) analysis method was proposed in this paper, which was then applied for the schizophrenia study. First, three frequency bands (Conventional: 0.01–0.08 Hz; Slow-5: 0.0111–0.0302 Hz; Slow-4: 0.0302–0.0820 Hz) were obtained by using Fast Fourier Transform. Next, the fractional amplitude of low frequency fluctuations was used to identify abnormal regions of interest (ROIs) of schizophrenia, and dFC among these abnormal ROIs was implemented by sliding time window method at four widow-widths. Finally, the recursive feature elimination was employed to select features, and the support vector machine was applied for the classification of schizophrenia patients and healthy controls. The experimental results showed that the proposed multi-frequency method (Combined: Slow-5 and Slow-4) had a better classification performance compared with conventional method at shorter sliding window-widths. In conclusion, our results revealed that the dFCs among the abnormal ROIs varied at different frequency bands and the efficiency of combining multiple features from different frequency bands can improve classification performance. Therefore, it would be a promising approach for identifying brain alterations in schizophrenia.