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Therefore, in this study, we performed resting-state functional magnetic resonance imaging (rs-fMRI) analysis to detect differences in the spontaneous neural activity between the meth abstainers and the healthy controls, and identify the physiological mechanisms underlying the high relapse rate among the meth abstainers. The fluctuations and time variations in the blood oxygenation level-dependent (BOLD) signal of the local brain activity was analyzed from the pre-processed rs-fMRI data of 11 meth abstainers and 11 healthy controls and estimated the amplitude of low-frequency fluctuations (ALFF) and the dynamic ALFF (dALFF). In comparison with the healthy controls, meth abstainers showed higher ALFF in the anterior central gyrus, posterior central gyrus, trigonal-inferior frontal gyrus, middle temporal gyrus, dorsolateral superior frontal gyrus, and the insula, and reduced ALFF in the paracentral lobule and middle occipital gyrus. Furthermore, the meth abstainers showed significantly reduced dALFF in the supplementary motor area, orbital inferior frontal gyrus, middle frontal gyrus, medial superior frontal gyrus, middle occipital gyrus, insula, middle temporal gyrus, anterior central gyrus, and the cerebellum compared to the healthy controls ($ P &amp;lt; 0.05 $). These data showed abnormal spontaneous neural activity in several brain regions related to the cognitive, executive, and other social functions in the meth abstainers and potentially represent the underlying physiological mechanisms that are responsible for the high relapse rate. In conclusion, a combination of ALFF and dALFF analytical methods can be used to estimate abnormal spontaneous brain activity in the meth abstainers and make a more reasonable explanation for the high relapse rate of meth abstainers.&lt;\/p&gt;\n\n&lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2023593","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T10:55:02Z","timestamp":1686308102000},"page":"13318-13333","source":"Crossref","is-referenced-by-count":7,"title":["Abnormal static and dynamic amplitude of low-frequency fluctuations in multiple brain regions of methamphetamine abstainers"],"prefix":"10.3934","volume":"20","author":[{"given":"Guixiang","family":"Liang","sequence":"first","affiliation":[{"name":"Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"},{"name":"Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"},{"name":"Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"},{"name":"Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Sun","sequence":"additional","affiliation":[{"name":"Affiliation Shandong Detoxification Monitoring and Treatment Institute, Zibo 255000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijun","family":"Qin","sequence":"additional","affiliation":[{"name":"Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"},{"name":"Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benzheng","family":"Wei","sequence":"additional","affiliation":[{"name":"Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"},{"name":"Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2023593-1","doi-asserted-by":"publisher","unstructured":"B. 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