Neuroplastic changes in resting-state functional connectivity after rTMS intervention for methamphetamine craving

Su, Hang; Liu, Yilin; Yin, Dazhi; Chen, Tianzhen; Li, Xiaotong; Zhong, Na; Jiang, Haifeng; Wang, Jijun; Du, Jing; Ke, Xiao; Xu, Ding; Zeljic, Kristina; Wang, Zheng; Zhao, Min

doi:10.1016/j.neuropharm.2020.108177

Cited by 36 publications

(31 citation statements)

References 54 publications

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“…The effects of transcranial magnetic stimulation on decreasing craving and attentional bias were significant during treatment and follow-up. These findings regarding craving decrease are consistent with a previous study showing significant craving decrease after rTMS treatment in the lateral-dorsal region of the cortex's (Luigjes et al 2019;Su et al 2020). This finding was also observed years after by Lee et al studying methamphetamine users (Rostami et al, 2013).…”

Section: Discussionsupporting

confidence: 92%

Comparison of the Efficacy of Matrix Therapy, Transcranial Magnetic Stimulation, and Cognitive Rehabilitation in Attention Bias Modification and Craving Reduction in Stimulant Drug Users

Esmaeili¹,

Taremian²,

Rezaei³

et al. 2021

BCN

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Introduction: Previous studies have shown that Matrix, repetitive transcranial magnetic stimulation (rTMS), and cognitive modification treatments could lead to numerous psychological improvements in patients suffering from substance use disorders. Previous research has shown that other therapeutic interventions could be useful in managing stimulant abuse in addition to Matrix treatment The present study aimed at comparison of the effectiveness of matrix therapy, transcranial magnetic stimulation and cognitive rehabilitation treatments in attention bias modification and craving reduction in amphetamine drug users. Methods: This study was quasi-experimental with pretest-posttest and three-month follow-up. Forty subjects taking amphetamine were selected by convenient sampling method and were randomly divided into three groups, including the matrix therapy, cognitive rehabilitation and rTMS as intervention groups and control groups (10 subjects in each group). The matrix group received 24 treatment sessions, 3 sessions per week and cognitive rehabilitation group recivied 15 treatment sessions (3 sessions per week). The group with rTMS treatment participated in a total of 10 sessions every other day. Questionnaires and dot-Probe tasks were performed as the cognitive software before and after the therapeutic interventions and one month after the interventions as a follow-up test. Results: The results showed that the effect of time on craving assessment in three stages was significant in all the study's four groups. In the experimental groups of Matrix, rTMS, and cognitive rehabilitation, the effect of time on the severity of addiction dependence and attentional bias was significantly different in three stages. In the control group, the effect of time in three stages of assessment of addiction severity and attention bias was not significant. Also, there was a significant decrease in the mean of craving in Matrix, rTMS, and cognitive rehabilitation groups from pre-treatment to post-treatment and from pre-treatment to follow-up stages. The mean addiction severity was significantly decreased from pre-treatment to post-treatment and from pre-treatment to follow-up stage in the Matrix, rTMS, and cognitive rehabilitation groups. Conclusion: The current study's findings indicated that all three interventions effectively reduced amphetamine craving and attentional bias. All three approaches produced positive therapeutic outcomes.

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Section: Discussionsupporting

confidence: 92%

Comparison of the Efficacy of Matrix Therapy, Transcranial Magnetic Stimulation, and Cognitive Rehabilitation in Attention Bias Modification and Craving Reduction in Stimulant Drug Users

Esmaeili¹,

Taremian²,

Rezaei³

et al. 2021

BCN

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show abstract

“…These evidences coincide with our results that the brain network of MA abstainers after withdrawal for a period of time still has a big difference in DDMN. Long-term use of MA can lead to dysfunction of the brain network, and similar experiments have also found that the brain networks of MA abstainers have been damaged ( Su et al, 2020 ). We also found that similar damage still exists after a period of treatment.…”

Section: Discussionmentioning

confidence: 94%

Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging

Dong

Huang

et al. 2021

Front. Psychol.

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Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.

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“…The reflecting system in IA and substance addiction was consistently found to be dysfunctional when performing inhibition control tasks (Darnai et al, 2019;Antons and Matthias, 2020;Suarez-Suarez et al, 2020); therefore, it was regarded as a therapeutic target for addiction. After noninvasive treatment targeted to the system, the addiction symptoms were significantly relieved (Newman-Norlund et al, 2020;Su et al, 2020), indicating the critical role of the reflective system in addiction.…”

Section: Hypoactive Reflecting Systemmentioning

confidence: 99%

Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study

Wang

Qin

et al. 2021

Front. Neurosci.

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Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA.

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Neuroplastic changes in resting-state functional connectivity after rTMS intervention for methamphetamine craving

Cited by 36 publications

References 54 publications

Comparison of the Efficacy of Matrix Therapy, Transcranial Magnetic Stimulation, and Cognitive Rehabilitation in Attention Bias Modification and Craving Reduction in Stimulant Drug Users

Comparison of the Efficacy of Matrix Therapy, Transcranial Magnetic Stimulation, and Cognitive Rehabilitation in Attention Bias Modification and Craving Reduction in Stimulant Drug Users

Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging

Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study

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