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Background: Increasing evidence suggests that heroin addiction may be related to the dysfunction among the triple brain network (default mode network [DMN], salience network [SN] and executive control network [ECN]). However, the characteristics of glucose metabolism and metabolic connectivity among core regions of the triple brain network remain unknown. Therefore, we hypothesized that individuals with heroin dependence would show abnormal glucose metabolism and accompanied abnormal metabolic connectivity within the triple brain network. Methods: Individuals with heroin dependence and healthy controls matched for age and sex underwent integrated positron emission tomography/magnetic resonance imaging (PET/MRI). Differences in glucose metabolism and metabolic connectivity among the DMN, SN and ECN were analyzed based on [sup.18]F-fluorodeoxyglucose PET and resting-state fMRI data. Results: We included 36 individuals with heroin dependence and 30 matched healthy controls in our study. The heroin dependence group showed a significant reduction of glucose metabolism in the bilateral anterior insula (AI) and inferior parietal lobule (IPL), and a significantly decreased metabolic connectivity between the right AI and the left dorsolateral prefrontal cortex (DLPFC). The daily dose of methadone was negatively correlated with glucose metabolism of the right AI and right IPL. Limitations: The results revealed the glucose metabolism alterations and metabolic connectivity only within the triple brain network in individuals with heroin dependence; additional brain networks should be investigated in future studies. Although methadone is an opioid with a similar neurophysiological mechanism as heroin, the specific chronic effects of methadone on cerebral metabolism and metabolic connectivity should also be investigated in future studies. Conclusion: Our findings suggest that long-term opioid use might, to some extent, be associated with reduced synergistic ability between the SN and ECN, which may be associated with the dysfunction of cognitive control. In particular, the right AI, which showed hypometabolism and related reduction in SN-ECN metabolic connectivity, should receive increasing attention in future studies., Introduction Heroin addiction is characterized by excessive drug-seeking and drug-taking behaviours despite serious adverse consequences, and it manifests clinically as a chronic relapsing cycle of binging, intoxication, withdrawal and craving. [...]

The abnormal interactions of three key large‐scale brain networks (default mode [DMN], salience and executive control [ECN]) were showed underlie dysfunctions in heroin addiction. Repetitive transcranial magnetic stimulation (rTMS) targeting the left dorsolateral prefrontal cortex (DLPFC) is a potential treatment for heroin addiction. It is unclear whether impaired coupling among the large‐scale brain networks would be improved by rTMS in treated heroin‐dependent individuals. Thirty‐five heroin‐dependent individuals were included in this sham‐controlled, randomized study. The patients received either active or sham rTMS for 1 week. The craving for heroin and resting‐state functional magnetic resonance imaging data were collected before and after 1‐week rTMS. Twenty‐two healthy subjects were included as controls not receiving rTMS. After 1‐week rTMS, only the active rTMS group showed a significant decrease in spontaneous and heroin cue‐induced craving. The coupling between left DLPFC (a key node of left ECN) and left parahippocampal gyrus (PHG, included in DMN) significantly increased for the active group with a tendency towards that of controls. The coupling between the right precentral gyrus and three key regions included in DMN (posterior cingulate cortex/precuneus and bilateral inferior parietal cortex) significantly decreased for the active group with a tendency towards that of healthy controls. For the active rTMS individuals, the left DLPFC‐PHG coupling negatively correlated with the spontaneous craving and the drug cue‐induced craving. It suggested that the rTMS could reduce heroin craving, which might be related to the modulation of ECN‐DMN coupling. This finding might shed light on the mechanism of rTMS for heroin addiction treatment. [ABSTRACT FROM AUTHOR]

To assess the clinical value of voxel-based automatic quantitative analysis using a normal brain glucose metabolism database in the preoperative localization of focal intractable temporal lobe epilepsy patients. Patients with refractory temporal lobe epilepsy who underwent 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging were retrospectively enrolled from January to June 2017. Visual analysis was performed by two nuclear medicine radiologists, and the automatic quantitative analysis was carried out using MIMneuro software based the age- and gender-stratified normal brain glucose metabolism database. Setting postoperative outcomes as reference, the consistency between visual analysis and automatic quantitative analysis was tested by Cohen's kappa coefficient, and differences in localization of epileptic foci of the two methods were compared by Chi-square test. A total of 32 patients intractable temporal lobe epilepsy were included in this study. There was a moderate agreement between the automatic quantitative analysis based on MIMneuro software and visual analysis (kappa coefficient = 0.472, p = 0.002). In terms of the efficiency of focus localization, the voxel-based automatic quantitative analysis was higher than that of visual analysis (Chi-square value = 6.969, p = 0.008). The voxel-based automatic quantitative analysis combined with normal brain glucose metabolism database had a certain clinical application value for detection temporal lobe epilepsy. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to measure the global brain glucose metabolism of patients with temporal lobe epilepsy (TLE) using MIMneuro software based on the normal brain glucose metabolism database.Methods: In this cross-sectional study, 23 patients (11 males and 12 females, mean age 25.6 ± 10.1 years) with TLE who underwent 18F-labeled fluoro-2-deoxyglucose positron emission tomography (18F-FDG PET) were enrolled. 18F-FDG PET images were then imported into MIMneuro software, which can automatically analyze the differences in regional brain glucose metabolism between patients and a normal database, and the results of different brain regions were presented by values of Z-score.Results: In patients with TLE, 18F-FDG PET imaging showed that in addition to the presence of temporal lobe hypometabolism, there was hypometabolism in the ipsilateral hippocampus, parahippocampal gyrus, insula, amygdala, temporal operculum, and bilateral cerebellar hemisphere, while hypermetabolism was found in the contralateral temporal lobe, frontal lobe, parietal lobe, parietal lobule, angular gyrus, and precentral gyrus. There was no significant difference in brain areas between the left and the right temporal lobe seizures (P > 0.05).Conclusions: We found that TLE has a specific characteristic in terms of brain glucose metabolism, and the underlying mechanism needs to be further studied that may be helpful to localize seizure focus. [ABSTRACT FROM AUTHOR]

Purpose: To create a standard Western Chinese normal functional brain database for quantitative analysis using 2-deoxy-2-[18F] fluoro-D-glucose (18F-FDG) positron emission tomography (PET) images and MIMneuro software. Methods: 78 healthy right-handed Chinese volunteers from Tangdu Hospital were scanned using 18F-FDG PET to evaluate brain metabolism between March and October 2016. All PET images were processed using MIMneuro software to create a normal database platform. The platform included anatomical optimization to facilitate spatial localization of abnormalities and a statistical comparison with normal cases utilizing the Z-scores, which represent the number of standard deviations from the mean of the normal controls in the database. Results: The novel Chinese brain metabolism database platform including 78 healthy volunteers (male: female 40:38; age 3-78 years, mean age, 45 years) was constructed based on the MIMneuro software, which increased the diagnostic confidence in the test patient by quantifying and emphasizing the abnormality. The BrainAlignTM deformation algorithm of MIMneuro matched the size, shape, and orientation of the patient's brain scan to a template brain for comparison against a database of normal controls. The quantitative analysis performed on a voxel and regional level was useful in assessing the areas of abnormalities. Conclusions: A novel Chinese 18F-FDG PET-based normal brain function database was created to highlight the local regions of abnormal metabolic activity through quantitative comparisons against the normal database. The Z-scores obtained by MIMneuro potentially aid in visualizing and quantifying the subtle lesions on 18FDG-PET scan images as observed in a patient diagnosed with epilepsy. [ABSTRACT FROM AUTHOR]

Purpose: To create a standard Western Chinese normal functional brain database for quantitative analysis using 2-deoxy-2-[18F] fluoro-d-glucose ( 18 F-FDG) positron emission tomography (PET) images and MIMneuro software., Methods: 78 healthy right-handed Chinese volunteers from Tangdu Hospital were scanned using 18 F-FDG PET to evaluate brain metabolism between March and October 2016. All PET images were processed using MIMneuro software to create a normal database platform. The platform included anatomical optimization to facilitate spatial localization of abnormalities and a statistical comparison with normal cases utilizing the Z-scores, which represent the number of standard deviations from the mean of the normal controls in the database., Results: The novel Chinese brain metabolism database platform including 78 healthy volunteers (male: female 40:38; age 3-78 years, mean age, 45 years) was constructed based on the MIMneuro software, which increased the diagnostic confidence in the test patient by quantifying and emphasizing the abnormality. The BrainAlign TM deformation algorithm of MIMneuro matched the size, shape, and orientation of the patient's brain scan to a template brain for comparison against a database of normal controls. The quantitative analysis performed on a voxel and regional level was useful in assessing the areas of abnormalities., Conclusions: A novel Chinese 18 F-FDG PET-based normal brain function database was created to highlight the local regions of abnormal metabolic activity through quantitative comparisons against the normal database. The Z-scores obtained by MIMneuro potentially aid in visualizing and quantifying the subtle lesions on 18 FDG-PET scan images as observed in a patient diagnosed with epilepsy.