Effects of methamphetamine on neural responses to visual stimuli

An electroencephalographic signature predicts craving for methamphetamine

Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

Hyperconnectivity of the lateral amygdala in long-term methamphetamine abstainers negatively correlated with withdrawal duration

Introduction: Several studies have reported structural and functional abnormalities of the amygdala caused by methamphetamine addiction. However, it is unknown whether abnormalities in amygdala function persist in long-term methamphetamine abstainers. Methods: In this study, 38 long-term male methamphetamine abstainers (>12 months) and 40 demographically matched male healthy controls (HCs) were recruited. Considering the heterogeneous nature of the amygdala structure and function, we chose 4 amygdala subregions (i.e., left lateral, left medial, right lateral, and right medial) as regions of interest (ROI) and compared the ROI-based resting-state functional connectivity (FC) at the whole-brain voxel-wise between the two groups. We explored the relationship between the detected abnormal connectivity, methamphetamine use factors, and the duration of withdrawal using correlation analyses. We also examined the effect of methamphetamine use factors, months of withdrawal, and sociodemographic data on detected abnormal connectivity through multiple linear regressions. Results: Compared with HCs, long-term methamphetamine abstainers showed significant hyperconnectivity between the left lateral amygdala and a continuous area extending to the left inferior/middle occipital gyrus and left middle/superior temporal gyrus. Abnormal connections negatively correlated with methamphetamine withdrawal time (r = -0.85, p < 0.001). The linear regression model further demonstrated that the months of withdrawal could identify the abnormal connectivity (βadj = -0.86, 95%CI: -1.06 to -0.65, p < 0.001). Discussion: The use of methamphetamine can impair the neural sensory system, including the visual and auditory systems, but this abnormal connectivity can gradually recover after prolonged withdrawal of methamphetamine. From a neuroimaging perspective, our results suggest that withdrawal is an effective treatment for methamphetamine.

The Role of a Dopamine-Dependent Limbic-Motor Network in Sensory Motor Processing in Parkinson Disease

Limbic and motor integration is enabled by a mesial temporal to motor cortex network. Parkinson disease (PD) is characterized by a loss of dorsal striatal dopamine but relative preservation of mesolimbic dopamine early in disease, along with changes to motor action control. Here, we studied 47 patients with PD using the Simon conflict task and [18F]fallypride PET imaging. Additionally, a cohort of 16 patients participated in a single-blinded dextroamphetamine (dAMPH) study. Task performance was evaluated using the diffusion model for conflict tasks, which allows for an assessment of interpretable action control processes. First, a voxel-wise examination disclosed a negative relationship, such that longer non-decision time is associated with reduced D2-like binding potential (BPND) in the bilateral putamen, left globus pallidus, and right insula. Second, an ROI analysis revealed a positive relationship, such that shorter non-decision time is associated with reduced D2-like BPND in the amygdala and ventromedial OFC. The difference in non-decision time between off-dAMPH and on-dAMPH trials was positively associated with D2-like BPND in the globus pallidus. These findings support the idea that dysfunction of the traditional striatal-motor loop underlies action control deficits but also suggest that a compensatory parallel limbic-motor loop regulates motor output.

Substance-induced psychosis and cognitive functioning: A systematic review

Longitudinal studies of substance-induced psychosis (SIP) suggest that approximately 11-46% of persons will progress to schizophrenia with differential risk of progression depending on the type of substance used. The findings suggest SIP may be a distinct variant of a psychotic disorder, yet SIP is understudied and the disease expression is not well characterized, particularly the cognitive phenotype. There is some evidence for cognitive dysfunction in SIP, but a synthesis of this literature has not been undertaken. We systematically reviewed all empirical research (up to December 31, 2020) that examined cognition in SIP using clinical neuropsychological measures. The cognitive outcomes are summarized by type of SIP (methamphetamine, other stimulants, alcohol, cannabis, undifferentiated). There was evidence for global and domain-specific cognitive dysfunction in SIP compared to controls and non-psychotic persons who use substances. Impairments were of similar magnitude compared to persons with schizophrenia. Delineation of a specific cognitive profile in SIP was precluded by lack of literature with comparable study designs and outcomes. Variation in visual-based cognition may be a distinct feature of SIP, but this requires further investigation. More rigorously controlled studies of cognition in SIP are needed to inform differential diagnosis and identify the unique clinical needs of this population.

Difference in topological organization of white matter structural connectome between methamphetamine and heroin use disorder

The psychological symptoms caused by heroin and methamphetamine are significantly different in people with substance use disorders. The topological organization of structural connections that may underlie these differences remains unknown. The study sample consisted of 23 males with methamphetamine use disorder (MAUD), 20 males with heroin use disorder (HUD), and 21 male healthy controls (HCs) who were demographically matched. Diffusion tensor imaging and probabilistic tractography were used for white matter network construction. Psychological symptoms were evaluated by the Symptom Checklist-90. Using graph theoretical analysis, we examined the difference in graph-level and nodal-level properties among the groups. The network Hubs distribution and the relationship between the network alterations and psychological symptoms were identified. The MAUD group demonstrated significantly higher scores on anxiety, hostility, and symptoms of schizophrenia than the HUD and HCs groups. The HUD group showed significantly higher global efficiency and network strength than the HCs group, and higher network strength than the MAUD group. Compared with the HUD group, the MAUD group showed significantly lower Nodal Strength and efficiency, distributed mainly in the temporal, parietal, and occipital regions. We also found the network Hubs were decreased in the MAUD group, but increased in the HUD group. The Nodal Strength in the right superior temporal gyrus was significantly correlated with psychological symptoms in the MAUD group. These findings reflect the significant differences in topological structural connection between HUD and MAUD. This evidence helps shed some light on the neurobiological mechanisms of the psychological differences between HUD and MAUD, and extend our understanding of the structural disruption underlying MAUD-related psychological symptoms.

Effects of Methamphetamine on Within- and Between-Network Connectivity in Healthy Adults

Methamphetamine (MA) abuse remains an urgent public health problem. Understanding how the drug affects brain function will help to identify how it leads to abuse and dependence. Previous studies indicate that MA and other stimulants have complex effects on resting state functional connectivity. Here, we used a hypothesis-free approach to examine the acute effects of MA (20 mg oral) versus placebo on neural connectivity in healthy adults. Using networks identified by an independent component analysis with placebo data, we examined the effects of MA on connectivity within and between resting state networks. The drug did not significantly alter connectivity within networks. MA did alter connectivity between some networks: it increased connectivity between both the thalamus and cerebellum to sensorimotor and middle temporal gyrus. However, MA decreased connectivity between sensorimotor and middle temporal gyrus networks. MA produced its expected subjective effects, but these were not significantly related to connectivity. The findings extend our knowledge of how MA affects connectivity, by reporting that it affects between-network connectivity but not within-network connectivity. Future studies with other behavioral measures may reveal relationships between the neural and behavioral actions of the drug.

Treatment Response Prediction and Individualized Identification of Short-Term Abstinence Methamphetamine Dependence Using Brain Graph Metrics

Background: The abuse of methamphetamine (MA) worldwide has gained international attention as the most rapidly growing illicit drug problem. The classification and treatment response prediction of MA addicts are thereby paramount, in order for effective treatments to be more targeted to individuals. However, there has been limited progress. Methods: In the present study, 43 MA-dependent participants and 38 age- and gender-matched healthy controls were enrolled, and their resting-state functional magnetic resonance imaging data were collected. MA-dependent participants who showed 50% reduction in craving were defined as responders to treatment. The present study used the machine learning method, which is a support vector machine (SVM), to detect the most relevant features for discriminating and predicting the treatment response for MA-dependent participants based on the features extracted from the functional graph metrics. Results: A classifier was able to differentiate MA-dependent subjects from normal controls, with a cross-validated prediction accuracy, sensitivity, and specificity of 73.2% [95% confidence interval (CI) = 71.23-74.17%), 66.05% (95% CI = 63.06-69.04%), and 80.35% (95% CI = 77.77-82.93%), respectively, at the individual level. The most accurate combination of classifier features included the nodal efficiency in the right middle temporal gyrus and the community index in the left precentral gyrus and cuneus. Between these two, the community index in the left precentral gyrus had the highest importance. In addition, the classification performance of the other classifier used to predict the treatment response of MA-dependent subjects had an accuracy, sensitivity, and specificity of 71.2% (95% CI = 69.28-73.12%), 86.75% (95% CI = 84.48-88.92%), and 55.65% (95% CI = 52.61-58.79%), respectively, at the individual level. Furthermore, the most accurate combination of classifier features included the nodal clustering coefficient in the right orbital part of the superior frontal gyrus, the nodal local efficiency in the right orbital part of the superior frontal gyrus, and the right triangular part of the inferior frontal gyrus and right temporal pole of middle temporal gyrus. Among these, the nodal local efficiency in the right temporal pole of the middle temporal gyrus had the highest feature importance. Conclusion: The present study identified the most relevant features of MA addiction and treatment based on SVMs and the features extracted from the graph metrics and provided possible biomarkers to differentiate and predict the treatment response for MA-dependent patients. The brain regions involved in the best combinations should be given close attention during the treatment of MA.

Visual features influence thought content in the absence of overt semantic information

It has recently been shown that the perception of visual features of the environment can influence thought content. Both low-level (e.g., fractalness) and high-level (e.g., presence of water) visual features of the environment can influence thought content in real-world and experimental settings where these features can make people more reflective and contemplative in their thoughts. It remains to be seen, however, if these visual features retain their influence on thoughts in the absence of overt semantic content, which could indicate a more fundamental mechanism for this effect. In this study, we removed this limitation by creating scrambled edge versions of images, which maintain edge content from the original images but remove scene identification. Nonstraight edge density is one visual feature that has been shown to influence many judgements about objects and landscapes and has also been associated with thoughts of spirituality. We extend previous findings by showing that nonstraight edges retain their influence on the selection of a Spiritual & Life Journey topic after scene-identification removal. These results strengthen the implication of a causal role for the perception of low-level visual features on the influence of higher order cognitive function, by demonstrating that in the absence of overt semantic content, low-level features, such as edges, influence cognitive processes.