Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG

Point-of-care testing of methamphetamine and cocaine utilizing wearable sensors

The imperative for the point-of-care testing of methamphetamine and cocaine in drug abuse prevention necessitates innovative solutions. To address this need, we have introduced a multi-channel wearable sensor harnessing CRISPR/Cas12a system. A CRISPR/Cas12a based system, integrated with aptamers specific to methamphetamine and cocaine, has been engineered. These aptamers function as signal-mediated intermediaries, converting methamphetamine and cocaine into nucleic acid signals, subsequently generating single-stranded DNA to activate the Cas12 protein. Additionally, we have integrated a microfluidic system and magnetic separation technology into the CRISPR system, enabling rapid and precise detection of cocaine and methamphetamine. The proposed sensing platform demonstrated exceptional sensitivity, achieving a detection limit as low as 0.1 ng/mL. This sensor is expected to be used for on-site drug detection in the future.

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.

An automated drug dependence detection system based on EEG

OBJECTIVE

Substance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser.

METHODS

EEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification.

RESULTS

The results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score.

CONCLUSIONS AND SIGNIFICANCE

The nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.

Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction

Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.

High-frequency repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex reduces drug craving and improves decision-making ability in methamphetamine use disorder

Methamphetamine abuse is escalating worldwide. Its strong and irreversible neurotoxicity generally causes structural and functional changes in the brain. Repetitive transcranial magnetic stimulation (rTMS) as a non-invasive tool can be used to modulate neuronal activity, cortical excitability, and dopaminergic neurotransmission. This study aims to explore the efficacy of high-frequency rTMS in reducing drug craving and increasing decision-making ability for methamphetamine use disorder patients. Sixty-four methamphetamine use disorder patients were randomized to sham rTMS group and 10-Hz rTMS group. Visual analog scale (VAS) and Iowa game test (IGT) were used to evaluate drug craving and cognitive decision-making ability before and after treatment. Before the treatment, the two groups had no differences in the scores of VAS and IGT. After the intervention, VAS scores of 10-Hz rTMS group were significantly lower than that of sham rTMS group. In addition, the two groups had significant differences in the net score of IGT on block 4 and block 5, which favoured the 10-Hz rTMS group. Taken together, the present results suggest that High-frequency rTMS can be used to reduce drug craving and improve decision-making function for methamphetamine use disorder.

NDCN-Brain: An Extensible Dynamic Functional Brain Network Model

As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain’s connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases.

The Counterproductive Effect of Right Anodal/Left Cathodal Transcranial Direct Current Stimulation Over the Dorsolateral Prefrontal Cortex on Impulsivity in Methamphetamine Addicts

The current study aimed to evaluate the effect of transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (DLPFC) on behavioral impulsivity in methamphetamine addicts. Forty-five methamphetamine addicts were recruited and randomly divided into active tDCS and sham tDCS groups to receive a daily tDCS intervention for 5 days, with the intensity set to 2 mA for the active group and 0 mA for the sham group. Anodal and cathodal electrodes were, respectively, placed over the right and left DLPFC. Behavioral impulsivity in methamphetamine addicts was examined by the 2-choice oddball task at 3-time points: before tDCS intervention (baseline), after the first intervention (day 1), and after 5 repeated interventions (day 5). Besides, twenty-four healthy male participants were recruited as the healthy controls who completed a 2-choice oddball task. Analysis of accuracy for the 2-choice oddball task showed that behavioral impulsivity was counterproductively increased in the active group, which was shown by the decreased accuracy for the deviant stimulus. The results suggested that the present protocol may not be optimal and other protocols should be considered for the intervention of methamphetamine addicts in the future.

The Effectiveness of Mindfulness-Based Relapse Prevention on Chinese Methamphetamine Dependent Patients: A Pilot Study

Methamphetamine use is a serious problem in China. Compulsory isolation detoxification is the main treatment measure for drug dependents, whereas psychological interventions in compulsory isolation detoxification centers are extremely inadequate. The current study aimed to examine the effects of mindfulness-based relapse prevention (MBRP) on methamphetamine dependence patients in Chinese compulsory isolation detoxification treatment institutions. Forty-one methamphetamine dependent patients received 16-sessions of MBRP in 8 weeks and assessments were conducted at the baseline, 4-, 8-week (after the whole intervention). Results of repeated measured ANOVAs showed there was no significant effect on emotions and cravings. Findings indicated that the effects of MBRP are still difficult to make firm conclusions due to the insignificant results. Future studies should modify the MBRP and ensure that it is suitable for compulsory isolation detoxification treatment institutions in China.

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations.

Brain functional network modeling and analysis based on fMRI: a systematic review

In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.

Effects of exercise programs on neuroelectric dynamics in drug addiction

Exercise interventions have been considered to be an effective treatment for drug addiction. However, there is little dirct evidence that exercise affects brain activity in individuals afftected by drug addiction. Therefore, the aim of the present study was to investigate the effects of different exercise programs on detoxification. Cognitive recovery with 64-channel electroencephalography (EEG) recordings was obtained before and after three months of daily aerobic and anaerobic exercise. A total of 63 subjects with methamphetamine addiction were recruited and randomly divided into three groups for cognitive study in four behavioral states: an anaerobic resistance treatment group, an aerobic cycling treatment group and a control group. In addition, four behavioral states were examined: eyes-closed and eyes-open resting states, and exploratory behavior states following either drug- or neutral-cue exposure. Over a 12-week period,the alpha block ratio in the control group showed a slight decrease, while clear increases were observed in the resistance exercise and cycling treatment groups, particularly under the frontal and temporal regions in the eyes-open and drug-cue conditions. The major EEG activity frequency in the resistance treatment group during the drug-cue behavior task decreased compared with the frequencies of the cycling exercise and control groups. Meanwhile, the power of higher brain rhythms in the resistance treatment group was increased. Finally, the brain alpha wave left-lateralization index from EEG recording sites, F1-F2, in the resistance and cycling treatment groups under the eyes-closed condition positively decreased, while the control groups only showed slight decreases. Taken together, these results suggest that different types of exercise may induce distince and different positive therapeutic effects to facilitate detoxification.

Energy features in spontaneous up and down oscillations

Spontaneous brain activities consume most of the brain's energy. So if we want to understand how the brain operates, we must take into account these spontaneous activities. Up and down transitions of membrane potentials are considered to be one of significant spontaneous activities. This kind of oscillation always shows bistable and bimodal distribution of membrane potentials. Our previous theoretical studies on up and down oscillations mainly looked at the ion channel dynamics. In this paper, we focus on energy feature of spontaneous up and down transitions based on a network model and its simulation. The simulated results indicate that the energy is a robust index and distinguishable of excitatory and inhibitory neurons. Meanwhile, one the whole, energy consumption of neurons shows bistable feature and bimodal distribution as well as the membrane potential, which turns out that the indicator of energy consumption encodes up and down states in this spontaneous activity. In detail, energy consumption mainly occurs during up states temporally, and mostly concentrates inside neurons rather than synapses spatially. The stimulation related energy is small, indicating that energy consumption is not driven by external stimulus, but internal spontaneous activity. This point of view is also consistent with brain imaging results. Through the observation and analysis of the findings, we prove the validity of the model again, and we can further explore the energy mechanism of more spontaneous activities.

The late parietal event-related potential component is hierarchically sensitive to chunk tightness during chunk decomposition

The current study analyzed event-related potentials (ERPs) associated with visuo-spatial transformation in order to examine how "chunk tightness" affects the difficulty of chunk decomposition problems. Participants completed a Chinese character decomposition task in three conditions according to the tightness of the to-be-decomposed chunk (tight vs. medium vs. loose). Behavioral data showed that performance became worse (longer reaction time, lower accuracy) as chunk tightness increased. ERP data showed that, as chunk tightness increased, the LPC exhibited a significant decrease at posterior electrode sites. The results indicate that chunk tightness might exert its primary effect on chunk decomposition difficulty by increasing the difficulty of visuo-spatial transformation, a process linked to the parietal LPC.

The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi's Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naïve Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

This study aimed to examine the effects of chronic methamphetamine use on the topological organization of whole-brain functional connectivity network (FCN) by reconstruction of neural-activity time series at resting-state. The EEG of 36 individuals with methamphetamine use disorder (IWMUD) and 24 normal controls (NCs) were recorded, pre-processed and source-reconstructed using standardized low-resolution tomography (sLORETA). The brain FCNs of participants were constructed and between-group differences in network topological properties were investigated using graph theoretical analysis. IWMUD showed decreased characteristic path length, increased clustering coefficient and small-world index at delta and gamma frequency bands compared to NCs. Moreover, abnormal changes in inter-regional connectivity and network hubs were observed in all the frequency bands. The results suggest that the IWMUD and NCs have distinct FCNs at all the frequency bands, particularly at the delta and gamma bands, in which deviated small-world brain topology was found in IWMUD.