White Matter Abnormalities Based on TBSS and Its Correlation With Impulsivity Behavior of Methamphetamine Addicts

Coupling analysis of diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) with abnormal cerebral blood flow in methamphetamine-dependent patients and its application in machine-learning-based classification

BACKGROUND

Diffusion tensor imaging (DTI) analysis along the perivascular space (ALPS) index is currently widely employed to evaluate the neurophysiological activity in various neuropsychiatric disorders. However, there remains a scarcity of studies assessing the DTI-ALPS index in individuals with methamphetamine (MA) dependence. Recent studies on animals have demonstrated a significant correlation between glymphatic activity and alterations in cerebral blood flow (CBF). Hence, investigating the DTI-ALPS index and its coupling with CBF could yield novel insights for MA-dependent patients.

METHODS

In this study, we employed DTI and arterial spin labeling to investigate the ALPS index and CBF in 46 MA-dependent patients and 46 control subjects. By using DTI-ALPS, we evaluated a comprehensive diffusivity parameter that encompasses contributions from both the perivascular spaces and fiber tracts. Furthermore, a two-sample t-test was employed to assess inter-group differences. Partial correlation analysis was used to evaluate the correlations of the ALPS index with age, clinical parameters, and CBF, respectively. In addition, a causal mediation analysis was conducted to explore whether CBF mediates the causal relationship between MA-related clinical characteristics and the ALPS index. Finally, a support vector machine (SVM) was trained by the ALPS-related features and CBF features for the purpose of distinguishing MA-dependent subjects from control subjects.

RESULTS

Compared to the control group, the MA-dependent group presented a decreased ALPS index, particularly in the right hemisphere. Moreover, increased diffusivities were observed along the projection fibers in the right Y-axis and the association fibers in the right Z-axis, while the AI of the diffusivity along the Z-axis association fibers decreased in patients with MA dependence. The study observed a tight coupling between the ALPS index and CBF in MA-dependent patients, and revealed significant positive correlations between the ALPS index and CBF in specific brain regions, including the right precentral sulcus, right anterior transverse collateral sulcus, left postcentral sulcus, left superior parietal lobule, left superior occipital sulcus and transverse occipital sulcus, and right temporal pole. The causal mediation analysis suggested that CBF partially mediated the alteration of the ALPS index induced by the duration of MA consumption in MA-dependent patients. Additionally, CBF/ALPS ratio was lower in the MA-dependent group compared to the controls group. An SVM trained with the ALPS-related indicators and CBF indicators achieved classification accuracy, sensitivity, specificity, and kappa values of 93.31 % ± 5.72 %, 91.56 % ± 9.14 %, 95.05 % ± 7.91 % and 86.60 % ± 11.44 %, respectively, for identifying patients with MA dependence.

CONCLUSIONS

The study identified abnormal ALPS index, which has the potential to be a meaningful imaging marker for MA-dependent patients. The findings emphasized the strong coupling between the ALPS index and CBF in MA-dependent individuals, providing indirect imaging references for future research on the relationship between the glymphatic system and CBF. Moreover, the abnormal ALPS-related features and CBF features hold promise as valuable features for developing highly effective classification models.

Frontal white and gray matter abnormality in gambling disorder: A multimodal MRI study

Background

Changes in brain structural connections appear to be important in the pathophysiology of substance use disorders, but their role in behavioral addictions, such as gambling disorder (GD), is unclear. GD also offers a model to study addiction mechanisms without pharmacological confounding factors. Here, we used multimodal MRI data to examine the integrity of white matter connections in individuals with GD. We hypothesized that the affected areas would be in the fronto-striatal-thalamic circuit.

Methods

Twenty individuals with GD (mean age: 64 years, GD duration: 15.7 years) and 40 age- and sex-matched healthy controls (HCs) underwent detailed clinical examinations together with brain 3T MRI scans (T1, T2, FLAIR and DWI). White matter (WM) analysis involved fractional anisotropy and lesion load, while gray matter (GM) analysis included voxel- and surface-based morphometry. These measures were compared between groups, and correlations with GD-related behavioral characteristics were examined.

Results

Individuals with GD showed reduced WM integrity in the left and right frontal parts of the corona radiata and corpus callosum (pFWE < 0.05). WM gambling symptom severity (SOGS score) was negatively associated to WM integrity in these areas within the left hemisphere (p < 0.05). Individuals with GD also exhibited higher WM lesion load in the left anterior corona radiata (pFWE < 0.05). GM volume in the left thalamus and GM thickness in the left orbitofrontal cortex were reduced in the GD group (pFWE < 0.05).

Conclusions

Similar to substance addictions, the fronto-striatal-thalamic circuit is also affected in GD, suggesting that this circuitry may have a crucial role in addictions, independent of pharmacological substances.

Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification

Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models.

Beyond BMI: cardiometabolic measures as predictors of impulsivity and white matter changes in adolescents

Obesity is characterized by cardiometabolic and neurocognitive changes. However, how these two factors relate to each other in this population is unknown. We tested the association that cardiometabolic measures may have with impulse behaviors and white matter microstructure in adolescents with and without an excess weight. One hundred and eight adolescents (43 normal-weight and 65 overweight/obesity; 11-19 years old) were medically and psychologically (Temperament Character Inventory Revised, Three-Factor Eating Questionnaire-R18, Conners' Continuous Performance Test-II, Stroop Color and Word Test, Wisconsin Card Sorting Test, Kirby Delay Discounting Task) evaluated. A subsample of participants (n = 56) underwent a brain magnetic resonance imaging acquisition. In adolescents, higher triglycerides and having a body mass index indicative of overweight/obesity predicted a more impulsive performance in Conners' Continuous Performance Test-II (higher commission errors). In addition, higher glucose and diastolic blood pressure values predicted increments in the Three-Factor Eating Questionnaire-R18 emotional eating scale. Neuroanatomically, cingulum fractional anisotropy showed a negative relationship with glycated hemoglobin. The evaluation of the neurocognitive differences associated with obesity, usually based on body mass index, should be complemented with cardiometabolic measures.

Disrupted white matter microstructure correlates with impulsivity in children and adolescents with bipolar disorder

Altered white matter (WM) microstructure likely occurs in children with bipolar disorder (BD) with impulsivity representing one of the core features. However, altered WM microstructures and their age-related trendlines in children with BD and those at high-risk of developing BD, as well as correlations of WM microstructures with impulsivity, have been poorly investigated. In this study, diffusion MRI, cognitive, and impulsivity assessments were obtained from children/adolescents diagnosed with BD, offspring of individuals with BD (high-risk BD) and age-matched healthy controls. A novel atlas-based WM skeleton measurement approach was used to quantify WM microstructural integrity with all diffusion-tensor-imaging (DTI) metrics including fractional anisotropy, axial, mean and radial diffusivity to survey entire WM tracts and ameliorate partial volume effects. Among all DTI-derived metric measures, radial diffusivity quantifying WM myelination was found significantly higher primarily in corpus callosum and in the corona radiata in children with BD compared to controls. Distinguished from age-related progressively decreasing diffusivities and increasing fractional anisotropy in healthy controls, flattened age-related trendlines were found in BD group, and intermediate developmental rates were observed in high-risk group. Larger radial diffusivity in the corpus callosum and corona radiata significantly correlated with shorter response times to affective words that indicate higher impulsivity in the BD group, whereas no such correlation was found in the healthy control group. This work corroborates the progressive nature of pediatric BD and suggests that WM microstructural disruption involved in affective regulation and sensitive to impulsivity may serve as a biomarker of pediatric BD progression.

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.

Association between white matter microstructure and cognitive function in patients with methamphetamine use disorder

Methamphetamine use disorder (MUD) has been associated with broad neurocognitive impairments. While the cognitive impairments of MUD have been demonstrated, the neuropathological underpinnings remain inadequately understood. To date, the published human diffusion tensor imaging (DTI) studies involving the correlation between diffusion parameters and neurocognitive function in MUD are limited. Hence, the present study aimed to examine the association between cognitive performance and white matter microstructure in patients with MUD. Forty-five patients with MUD and 43 healthy controls (HCs) completed their demographic information collection, cognitive assessments, and DTI imaging. DTI images were preprocessed to extract fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of various fiber tracts. Univariate tests were used to examine group differences in cognitive assessments and DTI metrics. Linear regression was used to examine the relationship between these two metrics. The results revealed that patients with MUD had lower subset scores of the MATRICS Consensus Cognitive Battery (MCCB), which reflects five cognitive domains: processing speed, attention, verbal learning, visual learning, problem-solving. Patients with MUD also had significantly higher AD, MD, and RD values of the left superior longitudinal fasciculus than HCs. Furthermore, the RD value of the left superior longitudinal fasciculus was a significant predictor of processing speed and problem-solving ability, as shown by the digit-symbol coding test and NAB-Mazes scores, respectively. Findings extended our understanding of white matter microstructure that is related to neurocognitive deficits in MUD and provided potential targets for the prevention and treatment of this chronic disorder.

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.

Gradient Patterns of Age-Related Diffusivity Changes in Cerebral White Matter

The current concept of brain aging proposes three gradient patterns of changes in white matter that occur during healthy brain aging: antero-posterior, supero-inferior, and the myelodegeneration-retrogenesis (or the “last-in-first-out”) concept. The aim of this study was to correlate white matter diffusivity measures (fractional anisotropy-FA, mean diffusivity-MD, radial diffusivity-RD, and axial diffusivity-AD) in healthy volunteers with chronological age and education level, in order to potentially incorporate the findings with proposed patterns of physiological brain aging. The study was performed on 75 healthy participants of both sexes, with an average age of 37.32 ± 11.91 years underwent brain magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI). DTI was performed using tract-based spatial statistics (TBSS), with the analysis of four parameters: FA, MD, RD, and AD. Skeletonized measures were averaged in 29 regions of interest in white matter. Correlations between age and DTI measures and between education-level and DTI measures were performed using Pearson's correlation test. To correct for multiple comparisons, we applied a Bonferroni correction to the p-values. Significance was set at p ≤ 0.001. A significant negative correlation of FA with age was observed in posterior thalamic radiation (PTR) (p< 0.001). A significant positive correlation between age and MD was observed in sagittal stratum (SS) (p< 0.001), between age and RD in PTR, SS, and retrolenticular internal capsule (p< 0.001), and between age and AD in the body of the corpus callosum (p< 0.001). There were no significant correlations of DTI parameters with educational level. According to our study, RD showed the richest correlations with age, out of all DTI metrics. FA, MD, and RD showed significant changes in the diffusivity of projection fibers, while AD presented diffusivity changes in the commissural fibers. The observed heterogeneity in diffusivity changes across the brain cannot be explained by a single aging gradient pattern, since it seems that different patterns of degradation are true for different fiber tracts that no currently available theory can globally explain age-related changes in the brain. Additional factors, such as the effect of somatosensory decline, should be included as one of the important covariables to the existing patterns.

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.

Impulsive trait mediates the relationship between white matter integrity of prefrontal-striatal circuits and the severity of dependence in alcoholism

BACKGROUND

Alcohol dependence (AD) remains one of the major public health concerns. Impulsivity plays a central role in the transfer from recreational alcohol use to dependence and relapse. White matter dysfunction has been implicated in alcohol addiction behaviors and impulsivity. However, little is known about the role of systematic striatal structural connections underlying the mechanism of impulsive traits in AD.

METHODS

In our study, we used seed-based classification by probabilistic tractography with five target masks of striatal circuits to explore the differences in white matter integrity (fractional anisotropy, FA) in AD male patients (N = 51) and healthy controls (N = 27). We mainly explored the correlation between FA of the striatal circuits and impulsive traits (Barratt Impulsiveness Scale, BIS-11), and the mediation role of impulsivity in white matter integrity and the severity of alcohol dependence.

RESULTS

Compared with healthy controls, AD showed much lower FA in the left and right striatum-supplementary motor area (SMA) and left striatum-amygdala. We also found the decreased FA of right striatum-vlPFC was correlated with higher impulsivity. Besides, the relationship between reduced FA of right striatum-vlPFC and severity of dependence could be mediated by impulsivity.

CONCLUSION

In our study, we found disrupted white matter integrity in systematic striatal circuits in AD and the decreased FA of right striatum-vlPFC was correlated with higher impulsivity in AD. Our main findings provide evidence for reduced white matter integrity of systematic striatal circuits and the underlying mechanisms of impulsivity in male AD individuals.

White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group

BACKGROUND

Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention.

METHODS

Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence.

RESULTS

The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014).

CONCLUSIONS

Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine.