The severity of gambling in clinical samples of gamblers: profiles and prediction of the impulsivity and emotions.

Impulsivity and emotion impairments have been noted in individuals with gambling disorder (GD). However, little research has investigated the influence of impulsivity and emotions on the severity of gambling in clinical populations. This study aimed to examine: (i) differences in emotions and impulsivity traits according to the severity of gambling in individuals with GD, (ii) the mediating effects of emotion in the relationship between impulsivity traits and gambling severity, and (iii) the predictive effects of emotion and impulsivity traits on GD severity. The study included 214 participants seeking treatment for GD who completed assessments for emotions (Patient Health Questionnaire-9 [PHQ-9], 7-item Generalized Anxiety [GAD-7]), impulsivity traits (Barratt Impulsiveness Scale [BIS], Self-control Scale [SCS]), and GD severity (DSM-5). Participants were categorized into mild (n = 78), moderate (n = 63), and severe (n = 73) gambling severity groups. Significant differences in emotions and impulsivity traits were observed across these groups. The severe GD group exhibited higher levels of depression, anxiety, and impulsivity traits, along with lower self-control, compared to the moderate and mild groups. Mediation analyses demonstrated that negative emotions mediated the association between impulsivity traits and the severity of gambling. More specifically, the indirect effects of impulsivity traits through PHQ-9 and GAD-7 were found to be significant, indicating a mediating role of emotions. Moreover, a predictive model incorporating emotion and impulsivity traits showed moderate accuracy in predicting the severity of gambling, with an area under the receiver operating characteristic curve of 0.714. This study highlights the distinct pathways through which impulsivity traits operate and emphasizes the need for prevention and treatment strategies that consider impulsivity traits and emotions for different levels of gambling severity.

In-vivoverified anatomically aware deep learning for real-time electric field simulation.

Objective.Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement.Approach.We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset.In-vivoTMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes.Significance.The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments.

Prolonged Longitudinal Transcutaneous Auricular Vagus Nerve Stimulation Effect on Striatal Functional Connectivity in Patients with Major Depressive Disorder.

BACKGROUND: Transcutaneous auricular vagus nerve stimulation (taVNS) is effective for treating major depressive disorder (MDD). We aimed to explore the modulating effect of prolonged longitudinal taVNS on the striatal subregions' functional connectivity (FC) in MDD patients. METHODS: Sixteen MDD patients were enrolled and treated with taVNS for 8 weeks. Sixteen healthy control subjects (HCs) were recruited without intervention. The resting-state FC (rsFC) based on striatal subregion seed points and the Hamilton Depression Scale (HAMD) were evaluated in the MDD patients and HCs at baseline and after 8 weeks. A two-way ANCOVA test was performed on each rsFC metric to obtain the (group-by-time) interactions. RESULTS: The rsFC values between the left ventral caudate (vCa) and right ventral prefrontal cortex (vPFC), and between the right nucleus accumbens (NAc) and right dorsal medial prefrontal cortex (dmPFC) and ventrolateral prefrontal cortex (vlPFC) are lower in the MDD patients compared to the HCs at baseline, and increase following taVNS; the rsFC values between the left vCa and right, superior occipital gyrus (SOG), and between the left dorsal caudate (dCa) and right cuneus are higher in MDD patients and decrease following taVNS. CONCLUSIONS: Prolonged longitudinal taVNS can modulate the striatum rsFC with the prefrontal cortex, occipital cortex, temporal cortex, and intra-striatum, and these changes partly underlie any symptomatic improvements. The results indicate that prolonged longitudinal taVNS may produce beneficial treatment effects by modulating the cortical striatum circuitry in patients with MDD.

Precise Modulation Strategies for Transcranial Magnetic Stimulation: Advances and Future Directions.

Transcranial magnetic stimulation (TMS) is a popular modulatory technique for the noninvasive diagnosis and therapy of neurological and psychiatric diseases. Unfortunately, current modulation strategies are only modestly effective. The literature provides strong evidence that the modulatory effects of TMS vary depending on device components and stimulation protocols. These differential effects are important when designing precise modulatory strategies for clinical or research applications. Developments in TMS have been accompanied by advances in combining TMS with neuroimaging techniques, including electroencephalography, functional near-infrared spectroscopy, functional magnetic resonance imaging, and positron emission tomography. Such studies appear particularly promising as they may not only allow us to probe affected brain areas during TMS but also seem to predict underlying research directions that may enable us to precisely target and remodel impaired cortices or circuits. However, few precise modulation strategies are available, and the long-term safety and efficacy of these strategies need to be confirmed. Here, we review the literature on possible technologies for precise modulation to highlight progress along with limitations with the goal of suggesting future directions for this field.

[Electric field simulation and analysis of modified electroconvulsive therapy and magnetic seizure therapy in a realistic human head model].

Modified electroconvulsive therapy (MECT) and magnetic seizure therapy (MST) are effective treatments for severe major depression. MECT has better efficacy in the treatment than MST, but it has cognitive and memorial side effects while MST does not. To study the causes of these different outcomes, this study contrasted the electric filed strength and spatial distribution induced by MECT and MST in a realistic human head model. Electric field strength induced by MECT and MST are simulated by the finite element method, which was based on a realistic human head model obtained by magnetic resonance imaging. The electrode configuration of MECT is standard bifrontal stimulation configuration, and the coil configuration of MST is circular. Maps of the ratio of the electric field strength to neural activation threshold are obtained to evaluate the stimulation strength and stimulation focality in brain regions. The stimulation strength induced by MECT is stronger than MST, and the activated region is wider. MECT stimulation strength in gray matter is 17.817 times of that by MST, and MECT stimulation strength in white matter is 23.312 times of that by MST. As well, MECT stimulation strength in hippocampi is 35.162 times of that by MST. More than 99.999% of the brain volume is stimulated at suprathreshold by MECT. However, MST activated only 0.700% of the brain volume. The stimulation strength induced by MECT is stronger than MST, and the activated region is wider may be the reason that MECT has better effectiveness. Nevertheless, the stronger stimulation strength in hippocampi induced by MECT may be the reason that MECT is more likely to give rise to side effects. Based on the results of this study, it is expected that a more accurate clinical quantitative treatment scheme should be studied in the future.