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Understanding the dose-response relationship is crucial in studying the effects of brain stimulation techniques, such as transcranial direct current stimulation (tDCS). The dose-response relationship refers to the relationship between the received stimulation dose and the resulting response, which can be described as a function of the dose at various levels, including single/multiple neurons, clusters, regions, or networks. Here, we are focused on the received stimulation dose obtained from computational head models and brain responses which are quantified by functional magnetic resonance imaging (fMRI) data. In this randomized, triple-blind, sham-controlled clinical trial, we recruited sixty participants with methamphetamine use disorders (MUDs) as a sample clinical population who were randomly assigned to receive either sham or active tDCS. Structural and functional MRI data, including high-resolution T1 and T2-weighted MRI, resting-state functional MRI, and a methamphetamine cue-reactivity task fMRI, were acquired before and after tDCS. Individual head models were generated using the T1 and T2-weighted MRI data to simulate electric fields. In a linear approach, we investigated the associations between electric fields (received dose) and changes in brain function (response) at four different levels: voxel level, regional level (using atlas-based parcellation), cluster level (identifying active clusters), and network level (task-based functional connectivity). At the voxel level, regional level, and cluster level, no FDR-corrected significant correlation was observed between changes in functional activity and electric fields. However, at the network level, a significant positive correlation was found between frontoparietal connectivity and the electric field at the frontopolar stimulation site (r = 0.42, p corrected = 0.02; medium effect size). Our proposed pipeline offers a methodological framework for analyzing tDCS effects by exploring dose-response relationships at different levels, enabling a direct link between electric field variability and the neural response to tDCS. The results indicate that network-based analysis provides valuable insights into the dependency of tDCS neuromodulatory effects on the individual’s regional current dose. Integration of dose-response relationships can inform dose optimization, customization, or the extraction of predictive/treatment-response biomarkers in future brain stimulation studies.

Two challenges to optimizing transcranial direct current stimulation (tDCS) are selecting between, often similar, electrode montages and accounting for inter-individual differences in response. These two factors are related by how tDCS montage determines current flow through the brain considered across or within individuals. MRI-based computational head models (CHMs) predict how brain anatomy determines electric field (EF) patterns for a given tDCS montage. Because conventional tDCS produces diffuse brain current flow, stimulation outcomes may be understood as modulation of global networks. Therefore, we developed a network-led, rather than region-led, approach. We specifically considered two common “frontal” tDCS montages that nominally target the dorsolateral prefrontal cortex; asymmetric “unilateral” (anode/cathode: F4/Fp1) and symmetric “bilateral” (F4/F3) electrode montages. CHMs of 66 participants were constructed. We showed that cathode location significantly affects EFs in the limbic network. Furthermore, using a finer parcellation of large-scale networks, we found significant differences in some of the main nodes within a network, even if there is no difference at the network level. This study generally demonstrates a methodology for considering the components of large-scale networks in CHMs instead of targeting a single region and specifically provides insight into how symmetric vs asymmetric frontal tDCS may differentially modulate networks across a population.

The brain response to drug-related cues is an important marker in addiction-medicine. However, the temporal dynamics of this response in repeated exposure to cues are not well known. In an fMRI drug cue-reactivity task, the presence of rapid habituation or sensitization was investigated by modeling time and its interaction with condition (drug>neutral) using an initial discovery-sample. Replication of this temporal response was tested in two other clinical populations all abstinent during their early recovery (treatment). Sixty-five male participants (35.8 ± 8.4 years-old) with methamphetamine use disorder (MUD) were recruited as the discovery-sample from an abstinence-based residential treatment program. A linear mixed effects model was used to identify areas with a time-by-condition interaction in the discovery-sample. Replication of these effects was tested in two other samples (29 female with MUD from a different residential program and 22 male with opioid use disorder from the same residential program as the discovery sample). The second replication sample was re-tested within two weeks. In the discovery-sample, clusters within the VMPFC, amygdala and ventral striatum showed both a main effect of condition and a condition-by-time interaction, indicating a habituating response to drug-related but not neutral cues. The estimates for the main effects and interactions were generally consistent between the discovery and replication-samples across all clusters. The re-test data showed a consistent lack of drug > neutral and habituation response within all selected clusters in the second cue-exposure session. The VMPFC, amygdala and ventral striatum show habituation in response to drug-related cues which is consistent among different clinical populations. This habituated response in the first session of cue-exposure and lack of reactivity in the second session of exposure may be important for informing the development of cue-desensitization interventions. [Display omitted]

Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that has been reintroduced in the last decade and is now mainly used as a cognitive modulator in human neuroscience research. tDCS delivers a weak direct current (usually up to 2 mA) over the scalp and creates a constant electric field in the brain which can lead to acute alterations of the excitability of cortical areas by its subthreshold depolarizing or hyperpolarizing effects on neuronal resting membrane potentials (Nitsche and Paulus, 2000). The neuroplastic effects resemble LTP- and LTD-like plasticity of glutamatergic synapses (Liebetanz et al., 2002; Nitsche et al., 2003a). [...]this technique allows us to study neuroplasticity of the human brain in a reversible manner and to modulate plasticity-related functions such as memory or learning, which critically depend on neuroplasticity, in healthy and clinical populations. [...]a unified systematic framework will be helpful for presenting study methods and for extracting data for systematic reviews and meta-analyses in a more practical way. [...]computational modeling for a better understanding of current flow

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.

Centuries' worth of cultural stories suggest that self-sacrifice may be a cornerstone of our moral concepts, yet this notion is largely absent from recent theories in moral psychology. For instance, in the footbridge version of the well-known trolley car problem the only way to save five people from a runaway trolley is to push a single man on the tracks. It is explicitly specified that the bystander cannot sacrifice himself because his weight is insufficient to stop the trolley. But imagine if this were not the case. Would people rather sacrifice themselves than push another? In Study 1, we find that people approve of self-sacrifice more than directly harming another person to achieve the same outcome. In Studies 2 and 3, we demonstrate that the effect is not broadly about sensitivity to self-cost, instead there is something unique about sacrificing the self. Important theoretical implications about agent-relativity and the role of causality in moral judgments are discussed.

In the modern obesogenic environment, heightened reactivity to food-associated cues plays a major role in overconsumption by evoking appetitive responses. Accordingly, functional magnetic resonance imaging (fMRI) studies have implicated regions of the salience and rewards processing in this dysfunctional food cue-reactivity, but the temporal dynamics of brain activation (sensitization or habituation over time) remain poorly understood. Forty-nine obese or overweight adults were scanned in a single fMRI session to examine brain activation during the performance of a food cue-reactivity task. A general linear model (GLM) was used to validate the activation pattern of food cue reactivity in food > neutral contrast. The linear mixed effect models were used to examine the effect of time on the neuronal response during the paradigm of food cue reactivity. Neuro-behavioral relationships were investigated with Pearson's correlation tests and group factor analysis (GFA). A linear mixed-effect model revealed a trend for the time-by-condition interactions in the left medial amygdala [t(289) = 2.21, β = 0.1, = 0.028], right lateral amygdala [t(289) = 2.01, β = 0.26, = 0.045], right nucleus accumbens (NAc) [t(289) = 2.81, β = 0.13, = 0.005] and left dorsolateral prefrontal cortex (DLPFC) [t(289) = 2.58, β = 0.14, = 0.01], as well as in the left superior temporal cortex [42 Area: t(289) = 2.53, β = 0.15, = 0.012; TE1.0_TE1.2 Area: t(289) = 3.13, β = 0.27, = 0.002]. Habituation of blood-oxygenation-level-dependent (BOLD) signal during exposure to food vs. neutral stimuli was evident in these regions. We have not found any area in the brain with significant increased response to food-related cues over time (sensitization). Our results elucidate the temporal dynamics of cue-reactivity in overweight and obese individuals with food-induced craving. Both subcortical areas involved in reward processing and cortical areas involved in inhibitory processing are getting habituated over time in response to food vs. neutral cues. There were significant bivariate correlations between self-report behavioral/psychological measures with individual habituation slopes for the regions with dynamic activity, but no robust cross-unit latent factors were identified between the behavioral, demographic, and self-report psychological groups. This work provides novel insights into dynamic neural circuit mechanisms supporting food cue reactivity, thereby suggesting pathways in biomarker development and cue-desensitization interventions.

Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing methods. Moreover, any systematic relationship between HM and variables of interest can introduce systematic bias. There is a large and growing interest in identifying neural biomarkers for psychiatric disorders using resting state fMRI (rsfMRI). However, the relationship between HM and different psychiatric symptoms domains is not well understood. The aim of this investigation was to determine whether psychiatric symptoms and other characteristics of the individual predict HM during rsfMRI. A sample of n = 464 participants (174 male) from the Tulsa1000, a naturalistic longitudinal study recruiting subjects with different levels of severity in mood/anxiety/substance use disorders based on the dimensional NIMH Research Domain Criteria framework was used for this study. Based on a machine learning (ML) pipeline with nested cross-validation to avoid overfitting, the stacked model with 15 anthropometric (like body mass index, BMI) and demographic (age and sex) variables identifies BMI and weight as the most important variables and explained 10.9 percent of the HM variance (95% CI: 9.9–11.8). In comparison ML models with 105 self-report measures for state and trait psychological characteristics identified nicotine and alcohol use variables as well as impulsivity inhibitory control variables but explain only 5 percent of HM variance (95% CI: 3.5–6.4). A combined ML model using all 120 variables did not perform significantly better than the model using only 15 physical variables (combined model 95% confidence interval: 10.2–12.4). Taken together, after considering physical variables, state or trait psychological characteristics do not provide additional power to predict motion during rsfMRI.

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants’ characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: ‘Participants’ Characteristics’, ‘General fMRI Information’, ‘General Task Information’, ‘Cue Information’, ‘Craving Assessment Inside Scanner’, ‘Craving Assessment Outside Scanner’ and ‘Pre- and Post-Scanning Considerations’. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the ‘General fMRI Information’ category were reported in 90.5% of the reviewed papers, items in the ‘Pre- and Post-Scanning Considerations’ category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.Cue reactivity measured by functional magnetic resonance imaging is used in studies of substance-use disorders. This Consensus Statement is the result of a Delphi process to arrive at parameters that should be reported in describing these studies.

One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.