Network state dynamics underpin basal craving in a transdiagnostic population.

Emerging fMRI methods quantifying brain dynamics present an opportunity to capture how fluctuations in brain responses give rise to individual variations in affective and motivation states. Although the experience and regulation of affective states affect psychopathology, their underlying time-varying brain responses remain unclear. Here, we present a novel framework to identify network states matched to an affective experience and examine how the dynamic engagement of these network states contributes to this experience. We apply this framework to investigate network state dynamics underlying basal craving, an affective experience with important clinical implications. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (total N = 252), we utilized connectome-based predictive modeling (CPM) to identify brain networks predictive of basal craving. An edge-centric timeseries approach was leveraged to quantify the moment-to-moment engagement of the craving-positive and craving-negative subnetworks during independent scan runs. We found that dynamic markers of network engagement, namely more persistence in a craving-positive network state and less dwelling in a craving-negative network state, characterized individuals with higher craving. We replicated the latter results in a separate dataset, incorporating distinct participants (N = 173) and experimental stimuli. The associations between basal craving and network state dynamics were consistently observed even when craving-predictive networks were defined in the replication dataset. These robust findings suggest that network state dynamics underpin individual differences in basal craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our affective experiences.

A Network Analysis of Cigarette Craving.

INTRODUCTION: Craving is considered a central process to addictive behavior including cigarette smoking, although the clinical utility of craving relies on how it is defined and measured. Network analysis enables examining the network structure of craving symptoms, identifying the most central symptoms of cigarette craving, and improving our understanding of craving and its measurement. AIMS AND METHODS: This study used network analysis to identify the central symptoms of self-reported cigarette craving as measured by the Craving Experience Questionnaire, which assesses both craving strength and craving frequency. Data were obtained from baseline of a randomized controlled trial of mindfulness training for smoking cessation. RESULTS: The most central symptoms in an overall cigarette craving network were the frequency of imagining its smell, imagining its taste, and intrusive thoughts. The most central symptoms of both craving frequency and craving strength sub-networks were imagining its taste, the urge to have it, and intrusive thoughts. CONCLUSIONS: The most central craving symptoms reported by individuals in treatment for cigarette smoking were from the frequency domain, demonstrating the value of assessing craving frequency along with craving strength. Central craving symptoms included multisensory imagery (taste, smell), intrusive thoughts, and urge, providing additional evidence that these symptoms may be important to consider in craving measurement and intervention. Findings provide insight into the symptoms that are central to craving, contributing to a better understanding of cigarette cravings, and suggesting potential targets for clinical interventions. IMPLICATIONS: This study used network analysis to identify central symptoms of cigarette craving. Both craving frequency and strength were assessed. The most central symptoms of cigarette craving were related to craving frequency. Central symptoms included multisensory imagery, intrusive thoughts, and urge. Central symptoms might be targeted by smoking cessation treatment.

E-cigarette Marketing Expenditures in the United States From 2016 to 2021: Targeted Media Outlets Geared Toward People Who Are at Increased Risk for Tobacco Use.

INTRODUCTION: E-cigarette advertising exposure is linked to e-cigarette initiation and use. Thus, monitoring trends in e-cigarette advertising practices is important to understand e-cigarette use patterns observed over recent years. AIMS AND METHODS: E-cigarette advertising expenditures (January 2016-July 2021; Numerator Ad Intel) for 154 U.S. market areas were harmonized with U.S. Census sociodemographic data through Nielsen zip code designations by market area. Descriptive statistics and multivariable linear regressions were used to examine trends in e-cigarette advertising expenditures across media outlets and associations between sociodemographic characteristics and e-cigarette advertising over time. RESULTS: E-cigarette advertising expenditures peaked in 2018/2019, followed by a sharp decline in 2020. Expenditures were concentrated primarily on print (58.9%), TV (20.6%), and radio (14.4%). Major print outlets were Sports Illustrated, Rolling Stone, and Star magazines. Top TV channels were AMC, Investigation Discovery, and TBS. TV advertisements were purchased commonly during popular movies and TV series (eg King of Queens, Everybody Loves Raymond, The Walking Dead). Higher expenditures were associated with U.S. market areas that had (1) a larger percentage of non-rural zip codes (radio), (2) smaller male populations (radio), and (3) larger White or Caucasian, Black or African American, American Indian or Alaska Native, Asian, and Other or Multiracial populations (radio, print, online display, and online video). CONCLUSIONS: E-cigarette companies advertised in print magazines geared toward males and youth and young adults, radio commercials focused in urban areas with smaller male populations, and nationwide TV commercials. Declines in e-cigarette advertising expenditures in 2020 demonstrate the potential impact that federal policies may have on protecting populations who are at higher risk for tobacco use from predatory advertising practices. IMPLICATIONS: E-cigarette advertising exposure is associated with the initiation and use of e-cigarettes. This study shows how e-cigarette marketing expenditures in the United States may have targeted specific consumers (eg youth and young adults) between 2016 and 2021. The precipitous drop in advertising expenditures across all outlets during early 2020 corresponds with the implementation of the Tobacco 21 federal policy, the federal enforcement policy to remove most unauthorized flavored e-cigarette cartridges from the U.S. market, preparations for FDA's premarket review of e-cigarette products, and the decision by several TV broadcast companies to stop showing e-cigarette ads. The potential impact of federal policies may have far-reaching implications for protecting populations who are at high risk for tobacco use and its health consequences.

Smartphone Apps for Smoking Cessation: Systematic Framework for App Review and Analysis.

BACKGROUND: Cigarette smoking is a leading cause of preventable death, and identifying novel treatment approaches to promote smoking cessation is critical for improving public health. With the rise of digital health and mobile apps, these tools offer potential opportunities to address smoking cessation, yet the functionality of these apps and whether they offer scientifically based support for smoking cessation are unknown. OBJECTIVE: The goal of this research was to use the American Psychiatric Association app evaluation model to evaluate the top-returned apps from Android and Apple app store platforms related to smoking cessation and investigate the common app features available for end users. METHODS: We conducted a search of both Android and iOS app stores in July 2021 for apps related to the keywords "smoking," "tobacco," "smoke," and "cigarette" to evaluate apps for smoking cessation. Apps were screened for relevance, and trained raters identified and analyzed features, including accessibility (ie, cost), privacy, clinical foundation, and features of the apps, using a systematic framework of 105 objective questions from the American Psychiatric Association app evaluation model. All app rating data were deposited in mindapps, a publicly accessible database that is continuously updated every 6 months given the dynamic nature of apps available in the marketplace. We characterized apps available in July 2021 and November 2022. RESULTS: We initially identified 389 apps, excluded 161 due to irrelevance and nonfunctioning, and rated 228, including 152 available for Android platforms and 120 available for iOS platforms. Some of the top-returned apps (71/228, 31%) in 2021 were no longer functioning in 2022. Our analysis of rated apps revealed limitations in accessibility and features. While most apps (179/228, 78%) were free to download, over half had costs associated with in-app purchases or full use. Less than 65% (149/228) had a privacy policy addressing the data collected in the app. In terms of intervention features, more than 56% (128/228) of apps allowed the user to set and check in on goals, and more than 46% (106/228) of them provided psychoeducation, although few apps provided evidence-based support for smoking cessation, such as peer support or skill training, including mindfulness and deep breathing, and even fewer provided evidence-based interventions, such as acceptance and commitment therapy or cognitive behavioral therapy. Only 12 apps in 2021 and 11 in 2022 had published studies supporting the feasibility or efficacy for smoking cessation. CONCLUSIONS: Numerous smoking cessation apps were identified, but analysis revealed limitations, including high rates of irrelevant and nonfunctioning apps, high rates of turnover, and few apps providing evidence-based support for smoking cessation. Thus, it may be challenging for consumers to identify relevant, evidence-based apps to support smoking cessation in the app store, and a comprehensive evaluation system of mental health apps is critically important.

Drinking and responses to antidrinking messages among young adults: An fMRI study.

Young adults consume most of their alcohol by binge drinking, and more than one-third report binge drinking in the past month. Some will transition out of excessive drinking, while others will maintain or increase alcohol use into adulthood. Public health campaigns depicting negative consequences of drinking have shown some efficacy at reducing this behavior. However, substance use in dependent individuals is governed in part by automatic or habitual responses to drug cues rather than the consequences. This study used functional magnetic resonance imaging to measure neural responses to drinking cues and drinking cues paired with antidrinking messages among young adults who binge drink (N = 30). This study also explored responses to smoking cues and antismoking messages. Neural responses were also compared between drinking/smoking and neutral cues. Self-reported drinking and smoking were collected at baseline, postscan, and 1 month. Results indicate that activity in the ventral striatum-implicated in reward processing-was lower for drinking cues paired with antidrinking messages than drinking cues. This difference was less pronounced in young adults who reported greater baseline past month drinking quantity. Past month drinking quantity decreased from baseline to 1 month. Further, young adults who showed higher activity during antidrinking messages in the medial prefrontal cortex-implicated in processing message self-relevance- reported a greater decrease in past month drinking frequency from baseline to 1 month. Findings may help to identify young adults who are at risk for continued heavy drinking in adulthood and inform interventions aimed to reduce drinking and reward in young adults.

Craving to Quit: A Randomized Controlled Trial of Smartphone App-Based Mindfulness Training for Smoking Cessation.

INTRODUCTION: Mindfulness training may reduce smoking rates and lessen the association between craving and smoking. This trial tested the efficacy of mindfulness training via smartphone app to reduce smoking. Experience sampling (ES) was used to measure real-time craving, smoking, and mindfulness. METHODS: A researcher-blind, parallel randomized controlled trial compared the efficacy of mobile mindfulness training with experience sampling (MMT-ES; Craving to Quit) versus experience sampling only (ES) to (1) increase 1-week point-prevalence abstinence rates at 6 months, and (2) lessen the association between craving and smoking. A modified intent-to-treat approach was used for treatment starters (MMT-ES n = 143; ES n = 182; 72% female, 81% white, age 41 ± 12 year). RESULTS: No group difference was found in smoking abstinence at 6 months (overall, 11.1%; MMT-ES, 9.8%; ES, 12.1%; χ2(1) = 0.43, p = .51). From baseline to 6 months, both groups showed a reduction in cigarettes per day (p < .0001), craving strength (p < .0001) and frequency (p < .0001), and an increase in mindfulness (p < .05). Using ES data, a craving by group interaction was observed (F(1,3785) = 3.71, p = .05) driven by a stronger positive association between craving and cigarettes per day for ES (t = 4.96, p < .0001) versus MMT-ES (t = 2.03, p = .04). Within MMT-ES, the relationship between craving and cigarettes per day decreased as treatment completion increased (F(1,104) = 4.44, p = .04). CONCLUSIONS: Although mindfulness training via smartphone app did not lead to reduced smoking rates compared with control, our findings provide preliminary evidence that mindfulness training via smartphone app may help lessen the association between craving and smoking, an effect that may be meaningful to support quitting in the longer term. IMPLICATIONS: This is the first reported full-scale randomized controlled trial of any smartphone app for smoking cessation. Findings provide preliminary evidence that smartphone app-based MMT-ES may lessen the association between craving and smoking. TRIAL REGISTRATION: Clinicaltrials.gov NCT02134509.

Laterality of Poststroke Cortical Motor Activity during Action Observation Is Related to Hemispheric Dominance.

Background: Increased activity in the lesioned hemisphere has been related to improved poststroke motor recovery. However, the role of the dominant hemisphere-and its relationship to activity in the lesioned hemisphere-has not been widely explored. Objective: Here, we examined whether the dominant hemisphere drives the lateralization of brain activity after stroke and whether this changes based on if the lesioned hemisphere is the dominant hemisphere or not. Methods: We used fMRI to compare cortical motor activity in the action observation network (AON), motor-related regions that are active both during the observation and execution of an action, in 36 left hemisphere dominant individuals. Twelve individuals had nondominant, right hemisphere stroke, twelve had dominant, left-hemisphere stroke, and twelve were healthy age-matched controls. We previously found that individuals with left dominant stroke show greater ipsilesional activity during action observation. Here, we examined if individuals with nondominant, right hemisphere stroke also showed greater lateralized activity in the ipsilesional, right hemisphere or in the dominant, left hemisphere and compared these results with those of individuals with dominant, left hemisphere stroke. Results: We found that individuals with right hemisphere stroke showed greater activity in the dominant, left hemisphere, rather than the ipsilesional, right hemisphere. This left-lateralized pattern matched that of individuals with left, dominant hemisphere stroke, and both stroke groups differed from the age-matched control group. Conclusions: These findings suggest that action observation is lateralized to the dominant, rather than ipsilesional, hemisphere, which may reflect an interaction between the lesioned hemisphere and the dominant hemisphere in driving lateralization of brain activity after stroke. Hemispheric dominance and laterality should be carefully considered when characterizing poststroke neural activity.

Functional Connectivity During Exposure to Favorite-Food, Stress, and Neutral-Relaxing Imagery Differs Between Smokers and Nonsmokers.

INTRODUCTION: Tobacco-use disorder is a complex condition involving multiple brain networks and presenting with multiple behavioral correlates including changes in diet and stress. In a previous functional magnetic resonance imaging (fMRI) study of neural responses to favorite-food, stress, and neutral-relaxing imagery, smokers versus nonsmokers demonstrated blunted corticostriatal-limbic responses to favorite-food cues. Based on other recent reports of alterations in functional brain networks in smokers, the current study examined functional connectivity during exposure to favorite-food, stress, and neutral-relaxing imagery in smokers and nonsmokers, using the same dataset. METHODS: The intrinsic connectivity distribution was measured to identify brain regions that differed in degree of functional connectivity between groups during each imagery condition. Resulting clusters were evaluated for seed-to-voxel connectivity to identify the specific connections that differed between groups during each imagery condition. RESULTS: During exposure to favorite-food imagery, smokers versus nonsmokers showed lower connectivity in the supramarginal gyrus, and differences in connectivity between the supramarginal gyrus and the corticostriatal-limbic system. During exposure to neutral-relaxing imagery, smokers versus nonsmokers showed greater connectivity in the precuneus, and greater connectivity between the precuneus and the posterior insula and rolandic operculum. During exposure to stress imagery, smokers versus nonsmokers showed lower connectivity in the cerebellum. CONCLUSIONS: These findings provide data-driven insights into smoking-related alterations in brain functional connectivity patterns related to appetitive, relaxing, and stressful states. IMPLICATIONS: This study uses a data-driven approach to demonstrate that smokers and nonsmokers show differential patterns of functional connectivity during guided imagery related to personalized favorite-food, stress, and neutral-relaxing cues, in brain regions implicated in attention, reward-related, emotional, and motivational processes. For smokers, these differences in connectivity may impact appetite, stress, and relaxation, and may interfere with smoking cessation.

The (in)stability of functional brain network measures across thresholds.

The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.

Meditation leads to reduced default mode network activity beyond an active task.

Meditation has been associated with relatively reduced activity in the default mode network, a brain network implicated in self-related thinking and mind wandering. However, previous imaging studies have typically compared meditation to rest, despite other studies having reported differences in brain activation patterns between meditators and controls at rest. Moreover, rest is associated with a range of brain activation patterns across individuals that has only recently begun to be better characterized. Therefore, in this study we compared meditation to another active cognitive task, both to replicate the findings that meditation is associated with relatively reduced default mode network activity and to extend these findings by testing whether default mode activity was reduced during meditation, beyond the typical reductions observed during effortful tasks. In addition, prior studies had used small groups, whereas in the present study we tested these hypotheses in a larger group. The results indicated that meditation is associated with reduced activations in the default mode network, relative to an active task, for meditators as compared to controls. Regions of the default mode network showing a Group × Task interaction included the posterior cingulate/precuneus and anterior cingulate cortex. These findings replicate and extend prior work indicating that the suppression of default mode processing may represent a central neural process in long-term meditation, and they suggest that meditation leads to relatively reduced default mode processing beyond that observed during another active cognitive task.

A randomized controlled trial of smartphone-based mindfulness training for smoking cessation: a study protocol.

BACKGROUND: Tobacco use is responsible for the death of about 1 in 10 individuals worldwide. Mindfulness training has shown preliminary efficacy as a behavioral treatment for smoking cessation. Recent advances in mobile health suggest advantages to smartphone-based smoking cessation treatment including smartphone-based mindfulness training. This study evaluates the efficacy of a smartphone app-based mindfulness training program for improving smoking cessation rates at 6-months follow-up. METHODS/DESIGN: A two-group parallel-randomized clinical trial with allocation concealment will be conducted. Group assignment will be concealed from study researchers through to follow-up. The study will be conducted by smartphone and online. Daily smokers who are interested in quitting smoking and own a smartphone (n = 140) will be recruited through study advertisements posted online. After completion of a baseline survey, participants will be allocated randomly to the control or intervention group. Participants in both groups will receive a 22-day smartphone-based treatment program for smoking. Participants in the intervention group will receive mobile mindfulness training plus experience sampling. Participants in the control group will receive experience sampling-only. The primary outcome measure will be one-week point prevalence abstinence from smoking (at 6-months follow-up) assessed using carbon monoxide breath monitoring, which will be validated through smartphone-based video chat. DISCUSSION: This is the first intervention study to evaluate smartphone-based delivery of mindfulness training for smoking cessation. Such an intervention may provide treatment in-hand, in real-world contexts, to help individuals quit smoking. TRIAL REGISTRATION: Clinicaltrials.gov NCT02134509 . Registered 7 May 2014.

Neuroimaging and biomarkers in addiction treatment.

Neuroimaging studies have made a significant contribution to the efforts to identify measurable indices, or biomarkers, of addictions and their treatments. Biomarkers in addiction treatment are needed to provide targets for treatment, detect treatment subgroups, predict treatment response, and broadly improve outcomes. Neuroimaging is important to biomarkers research as it relates neural circuits to both molecular mechanisms and behavior. A focus of recent efforts in neuroimaging in addiction has been to elucidate the neural correlates associated with dimensions of functioning in substance-use and related disorders, such as cue-reactivity, impulsivity, and cognitive control, among others. These dimensions of functioning have been related to addiction treatment outcomes and relapse, and therefore, a better understanding of these dimensions and their neural correlates may help to identify brain-behavior biomarkers of treatment response. This paper reviews recent neuroimaging studies that report potential biomarkers in addiction treatment related to cue-reactivity, impulsivity, and cognitive control, as well as recent advances in neuroimaging that may facilitate efforts to determine reliable biomarkers. This important initial work has begun to identify possible mediators and moderators of treatment response, and multiple promising indices are being tested.

Behavioral Precursors of Nicotine Product Use Trajectories Among Youth.

INTRODUCTION: Preventing nicotine use among youth is a public health priority. Nicotine use emerges from complex relationships between numerous factors. This project used network analysis to model behavioral precursors of nicotine use (knowledge, attitudes, perceptions, intentions) among youth who had never used nicotine and determine which predicted future trajectories of use across multiple nicotine products. METHODS: Data were from the Population Assessment of Tobacco and Health study (2013-2018; analyzed 2023-2024), youth ages 12-17, who reported never using nicotine at Wave 1. Network structure was determined for behavioral precursors at Wave 1 and central network nodes were identified (N=5,087). Central nodes were then tested as predictors of trajectories of use across multiple nicotine products during Waves 1-4 (N=3,851). RESULTS: Central nodes of the Wave 1 network were harm perception, expectancy that tobacco would calm anger/reduce stress, and intention to try. Those with lower intent to try at Wave 1 had lower odds of being in an Experimentation or Use class versus a Nonuse class during Waves 1-4 (p<0.0001). Those with more accurate harm perception had lower odds of being in an Experimentation versus Nonuse class (p=0.004). Those with positive expectancies had higher odds of being in an Experimentation versus Nonuse or Use class (p=0.04, 0.02). CONCLUSIONS: Findings suggest a network model of behavioral precursors of nicotine use that can be tested, including central nodes that predicted trajectories of use across multiple nicotine products, and therefore may be priority intervention targets.

Smartband-based smoking detection and real-time brief mindfulness intervention: findings from a feasibility clinical trial.

BACKGROUND: Smartbands can be used to detect cigarette smoking and deliver real time smoking interventions. Brief mindfulness interventions have been found to reduce smoking. OBJECTIVE: This single arm feasibility trial used a smartband to detect smoking and deliver brief mindfulness exercises. METHODS: Daily smokers who were motivated to reduce their smoking wore a smartband for 60 days. For 21 days, the smartband monitored, detected and notified the user of smoking in real time. After 21 days, a 'mindful smoking' exercise was triggered by detected smoking. After 28 days, a 'RAIN' (recognize, allow, investigate, nonidentify) exercise was delivered to predicted smoking. Participants received mindfulness exercises by text message and online mindfulness training. Feasibility measures included treatment fidelity, adherence and acceptability. RESULTS: Participants (N=155) were 54% female, 76% white non-Hispanic, and treatment starters (n=115) were analyzed. Treatment fidelity cutoffs were met, including for detecting smoking and delivering mindfulness exercises. Adherence was mixed, including moderate smartband use and low completion of mindfulness exercises. Acceptability was mixed, including high helpfulness ratings and mixed user experiences data. Retention of treatment starters was high (81.9%). CONCLUSIONS: Findings demonstrate the feasibility of using a smartband to track smoking and deliver quit smoking interventions contingent on smoking.

Real-time fMRI links subjective experience with brain activity during focused attention.

Recent advances in brain imaging have improved the measure of neural processes related to perceptual, cognitive and affective functions, yet the relation between brain activity and subjective experience remains poorly characterized. In part, it is a challenge to obtain reliable accounts of participant's experience in such studies. Here we addressed this limitation by utilizing experienced meditators who are expert in introspection. We tested a novel method to link objective and subjective data, using real-time fMRI (rt-fMRI) to provide participants with feedback of their own brain activity during an ongoing task. We provided real-time feedback during a focused attention task from the posterior cingulate cortex, a hub of the default mode network shown to be activated during mind-wandering and deactivated during meditation. In a first experiment, both meditators and non-meditators reported significant correspondence between the feedback graph and their subjective experience of focused attention and mind-wandering. When instructed to volitionally decrease the feedback graph, meditators, but not non-meditators, showed significant deactivation of the posterior cingulate cortex. We were able to replicate these results in a separate group of meditators using a novel step-wise rt-fMRI discovery protocol in which participants were not provided with prior knowledge of the expected relationship between their experience and the feedback graph (i.e., focused attention versus mind-wandering). These findings support the feasibility of using rt-fMRI to link objective measures of brain activity with reports of ongoing subjective experience in cognitive neuroscience research, and demonstrate the generalization of expertise in introspective awareness to novel contexts.

A Network Investigation of Core Mindfulness Processes.

Objective: Mindfulness has been associated with improved psychological well-being and health, although outcomes from mindfulness-based interventions are mixed. One challenge is a limited understanding about which specific processes are core to mindfulness. Network analysis offers a method to characterize the core processes of mindfulness. Methods: This study used network analysis to identify which processes are central to mindfulness (have the strongest connectivity with other mindfulness processes) as measured by the Five Facets Mindfulness Questionnaire- Short Form, analyzed at the item-level. Data were obtained from baseline of a randomized clinical trial of smartphone app-based mindfulness training for smoking cessation. Results: The most central processes in the mindfulness network included, "I think some of my emotions are bad or inappropriate and I shouldn't feel them," an aspect of Nonjudgment/acceptance; as well as "I can easily put my beliefs, opinions, and expectations into words," and "It's hard for me to find the words to describe what I'm thinking," aspects of Describing. Conclusions: Findings help to clarify which processes are to mindfulness, contributing to a better understanding of the definition of mindfulness, and suggest factors that may be promising to target in mindfulness-based interventions. Future research should examine if mindfulness-based interventions may be improved by targeting these core mindfulness processes.

Transdiagnostic Connectome-Based Prediction of Craving.

OBJECTIVE: Craving is a central construct in the study of motivation and human behavior and is also a clinical symptom of substance and non-substance-related addictive disorders. Thus, craving represents a target for transdiagnostic modeling. METHODS: The authors applied connectome-based predictive modeling (CPM) to functional connectivity data in a large (N=274) transdiagnostic sample of individuals with and without substance use-related conditions, to predict self-reported craving. Functional connectomes derived from three guided imagery conditions of personalized appetitive, stress, and neutral-relaxing experiences were used to predict craving rated before and after each imagery condition. The generalizability of the "craving network" was tested in an independent sample using functional connectomes derived from a cue-induced craving task collected before and after fasting to predict craving rated during fasting. RESULTS: CPM successfully predicted craving, thereby identifying a transdiagnostic "craving network." Anatomical localization of model contribution suggested that the strongest predictors of craving were regions of the salience, subcortical, and default mode networks. As external validation, in an independent sample, the "craving network" predicted food craving during fasting using data from a cue-induced craving task. CONCLUSIONS: These data provide a transdiagnostic perspective to a key phenomenological feature of addictive disorders-craving-and identify a common "craving network" across individuals with and without substance use-related disorders, thereby suggesting a neural signature for craving or urge for motivated behaviors.

Network state dynamics underpin craving in a transdiagnostic population.

Emerging fMRI brain dynamic methods present a unique opportunity to capture how brain region interactions across time give rise to evolving affective and motivational states. As the unfolding experience and regulation of affective states affect psychopathology and well-being, it is important to elucidate their underlying time-varying brain responses. Here, we developed a novel framework to identify network states specific to an affective state of interest and examine how their instantaneous engagement contributed to its experience. This framework investigated network state dynamics underlying craving, a clinically meaningful and changeable state. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (N=252), we utilized connectome-based predictive modeling (CPM) to identify craving-predictive edges. An edge-centric timeseries approach was leveraged to quantify the instantaneous engagement of the craving-positive and craving-negative networks during independent scan runs. Individuals with higher craving persisted longer in a craving-positive network state while dwelling less in a craving-negative network state. We replicated the latter results externally in an independent group of healthy controls and individuals with alcohol use disorder exposed to different stimuli during the scan (N=173). The associations between craving and network state dynamics can still be consistently observed even when craving-predictive edges were instead identified in the replication dataset. These robust findings suggest that variations in craving-specific network state recruitment underpin individual differences in craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our changing affective experiences.

Smartphone Apps Targeting Youth Tobacco Use Prevention and Cessation: An Assessment of Credibility and Quality.

Purpose of Review: The goals of this study were to identify smartphone apps targeting youth tobacco use prevention and/or cessation discussed in the academic literature and/or available in the Apple App Store and to review and rate the credibility of the apps. We took a multiphase approach in a non-systematic review that involved conducting parallel literature and App Store searches, screening the returned literature and apps for inclusion, characterizing the studies and apps, and evaluating app quality using a standardized rating scale. Recent Findings: The negative consequences of youth tobacco use initiation are profound and far-reaching. Half of the youth who use nicotine want to quit, but quit rates are low. The integration of smartphone apps shows promise in complementing and enhancing evidence-based youth tobacco prevention and treatment methods. Summary: Consistent with prior reviews, we identified a disconnect between apps that are readily accessible and those that have an evidence base, and many popular apps received low quality scores. Findings suggest a need for better integration between evidence-based and popular, available apps targeting youth tobacco use.

Systematic review of structural and functional neuroimaging studies of cannabis use in adolescence and emerging adulthood: evidence from 90 studies and 9441 participants.

Cannabis use peaks in adolescence, and adolescents may be more vulnerable to the neural effects of cannabis and cannabis-related harms due to ongoing brain development during this period. In light of ongoing cannabis policy changes, increased availability, reduced perceptions of harm, heightened interest in medicinal applications of cannabis, and drastic increases in cannabis potency, it is essential to establish an understanding of cannabis effects on the developing adolescent brain. This systematic review aims to: (1) synthesize extant literature on functional and structural neural alterations associated with cannabis use during adolescence and emerging adulthood; (2) identify gaps in the literature that critically impede our ability to accurately assess the effect of cannabis on adolescent brain function and development; and (3) provide recommendations for future research to bridge these gaps and elucidate the mechanisms underlying cannabis-related harms in adolescence and emerging adulthood, with the long-term goal of facilitating the development of improved prevention, early intervention, and treatment approaches targeting adolescent cannabis users (CU). Based on a systematic search of Medline and PsycInfo and other non-systematic sources, we identified 90 studies including 9441 adolescents and emerging adults (n = 3924 CU, n = 5517 non-CU), which provide preliminary evidence for functional and structural alterations in frontoparietal, frontolimbic, frontostriatal, and cerebellar regions among adolescent cannabis users. Larger, more rigorous studies are essential to reconcile divergent results, assess potential moderators of cannabis effects on the developing brain, disentangle risk factors for use from consequences of exposure, and elucidate the extent to which cannabis effects are reversible with abstinence. Guidelines for conducting this work are provided.