Connectome-based prediction of craving in gambling disorder and cocaine use disorder

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.

The Neurocircuitry of Substance Use Disorder, Treatment, and Change: A Resource for Clinical Psychiatrists

Substance use disorder (SUD) is common in psychiatric patients and has a negative impact on health and well-being. However, SUD often goes untreated, and there is a need for psychiatrists, of all specialties, to address this pervasive clinical problem. In this review, the authors' goal is to provide a resource that describes treatments for SUD, using neuroscience as a framework. They discuss the effect of pharmacotherapy on craving, intoxication, and withdrawal and its ability to interrupt the cycle of substance use in SUD. The neuroscience of stress is reviewed, including medications targeting neurotransmitter systems activated by alarm and fear. Neuroplasticity and promising treatments that use this mechanism, including ketamine, psilocybin, and transcranial magnetic stimulation (TMS), are discussed. The authors conclude by listing resources and practice guidelines for physicians interested in learning more about treatments for SUD.

Prediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods

Craving is a central feature of substance use disorders and disorders due to addictive behaviors. Considerable research has investigated neural mechanisms involved in the development and processing of craving. Recently, connectome-based predictive modeling, a data-driven method, has been used in four studies aiming to predict craving related to substance use, addictive behaviors, and food. Studies differed in methods, samples, and conceptualizations of craving. Within the commentary we aim to compare, contrast and consolidate findings across studies by considering conceptual and methodological features of the studies. We derive a theoretical model on the functional connectivity-craving relationships across studies.

Neural signatures of imaginary motivational states: desire for music, movement and social play

The literature has demonstrated the potential for detecting accurate electrical signals that correspond to the will or intention to move, as well as decoding the thoughts of individuals who imagine houses, faces or objects. This investigation examines the presence of precise neural markers of imagined motivational states through the combining of electrophysiological and neuroimaging methods. 20 participants were instructed to vividly imagine the desire to move, listen to music or engage in social activities. Their EEG was recorded from 128 scalp sites and analysed using individual standardized Low-Resolution Brain Electromagnetic Tomographies (LORETAs) in the N400 time window (400-600 ms). The activation of 1056 voxels was examined in relation to the 3 motivational states. The most active dipoles were grouped in eight regions of interest (ROI), including Occipital, Temporal, Fusiform, Premotor, Frontal, OBF/IF, Parietal, and Limbic areas. The statistical analysis revealed that all motivational imaginary states engaged the right hemisphere more than the left hemisphere. Distinct markers were identified for the three motivational states. Specifically, the right temporal area was more relevant for "Social Play", the orbitofrontal/inferior frontal cortex for listening to music, and the left premotor cortex for the "Movement" desire. This outcome is encouraging in terms of the potential use of neural indicators in the realm of brain-computer interface, for interpreting the thoughts and desires of individuals with locked-in syndrome.

Multimodal frontal neuroimaging markers predict longitudinal craving reduction in abstinent individuals with heroin use disorder

The variation in improvement among individuals with addiction after abstinence is a critical issue. Here, we aimed to identify robust multimodal markers associated with high response to 8-month abstinence in the individuals with heroin use disorder (HUD) and explore whether the identified markers could be generalized to the individuals with methamphetamine use disorder (MUD). According to the median of craving changes, 53 individuals with HUD with 8-month abstinence were divided into two groups: higher craving reduction and lower craving reduction. At baseline, clinical variables, cortical thickness and subcortical volume, fractional anisotropy (FA) of fibers and resting-state functional connectivity (RSFC) were extracted. Different strategies (single metric, multimodal neuroimaging fusion and multimodal neuroimaging-clinical data fusion) were used to identify reliable features for discriminating the individuals with HUD with higher craving reduction from those with lower reduction. The generalization ability of the identified features was validated in the 21 individuals with MUD. Multimodal neuroimaging-clinical fusion features with best performance was achieved an 87.1 ± 3.89% average accuracy in individuals with HUD, with a moderate accuracy of 66.7% when generalizing to individuals with MUD. The multimodal neuroimaging features, primarily converging in frontal regions (e.g., the left superior frontal (LSF) thickness, FA of the LSF-occipital tract, and RSFC of left middle frontal-right superior temporal lobe), collectively contributed to prediction alongside dosage and attention impulsiveness. In this study, we identified the validated multimodal frontal neuroimaging markers associated with higher response to long-term abstinence and revealed insights for the neural mechanisms of addiction abstinence, contributing to clinical strategies and treatment for addiction.

Intrinsic connectivity demonstrates a shared role of the posterior cingulate for cue reactivity in both gambling and cocaine use disorders

Cue reactivity is relevant across addictive disorders as a process relevant to maintenance, relapse, and craving. Understanding the neurobiological foundations of cue reactivity across substance and behavioral addictions has important implications for intervention development. The present study used intrinsic connectivity distribution methods to examine functional connectivity during a cue-exposure fMRI task involving gambling, cocaine and sad videos in 22 subjects with gambling disorder, 24 with cocaine use disorder, and 40 healthy comparison subjects. Intrinsic connectivity distribution implicated the posterior cingulate cortex (PCC) at a stringent whole-brain threshold. Post-hoc analyses investigating the nature of the findings indicated that individuals with gambling disorder and cocaine use disorder exhibited decreased connectivity in the posterior cingulate during gambling and cocaine cues, respectively, as compared to other cues and compared to other groups. Brain-related cue reactivity in substance and behavioral addictions involve PCC connectivity in a content-to-disorder specific fashion. The findings suggesting that PCC-related circuitry underlies cue reactivity across substance and behavioral addictions suggests a potential biomarker for targeting in intervention development.

Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking

Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These "neural fingerprints" elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.

Efficacy and Neural Mechanisms of Mindfulness Meditation Among Adults With Internet Gaming Disorder: A Randomized Clinical Trial

Importance

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), recently identified internet gaming disorder (IGD) as a condition warranting more research, and few empirically validated treatments exist. Mindfulness meditation (MM) has multiple health benefits; however, its efficacy in treating IGD and potential neural mechanisms underlying MM treatment of the disorder remain largely unknown.

Objective

To explore the efficacy of MM used to treat adults with IGD and to identify neural mechanisms underlying MM.

Design, Setting, and Participants

This randomized clinical trial was performed from October 1 to November 30, 2023, at Hangzhou Normal University in Hangzhou, China. Adults (aged ≥18 years) who met at least 6 of the 9 DSM-5-TR proposed criteria for IGD were recruited to receive either MM or progressive muscle relaxation (PMR). Data analysis was performed on December 1, 2023.

Intervention

Participants underwent MM training (an 8-session meditation program that focuses on attention and acceptance) and PMR training (an 8-time program for body relaxation) delivered in groups that met 2 times each week for 4 weeks.

Main Outcomes and Measures

This per-protocol analysis included only participants who finished the pretest assessment, 8 training sessions, and posttest assessment. The main outcomes were addiction severity (measured with the DSM-5-TR proposed criteria for IGD and with Internet Addiction Test scores), gaming craving (measured with Questionnaire for Gaming Urges scores), and blood oxygen level-dependent signals assessed with cue-craving tasks on fMRI. Behavioral and brain measurements were compared using analysis of variance. Functional connectivity (FC) among identified brain regions was measured to test connectivity changes associated with MM.

Results

This study included 64 adults with IGD. A total of 32 participants received MM (mean [SD] age, 20.3 [1.9] years; 17 women [53%]) and 32 received PMR (mean [SD] age, 20.2 [1.5] years; 16 women [50%]). The severity of IGD decreased in the MM group (pretest vs posttest: mean [SD], 7.0 [1.1] vs 3.6 [0.8]; P < .001) and in the PMR group (mean [SD], 7.1 [0.9] vs 6.0 [0.9]; P = .04). The MM group had a greater decrease in IGD severity than the PMR group (mean [SD] score change for the MM group vs the PMR group, -3.6 [0.3] vs -1.1 [0.2]; P < .001). Mindfulness meditation was associated with decreased brain activation in the bilateral lentiform nuclei (r = 0.40; 95% CI, 0.19 to 0.60; P = .02), insula (r = 0.35; 95% CI, 0.09 to 0.60; P = .047), and medial frontal gyrus (MFG; r = 0.43; 95% CI, 0.16 to 0.70; P = .01). Increased MFG-lentiform FC and decreased craving (pretest vs posttest: mean [SD], 58.8 [15.7] vs 33.6 [12.0]; t = -8.66; ƞ2 = 0.30; P < .001) was observed after MM, and changes in MFG-lentiform FC mediated the relationship between increased mindfulness and decreased craving (mediate effect, -0.17; 95% CI, -0.32 to -0.08; P = .03).

Conclusions and Relevance

In this study, MM was more effective in decreasing addiction severity and gaming cravings compared with PMR. These findings indicate that MM may be an effective treatment for IGD and may exert its effects by altering frontopallidal pathways.

Trial Registration

Chinese Clinical Trial Registry Identifier: ChiCTR2300075869.

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.

Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach

Objective

It is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e., increased or decreased connectivity. In this study, we aimed to determine the direction of the connectivity alteration associated with schizophrenia using a multivariate, data-driven approach.

Methods

Resting-state functional magnetic resonance imaging data were acquired from 109 individuals with schizophrenia and 120 controls across two openly available datasets. A whole-brain resting-state functional connectivity (rsFC) matrix was computed for each individual. A modified connectome-based predictive model (CPM) with a support vector machine (SVM) was used to classify patients and controls. We conducted a series of multivariate classification analyses using three different feature sets, increased, decreased, and both increased and decreased rsFC.

Results

For both datasets, combining information from both increased and decreased rsFC substantially improved prediction accuracy (Dataset 1: accuracy = 70.2%, permutation p = 0.001; Dataset 2: accuracy = 64.4%, permutation p = 0.003). When tested across datasets, the prediction model using decreased rsFC performed best. The identified predictive features of decreased rsFC were distributed mostly in the motor network for both datasets.

Conclusion

These findings suggest that bidirectional alterations in rsFC are distributed in schizophrenia patients, with the pattern of decreased rsFC being more similar across different populations.