An Interpretable and Predictive Connectivity-Based Neural Signature for Chronic Cannabis Use

Neuromarkers in addiction: definitions, development strategies, and recent advances

Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.

Abnormal developmental of structural covariance networks in young adults with heavy cannabis use: a 3-year follow-up study

Heavy cannabis use (HCU) exerts adverse effects on the brain. Structural covariance networks (SCNs) that illustrate coordinated regional maturation patterns are extensively employed to examine abnormalities in brain structure. Nevertheless, the unexplored aspect remains the developmental alterations of SCNs in young adults with HCU for three years, from the baseline (BL) to the 3-year follow-up (FU). These changes demonstrate dynamic development and hold potential as biomarkers. A total of 20 young adults with HCU and 22 matched controls were recruited. All participants underwent magnetic resonance imaging (MRI) scans at both the BL and FU and were evaluated using clinical measures. Both groups used cortical thickness (CT) and cortical surface area (CSA) to construct structural covariance matrices. Subsequently, global and nodal network measures of SCNs were computed based on these matrices. Regarding global network measures, the BL assessment revealed significant deviations in small-worldness and local efficiency of CT and CSA in young adults with HCU compared to controls. However, no significant differences between the two groups were observed at the FU evaluation. Young adults with HCU displayed changes in nodal network measures across various brain regions during the transition from BL to FU. These alterations included abnormal nodal degree, nodal efficiency, and nodal betweenness in widespread areas such as the entorhinal cortex, superior frontal gyrus, and parahippocampal cortex. These findings suggest that the topography of CT and CSA plays a role in the typical structural covariance topology of the brain. Furthermore, these results indicate the effect of HCU on the developmental changes of SCNs in young adults.

Distinct neural networks predict cocaine versus cannabis treatment outcomes

Treatment outcomes for individuals with substance use disorders (SUDs) are variable and more individualized approaches may be needed. Cross-validated, machine-learning methods are well-suited for probing neural mechanisms of treatment outcomes. Our prior work applied one such approach, connectome-based predictive modeling (CPM), to identify dissociable and substance-specific neural networks of cocaine and opioid abstinence. In Study 1, we aimed to replicate and extend prior work by testing the predictive ability of the cocaine network in an independent sample of 43 participants from a trial of cognitive-behavioral therapy for SUD, and evaluating its ability to predict cannabis abstinence. In Study 2, CPM was applied to identify an independent cannabis abstinence network. Additional participants were identified for a combined sample of 33 with cannabis-use disorder. Participants underwent fMRI scanning before and after treatment. Additional samples of 53 individuals with co-occurring cocaine and opioid-use disorders and 38 comparison subjects were used to assess substance specificity and network strength relative to participants without SUDs. Results demonstrated a second external replication of the cocaine network predicting future cocaine abstinence, however it did not generalize to cannabis abstinence. An independent CPM identified a novel cannabis abstinence network, which was (i) anatomically distinct from the cocaine network, (ii) specific for predicting cannabis abstinence, and for which (iii) network strength was significantly stronger in treatment responders relative to control particpants. Results provide further evidence for substance specificity of neural predictors of abstinence and provide insight into neural mechanisms of successful cannabis treatment, thereby identifying novel treatment targets. Clinical trials registation: "Computer-based training in cognitive-behavioral therapy web-based (Man VS Machine)", registration number: NCT01442597 . "Maximizing the Efficacy of Cognitive Behavior Therapy and Contingency Management", registration number: NCT00350649 . "Computer-Based Training in Cognitive Behavior Therapy (CBT4CBT)", registration number: NCT01406899 .

The effects of prefrontal vs. parietal cortex transcranial direct current stimulation on craving, inhibition, and measures of self-esteem

While prefrontal cortex dysfunction has been implicated in high food cravings, other cortical regions, like the parietal cortex, are potentially also involved in regulating craving. This study explored the effects of stimulating the inferior parietal lobule (IPL) and dorsolateral prefrontal cortex (DLPFC) on food craving state and trait. Transcranial direct current stimulation (tDCS) was administered at 1.5 mA for 5 consecutive days. Participants received 20 min of IPL, DLPFC, or sham stimulation (SHAM) each day which consisted of two rounds of 10-min stimulation, divided by a 10-min mindfulness task break. In addition, we studied inhibition and subjective psychological aspects like body image and self-esteem state and trait. To decompose immediate and cumulative effects, we measured the following on days 1 and 5: inhibition through the Go/No-go task; and food craving, self-esteem, and body appreciation through a battery of questionnaires. We found that false alarm errors decreased in the participants receiving active stimulation in the DLPFC (DLPFC-group). In contrast, false alarm errors increased in participants receiving active stimulation in the IPL (IPL-group). At the same time, no change was found in the participants receiving SHAM (SHAM-group). There was a trending reduction in craving trait in all groups. Momentary craving was decreased in the DLPFC-group and increased in IPL-group, yet a statistical difference was not reached. According to time and baseline, self-esteem and body perception improved in the IPL-group. Furthermore, self-esteem trait significantly improved over time in the DLPFC-group and IPL-group. These preliminary results indicate that tDCS modulates inhibition in frontoparietal areas with opposite effects, enhancing it in DLPFC and impairing it in IPL. Moreover, craving is moderately linked to inhibition, self-esteem, and body appreciation which seem not to be affected by neuromodulation but may rely instead on broader regions as more complex constructs. Finally, the fractionated protocol can effectively influence inhibition with milder effects on other constructs.