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Federal government websites often end in. The site is secure. Preview improvements coming to the PMC website in October Learn More or Try it out now. To identify a brain-based predictor of cocaine abstinence using a recently developed machine learning approach, connectome-based predictive modeling CPM. Fifty-three individuals participated in neuroimaging protocols at the start of treatment for cocaine-use disorder, and again at the end of week treatment. CPM with leave-one-out cross-validation was run to identify pre-treatment networks that predicted abstinence percent cocaine-negative urines during treatment. Networks were applied to post-treatment fMRI data to assess changes over time and ability to predict abstinence during follow-up. These data demonstrate that individual differences in large-scale neural networks contribute to variability in treatment outcomes for cocaine use disorder. They identify specific abstinence networks that may be targeted in novel interventions. Addictions are a leading cause of disability worldwide. Despite advances, the effectiveness of most substance-use interventions remains highly variable across individuals and multiple quit-attempts are standard. While a growing body of research suggests that variability in treatment response is linked to individual differences in neural functioning 1 - 6 , the search for brain-based predictors has yet to yield a reliable indicator of future treatment response or abstinence 7 , 8. Identification of brain-based predictors of abstinence may not only expand existing biological knowledge of addictions pathophysiology which may itself be used to refine existing interventions , but may ultimately be used to directly inform real-world clinical practice via assignment of patients to therapies based on individual patterns of neural function; i. However, true predictive models require application of the model to novel data 8 , 11 , 13 — Newly available alternatives such as machine-learning allow for actual prediction 9 , 11 , 12 , but have not yet been used to identify pre-treatment predictors of abstinence. Nonetheless, prior research indicates that alterations within well-established neural networks — e. For example, functional connectivity strength between the medial prefrontal cortex and temporal pole, when combined with years of education, has been identified as a predictor of relapse 5. However, no prior study has used a whole-brain, machine learning approach to identify neuromarkers of future abstinence. Unlike correlation or regression models, CPM with built-in cross validation protects against over-fitting by testing the strength of the relationship in a novel sample, increasing the likelihood of replication in future studies and thus the applicability to other clinical samples Unlike other machine-learning approaches previously employed to study addictions, CPM is entirely data-driven and requires no a priori selection of networks. CPM has previously been used to identify neural fingerprints of IQ and attention using whole-brain functional connectivity data acquired during neurocognitive task performance 17 — 19 , but has not been previously used to predict future behaviors or a clinical outcome. Here, we use dimensional CPM to identify neural networks predictive of future abstinence from cocaine by applying CPM to fMRI reward task data acquired at the start of a week treatment for cocaine-use-disorder. We further test the stability of these networks over time and in relation to post-treatment abstinence. Finally, we test the ability of identified networks to predict treatment response in a heterogeneous replication sample. Based on prior work focusing on selected networks 5 , 16 , we hypothesized that increased connectivity within- and between medial frontal, frontoparietal and salience networks would positively predict abstinence. Demographic and clinical characteristics are shown in Table 1. In addition to seeking treatment for cocaine, all participants were currently enrolled in methadone maintenance treatment for opioid-use disorder. Consistent with the parent trial 20 , the fMRI sample was predominantly male Further details on motion controls and follow-up analyses controlling for time of scanning are provided in the Supplemental Materials. As in our prior work 2 , 4 , abstinence during treatment was determined based on results of biweekly urine testing and defined as the percentage of urines negative for cocaine provided during treatment. All participants provided written informed consent following a complete description of study procedures. Whole-brain functional connectivity analyses were conducted using the BioImage suite, using previously described methods 17 — Network nodes were defined using the Shen node brain atlas which includes the cortex, subcortex and cerebellum 22 , as in other prior CPM work 17 — 19 further details in Supplemental Materials. This involved computation of mean time courses for each of the nodes i. Briefly, CPM takes group connectivity matrices and behavioral data in this case, percentage of cocaine-negative urines during treatment as input to generate a predictive model of the behavioral data from connectivity matrices Positive networks are networks for which increased edge weights increased connectivity are associated with the variable of interest, whereas negative networks are networks for which decreased edge weights decreased connectivity are associated with the variable of interest. While both networks are used for predicting the same variable, they are by definition independent - as a single edge cannot be both a positive and negative predictor. Single subject summary statistics are then created as the sum of the significant edge weights in each network and entered into predictive models assuming linear relationships with behavioral data. Resultant polynomial coefficients linear equation including slope and intercept are then applied to the test dataset to predict behaviors. Model performance i. When using leave-one-out cross-validation, analyses in the leave-one-out folds are not wholly independent and the number of degrees of freedom is thus overestimated for parametric p-values based on correlation. Instead of parametric testing, we therefore performed permutation testing. To generate null distributions for significance testing, we randomly shuffled the correspondence between behavior variables and connectivity matrices 5, times and re-ran the CPM analysis with the shuffled data. Based on these null distributions, the p-values for leave-one-out predictions were calculated as in prior work 13 , Details on characterization of resultant network anatomy are provided in the Supplemental Materials. For comparison with CPM findings, a machine learning analysis i. To control for putative effects of residual motion, CPM analyses were conducted both with and without motion as a covariate. Both approaches yielded similar results further details in Supplemental Materials. For simplicity, findings including motion as a covariate are presented here unless otherwise specified. Figure 1A shows positive red and negative blue abstinence networks. For the positive network, increased edge weights i. For the negative network, decreased edge weights i. Larger smaller spheres indicate nodes with more less edges. Figure 1B shows the correspondence between actual x-axis and predicted y-axis abstinence values generated using CPM. Abstinence values correspond to the percentage of urines negative for cocaine provided during treatment. Predictions remained significant in follow-up analyses controlling for clinical variables including years-of-cocaine-use and treatment retention Supplementary Materials. Post-hoc correlations indicated significant correspondence between network strengths and other abstinence indices e. Figure 2 summarizes positive and negative abstinence networks based on connectivity between macroscale brain regions note that brain regions are presented in approximate anatomical order, such that longer range connections are represented by longer lines. Consistent with prior CPM work 17 — 19 , network anatomies for both networks were complex and included connections between frontal, parietal, occipital and temporal lobes. Highest degree nodes i. Highest degree nodes for the negative network also included a temporal node with connections to limbic, parietal and prefrontal nodes, as well as with connections to cerebellar and subcortical nodes. Both abstinence networks included short- and long-range connections. Figure 2 summarizes positive and negative abstinence networks based on connectivity between macroscale brain regions. From top, brain regions are presented in approximate anatomical order, such that longer range connections are represented by longer lines. To facilitate characterization of identified abstinence networks, Figure 3 summarizes connectivity based on the number of connections within and between canonical neural networks e. By definition, positive and negative networks do not contain overlapping connections as a single edge cannot be both a positive and negative predictor. However, positive and negative abstinence networks included connections within and between similar large-scale canonical neural networks. Within- and between-network connectivity for the positive network A , negative network B and for positive — negative networks C are summarized based on overlap with canonical neural networks. For A and B, cells represent the total number of edges connecting nodes within and between each network with darker colors indicating a greater number of edges. Despite this visual simplification, it is important to note that, by definition positive and negative networks do not contain overlapping edges for further information on network definitions, see Supplemental Materials. Individuals enrolled in the trial were also invited to participate in post-treatment fMRI scanning. Following exclusion for excess motion as described above this included 40 participants. Resultant scores from post-treatment matrices were entered into correlation analyses with abstinence during 6month follow-up as defined via self-report using the timeline follow-back method, assessed at monthly intervals. As in our original analyses, residual motion was included as a covariate. Further details on exclusion for motion and related analyses are provided in the supplemental materials. Individual participant summary scores were extracted from functional-connectivity matrices, as above, and entered into regression analyses with within-treatment abstinence values. The translation of brain imaging findings into real-world clinical settings is one of the primary challenges of modern neuropsychiatry 7 — 9 , 12 , 13 , Here, we demonstrate the ability of a recently developed connectome-based machine learning approach to predict treatment outcomes abstinence from cocaine during week treatment using baseline patterns of connectivity. We further demonstrate that post-treatment patterns of connectivity within these networks predict abstinence during six-month follow-up. Finally, we demonstrate that the same networks can be used to predict treatment response in an independent, heterogeneous sample. Despite this predictive ability, identified networks could be considered potential treatment targets 3 , 5 , 16 , and further replication and model refinement is needed prior to direct application of findings to clinical decision-making. Consistent with the connectome-based approach, abstinence networks were complex and included connections between multiple well-established neural networks 17 , Based on these findings, Figure 4 presents a theoretical network model of abstinence. Large-scale patterns of between-network connectivity for abstinence networks identified using CPM are summarized based on relative number of connections within positive red versus negative blue networks. Stronger connectivity i. Weaker connectivity between these two systems i. In addition, based on prior resting state work in cocaine-use disorder 28 , appropriate separation between these two systems is theorized to relate to greater behavioral flexibility or to decreased compulsivity , as would be required for behavior change during treatment. Findings are further consistent with recent data prospectively linking medial prefrontal, frontoparietal and salience networks to cocaine relapse 5 , 16 , as well as with data from activation-based studies linking individual differences in brain reward responses to treatment outcomes in addiction 2 , 6 , More generally, these data add to emerging evidence that manipulation of brain states e. For example, CPMs derived from task-based data have consistently out-performed those derived from resting state data in non-addicted populations However, further work across different brain states and in relation to diverse substance-use behaviors is needed to test this hypothesis within the specific context of addictions. Connectivity strength within abstinence networks did not differ from pre- to posttreatment. Prior activation-based studies have demonstrated changes in neural responses following substance-use treatments; however, comparatively little is known about network-level changes with treatment. For example, individual differences in connectivity have been found to predict subsequent relapse to cocaine 5 , 16 , 33 , yet no prior study has compared connectivity before and after treatment for cocaine-use-disorder. Our findings suggest relative stability of identified networks over week treatment, raising the possibility that abstinence may be more closely linked to pre-treatment neural function than to within-treatment neuroplasticity. Within this context, it is possible that pre-treatment interventions influencing connectivity within the identified networks e. For example, prior CPM work has demonstrated that connectivity strength within networks predictive of ADHD symptoms is changed following methylphenidate administration Thus it is possible that effective treatments for addictions might also influence connectivity within complex networks. It is further possible that networks contributing to treatment response are distinct from those directly implicated in disease pathology or that change with treatment. Brain regions predictive of treatment responses in other disorders often have limited overlap with regions consistently found to differentiate patients from controls Clinically, factors that predict treatment response e. Thus, the same may be true for neural networks. Further, it is possible that changes within abstinence networks may take time to emerge and thus may only be detectable following treatment, as would be consistent with data indicating protracted emergence of treatment effects Additional work is therefore needed to characterize network-level changes over time and in relation to addiction pathology, per se. While continuous prediction approaches, which maximize individual differences, are optimal for feature selection in heterogeneous clinical samples 11 , the practical value of predictive modeling within a clinical context will likely involve binary prediction e. Connectivity within the identified networks successfully predicted both categorical and dimensional treatment response in our replication sample. In our replication sample, our model had high sensitivity but low specificity. In this instance, low sensitivity would translate to under identification of responders and thus under assignment of individuals to effective treatment , whereas low specificity would translate to under identification of non-responders over-assignment to ineffective treatment. Given that multiple failed treatment attempts are common in addictions — and that only resources are lost in the instance of over assignment to ineffective treatment — maximizing sensitivity in this instance appears paramount. This study has several strengths, including use of a recently developed whole-brain predictive modeling approach, multiple time point fMRI data pre- and post-treatment and out-of-sample replication. However, several limitations should be noted. In addition, the functional significance of the identified networks in relation to other aspects of substance-use pathology remains to be determined. While networks were relatively robust and not significantly changed in follow-up analyses controlling for other factors, we cannot entirely exclude the effects of other clinical variables, such as concurrent use of other substances or even acute intoxication, on connectivity strength. Given the relatively limited temporal specificity of urine toxicology analyses, future studies should consider incorporation of salivary testing for acute drug effects. To avoid circularity, we did not test the ability of pre-treatment data to predict abstinence during follow-up; however, this will be an important next step for future studies 5. To facilitate replication, we have made the positive and negative abstinence network masks publicly available at our website to be posted upon manuscript publication Predictive models are less likely to overfit a specific dataset, leading to both increased likelihood of outof-sample-replication as well as typically decreased more realistic effect size estimates 42 — This study demonstrates that baseline patterns of whole-brain connectivity can predict a complex clinical outcome — in this case, cocaine abstinence. Consistent with the parent randomized controlled trial 20 , participants had significant addiction histories including multiple prior quit-attempts, legal problems and concurrent methadone treatment for opioid-use disorder. Despite this clinically rich profile, baseline connectivity within the identified networks successfully predicted within-treatment abstinence, even after controlling for other baseline variables including other drug-use history and treatment assignment. The predictive ability of these networks translated to a separate, heterogeneous sample of individuals including non-methadone maintained individuals with cocaine-use disorder scanned prior to enrollment in a different treatment trial. The authors would like to thank Charla Nich and Karen Hunkele for help with non-imaging statistical analyses. Disclosure of financial relationships: Drs. Yip and Scheinost report no financial relationships with commercial interest. Potenza has received financial support or compensation for the following: Dr. As a library, NLM provides access to scientific literature. Am J Psychiatry. Author manuscript; available in PMC Feb 1. Sarah W. Carroll , PhD 1. Marc N. Kathleen M. PMC Copyright notice. The publisher's final edited version of this article is available at Am J Psychiatry. Associated Data Supplementary Materials supplement. Abstract Objective: To identify a brain-based predictor of cocaine abstinence using a recently developed machine learning approach, connectome-based predictive modeling CPM. Methods: Fifty-three individuals participated in neuroimaging protocols at the start of treatment for cocaine-use disorder, and again at the end of week treatment. Conclusions: These data demonstrate that individual differences in large-scale neural networks contribute to variability in treatment outcomes for cocaine use disorder. Introduction Addictions are a leading cause of disability worldwide. Open in a separate window. Neuroimaging data acquisition fMRI data were acquired during performance of a well-validated Monetary Incentive Delay task 21 Supplemental Figure 1 ; details on acquisition in Supplemental Materials. Functional connectivity Whole-brain functional connectivity analyses were conducted using the BioImage suite, using previously described methods 17 — Predicting within-treatment abstinence To control for putative effects of residual motion, CPM analyses were conducted both with and without motion as a covariate. Figure 1 —. CPM model performance and positive and negative abstinence networks Figure 1A shows positive red and negative blue abstinence networks. Network anatomy Figure 2 summarizes positive and negative abstinence networks based on connectivity between macroscale brain regions note that brain regions are presented in approximate anatomical order, such that longer range connections are represented by longer lines. Figure 2 —. Positive and negative abstinence networks summarized by connectivity between macroscale brain regions Figure 2 summarizes positive and negative abstinence networks based on connectivity between macroscale brain regions. Overlap with canonical neural networks To facilitate characterization of identified abstinence networks, Figure 3 summarizes connectivity based on the number of connections within and between canonical neural networks e. Figure 3 —. Positive and negative abstinence networks summarized by overlap with canonical neural networks Within- and between-network connectivity for the positive network A , negative network B and for positive — negative networks C are summarized based on overlap with canonical neural networks. Relationship to post-treatment abstinence Individuals enrolled in the trial were also invited to participate in post-treatment fMRI scanning. Discussion The translation of brain imaging findings into real-world clinical settings is one of the primary challenges of modern neuropsychiatry 7 — 9 , 12 , 13 , Figure 4 —. Five network model of abstinence Large-scale patterns of between-network connectivity for abstinence networks identified using CPM are summarized based on relative number of connections within positive red versus negative blue networks. Strengths and limitations This study has several strengths, including use of a recently developed whole-brain predictive modeling approach, multiple time point fMRI data pre- and post-treatment and out-of-sample replication. Conclusions This study demonstrates that baseline patterns of whole-brain connectivity can predict a complex clinical outcome — in this case, cocaine abstinence. Supplementary Material supplement Click here to view. Acknowledgment section: The authors would like to thank Charla Nich and Karen Hunkele for help with non-imaging statistical analyses. References 1. Psychol Addict Behav. Drug Alcohol Depend. Moeller SJ, Paulus MP: Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder. Curr Opin Neurobiol. Mol Psychiatry. Current addiction reports. Biol Psychiatry. Nat Protocols. Perspectives on psychological science : a journal of the Association for Psychological Science. Addict Biol. Nat Neurosci. J Neurosci. J Clincal Psychiatry. New York, The Guilford Press; Hu Y, Salmeron B, Gu H, Stein EA, Yang Y: Impaired functional connectivity within and between frontostriatal circuits and its association with compulsive drug use and trait impulsivity in cocaine addiction. JAMA psychiatry. Nature communications. In Press. Front Psychiatry. Brain Res. J Subst Abuse Treat. Shmueli G: To Explain or to Predict? Statistical Science. Whelan R, Garavan H: When optimism hurts: inflated predictions in psychiatric neuroimaging. PLoS One. Copy Download. Smoke, No. Snort, No. IV, No. Speedball, No.

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