Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 176 (2), 156-164

Connectome-Based Prediction of Cocaine Abstinence


Connectome-Based Prediction of Cocaine Abstinence

Sarah W Yip et al. Am J Psychiatry.


Objective: The authors sought to identify a brain-based predictor of cocaine abstinence by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. CPM is a predictive tool and a method of identifying networks that underlie specific behaviors ("neural fingerprints").

Methods: Fifty-three individuals participated in neuroimaging protocols at the start of treatment for cocaine use disorder, and again at the end of 12 weeks of treatment. CPM with leave-one-out cross-validation was conducted to identify pretreatment networks that predicted abstinence (percent cocaine-negative urine samples during treatment). Networks were applied to posttreatment functional MRI data to assess changes over time and ability to predict abstinence during follow-up. The predictive ability of identified networks was then tested in a separate, heterogeneous sample of individuals who underwent scanning before treatment for cocaine use disorder (N=45).

Results: CPM predicted abstinence during treatment, as indicated by a significant correspondence between predicted and actual abstinence values (r=0.42, df=52). Identified networks included connections within and between canonical networks implicated in cognitive/executive control (frontoparietal, medial frontal) and in reward responsiveness (subcortical, salience, motor/sensory). Connectivity strength did not change with treatment, and strength at posttreatment assessment also significantly predicted abstinence during follow-up (r=0.34, df=39). Network strength in the independent sample predicted treatment response with 64% accuracy by itself and 71% accuracy when combined with baseline cocaine use.

Conclusions: These data demonstrate that individual differences in large-scale neural networks contribute to variability in treatment outcomes for cocaine use disorder, and they identify specific abstinence networks that may be targeted in novel interventions.

Keywords: Cocaine; Cognitive Neuroscience; Psychoactive Substance Use Disorder.


Figure 1 –
Figure 1 –. CPM model performance and positive and negative abstinence networks
Figure 1A shows positive (red) and negative (blue) abstinence networks. For the positive network, increased edge weights (i.e., increased functional connectivity) predict more within-treatment abstinence. For the negative network, decreased edge weights (i.e., decreased functional connectivity) predict more within-treatment abstinence. 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. Despite the clinical complexity of the population (Table 1), CPM successfully predicted within-treatment abstinence (p’s<.005). Predictions remained significant in follow-up analyses controlling for clinical variables including years-of-cocaine-use and treatment retention (Supplementary Materials).
Figure 2 –
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. From top, brain regions are presented in approximate anatomical order, such that longer range connections are represented by longer lines.
Figure 3 –
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. 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. For C, cells represent the number of positive versus negative edges connecting nodes within (and between) each network with warmer colors (orange/yellow) indicating more edges in the positive network and cooler colors (blue/green) indicating more edges in the negative network. 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).
Figure 4 –
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. Stronger connectivity (i.e., network integration) between frontoparietal and medial-frontal networks (top) and between sensory-motor, salience and subcortical networks (bottom) positively predicted within treatment abstinence. Weaker connectivity between these two systems (i.e., network segregation) also predicted more within treatment abstinence.

Similar articles

See all similar articles

Cited by 2 articles

Publication types

MeSH terms