Background A link between attention‐deficit/hyperactivity disorder (ADHD) and alcohol use disorder (AUD) has been widely demonstrated. In this study, we used neuroimaging to investigate the connectivity traits that may contribute to the comorbidity of these disorders. Methods The study included an AUD group (N = 18), an ADHD group (N = 17), a group with AUD + ADHD comorbidity (N = 12) and a control group (N = 18). We used resting‐state functional connectivity in a seed‐based approach in the default mode networks, the dorsal attention network, and the salience network. Results Within the default mode networks, all affected groups shared greater connectivity toward the temporal gyrus when compared to the control group. Regarding the dorsal attention network, the Brodmann area 6 presented greater connectivity for each affected group in comparison with the control group, displaying the strongest aberrations in the AUD + ADHD group. In the salience network, the prefrontal cortex showed decreased connectivity in each affected group compared to the control group. Conclusions Despite the small and unequal sample sizes, our findings show evidence of common neurobiological alterations in AUD and ADHD, supporting the hypothesis that ADHD could be a risk factor for the development of AUD. The results highlight the importance of an early ADHD diagnosis and treatment to reduce the risk of a subsequent AUD.

BackgroundIn mental health, comorbidities are the norm rather than the exception. However, current meta-analytic methods for summarizing the neural correlates of mental disorders do not consider comorbidities, reducing them to a source of noise and bias rather than benefitting from their valuable information.ObjectivesWe describe and validate a novel neuroimaging meta-analytic approach that focuses on comorbidities. In addition, we present the protocol for a meta-analysis of all major mental disorders and their comorbidities.MethodsThe novel approach consists of a modification of Seed-based d Mapping—with Permutation of Subject Images (SDM-PSI) in which the linear models have no intercept. As in previous SDM meta-analyses, the dependent variable is the brain anatomical difference between patients and controls in a voxel. However, there is no primary disorder, and the independent variables are the percentages of patients with each disorder and each pair of potentially comorbid disorders. We use simulations to validate and provide an example of this novel approach, which correctly disentangled the abnormalities associated with each disorder and comorbidity. We then describe a protocol for conducting the new meta-analysis of all major mental disorders and their comorbidities. Specifically, we will include all voxel-based morphometry (VBM) studies of mental disorders for which a meta-analysis has already been published, including at least 10 studies. We will use the novel approach to analyze all included studies in two separate single linear models, one for children/adolescents and one for adults.DiscussionThe novel approach is a valid method to focus on comorbidities. The meta-analysis will yield a comprehensive atlas of the neuroanatomy of all major mental disorders and their comorbidities, which we hope might help develop potential diagnostic and therapeutic tools.