Neuroimaging meta-analytics for addiction: Nodes, networks, and new heuristics Grant

Neuroimaging meta-analytics for addiction: Nodes, networks, and new heuristics .


  • PROJECT SUMMARY/ABSTRACTDespite strong theoretical and clinical interest, characterization of the common and distinct neurobiologicalalterations across drug and behavioral addictions cannot be feasibly addressed within a single neuroimagingstudy. This research project will fill this knowledge gap by integratively using neuroimaging meta-analytic toolsand a large amalgamated resting state fMRI (rs-fMRI) data set to rigorously characterize common (addiction-general) and distinct (drug/condition-specific) network-level brain alterations across addictive disorders.Available neuroimaging meta-analytic tools allow for synthesis of the extant literature and can be exploited toinform common and distinct neurobiological alterations across addiction. In addition, assessment of large-scalebrain networks through (meta-analytic and rs-fMRI approaches) provides a more complete and coherentframework to appreciate such addiction-related alterations. As such, the innovative combination of such datastreams offers the ability to inform heuristic frameworks guiding future research, fractionation of the addictionphenotype, and identification of neurobiological intervention targets. The overall objective of this project is toquantitatively synthesize the addiction-related neuroimaging literature (Aim 1), that then inform mega-analysisof a large amalgamated rs-fMRI data set (Aim 2), the behavioral interpretation of which will be facilitated byemerging meta-analytic techniques (Aim 3), thereby enabling cross-drug comparisons of network-level brainalterations. The feasibility of this overall analytic framework is evidenced by significant preliminary work innicotine addiction. Specifically, this project will comprehensively synthesize the addiction-related neuroimagingliterature to identify disrupted addiction-general and drug/condition-specific regional nodes across drug andbehavioral addictions (e.g., alcohol, nicotine, marijuana, stimulants, opiates) and behavioral addictions (e.g.,gambling, internet gaming) as well as obesity (Aim 1). Harnessing the accumulated volume of publishedneuroimaging results will allow for direct comparison of conditions that were never compared with each other inthe primary studies. Meta-analytically informed hypotheses will be applied to an amalgamated rs-fMRI data setfor targeted testing of altered functional connectivity across large-scale brain networks (Aim 2). To more fullycontextualize the behavioral consequences of such alterations, we will employ network-level meta-analytictechniques to quantitatively delineate behavioral phenomena linked with regional and network-level alterationsimpacted by addiction (Aim 3). Efforts to archive, mine, and synthesize the accumulated knowledge ofaddictions impact on the brain are critical to inform analysis of large neuroimaging data sets generated throughamalgamated sources or new data collection efforts.

date/time interval

  • June 1, 2017 - April 30, 2022

administered by

sponsor award ID

  • 1R01DA041353-01A1



  • Address
  • Adoption
  • Alcohols
  • Archives
  • Base of the Brain
  • Behavior
  • Behavioral
  • Brain
  • Brain imaging
  • Chronic
  • Clinical
  • Cocaine
  • Data
  • Data Analyses
  • Data Analytics
  • Data Collection
  • Data Set
  • Development
  • Disease
  • Drug
  • addiction
  • analytical method
  • analytical tool
  • cohesion