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Project Proposals


 

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PI- Valentina Lorenzetti: Investigating the independent, and interactive roles of sex, and dependence, on hippocampal and amygdala subregions in cannabis users: Findings from a multi-site study from the ENIGMA Addiction Working Group.

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PI- Sutherland/Laird: Protocol to organize ENIGMA Addiction resting-state fMRI datasets: Effects of substance use disorder on the functional connectivity of the ventral striatum.

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PI- Patricia Conrod: Using Deep learning classification to highlight general markers of illicit substance use and specific of markers cannabis use:

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PI- Dan Stein: Brain-age gap in substance dependence.

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PI- Edyth London: Sex differences in cortical thickness of cigarette smokers.

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PI- Valentina Lorenzetti: Sex differences in the neuroanatomy of alcohol dependence: a focus on hippocampal subregionsand amygdala nuclei

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Click here to download a blank new ENIGMA-Addiction Project Proposal.

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PI- Garza/Lorenzetti/Harding: Cerebellar volumes in substance use disorders.

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PI- Mackey/Garavan: Neural correlates of polygenic risk for cannabis use and cannabis use disorder

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PI- Mackey/Garavan: Machine learning based biotyping.

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PI- Matthias Kirschner: Network modeling.

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PI- Rachel Rabin: Investigating the Independent and Interactive Effects of Tobacco and Cannabis Use on Brain Morphology.

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PI- Anne Lingford-Hughes: Structural Covariance Network Changes in Alcohol Dependence/Abstinent.

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PI- Zilverstand/Ekhtiari: Cue reactivity fMRI.

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PI- Bernice Porjesz: Multi-modal deep learning AUD classifiers on structural voxel-based morphometry and resting state fMRI phenotypes with age/sex-specific modeling.

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PI- Vince Calhoun: Cocaine related structural differences assessed by COINSTAC.

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PI- Pablo Villoslada: Checking SUD locations.

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PI- Kirsten Morley: Comorbidity Addiction PTSD.

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PI- Scott Mackey: Graph theory metrics of resting state FC for multiple substance use disorders.

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PI- Alex Hatoum: Biotyping AUD in the ENIGMA-Addiction datasets using a k-means approach along with other methods.

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PI- Jinbo Bi: Explanation methods for the deep learning models and interpretable GNN.

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PI- Xiaochu Zhang: Utilize the eigenmodes generated from the brain geometry (using T1 structural data) to explore the similarities and differences in brain activity patterns (using Resting-state data) among three substantial additions (nicotine, alcohol, and cocaine).

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