Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study

Alcohol use disorder (AUD) is a disorder of clinical and public health significance requiring novel and improved therapeutic solutions. Both environmental and genetic factors play a significant role in its pathophysiology. However, the underlying epigenetic molecular mechanisms that link the gene-en...

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Published in:	iScience Vol. 27; no. 7; p. 110159
Main Authors:	Dagnew, Tewodros Mulugeta, Tseng, Chieh-En J., Yoo, Chi-Hyeon, Makary, Meena M., Goodheart, Anna E., Striar, Robin, Meyer, Tyler N., Rattray, Anna K., Kang, Leyi, Wolf, Kendall A., Fiedler, Stephanie A., Tocci, Darcy, Shapiro, Hannah, Provost, Scott, Sultana, Eleanor, Liu, Yan, Ding, Wei, Chen, Ping, Kubicki, Marek, Shen, Shiqian, Catana, Ciprian, Zürcher, Nicole R., Wey, Hsiao-Ying, Hooker, Jacob M., Weiss, Roger D., Wang, Changning
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 19-07-2024 Elsevier
Subjects:	addiction medicine artificial intelligence neuroscience techniques in neuroscience techniques in neuroscience addiction medicine neuroscience artificial intelligence
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Summary:	Alcohol use disorder (AUD) is a disorder of clinical and public health significance requiring novel and improved therapeutic solutions. Both environmental and genetic factors play a significant role in its pathophysiology. However, the underlying epigenetic molecular mechanisms that link the gene-environment interaction in AUD remain largely unknown. In this proof-of-concept study, we showed, for the first time, the neuroepigenetic biomarker capability of non-invasive imaging of class I histone deacetylase (HDAC) epigenetic enzymes in the in vivo brain for classifying AUD patients from healthy controls using a machine learning approach in the context of precision diagnosis. Eleven AUD patients and 16 age- and sex-matched healthy controls completed a simultaneous positron emission tomography-magnetic resonance (PET/MR) scan with the HDAC-binding radiotracer [11C]Martinostat. Our results showed lower HDAC expression in the anterior cingulate region in AUD. Furthermore, by applying a genetic algorithm feature selection, we identified five particular brain regions whose combined [11C]Martinostat relative standard uptake value (SUVR) features could reliably classify AUD vs. controls. We validate their promising classification reliability using a support vector machine classifier. These findings inform the potential of in vivo HDAC imaging biomarkers coupled with machine learning tools in the objective diagnosis and molecular translation of AUD that could complement the current diagnostic and statistical manual of mental disorders (DSM)-based intervention to propel precision medicine forward. [Display omitted] •Generic neuromolecular imaging biomarker identification pipeline for brain-related disorders•Classification using neuroepigenetic histone deacetylase (HDAC) enzyme imaging•[11C]Martinostat radiotracer, PET/MR brain imaging and Machine Learning for diagnosis•Application to alcohol use disorder Addiction medicine; Neuroscience; Techniques in neuroscience; Artificial intelligence; PET/MR brain imaging; Neuroepigenetics
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Lead contact
ISSN:	2589-0042 2589-0042
DOI:	10.1016/j.isci.2024.110159