[Correction added to online publication 16 May 2012: title changed from “How Acute and Chronic Alcohol Consumption Affects Brain Networks: Insights into Multimodal Neuroimaging” to “How Acute and Chronic Alcohol Consumption Affects Brain Networks: Insights from Multimodal Neuroimaging”.]
How Acute and Chronic Alcohol Consumption Affects Brain Networks: Insights from Multimodal Neuroimaging
Article first published online: 11 MAY 2012
Copyright © 2012 by the Research Society on Alcoholism
Alcoholism: Clinical and Experimental Research
Volume 36, Issue 12, pages 2017–2027, December 2012
How to Cite
Schulte, T., Oberlin, B. G., Kareken, D. A., Marinkovic, K., Müller-Oehring, E. M., Meyerhoff, D. J. and Tapert, S. (2012), How Acute and Chronic Alcohol Consumption Affects Brain Networks: Insights from Multimodal Neuroimaging. Alcoholism: Clinical and Experimental Research, 36: 2017–2027. doi: 10.1111/j.1530-0277.2012.01831.x
- Issue published online: 11 DEC 2012
- Article first published online: 11 MAY 2012
- Manuscript Accepted: 13 MAR 2012
- Manuscript Received: 5 JAN 2012
- Multimodal Neuroimaging;
- Acute and Chronic Alcohol
Multimodal imaging combining 2 or more techniques is becoming increasingly important because no single imaging approach has the capacity to elucidate all clinically relevant characteristics of a network.
This review highlights recent advances in multimodal neuroimaging (i.e., combined use and interpretation of data collected through magnetic resonance imaging [MRI], functional MRI, diffusion tensor imaging, positron emission tomography, magnetoencephalography, MR perfusion, and MR spectroscopy methods) that leads to a more comprehensive understanding of how acute and chronic alcohol consumption affect neural networks underlying cognition, emotion, reward processing, and drinking behavior.
Several innovative investigators have started utilizing multiple imaging approaches within the same individual to better understand how alcohol influences brain systems, both during intoxication and after years of chronic heavy use.
Their findings can help identify mechanism-based therapeutic and pharmacological treatment options, and they may increase the efficacy and cost effectiveness of such treatments by predicting those at greatest risk for relapse.