Regulation of craving for real-time fMRI neurofeedback based on individual classification

作者：Dong‐Youl Kim, Jonathan Lisinski, Matthew Caton, Brooks Casas, Stephen M. LaConte, Pearl H. Chiu · 发表于：Philosophical Transactions of the Royal Society B Biological Sciences · 年份：2024 · DOI：10.1098/rstb.2023.0094 · 被引用次数：7 · 研究领域：Nicotinic Acetylcholine Receptors Study、Smoking Behavior and Cessation、Functional Brain Connectivity Studies

In previous real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) studies on smoking craving, the focus has been on within-region activity or between-region connectivity, neglecting the potential predictive utility of broader network activity. Moreover, there is debate over the use and relative predictive power of individual-specific and group-level classifiers. This study aims to further advance rtfMRI-NF for substance use disorders by using whole-brain rtfMRI-NF to assess smoking craving-related brain patterns, evaluate the performance of group-level or individual-level classification ( n = 31) and evaluate the performance of an optimized classifier across repeated NF runs. Using real-time individual-level classifiers derived from whole-brain support vector machines, we found that classification accuracy between crave and no-crave conditions and between repeated NF runs increased across repeated runs at both individual and group levels. In addition, individual-level accuracy was significantly greater than group-level accuracy, highlighting the potential increased utility of an individually trained whole-brain classifier for volitional control over brain patterns to regulate smoking craving. This study provides evidence supporting the feasibility of using whole-brain rtfMRI-NF to modulate smoking craving-related brain responses and the potential for learning individual strategies through optimization across repeated feedback runs. This article is part of ...