Search

Voxel-based morphometry (VBM) studies have revealed gray matter alterations in smokers, but this type of analysis has poor predictive value for individual cases, which limits its applicability in clinical diagnoses and treatment. A predictive model would essentially embody a complex biomarker that could be used to evaluate treatment efficacy. In this study, we applied VBM along with a multivariate classification method consisting of a support vector machine with recursive feature elimination to discriminate smokers from nonsmokers using their structural MRI data. Mean gray matter volumes in 1,024 cerebral cortical regions of interest created using a subparcellated version of the Automated Anatomical Labeling template were calculated from 60 smokers and 60 nonsmokers, and served as input features to the classification procedure. The classifier achieved the highest accuracy of 69.6% when taking the 139 highest ranked features via 10-fold cross-validation. Critically, these features were later validated on an independent testing set that consisted of 28 smokers and 28 nonsmokers, yielding a 64.04% accuracy level (binomial P = 0.01). Following classification, exploratory post hoc regression analyses were performed, which revealed that gray matter volumes in the putamen, hippocampus, prefrontal cortex, cingulate cortex, caudate, thalamus, pre-/postcentral gyrus, precuneus, and the parahippocampal gyrus, were inversely related to smoking behavioral characteristics. These results not only indicate that smoking related gray matter alterations can provide predictive power for group membership, but also suggest that machine learning techniques can reveal underlying smoking-related neurobiology., (Published 2015. This article is a U.S. Government work and is in the public domain in the USA.)

Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance., (Copyright © 2012 Wiley Periodicals, Inc.)

Real-time functional magnetic resonance imaging (rtfMRI) has been proposed as a method of providing feedback to develop a participant's ability to control his or her own neuronal activity. However, this BOLD signal is vulnerable to contamination from nonneuronal sources that can also be shaped by the feedback provided. Here we illustrate an artifact found while training participants to control signal from an ROI in the insula. As the artifact was directly behind the eye and the experiment used an echo-planar imaging (EPI) sequence with phase encoding direction that included the orbits and the insula in the same line, we hypothesized that the artifact was due to eye motion. We demonstrate a reduced training effect when eyeball signal is regressed out of the data and reproduce the artifact with block design voluntary eye movement. Further, using independent components analysis on historical data, we find the artifact is common in BOLD data, but typically not task-correlated, even in tasks where one might expect differing amounts of eye movement in the active task blocks. The artifact, thus, does not significantly impact group results in typical fMRI experiments. Finally, we demonstrate this particular artifact can be avoided in rtfMRI experiments by ensuring that the phase encoding direction does not project any eye movement related artifact onto the ROI being used for feedback training. Our findings underscore the importance of taking great care in designing rtfMRI feedback procedures to avoid contamination with nonneuronal sources of BOLD signal alteration., (Copyright © 2010 Wiley-Liss, Inc.)