Extended-release naltrexone modulates brain response to drug cues in abstinent heroin-dependent patients.

Drug cues play an important role in relapse to drug use. Naltrexone is an opioid antagonist that is used to prevent relapse in opioid dependence. Central opioidergic pathways may be implicated in the heightened drug cue-reactivity, but the effects of the opioid receptors' blockade on the brain responses to drug cues in opioid dependence are unknown. To pursue this question, we studied 17 abstinent i.v. heroin users with brain functional magnetic resonance imaging (fMRI) during exposure to visual heroin-related cues and matched neutral images before and 10-14 days after an injection of extended-release naltrexone (XRNTX). Whole brain analysis of variance of fMRI data showed main effect of XRNTX in the medial frontal gyrus, precentral gyrus, cuneus, precuneus, caudate and the amygdala. fMRI response was decreased in the amygdala, cuneus, caudate and the precentral gyrus and increased in the medial frontal gyrus and the precuneus. Higher plasma levels of naltrexone's major metabolite, 6-beta-naltrexol, were associated with larger reduction in the fMRI response to drug cues after XRNTX in the precentral, caudate and amygdala clusters. The present data suggest that XRNTX pharmacotherapy of opioid-dependent patients may, respectively, decrease and potentiate prefrontal and limbic cortical responses to drug cues and that this effect might be related to the XRNTX metabolism. Our findings call for further evaluation of the brain fMRI response to drug-related cues and of the 6-beta-naltrexol levels as potential biomarkers of XRNTX therapeutic effects in patients with opioid dependence.

Acute effect of methadone maintenance dose on brain FMRI response to heroin-related cues.

OBJECTIVE: Environmental drug-related cues have been implicated as a cause of illicit heroin use during methadone maintenance treatment of heroin dependence. The authors sought to identify the functional neuroanatomy of the brain response to visual heroin-related stimuli in methadone maintenance patients. METHOD: Event-related functional magnetic resonance imaging was used to compare brain responses to heroin-related stimuli and matched neutral stimuli in 25 patients in methadone maintenance treatment. Patients were studied before and after administration of their regular daily methadone dose. RESULTS: The heightened responses to heroin-related stimuli in the insula, amygdala, and hippocampal complex, but not the orbitofrontal and ventral anterior cingulate cortices, were acutely reduced after administration of the daily methadone dose. CONCLUSIONS: The medial prefrontal cortex and the extended limbic system in methadone maintenance patients with a history of heroin dependence remains responsive to salient drug cues, which suggests a continued vulnerability to relapse. Vulnerability may be highest at the end of the 24-hour interdose interval.

Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx.

Arterial spin labeling (ASL) perfusion fMRI data differ in important respects from the more familiar blood oxygen level-dependent (BOLD) fMRI data and require specific processing strategies. In this paper, we examined several factors that may influence ASL data analysis, including data storage bit resolution, motion correction, preprocessing for cerebral blood flow (CBF) calculations and nuisance covariate modeling. Continuous ASL data were collected at 3 T from 10 subjects while they performed a simple sensorimotor task with an epoch length of 48 s. These data were then analyzed using systematic variations of the factors listed above to identify the approach that yielded optimal signal detection for task activation. Improvements in statistical power were found for use of at least 10 bits for data storage at 3 T. No significant difference was found in motor cortex regarding using simple subtraction or sinc subtraction, but the former presented minor but significantly (P<.024) larger peak t value in visual cortex. While artifactual head motion patterns were observed in synthetic data and background-suppressed ASL data when label/control images were realigned to a common target, independent realignment of label and control images did not yield significant improvements in activation in the sensorimotor data. It was also found that CBF calculations should be performed prior to spatial normalization and that modeling of global fluctuations yielded significantly increased peak t value in motor cortex. The implementation of all ASL data processing approaches is easily accomplished within an open-source toolbox, ASLtbx, and is advocated for most perfusion fMRI data sets.

Strategies for reducing large fMRI data sets for independent component analysis.

In independent component analysis (ICA), principal component analysis (PCA) is generally used to reduce the raw data to a few principal components (PCs) through eigenvector decomposition (EVD) on the data covariance matrix. Although this works for spatial ICA (sICA) on moderately sized fMRI data, it is intractable for temporal ICA (tICA), since typical fMRI data have a high spatial dimension, resulting in an unmanageable data covariance matrix. To solve this problem, two practical data reduction methods are presented in this paper. The first solution is to calculate the PCs of tICA from the PCs of sICA. This approach works well for moderately sized fMRI data; however, it is highly computationally intensive, even intractable, when the number of scans increases. The second solution proposed is to perform PCA decomposition via a cascade recursive least squared (CRLS) network, which provides a uniform data reduction solution for both sICA and tICA. Without the need to calculate the covariance matrix, CRLS extracts PCs directly from the raw data, and the PC extraction can be terminated after computing an arbitrary number of PCs without the need to estimate the whole set of PCs. Moreover, when the whole data set becomes too large to be loaded into the machine memory, CRLS-PCA can save data retrieval time by reading the data once, while the conventional PCA requires numerous data retrieval steps for both covariance matrix calculation and PC extractions. Real fMRI data were used to evaluate the PC extraction precision, computational expense, and memory usage of the presented methods.

Support vector machine learning-based fMRI data group analysis.

To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.

Boost up the detection sensitivity of ASL perfusion fMRI through support vector machine.

Data analysis is challenging in arterial spin labeling (ASL) perfusion fMRI due to the intrinsic low SNR of ASL data. To boost up the detection sensitivity, this paper presented a multivariate method based group analysis approach to analyze ASL perfusion fMRI data. A spatial discriminance map (SDM) was first extracted for each subject by support vector machine learning (SVM) algorithm; a population inference about the discriminance was then given by a random effect analysis (RFX) on these individual SDMs. Evaluations were performed using 7 subjects' fingertapping ASL perfusion fMRI data, yielding similar activation patterns with enhanced sensitivity compared to the standard GLM based group analysis.

CRLS-PCA based independent component analysis for fMRI study.

Data reduction through conventional principal component analysis is impractical for temporal independent component analysis (tICA) on fMRI data, since the data covariance matrix is too huge to be manipulated. It is also computationally intensive for spatial ICA (sICA) on long time fMRI scans. To solve this problem, a cascade recursive least squared networks based PCA (CRLS-PCA) was used to reduce the fMRI data in this paper. Without the need to compute data covariance matrix CRLS-PCA can extract arbitrary number of PCs directly from the original data, which simultaneously saves time for data reduction. Experiment results were given to evaluate the performance of CRLS-PCA based tICA and sICA in fMRI study.