In preclinical models of alcohol use disorder, the corticotropin-releasing factor (CRF) receptor is upregulated, particularly in the extended amygdala. This upregulation is thought to play a role in stress-induced relapse to drinking by a mechanism that is independent of the hypothalamic-pituitary-adrenal axis. As part of a double-blind, placebo-controlled clinical study with pexacerfont, a selective, orally available, and brain-penetrant CRF1 receptor antagonist which has anti-anxiety effects in preclinical studies, we examined the effect of pexacerfont on the neural response to a social stress task adapted to fMRI. Subjects were 39 individuals (4 women) with high trait anxiety and moderate to severe alcohol use disorder randomized to receive pexacerfont or placebo. The task involved feedback of videoclips of an individual performing the Trier Social Stress Test. Pexacerfont had no effect on the neural response to self-observation under stress. The neural response to viewing oneself under stress vs an unknown other under stress activated prefrontal brain regions including insula, inferior frontal gyrus as well as medial, superior frontal gyri. These regions of activation overlap with those found in studies using similar paradigms. Potential applications of this task to probe neurocircuitry that is disrupted in addiction is discussed.

Extensive preclinical data implicate corticotropin-releasing hormone (CRH), acting through its CRH1 receptor, in stress- and dependence-induced alcohol seeking. We evaluated pexacerfont, an orally available, brain penetrant CRH1 antagonist for its ability to suppress stress-induced alcohol craving and brain responses in treatment seeking alcohol-dependent patients in early abstinence. Fifty-four anxious alcohol-dependent participants were admitted to an inpatient unit at the NIH Clinical Center, completed withdrawal treatment, and were enrolled in a double-blind, randomized, placebo-controlled study with pexacerfont (300 mg/day for 7 days, followed by 100 mg/day for 23 days). After reaching steady state, participants were assessed for alcohol craving in response to stressful or alcohol-related cues, neuroendocrine responses to these stimuli, and functional magnetic resonance imaging (fMRI) responses to alcohol-related stimuli or stimuli with positive or negative emotional valence. A separate group of 10 patients received open-label pexacerfont following the same dosing regimen and had cerebrospinal fluid sampled to estimate central nervous system exposure. Pexacerfont treatment had no effect on alcohol craving, emotional responses, or anxiety. There was no effect of pexacerfont on neural responses to alcohol-related or affective stimuli. These results were obtained despite drug levels in cerebrospinal fluid (CSF) that predict close to 90% central CRH1 receptor occupancy. CRH1 antagonists have been grouped based on their receptor dissociation kinetics, with pexacerfont falling in a category characterized by fast dissociation. Our results may indicate that antagonists with slow offset are required for therapeutic efficacy. Alternatively, the extensive preclinical data on CRH1 antagonism as a mechanism to suppress alcohol seeking may not translate to humans.

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function.

Analysis of functional MRI (fMRI) blood oxygenation level dependent (BOLD) data is typically carried out in the time domain where the data has a high temporal correlation. These analyses usually employ parametric models of the hemodynamic response function (HRF) where either pre-whitening of the data is attempted or autoregressive (AR) models are employed to model the noise. Statistical analysis then proceeds via regression of the convolution of the HRF with the input stimuli. This approach has limitations when considering that the time series collected are embedded in a brain image in which the AR model order may vary and pre-whitening techniques may be insufficient for handling faster sampling times. However fMRI data can be analyzed in the Fourier domain where the assumptions made as to the structure of the noise can be less restrictive and hypothesis tests are straightforward for single subject analysis, especially useful in a clinical setting. This allows for experiments that can have both fast temporal sampling and event-related designs where stimuli can be closely spaced in time. Equally important, statistical analysis in the Fourier domain focuses on hypothesis tests based on nonparametric estimates of the hemodynamic transfer function (HRF in the frequency domain). This is especially important for experimental designs involving multiple states (drug or stimulus induced) that may alter the form of the response function. In this context a univariate general linear model in the Fourier domain has been applied to analyze BOLD data sampled at a rate of 400 ms from an experiment that used a two-way ANOVA design for the deterministic stimulus inputs with inter-stimulus time intervals chosen from Poisson distributions of equal intensity.

The general linear model (GLM) has been extensively applied to fMRI data in the time domain. However, traditionally time series data can be analyzed in the Fourier domain where the assumptions made as to the noise in the signal can be less restrictive and statistical tests are mathematically more rigorous. A complex form of the GLM in the Fourier domain has been applied to the analysis of fMRI (BOLD) data. This methodology has a number of advantages over temporal methods: 1. Noise in the fMRI data is modeled more generally and closer to that actually seen in the data. 2. Any input function is allowed regardless of the timing. 3. Non-parametric estimation of the transfer functions at each voxel are possible. 4. Rigorous statistical inference of single subjects is possible. This is demonstrated in the analysis of an experimental design with random exponentially distributed stimulus inputs (a two way ANOVA design with input stimuli images of alcohol, non-alcohol beverage and positive or negative images) sampled at 400 milliseconds. This methodology applied to a pair of subjects showed precise and interesting results (e.g. alcoholic beverage images attenuate the response of negative images in an alcoholic as compared to a control subject).

A linear time invariant model is applied to functional fMRI blood flow data. This model assumes that the fMRI stochastic output sequence can be determined by a constant plus a linear filter (hemodynamic response function) of several fixed deterministic inputs and an error term, assumed stationary with zero mean and error spectrum. An on-off finger tapping experiment was performed where the subject repetitively tapped their fingers for 30 seconds and remained still for 30 seconds. Thirty three disjoint frequency bands, 3 wave numbers wide were chosen to analyze the data. At each band an F- statistical image was constructed to test ((alpha) equals .05/33) whether power from the input signal induced a response in the output signal. Activation was seen at frequency .0154 Hz close to the frequency for maximum power of the input signal, .0167 Hz in the contralateral motor strip and motor cortex. In conclusion, (1) No assumptions are made about the filter. (2) Several different deterministic inputs may be applied. (3) Problems with temporal correlation are avoided by performing the statistics in the Fourier domain. (4) Testing can be performed for differences in the hemodynamic transfer function at different spatial locations under different experimental conditions.

A linear time invariant model is applied to functional fMRI blood flow data. Based on traditional time series analysis, this model assumes that the fMRI stochastic output sequence can be determined by a constant plus a linear filter (hemodynamic response function) of several fixed deterministic inputs and an error term assumed stationary with zero mean. The input function consists of multiple exponential distributed (time delay between images) visual stimuli consisting of negative and erotic images. No a priori assumptions are made about the hemodynamic response function that, in essence, is calculated at each spatial position from the data. The sampling rate for the experiment is 400 ms in order to allow for filtering out higher frequencies associated with the cardiac rate. Since the statistical analysis is carried out in the Fourier domain, temporal correlation problems associated with inference in the time domain are avoided. This formal model easily lends itself to further development based on previously developed statistical techniques.

The Wisconsin Card Sorting Test (WCST) has been argued to be a sensitive indicator of frontal lobe function. However, several recent studies have failed to find a consistent relationship between structural damage to this cortical area and perseveration on the test. In the present study, positron emission tomography (PET) imaging with 18F-fluorodeoxyglucose was used to examine the relationship of regional brain metabolism to perseverative responding on the WCST in patients with a history of closed-head injury. An inverse relationship was found between perseverative responses and metabolism in the right, but not the left, dorsolateral prefrontal cortex and caudate nucleus. Perseverative responding was not related to metabolism in several other regions of the frontal lobes and basal ganglia, including the putamen and the frontal poles bilaterally. These data suggest that the functional integrity of the right dorsolateral frontal-subcortical circuit is critical for WCST performance.

The use of the wavelet transform is explored for the detection of differences between brain functional magnetic resonance images (fMRIs) acquired under two different experimental conditions. The method benefits from the fact that a smooth and spatially localized signal can be represented by a small set of localized wavelet coefficients, while the power of white noise is uniformly spread throughout the wavelet space. Hence, a statistical procedure is developed that uses the imposed decomposition orthogonality to locate wavelet-space partitions with large signal-to-noise ratio (SNR), and subsequently restricts the testing for significant wavelet coefficients to these partitions. This results in a higher SNR and a smaller number of statistical tests, yielding a lower detection threshold compared to spatial-domain testing and, thus, a higher detection sensitivity without increasing type I errors. The multiresolution approach of the wavelet method is particularly suited to applications where the signal bandwidth and/or the characteristics of an imaging modality cannot be well specified. The proposed method was applied to compare two different fMRI acquisition modalities, Differences of the respective useful signal bandwidths could be clearly demonstrated; the estimated signal, due to the smoothness of the wavelet representation, yielded more compact regions of neuroactivity than standard spatial-domain testing.