F. DuBois Bowman

The Neural Correlates of Social Anxiety Disorder and Response to Pharmacotherapy

This study attempted to define further the neural processing events underlying social anxiety in patients with social anxiety disorder (SAD) and their response to pharmacotherapy. Social anxiety-related changes in regional cerebral blood flow were defined by [15O]H2 positron emission tomography (PET) in medication-free individuals with generalized SAD (gSAD), and age- and sex-matched comparison subjects, and analyzed using a linear mixed effects model. PET studies were again acquired in the gSAD individuals following an 8-week, flexible dose treatment trial of nefazodone. Both script-guided mental imagery of an anxiogenic social situation and a confrontational mental arithmetic task were associated with marked increases in self-rated anxiety in both subject groups. For gSAD subjects, social anxiety induced by guided mental imagery was associated with increased activity in the left postcentral gyrus and lenticulate, and the right inferior frontal and middle temporal gyri. Social anxiety induced by the mental arithmetic task was associated with activation of the medial and left dorsolateral prefrontal cortex, cerebellum, thalamus, insula, and ventral striatum. Both tasks were associated with relative decreases in activity in the right amygdala and the hippocampus. A direct group comparison indicated that comparison subjects exhibited a differing pattern of social anxiety-related neural activations. Nefazodone treatment was associated with marked clinical improvement. Comparison of social anxiety-related neural activations prior to and after nefazodone administration indicated greater activity in the precentral gyrus, insula, midbrain/hypothalamus, and middle frontal and anterior cingulate gyrus prior to treatment, and greater activity in the left middle occipital and bilateral lingual gyri, postcentral gyrus, gyrus rectus, and hippocampus after treatment. The results of an analysis relating neural activity and treatment-related changes in symptom severity indicated differential neural responses associated with states of symptom remission vs partial response. The observed social anxiety-related changes in distributed neural activity are consistent with cognitive models of SAD and adaptive decreases in amygdala activity in response to social anxiogenics, and support the association of altered frontal cortical responses with treatment response.

Detection of Recurrent Prostate Carcinoma with anti-1-Amino-3-18F-Fluorocyclobutane-1-Carboxylic Acid PET/CT and 111In–Capromab Pendetide SPECT/CT

PURPOSE To compare the diagnostic performance of the synthetic amino acid analog radiotracer anti-1-amino-3-fluorine 18-fluorocyclobutane-1-carboxylic acid (anti-3-(18)F-FACBC) with that of indium 111 ((111)In)-capromab pendetide in the detection of recurrent prostate carcinoma. MATERIALS AND METHODS This prospective study was approved by the institutional review board and complied with HIPAA guidelines. Written informed consent was obtained. Fifty patients (mean age, 68.3 years ± 8.1 [standard deviation]; age range, 50-90 years) were included in the study on the basis of the following criteria: (a) Recurrence of prostate carcinoma was suspected after definitive therapy for localized disease, (b) bone scans were negative, and (c) anti-3-(18)F-FACBC positron emission tomography (PET)/computed tomography (CT) and (111)In-capromab pendetide single photon emission computed tomography (SPECT)/CT were performed within 6 weeks of each other. Studies were evaluated by two experienced interpreters for abnormal uptake suspicious for recurrent disease in the prostate bed and extraprostatic locations. The reference standard was a combination of tissue correlation, imaging, laboratory, and clinical data. Diagnostic performance measures were calculated and tests of the statistical significance of differences determined by using the McNemar χ(2) test as well as approximate tests based on the difference between two proportions. RESULTS For disease detection in the prostate bed, anti-3-(18)F-FACBC had a sensitivity of 89% (32 of 36 patients; 95% confidence interval [CI]: 74%, 97%), specificity of 67% (eight of 12 patients; 95% CI: 35%, 90%), and accuracy of 83% (40 of 48 patients; 95% CI: 70%, 93%). (111)In-capromab pendetide had a sensitivity of 69% (25 of 36 patients; 95% CI: 52%, 84%), specificity of 58% (seven of 12 patients; 95% CI: 28%, 85%), and accuracy of 67% (32 of 48 patients; 95% CI: 52%, 80%). In the detection of extraprostatic recurrence, anti-3-(18)F-FACBC had a sensitivity of 100% (10 of 10 patients; 95% CI: 69%, 100%), specificity of 100% (seven of seven patients; 95% CI: 59%, 100%), and accuracy of 100% (17 of 17 patients; 95% CI: 80%, 100%). (111)In-capromab pendetide had a sensitivity of 10% (one of 10 patients; 95% CI: 0%, 45%), specificity of 100% (seven of seven patients; 95% CI: 59%, 100%), and accuracy of 47% (eight of 17 patients; 95% CI: 23%, 72%). CONCLUSION anti-3-(18)F-FACBC PET/CT was more sensitive than (111)In-capromab pendetide SPECT/CT in the detection of recurrent prostate carcinoma and is highly accurate in the differentiation of prostatic from extraprostatic disease. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11102023/-/DC1.

A Bayesian approach to determining connectivity of the human brain

Recent work regarding the analysis of brain imaging data has focused on examining functional and effective connectivity of the brain. We develop a novel descriptive and inferential method to analyze the connectivity of the human brain using functional MRI (fMRI). We assess the relationship between pairs of distinct brain regions by comparing expected joint and marginal probabilities of elevated activity of voxel pairs through a Bayesian paradigm, which allows for the incorporation of previously known anatomical and functional information. We define the relationship between two distinct brain regions by measures of functional connectivity and ascendancy. After assessing the relationship between all pairs of brain voxels, we are able to construct hierarchical functional networks from any given brain region and assess significant functional connectivity and ascendancy in these networks. We illustrate the use of our connectivity analysis using data from an fMRI study of social cooperation among women who played an iterated “Prisoners Dilemma” game. Our analysis reveals a functional network that includes the amygdala, anterior insula cortex, and anterior cingulate cortex, and another network that includes the ventral striatum, orbitofrontal cortex, and anterior insula. Our method can be used to develop causal brain networks for use with structural equation modeling and dynamic causal models. Hum Brain Mapp, 2005.

A Bayesian Hierarchical Framework for Spatial Modeling of fMRI Data

Applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric, neurological, and substance abuse disorders and their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. Complementary approaches consider the ensemble of voxels constituting an anatomically defined region of interest (ROI) or summary statistics, such as means or quantiles, of the ROI. In this work, we present a Bayesian extension of voxel-level analyses that offers several notable benefits. Among these, it combines whole-brain voxel-by-voxel modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance matrix for regional mean parameters allows for the study of inter-regional (long-range) correlations, and the model employs an exchangeable correlation structure to capture intra-regional (short-range) correlations. Estimation is performed using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling. We apply our Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimers disease.

Anti-3-[18F]FACBC Positron Emission Tomography-Computerized Tomography and 111In-Capromab Pendetide Single Photon Emission Computerized Tomography-Computerized Tomography for Recurrent Prostate Carcinoma: Results of a Prospective Clinical Trial

PURPOSE We prospectively evaluated the amino acid analogue positron emission tomography radiotracer anti-3-[(18)F]FACBC compared to ProstaScint® ((111)In-capromab pendetide) single photon emission computerized tomography-computerized tomography to detect recurrent prostate carcinoma. MATERIALS AND METHODS A total of 93 patients met study inclusion criteria who underwent anti-3-[(18)F]FACBC positron emission tomography-computerized tomography plus (111)In-capromab pendetide single photon emission computerized tomography-computerized tomography for suspected recurrent prostate carcinoma within 90 days. Reference standards were applied by a multidisciplinary board. We calculated diagnostic performance for detecting disease. RESULTS In the 91 of 93 patients with sufficient data for a consensus on the presence or absence of prostate/bed disease anti-3-[(18)F]FACBC had 90.2% sensitivity, 40.0% specificity, 73.6% accuracy, 75.3% positive predictive value and 66.7% negative predictive value compared to (111)In-capromab pendetide with 67.2%, 56.7%, 63.7%, 75.9% and 45.9%, respectively. In the 70 of 93 patients with a consensus on the presence or absence of extraprostatic disease anti-3-[(18)F]FACBC had 55.0% sensitivity, 96.7% specificity, 72.9% accuracy, 95.7% positive predictive value and 61.7% negative predictive value compared to (111)In-capromab pendetide with 10.0%, 86.7%, 42.9%, 50.0% and 41.9%, respectively. Of 77 index lesions used to prove positivity histological proof was obtained in 74 (96.1%). Anti-3-[(18)F]FACBC identified 14 more positive prostate bed recurrences (55 vs 41) and 18 more patients with extraprostatic involvement (22 vs 4). Anti-3-[(18)F]FACBC positron emission tomography-computerized tomography correctly up-staged 18 of 70 cases (25.7%) in which there was a consensus on the presence or absence of extraprostatic involvement. CONCLUSIONS Better diagnostic performance was noted for anti-3-[(18)F]FACBC positron emission tomography-computerized tomography than for (111)In-capromab pendetide single photon emission computerized tomography-computerized tomography for prostate carcinoma recurrence. The former method detected significantly more prostatic and extraprostatic disease.

Interaction of Isoflurane with the Dopamine Transporter

Background Isoflurane administration is known to increase extracellular dopamine (DA) concentration. Because the dopamine transporter (DAT) is a key regulator of DA, it is likely affected by isoflurane. This study investigates the hypothesis that isoflurane inhibits DA reuptake by causing DAT to be trafficked into the cell. Methods Rhesus monkeys were scanned with positron emission tomography (PET) using [18F]FECNT (a highly specific DAT ligand) while anesthetized with 1% isoflurane. The isoflurane was increased to 2%, and the animals were rescanned. Uptake was analyzed with the tissue reference method using the cerebellum as the reference tissue to determine the binding potential in the putamen. Immunohistochemistry and Western blot analyses were performed in vivo in rats to determine if isoflurane administration would change the total amount of DAT. Rats breathed air plus 2% isoflurane for 30 min, and then striatal DAT assays were rapidly performed. In vitro immunocytochemistry experiments were performed using human embryonic kidney (HEK) cells stably transfected with human DAT. The cells were exposed to 4% isoflurane for 1 h while the location of DAT was observed with fluorescent confocal microscopy. Results The [18F]FECNT binding potential in rhesus monkeys decreased by 63 ± 6% (SEM, n = 5) when isoflurane was increased from 1 to 2% as compared with no significant change (0.7 ± 2.5%; SEM, n = 5) when the isoflurane concentration was not changed (P < 0.001). No difference in DAT staining between isoflurane-treated and control rats was apparent from visual inspection, and quantitative Western blot analyses showed no significant change in total DAT protein. After isoflurane treatment, focal puncta of intense fluorescence was visible inside the HEK cells. Conclusions The in vitro experiments indicate that DAT is trafficked into the cell by isoflurane without changing the total amount of DAT in the striatum. The PET data are consistent with this finding, provided that intracellular DAT acquires a conformation that has low affinity for [18F]FECNT. Thus, [18F]FECNT appears to be an excellent agent for measuring plasma membrane-expressed DAT and evaluating DAT trafficking in vivo.

Determining functional connectivity using fMRI data with diffusion-based anatomical weighting.

There is strong interest in investigating both functional connectivity (FC) using functional magnetic resonance imaging (fMRI) and structural connectivity (SC) using diffusion tensor imaging (DTI). There is also emerging evidence of correspondence between functional and structural pathways within many networks (Greicius, et al., 2009; Skudlarski et al., 2008; van den Heuvel et al., 2009), although some regions without SC exhibit strong FC (Honey et al., 2008). These findings suggest that FC may be mediated by (direct or indirect) anatomical connections, offering an opportunity to supplement fMRI data with DTI data when determining FC. We develop a novel statistical method for determining FC, called anatomically weighted FC (awFC), which combines fMRI and DTI data. Our awFC approach implements a hierarchical clustering algorithm that establishes neural processing networks using a new distance measure consisting of two components, a primary functional component that captures correlations between fMRI signals from different regions and a secondary anatomical weight reflecting probabilities of SC. The awFC approach defaults to conventional unweighted clustering for specific parameter settings. We optimize awFC parameters using a strictly functional criterion, therefore our approach will generally perform at least as well as an unweighted analysis, with respect to intracluster coherence or autocorrelation. AwFC also yields more informative results since it provides structural properties associated with identified functional networks. We apply awFC to two fMRI data sets: resting-state data from 6 healthy subjects and data from 17 subjects performing an auditory task. In these examples, awFC leads to more highly autocorrelated networks than a conventional analysis. We also conduct a simulation study, which demonstrates accurate performance of awFC and confirms that awFC generally yields comparable, if not superior, accuracy relative to a standard approach.

Predicting the brain response to treatment using a Bayesian hierarchical model with application to a study of schizophrenia

In vivo functional neuroimaging, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), is becoming increasingly important in defining the pathophysiology of psychiatric disorders such as schizophrenia, major depression, and Alzheimers disease. Furthermore, recent studies have begun to investigate the possibility of using functional neuroimaging to guide treatment selection for individual patients. By studying the changes between a patients pre‐ and post‐treatment brain activity, investigators are gaining insights into the impact of treatment on behavior‐related neural processing traits associated with particular psychiatric disorders. Furthermore, these studies may shed light on the neural basis of response and nonresponse to specific treatments. The practical limitation of such studies is that the post‐treatment scans offer little guidance to treatment selection in clinical settings, since treatment decisions precede the availability of post‐treatment brain scans. This shortcoming represents the impetus for developing statistical methodology that would provide clinicians with predictive information concerning the effect of treatment on brain activity and, ultimately, symptom‐related behaviors. We present a prediction algorithm that uses a patients pretreatment scans, coupled with relevant patient characteristics, to forecast the patients brain activity following a specified treatment regimen. We derive our predictive method from a Bayesian hierarchical model constructed on the pre‐ and post‐treatment scans of designated training data. We perform estimation using the expectation–maximization algorithm. We evaluate the accuracy of our proposed prediction method using K‐fold cross‐validation, quantifying the error using two new measures that we propose for neuroimaging data. The proposed method is applicable to both PET and fMRI studies. We illustrate its use with a PET study of working memory in patients with schizophrenia and an fMRI data example is also provided. Hum Brain Mapp 29:1092–1109, 2007.

Modeling the spatial and temporal dependence in FMRI data.

Functional magnetic resonance imaging (fMRI) data sets are large and characterized by complex dependence structures driven by highly sophisticated neurophysiology and aspects of the experimental designs. Typical analyses investigating task-related changes in measured brain activity use a two-stage procedure in which the first stage involves subject-specific models and the second-stage specifies group (or population) level parameters. Customarily, the first-level accounts for temporal correlations between the serial scans acquired during one scanning session. Despite accounting for these correlations, fMRI studies often include multiple sessions and temporal dependencies may persist between the corresponding estimates of mean neural activity. Further, spatial correlations between brain activity measurements in different locations are often unaccounted for in statistical modeling and estimation. We propose a two-stage, spatio-temporal, autoregressive model that simultaneously accounts for spatial dependencies between voxels within the same anatomical region and for temporal dependencies between a subjects estimates from multiple sessions. We develop an algorithm that leverages the special structure of our covariance model, enabling relatively fast and efficient estimation. Using our proposed method, we analyze fMRI data from a study of inhibitory control in cocaine addicts.

Determining Significant Connectivity by 4D Spatiotemporal Wavelet Packet Resampling of Functional Neuroimaging Data

An active area of neuroimaging research involves examining functional relationships between spatially remote brain regions. When determining whether two brain regions exhibit significant correlation due to true functional connectivity, one must account for the background spatial correlation inherent in neuroimaging data. We define background correlation as spatiotemporal correlation in the data caused by factors other than neurophysiologically based functional associations such as scanner induced correlations and image preprocessing. We develop a 4D spatiotemporal wavelet packet resampling method which generates surrogate data that preserves only the average background spatial correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships. We also extend an amplitude adjustment algorithm which adjusts our surrogate data to closely match the amplitude distribution of the original data. Our method improves upon existing wavelet-based methods and extends them to 4D. We apply our resampling technique to determine significant functional connectivity from resting state and motor task fMRI datasets.