Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology.

Emerging brain connectivity network studies suggest that interactions between various distributed neuronal populations may be characterized by an organized complex topological structure. Many neuropsychiatric disorders are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to detect group-level differentially expressed connectome patterns from the massive neuroimaging data. Recently, statistical methods have been developed to detect differentially expressed connectivity features at a subnetwork level, extending more commonly applied edge level analysis. However, the graph topological structures in these methods are limited to community/cliques which may not effectively uncover the underlying complex and disease-related brain circuits/subnetworks. Building on these previous subnetwork detection methods, a new statistical approach is developed to automatically identify the latent differentially expressed brain connectivity subnetworks with k-partite graph topological structures from large brain connectivity matrices. In addition, statistical inferential techniques are provided to test the detected topological structure. The new methods are evaluated via extensive simulation studies and then applied to resting state fMRI data (24 cases and 18 controls) for Parkinson's disease research. A differentially expressed connectivity network with the k-partite graph topological structure is detected which reveals underlying neural features distinguishing Parkinson's disease patients from healthy control subjects.

A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs.

To establish brain network properties associated with major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (Rs-fMRI) data, we develop a multi-attribute graph model to construct a region-level functional connectivity network that uses all voxel level information. For each region pair, we define the strength of the connectivity as the kernel canonical correlation coefficient between voxels in the two regions; and we develop a permutation test to assess the statistical significance. We also construct a network based classifier for making predictions on the risk of MDD. We apply our method to Rs-fMRI data from 20 MDD patients and 20 healthy control subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Using this method, MDD patients can be distinguished from healthy control subjects based on significant differences in the strength of regional connectivity. We also demonstrate the performance of the proposed method using simulationstudies.

White matter tract covariance patterns predict age-declining cognitive abilities.

UNLABELLED: Previous studies investigating the relationship of white matter (WM) integrity to cognitive abilities and aging have either focused on a global measure or a few selected WM tracts. Ideally, contribution from all of the WM tracts should be evaluated at the same time. However, the high collinearity among WM tracts precludes systematic examination of WM tracts simultaneously without sacrificing statistical power due to stringent multiple-comparison corrections. Multivariate covariance techniques enable comprehensive simultaneous examination of all WM tracts without being penalized for high collinearity among observations. METHOD: In this study, Scaled Subprofile Modeling (SSM) was applied to the mean integrity of 18 major WM tracts to extract covariance patterns that optimally predicted four cognitive abilities (perceptual speed, episodic memory, fluid reasoning, and vocabulary) in 346 participants across ages 20 to 79years old. Using expression of the covariance patterns, age-independent effects of white matter integrity on cognition and the indirect effect of WM integrity on age-related differences in cognition were tested separately, but inferences from the indirect analyses were cautiously made given that cross-sectional data set was used in the analysis. RESULTS: A separate covariance pattern was identified that significantly predicted each cognitive ability after controlling for age except for vocabulary, but the age by WM covariance pattern interaction was not significant for any of the three abilities. Furthermore, each of the patterns mediated the effect of age on the respective cognitive ability. A distinct set of WM tracts was most influential in each of the three patterns. The WM covariance pattern accounting for fluid reasoning showed the most number of influential WM tracts whereas the episodic memory pattern showed the least number. CONCLUSION: Specific patterns of WM tracts make significant contributions to the age-related differences in perceptual speed, episodic memory, and fluid reasoning but not vocabulary. Other measures of brain health will need to be explored to reveal the major influences on the vocabulary ability.

Abnormal reward circuitry in anorexia nervosa: A longitudinal, multimodal MRI study.

Anorexia nervosa (AN) is a debilitating illness and existing interventions are only modestly effective. This study aimed to determine whether AN pathophysiology is associated with altered connections within fronto-accumbal circuitry subserving reward processing. Diffusion and resting-state functional MRI scans were collected in female inpatients with AN (n = 22) and healthy controls (HC; n = 18) between the ages of 16 and 25 years. Individuals with AN were scanned during the acute, underweight phase of the illness and again following inpatient weight restoration. HC were scanned twice over the same timeframe. Based on univariate and multivariate analyses of fronto-accumbal circuitry, underweight individuals with AN were found to have increased structural connectivity (diffusion probabilistic tractography), increased white matter anisotropy (tract-based spatial statistics), increased functional connectivity (seed-based correlation in resting-state fMRI), and altered effective connectivity (spectral dynamic causal modeling). Following weight restoration, fronto-accumbal structural connectivity continued to be abnormally increased bilaterally with large (partial η2 = 0.387; right NAcc-OFC) and moderate (partial η2 = 0.197; left NAcc-OFC) effect sizes. Increased structural connectivity within fronto-accumbal circuitry in the underweight state correlated with severity of eating disorder symptoms. Taken together, the findings from this longitudinal, multimodal neuroimaging study offer converging evidence of atypical fronto-accumbal circuitry in AN. Hum Brain Mapp 37:3835-3846, 2016. © 2016 Wiley Periodicals, Inc.

The NINDS Parkinson's disease biomarkers program.

BACKGROUND: Neuroprotection for Parkinson's disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke has therefore established the Parkinson's Disease Biomarkers Program to promote discovery of PD biomarkers for use in phase II and III clinical trials. METHODS: Using a novel consortium design, the Parkinson's Disease Biomarker Program is focused on the development of clinical and laboratory-based biomarkers for PD diagnosis, progression, and prognosis. Standardized operating procedures and pooled reference samples were created to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the research community for additional biomarker projects. RESULTS: Eleven consortium projects are ongoing, seven of which recruit participants and obtain biosamples. As of October 2014, 1,082 participants have enrolled (620 PD, 101 with other causes of parkinsonism, 23 essential tremor, and 338 controls), 1,040 of whom have at least one biosample. Six thousand eight hundred ninety-eight total biosamples are available from baseline, 6-, 12-, and 18-month visits: 1,006 DNA, 1,661 RNA, 1,419 whole blood, 1,382 plasma, 1,200 serum, and 230 cerebrospinal fluid (CSF). Quality control analysis of plasma, serum, and CSF samples indicates that almost all samples are high quality (24 of 2,812 samples exceed acceptable hemoglobin levels). CONCLUSIONS: By making samples and data widely available, using stringent operating procedures based on existing standards, hypothesis testing for biomarker discovery, and providing a resource that complements existing programs, the Parkinson's Disease Biomarker Program will accelerate the pace of PD biomarker research. © 2015 International Parkinson and Movement Disorder Society.

A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data.

We propose a novel Bayesian hierarchical model for brain imaging data that unifies voxel-level (the most localized unit of measure) and region-level brain connectivity analyses, and yields population-level inferences. Functional connectivity generally refers to associations in brain activity between distinct locations. The first level of our model summarizes brain connectivity for cross-region voxel pairs using a two-component mixture model consisting of connected and nonconnected voxels. We use the proportion of connected voxel pairs to define a new measure of connectivity strength, which reflects the breadth of between-region connectivity. Furthermore, we evaluate the impact of clinical covariates on connectivity between region-pairs at a population level. We perform parameter estimation using Markov chain Monte Carlo (MCMC) techniques, which can be executed quickly relative to the number of model parameters. We apply our method to resting-state functional magnetic resonance imaging (fMRI) data from 32 subjects with major depression and simulated data to demonstrate the properties of our method.

Anti-3-[(18)F]FACBC positron emission tomography-computerized tomography and (111)In-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.

Identifying functional co-activation patterns in neuroimaging studies via poisson graphical models.

Studying the interactions between different brain regions is essential to achieve a more complete understanding of brain function. In this article, we focus on identifying functional co-activation patterns and undirected functional networks in neuroimaging studies. We build a functional brain network, using a sparse covariance matrix, with elements representing associations between region-level peak activations. We adopt a penalized likelihood approach to impose sparsity on the covariance matrix based on an extended multivariate Poisson model. We obtain penalized maximum likelihood estimates via the expectation-maximization (EM) algorithm and optimize an associated tuning parameter by maximizing the predictive log-likelihood. Permutation tests on the brain co-activation patterns provide region pair and network-level inference. Simulations suggest that the proposed approach has minimal biases and provides a coverage rate close to 95% of covariance estimations. Conducting a meta-analysis of 162 functional neuroimaging studies on emotions, our model identifies a functional network that consists of connected regions within the basal ganglia, limbic system, and other emotion-related brain regions. We characterize this network through statistical inference on region-pair connections as well as by graph measures.

Longitudinal Canonical Correlation Analysis.

This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modeled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space. Our numerical simulations showed that the longitudinal canonical correlation analysis (LCCA) effectively recovers underlying correlation patterns between two high-dimensional longitudinal data sets. We applied the proposed LCCA to data from the Alzheimer's Disease Neuroimaging Initiative and identified the longitudinal profiles of morphological brain changes and amyloid cumulation.

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.

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.

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 subject's 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.

Investigation of emission-transmission misalignment artifacts on rubidium-82 cardiac PET with adenosine pharmacologic stress.

PURPOSE: This study was undertaken to determine if artifacts from misalignment of cardiac emission to transmission data is present in adenosine stress studies and if the artifact could be reproduced by intentional misalignment in normal exams. PROCEDURES: Seventy consecutive 82Rb myocardial perfusion studies were reviewed. Utilizing a quality control program, misalignment was assessed. The study was reprocessed after manual realignment to determine if the defect extent changed. Emission and transmission acquisitions in six normal studies also were intentionally misaligned. RESULTS: Twenty of 69 rest studies (29.0%) and 17 of 69 (24.6%) stress studies demonstrated misalignment. In four patients with stress misalignment, there was a significant change in clinical interpretation. Upon intentionally misaligning six normal studies, a lateral wall defect was reproduced. CONCLUSIONS: Emission-transmission misalignment occurs in 29.0% and 24.6% of 82Rb rest and adenosine stress studies, respectively. While there is a positive correlation of artifactual defects with misalignment, the presence and size of artifacts is variable and unpredictable at seemingly lesser degrees of misalignment.

A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities.

Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The estimates of our model parameters are based on samples drawn from the joint posterior distribution using Markov Chain Monte Carlo (MCMC) methods. We evaluate our method by examining the prediction accuracy rates based on leave-one-out cross validation, and we employ an importance sampling strategy to reduce the computation time. We conduct both whole-brain and voxel-level prediction and identify the brain regions that are highly associated with the disease based on the voxel-level prediction results. We apply our model to multimodal brain imaging data from a study of Parkinson's disease. We achieve extremely high accuracy, in general, and our model identifies key regions contributing to accurate prediction including caudate, putamen, and fusiform gyrus as well as several sensory system regions.

On the Reliability of Individual Brain Activity Networks.

There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH2 network construction method outperforms other approaches at most data spatiotemporal resolutions.

Connectivity in fMRI: Blind Spots and Breakthroughs.

In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.

Cortical Thinning and Cognitive Impairment in Parkinson's Disease without Dementia.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized clinically by motor dysfunction (bradykinesia, rigidity, tremor, and postural instability), and pathologically by the loss of dopaminergic neurons in the substantia nigra of the basal ganglia. Growing literature supports that cognitive deficits may also be present in PD, even in non-demented patients. Gray matter (GM) atrophy has been reported in PD and may be related to cognitive decline. This study investigated cortical thickness in non-demented PD subjects and elucidated its relationship to cognitive impairment using high-resolution T1-weighted brain MRI and comprehensive cognitive function scores from 71 non-demented PD and 48 control subjects matched for age, gender, and education. Cortical thickness was compared between groups using a flexible hierarchical multivariate Bayesian model, which accounts for correlations between brain regions. Correlation analyses were performed among brain areas and cognitive domains as well, which showed significant group differences in the PD population. Compared to Controls, PD subjects demonstrated significant age-adjusted cortical thinning predominantly in inferior and superior parietal areas and extended to superior frontal, superior temporal, and precuneus areas (posterior probability >0.9). Cortical thinning was also found in the left precentral and lateral occipital, and right postcentral, middle frontal, and fusiform regions (posterior probability >0.9). PD patients showed significantly reduced cognitive performance in executive function, including set shifting (p = 0.005) and spontaneous flexibility (p = 0.02), which were associated with the above cortical thinning regions (p < 0.05).

Initial experience with the radiotracer anti-1-amino-3-18F-fluorocyclobutane-1-carboxylic acid with PET/CT in prostate carcinoma.

UNLABELLED: Conventional imaging techniques have serious limitations in the detection, staging, and restaging of prostate carcinoma. Anti-1-amino-3-(18)F-fluorocyclobutane-1-carboxylic acid (anti-(18)F-FACBC)is a synthetic l-leucine analog that has excellent in vitro uptake within the DU-145 prostate carcinoma cell line and orthotopically implanted prostate tumor in nude rats. There is little renal excretion compared with (18)F-FDG. The present study examines anti-(18)F-FACBC uptake in patients with newly diagnosed and recurrent prostate carcinoma. METHODS: Fifteen patients with a recent diagnosis of prostate carcinoma (n = 9) or suspected recurrence (n = 6) underwent 65-min dynamic PET/CT of the pelvis after intravenous injection of 300-410 MBq anti-(18)F-FACBC followed by static body images. Each study was evaluated qualitatively and quantitatively. Maximum standardized uptake values were recorded in the prostate or prostate bed, and within lymph nodes at 4.5 min (early) and 20 min (delayed), and correlated with clinical, imaging and pathologic follow-up. Time-activity curves were also generated for benign and malignant tissue. RESULTS: In the 8 patients with newly diagnosed prostate carcinoma who underwent dynamic scanning, visual analysis correctly identified the presence or absence of focal neoplastic involvement in 40 of 48 prostate sextants. Pelvic nodal status correlated with anti-(18)F-FACBC findings in 7 of 9 patients and was indeterminate in 2 of 9. In all 4 patients in whom there was proven recurrence, visual analysis was successful in identifying disease (1 prostate bed, 3 extraprostatic). In 3 of these patients, (111)In-capromab-pendetide had no significant uptake at nodal and skeletal foci. Malignant lymph node uptake in both the staging and restaging patients was significantly higher than benign nodal uptake. Though uptake faded with time, in all 6 patients with either lymph node metastases or recurrent prostate bed carcinoma, there was intense persistent uptake at 65 min. CONCLUSION: Anti-(18)F-FACBC is a promising radiotracer for imaging prostate carcinoma. Radiotracer uptake was demonstrated in primary and metastatic disease. Future research should investigate the mechanism of radiotracer uptake in normal and pathologic tissue and develop a clinical imaging strategy for initial staging and restaging.

Statistical approaches to functional neuroimaging data.

The field of statistics makes valuable contributions to functional neuroimaging research by establishing procedures for the design and conduct of neuroimaging experiments and providing tools for objectively quantifying and measuring the strength of scientific evidence provided by the data. Two common functional neuroimaging research objectives include detecting brain regions that reveal task-related alterations in measured brain activity (activations) and identifying highly correlated brain regions that exhibit similar patterns of activity over time (functional connectivity). This article highlights various statistical procedures for analyzing data from activation studies and functional connectivity studies, focusing on functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data. Also discussed are emerging statistical methods for prediction using fMRI and PET data, which stand to increase the translational significance of functional neuroimaging data to clinical practice.

Parkinson's disease biomarkers: perspective from the NINDS Parkinson's Disease Biomarkers Program.

Biomarkers for Parkinson's disease (PD) diagnosis, prognostication and clinical trial cohort selection are an urgent need. While many promising markers have been discovered through the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) and other mechanisms, no single PD marker or set of markers are ready for clinical use. Here we discuss the current state of biomarker discovery for platforms relevant to PDBP. We discuss the role of the PDBP in PD biomarker identification and present guidelines to facilitate their development. These guidelines include: harmonizing procedures for biofluid acquisition and clinical assessments, replication of the most promising biomarkers, support and encouragement of publications that report negative findings, longitudinal follow-up of current cohorts including the PDBP, testing of wearable technologies to capture readouts between study visits and development of recently diagnosed (de novo) cohorts to foster identification of the earliest markers of disease onset.