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Please see the Editorial Comment by Jonathan L. Mezrich discussing this article. Background: In community settings, radiologists commonly function as multispecialty radiologists, interpreting exami...

A growing body of evidence has revealed the potential utility of 5-aminolevulinic acid (5-ALA) as a surgical adjunct in selected lower-grade gliomas. However, a reliable means of identifying which lower-grade gliomas will fluoresce has not been established.To identify clinical and radiological factors predictive of intraoperative fluorescence in intermediate-grade gliomas. In addition, given that higher-grade gliomas are more likely to fluoresce than lower-grade gliomas, we also sought to develop a means of predicting glioma grade.We investigated a cohort of patients with grade II and grade III gliomas who received 5-ALA before resection at a single institution. Using a logistic regression-based model, we evaluated 14 clinical and molecular variables considered plausible determinants of fluorescence. We then distilled the most predictive features to develop a model for predicting both fluorescence and tumor grade. We also explored the relationship between intraoperative fluorescence and diagnostic molecular markers.One hundered seventy-nine subjects were eligible for inclusion. Our logistic regression classifier accurately predicted intraoperative fluorescence in our cohort with 91.9% accuracy and revealed enhancement as the singular variable in determining intraoperative fluorescence. There was a direct relationship between enhancement on MRI and the likelihood of observed fluorescence. Observed fluorescence correlated with MIB-1 index but not with isocitrate dehydrogenase (IDH) status, 1p19q codeletion, or methylguanine DNA methyltransferase promoter methylation.We demonstrate a strong correlation between enhancement on preoperative MRI and the likelihood of visible fluorescence during surgery in patients with intermediate-grade glioma. Our analysis provides a robust method for predicting 5-ALA-induced fluorescence in patients with grade II and grade III gliomas.

RATIONALE AND OBJECTIVES: Glioblastoma image evaluation utilizes MRI contrast-enhanced, T1-weighted and non-contrast T2-weighted FLAIR acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression. MATERIALS AND METHODS: We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRM(rFLAIR+)), decreased (PRM(rFLAIR−)), or unchanged (PRM(rFLAIR0)) rFLAIR intensity. We determined response by rFLAIR between pre-treatment and 10 weeks post-treatment. Risk of disease progression in a subset of subjects (N=26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRM(rFLAIR) between weeks 10 and 20 and continuously until the PRM(rFLAIR+) exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection. RESULTS: Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p

Over the past 2 decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical science. Although Bayesian methods can be an attractive alternative to classical methods for answering certain research questions, they involve a heavy "overhead" (e.g., advanced mathematical methods, complex computations), which pose significant barriers to researchers interested in adding Bayesian methods to their statistical toolbox. To increase the accessibility of Bayesian methods for psychopathology researchers, this article presents a gentle introduction of the Bayesian inference framework and a tutorial on implementation. We first provide a primer on the key concepts of Bayesian inference and major implementation considerations related to Bayesian estimation. We then demonstrate how to apply hierarchical Bayesian modeling (HBM) to experimental psychopathology data. Using a real dataset collected from two clinical groups (schizophrenia and bipolar disorder) and a healthy comparison sample on a psychophysical gaze perception task, we illustrate how to model individual responses and group differences with probability functions respectful of the presumed underlying data-generating process and the hierarchical nature of the data. We provide the code with explanations and the data used to generate and visualize the results to facilitate learning. Finally, we discuss interpretation of the results in terms of posterior probabilities and compare the results with those obtained using a traditional method. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

We prospectively investigated the performance of the prostate-specific membrane antigen (PSMA) ligand 68Ga-PSMA-11 for detecting prostate adenocarcinoma in patients with elevated levels of prostate-specific antigen (PSA) after initial therapy. Methods:68Ga-PSMA-11 hybrid PET was performed on 2,005 patients at the time of biochemically recurrent prostate cancer after radical prostatectomy (RP) (50.8%), definitive radiation therapy (RT) (19.7%), or RP with postoperative RT (PORT) (29.6%). The presence of prostate cancer was assessed qualitatively (detection rate = positivity rate) and quantitatively on a per-patient and per-region basis, creating a disease burden estimate from the presence or absence of local (prostate/prostate bed), nodal (N1: pelvis), and distant metastatic (M1: distant soft tissue and bone) disease. The primary study endpoint was the positive predictive value (PPV) of 68Ga-PSMA-11 PET/CT confirmed by histopathology. Results: After RP, the scan detection rate increased significantly with rising PSA level (44.8% at PSA 10 ng/mL; P

OBJECTIVE: The relative contribution of individual cognitive behavioral therapy (CBT) components to treatment outcomes for child anxiety disorders (CADs) is unclear. Recent meta-analyses suggest that exposure may be the primary active ingredient in CBT for CADs, and that relaxation may be relatively less effective. This brief report tests the hypothesis that exposure-focused CBT (EF-CBT) would outperform a relaxation-based active therapy control (Relaxation Mentorship Training; RMT) for the treatment of CADs. METHOD: Participants were 102 youth with CADs (mean age=11.91, 26 males; 76.4% White, 14.7% Multiracial, 3.9% Black, 3.9% Asian, 0.9% other/do not wish to identify) as part of an ongoing neuroimaging randomized controlled trial. Participants were randomly assigned (ratio 2:1) to receive 12 sessions of EF-CBT (n=70) or RMT (n=32). Clinical improvement was measured at Week 12 (Clinical Global Impression – Improvement scale; CGI-I); treatment response was defined as receiving a rating of ‘very much’ or ‘much improved’ on the CGI-I. Anxiety severity was measured at Weeks 1, 6, 9, 12 (Pediatric Anxiety Rating Scale; PARS). Outcome measures were completed by an independent evaluator unaware of condition. RESULTS: EF-CBT exhibited 2.98 times higher odds of treatment completion than RMT; 13 treatment non-completers were included in analyses. Estimated treatment response rates were higher for EF-CBT (57.3%) than for RMT (19.2%). Longitudinal analyses indicated that EF-CBT was associated with faster and more pronounced anxiety reductions than RMT on the PARS (Hedges’ g=.77). CONCLUSIONS: Results suggest that EF-CBT without relaxation is effective for CADs, and more effective than a relaxation-based intervention.

Background: Gaze perception is a basic building block of social cognition, which is impaired in schizophrenia and contributes to functional outcomes. Few studies, however, have investigated neural underpinnings of gaze perception and their relation to social cognition. We address this gap.Method: We recruited 77 schizophrenia patients and 71 healthy controls, who completed various social-cognition tasks. During fMRI, participants (62 schizophrenia, 54 controls) completed a gaze-perception task, where they judged whether faces with varying gaze angles were self-directed or averted; as a control condition, participants identified stimulus gender. Activation estimates were extracted based on 1) task vs. baseline, 2) gaze-perception vs. gender-identification, 3) parametric modulation by perception of stimuli as self-directed vs. averted, and 4) parametric modulation by stimulus gaze angle. We used latent variable analysis to test associations among diagnostic group, brain activation, gaze perception, and social cognition.Results: Preferential activation to gaze perception was observed throughout dorsomedial prefrontal cortex, superior temporal sulcus, and insula. Activation was modulated by stimulus gaze angle and perception of stimuli as self-directed vs. averted. More precise gaze perception and higher task-related activation were associated with better social cognition. Patients with schizophrenia showed hyperactivation within left pre-/post-central gyrus, which was associated with more precise gaze perception and fewer symptoms and thus may be a compensatory mechanism.Conclusions: Neural and behavioral indices of gaze perception were related to social cognition, across patients and controls. This suggests gaze perception is an important perceptual building block for more complex social cognition. Results are discussed in the context of dimensional psychopathology and clinical heterogeneity.

Parametric response mapping (PRM) is a novel computed tomography (CT) technology that has shown potential for assessment of bronchiolitis obliterans syndrome (BOS) after hematopoietic stem cell transplantation (HCT). The primary aim of this study was to evaluate whether variations in image acquisition under real-world conditions affect the PRM measurements of clinically diagnosed BOS. CT scans were obtained retrospectively from 72 HCT recipients with BOS and graft-versus-host disease from Fred Hutchinson Cancer Research Center, Karolinska Institute, and the University of Michigan. Whole lung volumetric scans were performed at inspiration and expiration using site-specific acquisition and reconstruction protocols. PRM and pulmonary function measurements were assessed. Patients with moderately severe BOS at diagnosis (median forced expiratory volume at 1 second [FEV1] 53.5% predicted) had similar characteristics between sites. Variations in site-specific CT acquisition protocols had a negligible effect on the PRM-derived small airways disease (SAD), that is, BOS measurements. PRM-derived SAD was found to correlate with FEV1% predicted and FEV1/ forced vital capacity (R = -0.236, P = .046; and R = -0.689, P < .0001, respectively), which suggests that elevated levels in the PRM measurements are primarily affected by BOS airflow obstruction and not CT scan acquisition parameters. Based on these results, PRM may be applied broadly for post-HCT diagnosis and monitoring of BOS.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

Background and purpose Astroblastoma is a rare type of glial tumor, histologically classified into two types with different prognoses: high and low grade. We aimed to investigate the CT and MRI findings of astroblastomas by collecting studies with analyzable neuroimaging data and extracting the imaging features useful for tumor grading. Methods We searched for reports of pathologically proven astroblastomas with analyzable neuroimaging data using PubMed, Scopus, and Embase. Sixty-five studies with 71 patients with astroblastomas met the criteria for a systematic review. We added eight patients from our hospital, resulting in a final study cohort of 79 patients. The proportion of high-grade tumors was compared in groups based on the morphology (typical and atypical) using Fisher's exact test. Results High- and low-grade tumors were 35/71 (49.3%) and 36/71 (50.7%), respectively. There was a significant difference in the proportion of high-grade tumors based on the tumor morphology (typical morphology: high-grade = 33/58 [56.9%] vs. atypical morphology, 2/13 [15.4%], p = .012). The reviews of neuroimaging findings were performed using the images included in each article. The articles had missing data due to the heterogeneity of the collected studies. Conclusions Detailed neuroimaging features were clarified, including tumor location, margin status, morphology, CT attenuation, MRI signal intensity, and contrast enhancement pattern. The classification of tumor morphology may help predict the tumor's histological grade, contributing to clinical care and future oncologic research.

We prospectively investigated the performance of the prostate-specific membrane antigen (PSMA) ligand

Summary Statistical modelling of functional magnetic resonance imaging data is challenging as the data are both spatially and temporally correlated. Spatially, measurements are taken at thousands of contiguous regions, called voxels, and temporally measurements are taken at hundreds of time points at each voxel. Recent advances in Bayesian hierarchical modelling have addressed the challenges of spatiotemporal structure in functional magnetic resonance imaging data with models incorporating both spatial and temporal priors for signal and noise. Whereas there has been extensive research on modelling the functional magnetic resonance imaging signal (i.e. the convolution of the experimental design with the functional choice for the haemodynamic response function) and its spatial variability, less attention has been paid to realistic modelling of the temporal dependence that typically exists within the functional magnetic resonance imaging noise, where a low order auto-regressive process is typically adopted. Furthermore, the auto-regressive order is held constant across voxels (e.g. AR(1) at each voxel). Motivated by an event-related functional magnetic resonance imaging experiment, we propose a novel hierarchical Bayesian model with automatic selection of the auto-regressive orders of the noise process that vary spatially over the brain. With simulation studies we show that our model is more statistically efficient and we apply it to our motivating example.

Image-on-image regression analysis, using images to predict images, is a challenging task, due to (1) the high dimensionality and (2) the complex spatial dependence structures in image predictors and image outcomes. In this work, we propose a novel image-on-image regression model, by extending a spatial Bayesian latent factor model to image data, where low-dimensional latent factors are adopted to make connections between high-dimensional image outcomes and image predictors. We assign Gaussian process priors to the spatially varying regression coefficients in the model, which can well capture the complex spatial dependence among image outcomes as well as that among the image predictors. We perform simulation studies to evaluate the out-of-sample prediction performance of our method compared with linear regression and voxel-wise regression methods for different scenarios. The proposed method achieves better prediction accuracy by effectively accounting for the spatial dependence and efficiently reduces image dimensions with latent factors. We apply the proposed method to analysis of multimodal image data in the Human Connectome Project where we predict task-related contrast maps using subcortical volumetric seed maps.

Social dysfunction is an intractable problem in a wide spectrum of psychiatric illnesses, undermining patients’ capacities for employment, independent living, and maintaining meaningful relationships. Identifying common markers of social impairment across disorders and understanding their mechanisms are prerequisites to developing targeted neurobiological treatments that can be applied productively across diagnoses and illness stages to improve functional outcome. This project focuses on eye gaze perception, the ability to accurately and efficiently discriminate others’ gaze direction, as a potential biomarker of social functioning that cuts across psychiatric diagnoses. This premise builds on both the monkey and human literatures showing gaze perception as a basic building block supporting higher-level social communication and social development, and reports of abnormal gaze perception in multiple psychiatric conditions accompanied by prominent social dysfunction (e.g., psychosis-spectrum disorders, autism-spectrum disorders, social phobia). A large sample (n = 225) of adolescent and young adult (age 14–30) psychiatric patients (regardless of diagnosis) with various degrees of impaired social functioning, and demographically-matched healthy controls (n = 75) will be recruited for this study. Participant’s psychiatric phenotypes, cognition, social cognition, and community functioning will be dimensionally characterized. Eye gaze perception will be assessed using a psychophysical task, and two metrics (precision, self-referential bias) that respectively tap into gaze perception disturbances at the visual perceptual and interpretation levels, independent of general deficits, will be derived using hierarchical Bayesian modeling. A subset of the participants (150 psychiatric patients, 75 controls) will additionally undergo multimodal fMRI to determine the functional and structural brain network features of altered gaze perception. The specific aims of this project are three-fold: (1) Determine the generality of gaze perception disturbances in psychiatric patients with prominent social dysfunction; (2) Map behavioral indices of gaze perception disturbances to dimensions of psychiatric phenotypes and core functional domains; and (3) Identify the neural correlates of altered gaze perception in psychiatric patients with social dysfunction. Successfully completing these specific aims will identify the specific basic deficits, clinical profile, and underlying neural circuits associated with social dysfunction that can be used to guide targeted, personalized treatments, thus advancing NIMH’s Strategic Objective 1 (describe neural circuits associated with mental illnesses and map the connectomes for mental illnesses) and Objective 3 (develop new treatments based on discoveries in neuroscience and behavioral science).

Motion artifacts are the most significant sources of noise in the context of pediatric brain imaging designs and data analyses, especially in applications of functional near-infrared spectroscopy (fNIRS), in which it can completely affect the quality of the data acquired. Different methods have been developed to correct motion artifacts in fNIRS data, but the relative effectiveness of these methods for data from child and infant subjects (which is often found to be significantly noisier than adult data) remains largely unexplored. The issue is further complicated by the heterogeneity of fNIRS data artifacts. We compared the efficacy of the six most prevalent motion artifact correction techniques with fNIRS data acquired from children participating in a language acquisition task, including wavelet, spline interpolation, principal component analysis, moving average (MA), correlation-based signal improvement, and combination of wavelet and MA. The evaluation of five predefined metrics suggests that the MA and wavelet methods yield the best outcomes. These findings elucidate the varied nature of fNIRS data artifacts and the efficacy of artifact correction methods with pediatric populations, as well as help inform both the theory and practice of optical brain imaging analysis.

Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. Spatial models and Bayesian approaches for inference in such models have advantages over more traditional mass univariate approaches; however, a major challenge for such analyses is the required computation. As a result, the neuroimaging community has embraced approximate Bayesian inference based on mean-field variational Bayes (VB) approximations. These approximations are implemented in standard software packages such as the popular statistical parametric mapping software. While computationally efficient, the quality of VB approximations remains unclear even though they are commonly used in the analysis of neuroimaging data. For reliable statistical inference, it is important that these approximations be accurate and that users understand the scenarios under which they may not be accurate. We consider this issue for a particular model that includes spatially varying coefficients. To examine the accuracy of the VB approximation, we derive Hamiltonian Monte Carlo (HMC) for this model and conduct simulation studies to compare its performance with VB in terms of estimation accuracy, posterior variability, the spatial smoothness of estimated images, and computation time. As expected, we find that the computation time required for VB is considerably less than that for HMC. In settings involving a high or moderate signal-to-noise ratio (SNR), we find that the 2 approaches produce very similar results suggesting that the VB approximation is useful in this setting. On the other hand, when one considers a low SNR, substantial differences are found, suggesting that the approximation may not be accurate in such cases and we demonstrate that VB produces Bayes estimators with larger mean squared error. A comparison of the 2 computational approaches in an application examining the hemodynamic response to face perception in addition to a comparison with the traditional mass univariate approach in this application is also considered. Overall, our work clarifies the usefulness of VB for the spatiotemporal analysis of fMRI data, while also pointing out the limitation of VB when the SNR is low and the utility of HMC in this case.

Spatial resolution plays an important role in functional magnetic resonance imaging studies as the signal-to-noise ratio increases linearly with voxel volume. In scientific studies, where functional magnetic resonance imaging is widely used, the standard spatial resolution typically used is relatively low which ensures a relatively high signal-to-noise ratio. However, for pre-surgical functional magnetic resonance imaging analysis, where spatial accuracy is paramount, high-resolution functional magnetic resonance imaging may play an important role with its greater spatial resolution. High spatial resolution comes at the cost of a smaller signal-to-noise ratio. This begs the question as to whether we can leverage the higher signal-to-noise ratio of a standard functional magnetic resonance imaging study with the greater spatial accuracy of a high-resolution functional magnetic resonance imaging study in a pre-operative patient. To answer this question, we propose to regress the statistic image from a high resolution scan onto the statistic image obtained from a standard resolution scan using a mixed-effects model with spatially varying coefficients. We evaluate our model via simulation studies and we compare its performance with a recently proposed model that operates at a single spatial resolution. We apply and compare the two models on data from a patient awaiting tumor resection. Both simulation study results and the real data analysis demonstrate that our newly proposed model indeed leverages the larger signal-to-noise ratio of the standard spatial resolution scan while maintaining the advantages of the high spatial resolution scan.

To report the chest radiograph measurement technique for placing bedside peripherally inserted central catheters (PICCs). Two hundred and thirty-two consecutive pediatric patients, mean age of 56.3 months (range: 0–203 months), underwent PICC placement from January 2015 to May 2017 (28 months) with a total of 232 PICCs placed. Measurements were taken from the cavoatrial junction to clavicle, clavicle to medial margin of mid-humeral head, and medial margin of mid-humeral head to mid-humerus. The difference between total radiographic measured length and actual PICC length was then calculated, and the percent difference (from actual cut length) was recorded. An equivalence test was performed using the two, one-sided test method. Mean ± standard deviation cavoatrial junction to clavicle length was 5.29 ± 2.20 cm (range: 2.1–12.6 cm). Mean clavicle to shoulder length was 8.20 ± 3.59 cm (range: 3.23–19.06 cm). Mean shoulder to mid-humerus length was 7.88 ± 3.87 cm (range: 2.01–16.8 cm). Mean total radiographic measured length was 21.37 ± 9.19 cm (range: 7.42–43.6 cm). Mean actual cut PICC length was 20.64 ± 8.72 cm (range: 8.5–44 cm). The mean difference between predicted, or total radiographic measured length, and actual cut PICC length was 0.73 ± 2.51 (range: − 5.42–8.60 cm). The mean percent difference was 4.07 ± 12.65% (range: − 23.84–47.80%). An equivalence test rejected the null hypothesis of the true percent difference greater/less than ± 6.67% with a p value of 0.002. The chest radiograph measurement technique is an accurate method to determine catheter length for PICC placement at bedside in the pediatric population.

Bipolar disorder (BD) is associated with a range of social cognitive deficits. This study investigated the functioning of the mentalizing brain system in BD probed by an eye gaze perception task during fMRI. Compared with healthy controls (n = 21), BD participants (n = 14) showed reduced preferential activation for self-directed gaze discrimination in the medial prefrontal cortex (mPFC) and temporo-parietal junction (TPJ), which was associated with poorer cognitive and social functioning. Aberrant functions of the mentalizing system should be further investigated as marker of social dysfunction and treatment targets.HighlightsSocial dysfunction in bipolar disorder (BD) may be due to altered mentalizing.Individuals with BD showed reduced activation in the mentalizing brain system.Aberrant activity of the mentalizing system was associated with poorer functioning.

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets.

Background Standardized definitions and methods of surveillance for local recurrence of nonmelanoma skin cancer are critical in determining cure rates attributed to treatment modalities. Objective We sought to offer a standard definition of local recurrence after surgical treatment of nonmelanoma skin cancer and to propose an acceptable surveillance period and tracking methods. Methods A literature search was performed for background definitions of local recurrence and tracking methods. The American College of Mohs Surgery (ACMS) Registry and Outcomes Committee then conducted a modified Delphi process to arrive at consensus definitions. Results We define local recurrence as a tumor with comparable histology, with contiguity to the surgical scar after treatment, and that arises within the area of the previously treated tumor. Limitations This project reports the results of a modified Delphi method process involving members of the ACMS. The model described may not be useful for nonexcision type treatments such as topical chemotherapy, electrodessication and curettage, or radiation treatment. Conclusions Previous definitions of recurrence and surveillance methods after surgical treatment of nonmelanoma skin cancer are variable and nonstandard. We describe consensus standards for defining and tracking recurrence that should allow for consistent scientific evaluation and development of performance data in skin cancer outcomes registries.

Assessment tools for early cystic fibrosis (CF) lung disease are limited. Detecting early pulmonary disease is crucial to increasing life expectancy by starting interventions to slow the progression of the pulmonary disease with the many treatment options available. To compare the utility of lung T1-mapping MRI with ultrashort echo time (UTE) MRI in children with cystic fibrosis in detecting early stage lung disease and monitoring pulmonary exacerbations. We performed a prospective study in 16 children between September 2017 and January 2018. In Phase 1, we compared five CF patients with normal spirometry (mean 11.2 years) to five age- and gender-matched healthy volunteers. In Phase 2, we longitudinally evaluated six CF patients (median 11 years) in acute pulmonary exacerbation. All children had non-contrast lung T1-mapping and UTE MRI and spirometry testing. We compared the mean normalized T1 value and percentage lung volume without T1 value in CF patients and healthy subjects in Phase 1 and during treatment in Phase 2. We also performed cystic fibrosis MRI scoring. We evaluated differences in continuous variables using standard statistical tests. In Phase 1, mean normalized T1 values of the lung were significantly lower in CF patients in comparison to healthy controls (P=0.02) except in the right lower lobe (P=0.29). The percentage lung volume without T1 value was also significantly higher in CF patients (P=0.006). UTE MRI showed no significant differences between CF patients and healthy volunteers (P=0.11). In Phase 2, excluding one outlier case who developed systemic disease in the course of treatment, the whole-lung T1 value increased (P=0.001) and perfusion scoring improved (P=0.02) following therapy. We observed no other significant changes in the MRI scoring. Lung T1-mapping MRI can detect early regional pulmonary CF disease in children and might be helpful in the assessment of acute pulmonary exacerbations.

BACKGROUND: Deficits in social cognition are pervasive in schizophrenia (SZ) and strong predictors of poor functional outcomes. Understanding of the mechanisms underlying critical social cognitive dysfunctions in SZ will advance our understanding of the disorder and help design targeted treatment. In this presentation, we examine a basic building block of social cognition—eye gaze perception—in SZ and bipolar psychosis (BP). Given the frequent documentations of visual perceptual anomaly in SZ, we specifically evaluate the role of visual disturbances in altered gaze processing in psychosis. We used psychophysics to isolate distinctive cognitive processes involved in gaze perception (Study 1) and applied dynamic causal modeling (DCM) to fMRI data to illuminate aberrant brain dynamics responsible for altered gaze processing (Study 2). METHODS: In Study 1, 157 participants (47 SZ; 55 BP; and 55 healthy controls, HC) viewed faces with varying gaze directions and made two-forced choice eye contact judgments (“yes” or “no”). In each individual, eye contact endorsement was examined as a logistic function of gaze direction. The slope and absolute threshold of this perception curve were used to index, respectively, visual perceptual sensitivity and self-referential bias. Individual measures and group differences were estimated using hierarchical Bayesian modeling. Markov Chain Monte Carlo (MCMC) implemented in WinBUGS was used to sample from the joint posterior distribution to estimate posterior probabilities of the parameters. In Study 2, 27 SZ participants and 22 HC completed a gaze perception task during BOLD fMRI. They viewed faces with varying gaze directions and made two-forced choice judgments of eye contact or gender (control task). Time series from the V2 visual cortex (V2), posterior superior temporal sulcus (pSTS), inferior parietal lobule (IPL), and posterior medial prefrontal cortex (pMPFC) were extracted and subject to DCM analysis. Initial model family comparison showed that models with gaze modulations on both bottom-up connections from V2 and top-down connections from pMFC had the highest evidence compared with models with gaze modulations on either bottom-up or top-down connections. Models from this model family were then subject to post-hoc Bayesian model selection to select the best model for each individual. Parameter estimates of the winning model were taken to group-level analyses using t-tests. RESULTS: In Study 1, both clinical groups showed high posterior probabilities of reduced perceptual sensitivity (SZ: 99.96%; BP: 98.7%) and increased self-referential bias (SZ: 98.7%; BP: 93.71%) when compared with HC during gaze perception. Although BP generally positioned in between SZ and HC, it was not statistically distinguishable from SZ. In Study 2, DCM revealed that, as expected, attending to gaze (vs. gender) strengthened bottom-up connections from V2 to IPL (t= 3.50, p = .001) and to pSTS (t= 2.53, p = .015) across participants. Compared with HC, SZ showed weakened sensory input to V2 (t = -2.12, p = .042) and reduced feedforward connectivity from V2 to IPL (t = -2.09, p = .045). When processing gaze (vs. gender), HC relaxed pMFC suppression of V2, perhaps to allow more data-driven processes; conversely, SZ increased pMFC inhibition of V2 (t = -3.22, p = .002), presumably to compensate for impaired bottom-up processes by relying more on higher-level cognition to determine the self-referential nature of gaze. CONCLUSIONS: These findings suggest that gaze perception in psychosis, regardless of diagnostic category, is characterized by altered cognitive processes at both the perceptual and interpretation levels. However, effective connectivity analyses suggest that abnormal gaze processing may originate from dysfunctions of the visual cortex, and that aberrant top-down processes may be a compensatory mechanism. We are currently working on replicating these fMRI findings and will be launching an experimental study using transcranial magnetic stimulation to confirm the causal role of visual cortical dysfunction in altered gaze processing in psychosis.

Purpose: Hyperglycemia affects FDG uptake in the brain, potentially emulating Alzheimer’s disease in normal individuals. This study investigates global and regional cerebral FDG uptake as a function of plasma glucose in a cohort of patients. Methods: 120 consecutive male patients with FDG PET/CT for initial oncologic staging (July–Dec 2015) were reviewed. Patients with dementia, cerebrovascular accident, structural brain lesion, prior oncology treatment or high metabolic tumor burden (recently shown affecting brain FDG uptake) were excluded. 53 (24 nondiabetic) eligible patients (age 65.7 ± 2.8 mean ± SE) were analyzed with parametric computer software, MIMneuro™. Regional Z-scores were evaluated as a function of plasma glucose and age using multi variable linear mixed effects models with false discovery analysis adjusting for multiple comparisons. If the regression slope was significantly (p

Objective Atrial fibrillation (AF) is frequently detected after ischemic stroke for the first time, and brain regions involved in autonomic control have been suspected to trigger AF. We examined whether specific brain regions are associated with newly detected AF after ischemic stroke. Methods Patients with acute cerebral infarctions on diffusion-weighted magnetic resonance imaging were included in this lesion mapping study. Lesions were mapped and modeled voxelwise using Bayesian Spatial Generalised Linear Mixed Modeling to determine differences in infarct locations between stroke patients with new AF, without AF and with AF already known before the stroke. Results 582 patients were included (median age 68 years; 63.2% male). AF was present in 109/582 patients [(18.7%); new AF: 39/109 (35.8%), known AF: 70/109 (64.2%)]. AF patients had larger infarct volumes than patients without AF (mean: 29.7 ± 45.8 ml vs. 15.2 ± 35.1 ml; p Conclusions In the search for brain areas potentially triggering cardiac arrhythmias infarct size should be accounted for. After controlling for infarct size, there is currently no evidence that ischemic stroke lesions of specific brain areas are associated with new AF compared to patients without AF. This challenges the neurogenic hypothesis of AF according to which a relevant proportion of new AF is triggered by ischemic brain lesions of particular locations.

Background Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. Objective To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. Materials and methods Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. Results QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. Conclusion The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

We assessed the value of fusion 18F-fluoromethylcholine (18F-choline) Positron Emission To-mography / Magnetic Resonance Imaging (PET/MRI) for image-guided (targeted) prostate biop-sies to detect significant prostate cancer (Gleason ≥ 3+4) compared to standard (systematic 12-core) biopsies. Methods: Within an ongoing prospective clinical trial, hybrid 18F-choline Positron Emission Tomography / Computed Tomography (PET/CT) and multi-parametric 3T MRI (mpMRI) of the pelvis were performed in 36 subjects with rising prostate specific antigen (PSA) for known (n = 15) or suspected (n = 21) prostate cancer prior to a prostate biopsy procedure. PET and T2-weighted (T2w) Magnetic Resonance (MR) volumes of the prostate were spatially registered using commercially available software. Biopsy targets were selected based on visual appearance on MRI and graded as low, intermediate or high risk for significant disease. Volumes of interest (VOI) were defined for MR-identified lesions. 18F-choline uptake measures were obtained from the MR-target and a mirrored background VOI. The biopsy procedure was performed after registration of real-time transrectal ultrasound (TRUS) with T2w MR and included image-guided cores plus standard cores. Histological results were determined from standard and targeted biopsy cores, as well as prostatectomy specimens (n = 10). Results: Fifteen subjects were ultimately identified with Gleason ≥ 3+4 prostate cancer, of which targeted biopsy identified significantly more (n = 12) than standard biopsies (n = 5; P = 0.002). A total of 52 lesions were identified by mpMRI (19 low; 18 intermediate; 15 high risk), and mpMRI-assigned risk was a strong predictor of final pathology (area under the curve (AUC) = 0.81; P < 0.001). Using the mean 18F-choline target-to-background ratio (TBR), the addition of 18F-choline to mpMRI significantly improved the prediction of Gleason ≥ 3+4 cancers over mpMRI alone (AUC = 0.92; p