The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

Development of the default mode and central executive networks across early adolescence: a longitudinal study

The mature brain is organized into distinct neural networks defined by regions demonstrating correlated activity during task performance as well as rest. While research has begun to examine differences in these networks between children and adults, little is known about developmental changes during early adolescence. Using functional magnetic resonance imaging (fMRI), we examined the Default Mode Network (DMN) and the Central Executive Network (CEN) at ages 10 and 13 in a longitudinal sample of 45 participants. In the DMN, participants showed increasing integration (i.e., stronger within-network correlations) between the posterior cingulate cortex (PCC) and the medial prefrontal cortex. During this time frame participants also showed increased segregation (i.e., weaker between-network correlations) between the PCC and the CEN. Similarly, from age 10 to 13, participants showed increased connectivity between the dorsolateral prefrontal cortex and other CEN nodes, as well as increasing DMN segregation. IQ was significantly positively related to CEN integration at age 10, and between-network segregation at both ages. These findings highlight early adolescence as a period of significant maturation for the brain's functional architecture and demonstrate the utility of longitudinal designs to investigate neural network development.

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.

Altered functional connectivity in frontal lobe circuits is associated with variation in the autism risk gene CNTNAP2

Genetic studies are rapidly identifying variants that shape risk for disorders of human cognition, but the question of how such variants predispose to neuropsychiatric disease remains. Noninvasive human brain imaging allows assessment of the brain in vivo, and the combination of genetics and imaging phenotypes remains one of the only ways to explore functional genotype-phenotype associations in human brain. Common variants in contactin-associated protein-like 2 (CNTNAP2), a neurexin superfamily member, have been associated with several allied neurodevelopmental disorders, including autism and specific language impairment, and CNTNAP2 is highly expressed in frontal lobe circuits in the developing human brain. Using functional neuroimaging, we have demonstrated a relationship between frontal lobar connectivity and common genetic variants in CNTNAP2. These data provide a mechanistic link between specific genetic risk for neurodevelopmental disorders and empirical data implicating dysfunction of long-range connections within the frontal lobe in autism. The convergence between genetic findings and cognitive-behavioral models of autism provides evidence that genetic variation at CNTNAP2 predisposes to diseases such as autism in part through modulation of frontal lobe connectivity.

Emerging Applications of Artificial Intelligence in Neuro-Oncology

Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.

Reduced functional integration and segregation of distributed neural systems underlying social and emotional information processing in autism spectrum disorders

A growing body of evidence suggests that autism spectrum disorders (ASDs) are related to altered communication between brain regions. Here, we present findings showing that ASD is characterized by a pattern of reduced functional integration as well as reduced segregation of large-scale brain networks. Twenty-three children with ASD and 25 typically developing matched controls underwent functional magnetic resonance imaging while passively viewing emotional face expressions. We examined whole-brain functional connectivity of two brain structures previously implicated in emotional face processing in autism: the amygdala bilaterally and the right pars opercularis of the inferior frontal gyrus (rIFGpo). In the ASD group, we observed reduced functional integration (i.e., less long-range connectivity) between amygdala and secondary visual areas, as well as reduced segregation between amygdala and dorsolateral prefrontal cortex. For the rIFGpo seed, we observed reduced functional integration with parietal cortex and increased integration with right frontal cortex as well as right nucleus accumbens. Finally, we observed reduced segregation between rIFGpo and the ventromedial prefrontal cortex. We propose that a systems-level approach-whereby the integration and segregation of large-scale brain networks in ASD is examined in relation to typical development-may provide a more detailed characterization of the neural basis of ASD.

Overreactive brain responses to sensory stimuli in youth with autism spectrum disorders

OBJECTIVES

Sensory over-responsivity (SOR), defined as a negative response to or avoidance of sensory stimuli, is both highly prevalent and extremely impairing in youth with autism spectrum disorders (ASD), yet little is known about the neurological bases of SOR. This study aimed to examine the functional neural correlates of SOR by comparing brain responses to sensory stimuli in youth with and without ASD.

METHOD

A total of 25 high-functioning youth with ASD and 25 age- and IQ-equivalent typically developing (TD) youth were presented with mildly aversive auditory and visual stimuli during a functional magnetic resonance imaging (fMRI) scan. Parents provided ratings of children's SOR and anxiety symptom severity.

RESULTS

Compared to TD participants, ASD participants displayed greater activation in primary sensory cortical areas as well as amygdala, hippocampus, and orbital-frontal cortex. In both groups, the level of activity in these areas was positively correlated with level of SOR severity as rated by parents, over and above behavioral ratings of anxiety.

CONCLUSIONS

This study demonstrates that youth with ASD show neural hyper-responsivity to sensory stimuli, and that behavioral symptoms of SOR may be related to both heightened responsivity in primary sensory regions as well as areas related to emotion processing and regulation.

Autism-associated promoter variant in MET impacts functional and structural brain networks

As genes that confer increased risk for autism spectrum disorder (ASD) are identified, a crucial next step is to determine how these risk factors impact brain structure and function and contribute to disorder heterogeneity. With three converging lines of evidence, we show that a common, functional ASD risk variant in the Met Receptor Tyrosine Kinase (MET) gene is a potent modulator of key social brain circuitry in children and adolescents with and without ASD. MET risk genotype predicted atypical fMRI activation and deactivation patterns to social stimuli (i.e., emotional faces), as well as reduced functional and structural connectivity in temporo-parietal regions known to have high MET expression, particularly within the default mode network. Notably, these effects were more pronounced in individuals with ASD. These findings highlight how genetic stratification may reduce heterogeneity and help elucidate the biological basis of complex neuropsychiatric disorders such as ASD.

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Artificial intelligence for precision education in radiology

In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.

Insights into multimodal imaging classification of ADHD

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD.

Neural signatures of autism spectrum disorders: insights into brain network dynamics

Neuroimaging investigations of autism spectrum disorders (ASDs) have advanced our understanding of atypical brain function and structure, and have recently converged on a model of altered network-level connectivity. Traditional task-based functional magnetic resonance imaging (MRI) and volume-based structural MRI studies have identified widespread atypicalities in brain regions involved in social behavior and other core ASD-related behavioral deficits. More recent advances in MR-neuroimaging methods allow for quantification of brain connectivity using diffusion tensor imaging, functional connectivity, and graph theoretic methods. These newer techniques have moved the field toward a systems-level understanding of ASD etiology, integrating functional and structural measures across distal brain regions. Neuroimaging findings in ASD as a whole have been mixed and at times contradictory, likely due to the vast genetic and phenotypic heterogeneity characteristic of the disorder. Future longitudinal studies of brain development will be crucial to yield insights into mechanisms of disease etiology in ASD sub-populations. Advances in neuroimaging methods and large-scale collaborations will also allow for an integrated approach linking neuroimaging, genetics, and phenotypic data.

Regional fMRI hypoactivation and altered functional connectivity during emotion processing in nonmedicated depressed patients with bipolar II disorder

OBJECTIVE

Although the amygdala and ventrolateral prefrontal cortex have been implicated in the pathophysiology of bipolar I disorder, the neural mechanisms underlying bipolar II disorder remain unknown. The authors examined neural activity in response to negative emotional faces during an emotion perception task that reliably activates emotion regulatory regions.

METHOD

Twenty-one nonmedicated depressed bipolar II patients and 21 healthy comparison subjects underwent functional MRI (fMRI) while performing an emotional face-matching task. Within- and between-group whole-brain fMRI activation and seed-based connectivity analyses were conducted.

RESULTS

In depressed bipolar II patients, random-effects between-group fMRI analyses revealed a significant reduction in activation in several regions, including the left and right ventrolateral prefrontal cortices (Brodmann's area [BA] 47) and the right amygdala, a priori regions of interest. Additionally, bipolar patients exhibited significantly reduced negative functional connectivity between the right amygdala and the right orbitofrontal cortex (BA 10) as well as the right dorsolateral prefrontal cortex (BA 46) relative to healthy comparison subjects.

CONCLUSIONS

These findings suggest that bipolar II depression is characterized by reduced regional orbitofrontal and limbic activation and altered connectivity in a fronto-temporal circuit implicated in working memory and emotional learning. While the amygdala hypoactivation observed in bipolar II depression is opposite to the direction seen in bipolar I mania and may therefore be state dependent, the observed orbitofrontal cortex hypoactivation is consistent with findings in bipolar I depression, mania, and euthymia, suggesting a physiologic trait marker of the disorder.

Altered structural brain connectivity in healthy carriers of the autism risk gene, CNTNAP2

Recently, carriers of a common variant in the autism risk gene, CNTNAP2, were found to have altered functional brain connectivity using functional MRI. Here, we scanned 328 young adults with high-field (4-Tesla) diffusion imaging, to test the hypothesis that carriers of this gene variant would have altered structural brain connectivity. All participants (209 women, 119 men, age: 23.4±2.17 SD years) were scanned with 105-gradient high-angular-resolution diffusion imaging (HARDI) at 4 Tesla. After performing a whole-brain fiber tractography using the full angular resolution of the diffusion scans, 70 cortical surface-based regions of interest were created from each individual's co-registered anatomical data to compute graph metrics for all pairs of cortical regions. In graph theory analyses, subjects homozygous for the risk allele (CC) had lower characteristic path length, greater small-worldness and global efficiency in whole-brain analyses, and lower [corrected] eccentricity (maximum path length) in 60 of the 70 nodes in regional analyses. These results were not reducible to differences in more commonly studied traits such as fiber density or fractional anisotropy. This is the first study that links graph theory metrics of brain structural connectivity to a common genetic variant linked with autism and will help us understand the neurobiology of the circuits implicated in the risk for autism.

The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis

Brain connectomics research has rapidly expanded using functional MRI (fMRI) and diffusion-weighted MRI (dwMRI). A common product of these varied analyses is a connectivity matrix (CM). A CM stores the connection strength between any two regions (“nodes”) in a brain network. This format is useful for several reasons: (1) it is highly distilled, with minimal data size and complexity, (2) graph theory can be applied to characterize the network's topology, and (3) it retains sufficient information to capture individual differences such as age, gender, intelligence quotient (IQ), or disease state. Here we introduce the UCLA Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an openly available website for brain network analysis and data sharing. The site is a repository for researchers to publicly share CMs derived from their data. The site also allows users to select any CM shared by another user, compute graph theoretical metrics on the site, visualize a report of results, or download the raw CM. To date, users have contributed over 2000 individual CMs, spanning different imaging modalities (fMRI, dwMRI) and disorders (Alzheimer's, autism, Attention Deficit Hyperactive Disorder). To demonstrate the site's functionality, whole brain functional and structural connectivity matrices are derived from 60 subjects' (ages 26–45) resting state fMRI (rs-fMRI) and dwMRI data and uploaded to the site. The site is utilized to derive graph theory global and regional measures for the rs-fMRI and dwMRI networks. Global and nodal graph theoretical measures between functional and structural networks exhibit low correspondence. This example demonstrates how this tool can enhance the comparability of brain networks from different imaging modalities and studies. The existence of this connectivity-based repository should foster broader data sharing and enable larger-scale meta-analyses comparing networks across imaging modality, age group, and disease state.

An fMRI investigation of responses to peer rejection in adolescents with autism spectrum disorders

Peer rejection is particularly pervasive among adolescents with autism spectrum disorders (ASD). However, how adolescents with ASD differ from typically developing adolescents in their responses to peer rejection is poorly understood. The goal of the current investigation was to examine neural responses to peer exclusion among adolescents with ASD compared to typically developing adolescents. Nineteen adolescents with ASD and 17 typically developing controls underwent fMRI as they were ostensibly excluded by peers during an online game called Cyberball. Afterwards, participants reported their distress about the exclusion. Compared to typically developing adolescents, those with ASD displayed less activity in regions previously linked with the distressing aspect of peer exclusion, including the subgenual anterior cingulate and anterior insula, as well as less activity in regions previously linked with the regulation of distress responses during peer exclusion, including the ventrolateral prefrontal cortex and ventral striatum. Interestingly, however, both groups self-reported equivalent levels of distress. This suggests that adolescents with ASD may engage in differential processing of social experiences at the neural level, but be equally aware of, and concerned about, peer rejection. Overall, these findings contribute new insights about how this population may differentially experience negative social events in their daily lives.

Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma

BACKGROUND

Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represent the effects of treatment, or pseudo-progression (PsP). We hypothesize that quantitative machine learning analysis of clinically acquired multiparametric magnetic resonance imaging (mpMRI) can identify subvisual imaging characteristics to provide robust, noninvasive imaging signatures that can distinguish true progression (TP) from PsP.

METHODS

We evaluated independent discovery (n = 40) and replication (n = 23) cohorts of glioblastoma patients who underwent second resection due to progressive radiographic changes suspicious for recurrence. Deep learning and conventional feature extraction methods were used to extract quantitative characteristics from the mpMRI scans. Multivariate analysis of these features revealed radiophenotypic signatures distinguishing among TP, PsP, and mixed response that compared with similar categories blindly defined by board-certified neuropathologists. Additionally, interinstitutional validation was performed on 20 new patients.

RESULTS

Patients who demonstrate TP on neuropathology are significantly different (P < .0001) from those with PsP, showing imaging features reflecting higher angiogenesis, higher cellularity, and lower water concentration. The accuracy of the proposed signature in leave-one-out cross-validation was 87% for predicting PsP (area under the curve [AUC], 0.92) and 84% for predicting TP (AUC, 0.83), whereas in the discovery/replication cohort, the accuracy was 87% for predicting PsP (AUC, 0.84) and 78% for TP (AUC, 0.80). The accuracy in the interinstitutional cohort was 75% (AUC, 0.80).

CONCLUSION

Quantitative mpMRI analysis via machine learning reveals distinctive noninvasive signatures of TP versus PsP after treatment of glioblastoma. Integration of the proposed method into clinical studies can be performed using the freely available Cancer Imaging Phenomics Toolkit.

Neuroimaging of Dilated Perivascular Spaces: From Benign and Pathologic Causes to Mimics

Perivascular spaces (PVSs), also known as Virchow-Robin spaces, are pial-lined, fluid-filled structures found in characteristic locations throughout the brain. They can become abnormally enlarged or dilated and in rare cases can cause hydrocephalus. Dilated PVSs can pose a diagnostic dilemma for radiologists because of their varied appearance, sometimes mimicking more serious entities such as cystic neoplasms, including dysembryoplastic neuroepithelial tumor and multinodular and vacuolating neuronal tumor, or cystic infections including toxoplasmosis and neurocysticercosis. In addition, various pathologic processes, including cryptococcosis and chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids, can spread into the brain via PVSs, resulting in characteristic magnetic resonance imaging appearances. This review aims to describe the key imaging characteristics of normal and dilated PVSs, as well as cystic mimics and pathologic processes that directly involve PVSs.

Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI

Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset

Supplemental material is available for this article. Keywords: Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration © RSNA, 2022.

Machine learning classification of mesial temporal sclerosis in epilepsy patients

BACKGROUND AND PURPOSE

Novel approaches applying machine-learning methods to neuroimaging data seek to develop individualized measures that will aid in the diagnosis and treatment of brain-based disorders such as temporal lobe epilepsy (TLE). Using a large cohort of epilepsy patients with and without mesial temporal sclerosis (MTS), we sought to automatically classify MTS using measures of cortical morphology, and to further relate classification probabilities to measures of disease burden.

MATERIALS AND METHODS

Our sample consisted of high-resolution T1 structural scans of 169 adults with epilepsy collected across five different 1.5T and four different 3T scanners at UCLA. We applied a multiple support vector machine recursive feature elimination algorithm to morphological measures generated from FreeSurfer's automated segmentation and parcellation in order to classify Epilepsy patients with MTS (n=85) from those without MTS (N=84).

RESULTS

In addition to hippocampal volume, we found that alterations in cortical thickness, surface area, volume and curvature in inferior frontal and anterior and inferior temporal regions contributed to a classification accuracy of up to 81% (p=1.3×10(-17)) in identifying MTS. We also found that MTS classification probabilities were associated with a longer duration of disease for epilepsy patients both with and without MTS.

CONCLUSIONS

In addition to implicating extra-hippocampal involvement of MTS, these findings shed further light on the pathogenesis of TLE and may ultimately assist in the development of automated tools that incorporate multiple neuroimaging measures to assist clinicians in detecting more subtle cases of TLE and MTS.

Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging

BACKGROUND AND PURPOSE

Most brain lesions are characterized by hyperintense signal on FLAIR. We sought to develop an automated deep learning-based method for segmentation of abnormalities on FLAIR and volumetric quantification on clinical brain MRIs across many pathologic entities and scanning parameters. We evaluated the performance of the algorithm compared with manual segmentation and existing automated methods.

MATERIALS AND METHODS

We adapted a U-Net convolutional neural network architecture for brain MRIs using 3D volumes. This network was retrospectively trained on 295 brain MRIs to perform automated FLAIR lesion segmentation. Performance was evaluated on 92 validation cases using Dice scores and voxelwise sensitivity and specificity, compared with radiologists' manual segmentations. The algorithm was also evaluated on measuring total lesion volume.

RESULTS

Our model demonstrated accurate FLAIR lesion segmentation performance (median Dice score, 0.79) on the validation dataset across a large range of lesion characteristics. Across 19 neurologic diseases, performance was significantly higher than existing methods (Dice, 0.56 and 0.41) and approached human performance (Dice, 0.81). There was a strong correlation between the predictions of lesion volume of the algorithm compared with true lesion volume (ρ = 0.99). Lesion segmentations were accurate across a large range of image-acquisition parameters on >30 different MR imaging scanners.

CONCLUSIONS

A 3D convolutional neural network adapted from a U-Net architecture can achieve high automated FLAIR segmentation performance on clinical brain MR imaging across a variety of underlying pathologies and image acquisition parameters. The method provides accurate volumetric lesion data that can be incorporated into assessments of disease burden or into radiologic reports.

Clinical Assessment of Deep Learning-based Super-Resolution for 3D Volumetric Brain MRI

Artificial intelligence (AI)-based image enhancement has the potential to reduce scan times while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study prospectively evaluated AI-based image enhancement in 32 consecutive patients undergoing clinical brain MRI. Standard-of-care (SOC) three-dimensional (3D) T1 precontrast, 3D T2 fluid-attenuated inversion recovery, and 3D T1 postcontrast sequences were performed along with 45% faster versions of these sequences using half the number of phase-encoding steps. Images from the faster sequences were processed by a Food and Drug Administration-cleared AI-based image enhancement software for resolution enhancement. Four board-certified neuroradiologists scored the SOC and AI-enhanced image series independently on a five-point Likert scale for image SNR, anatomic conspicuity, overall image quality, imaging artifacts, and diagnostic confidence. While interrater κ was low to fair, the AI-enhanced scans were noninferior for all metrics and actually demonstrated a qualitative SNR improvement. Quantitative analyses showed that the AI software restored the high spatial resolution of small structures, such as the septum pellucidum. In conclusion, AI-based software can achieve noninferior image quality for 3D brain MRI sequences with a 45% scan time reduction, potentially improving the patient experience and scanner efficiency without sacrificing diagnostic quality. Keywords: MR Imaging, CNS, Brain/Brain Stem, Reconstruction Algorithms © RSNA, 2022.

Frontostriatal connectivity in children during working memory and the effects of prenatal methamphetamine, alcohol, and polydrug exposure

Various abnormalities in frontal and striatal regions have been reported in children with prenatal alcohol and/or methamphetamine exposure. In a recent fMRI study, we observed a correlation between accuracy on a working-memory task and functional activation in the putamen in children with prenatal methamphetamine and polydrug exposure. Because the putamen is part of the corticostriatal motor loop whereas the caudate is involved in the executive loop, we hypothesized that a loss of segregation between distinct corticostriatal networks may occur in these participants. The current study was designed to test this hypothesis using functional connectivity MRI. We examined 50 children ranging in age from 7 to 15, including 19 with prenatal methamphetamine exposure (15 of whom had concomitant prenatal alcohol exposure), 13 with prenatal exposure to alcohol but not methamphetamine, and 18 unexposed controls. We measured the coupling between blood oxygenation level dependent (BOLD) fluctuations during a working-memory task in four striatal seed regions and those in the rest of the brain. We found that the putamen seeds showed increased connectivity with frontal brain regions involved in executive functions while the caudate seeds showed decreased connectivity with some of these regions in both groups of exposed subjects compared to controls. These findings suggest that localized brain abnormalities resulting from prenatal exposure to alcohol and/or methamphetamine lead to a partial rewiring of corticostriatal networks. These results represent important progress in the field, and could have substantial clinical significance in helping devise more targeted treatments and remediation strategies designed to better serve the needs of this population.