Faculty Bibliography

PURPOSE:To propose and validate a method for accurately quantifying renal plasma flow (RPF) with arterial spin labeling (ASL). MATERIALS AND METHODS:The proposed method employs a tracer-kinetic approach and derives perfusion from the slope of the ASL difference signal sampled at multiple inversion-times (TIs). To validate the method's accuracy, we performed a HIPAA-compliant and IRB-approved study with 15 subjects (9 male, 6 female; age range 24-73) to compare RPF estimates obtained from ASL to those from a more established dynamic contrast-enhanced (DCE) MRI method. We also investigated the impact of TI-sampling density on the accuracy of estimated RPF. RESULTS:Good agreement was found between ASL- and DCE-measured RPF, with a mean difference of 9±30ml/min and a correlation coefficient R=0.92 when ASL signals were acquired at 16 TIs and a mean difference of 9±57ml/min and R=0.81 when ASL signals were acquired at 5 TIs. RPF estimated from ASL signals acquired at only 2 TIs (400 and 1200ms) showed a low correlation with DCE-measured values (R=0.30). CONCLUSION:The proposed ASL method is capable of measuring RPF with an accuracy that is comparable to DCE MRI. At least 5 TIs are recommended for the ASL acquisition to ensure reliability of RPF measurements.

The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities. LEVEL OF EVIDENCE: 5 J. Magn. Reson. Imaging 2016.

BACKGROUND: Neuroimaging studies have shown structural alterations in several brain regions in children and adults with attention deficit hyperactivity disorder (ADHD). Through the formation of the international ENIGMA ADHD Working Group, we aimed to address weaknesses of previous imaging studies and meta-analyses, namely inadequate sample size and methodological heterogeneity. We aimed to investigate whether there are structural differences in children and adults with ADHD compared with those without this diagnosis. METHODS: In this cross-sectional mega-analysis, we used the data from the international ENIGMA Working Group collaboration, which in the present analysis was frozen at Feb 8, 2015. Individual sites analysed structural T1-weighted MRI brain scans with harmonised protocols of individuals with ADHD compared with those who do not have this diagnosis. Our primary outcome was to assess case-control differences in subcortical structures and intracranial volume through pooling of all individual data from all cohorts in this collaboration. For this analysis, p values were significant at the false discovery rate corrected threshold of p=0.0156. FINDINGS: Our sample comprised 1713 participants with ADHD and 1529 controls from 23 sites with a median age of 14 years (range 4-63 years). The volumes of the accumbens (Cohen's d=-0.15), amygdala (d=-0.19), caudate (d=-0.11), hippocampus (d=-0.11), putamen (d=-0.14), and intracranial volume (d=-0.10) were smaller in individuals with ADHD compared with controls in the mega-analysis. There was no difference in volume size in the pallidum (p=0.95) and thalamus (p=0.39) between people with ADHD and controls. Exploratory lifespan modelling suggested a delay of maturation and a delay of degeneration, as effect sizes were highest in most subgroups of children (<15 years) versus adults (>21 years): in the accumbens (Cohen's d=-0.19 vs -0.10), amygdala (d=-0.18 vs -0.14), caudate (d=-0.13 vs -0.07), hippocampus (d=-0.12 vs -0.06), putamen (d=-0.18 vs -0.08), and intracranial volume (d=-0.14 vs 0.01). There was no difference between children and adults for the pallidum (p=0.79) or thalamus (p=0.89). Case-control differences in adults were non-significant (all p>0.03). Psychostimulant medication use (all p>0.15) or symptom scores (all p>0.02) did not influence results, nor did the presence of comorbid psychiatric disorders (all p>0.5). INTERPRETATION: With the largest dataset to date, we add new knowledge about bilateral amygdala, accumbens, and hippocampus reductions in ADHD. We extend the brain maturation delay theory for ADHD to include subcortical structures and refute medication effects on brain volume suggested by earlier meta-analyses. Lifespan analyses suggest that, in the absence of well powered longitudinal studies, the ENIGMA cross-sectional sample across six decades of ages provides a means to generate hypotheses about lifespan trajectories in brain phenotypes. FUNDING: National Institutes of Health.

Stem cells self-renew and produce progenitors with limited proliferative potential. Reporting in Developmental Cell, Liu et al. (2017) demonstrate that in some neural stem cells, Notch activity is asymmetrically amplified by a positive feedback loop with the super elongation complex (SEC) to quickly differentiate between stem cells and progenitors.

The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC_(RA)neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC_(RA)cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks.

Two complex norditerpenoids, caribenols A and B, were accessed from a common building block. Our synthesis of caribenol A features the diastereoselective formation of the seven-membered ring through a Friedel-Crafts triflation and a late-stage oxidation of a furan ring. The first synthesis of caribenol B was achieved using an intramolecular organocatalytic alpha-arylation. An unusual intramolecular aldol addition was developed for the assembly of its cyclopentenone moiety, and the challenging trans-diol moiety was installed through a selective nucleophilic addition to a hydroxy 1,2-diketone. Our overall synthetic strategy, which also resulted in a second-generation synthesis of amphilectolide, confirms the usefulness of furans as powerful nucleophiles and versatile synthons.

BACKGROUND: Data-driven approaches can capture behavioral and biological variation currently unaccounted for by contemporary diagnostic categories, thereby enhancing the ability of neurobiological studies to characterize brain-behavior relationships. METHODS: A community-ascertained sample of individuals (N = 347, 18-59 years of age) completed a battery of behavioral measures, psychiatric assessment, and resting-state functional magnetic resonance imaging in a cross-sectional design. Bootstrap-based exploratory factor analysis was applied to 49 phenotypic subscales from 10 measures. Hybrid hierarchical clustering was applied to resultant factor scores to identify nested groups. Adjacent groups were compared via independent samples t tests and chi-square tests of factor scores, syndrome scores, and psychiatric prevalence. Multivariate distance matrix regression examined functional connectome differences between adjacent groups. RESULTS: Reduction yielded six factors, which explained 77.8% and 65.4% of the variance in exploratory and constrained exploratory models, respectively. Hybrid hierarchical clustering of these six factors identified two, four, and eight nested groups (i.e., phenotypic communities). At the highest clustering level, the algorithm differentiated functionally adaptive and maladaptive groups. At the middle clustering level, groups were separated by problem type (maladaptive groups; internalizing vs. externalizing problems) and behavioral type (adaptive groups; sensation-seeking vs. extraverted/emotionally stable). Unique phenotypic profiles were also evident at the lowest clustering level. Group comparisons exhibited significant differences in intrinsic functional connectivity at the highest clustering level in somatomotor, thalamic, basal ganglia, and limbic networks. CONCLUSIONS: Data-driven approaches for identifying homogenous subgroups, spanning typical function to dysfunction, not only yielded clinically meaningful groups, but also captured behavioral and neurobiological variation among healthy individuals.

The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.