Paused Pol II coordinates tissue morphogenesis in the Drosophila embryo.

Paused RNA polymerase (Pol II) is a pervasive feature of Drosophila embryos and mammalian stem cells, but its role in development is uncertain. Here, we demonstrate that a spectrum of paused Pol II determines the "time to synchrony"-the time required to achieve coordinated gene expression across the cells of a tissue. To determine whether synchronous patterns of gene activation are significant in development, we manipulated the timing of snail expression, which controls the coordinated invagination of ∼1,000 mesoderm cells during gastrulation. Replacement of the strongly paused snail promoter with moderately paused or nonpaused promoters causes stochastic activation of snail expression and increased variability of mesoderm invagination. Computational modeling of the dorsal-ventral patterning network recapitulates these variable and bistable gastrulation profiles and emphasizes the importance of timing of gene activation in development. We conclude that paused Pol II and transcriptional synchrony are essential for coordinating cell behavior during morphogenesis.

Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD.

Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network was used including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Future work is needed to build on this early attempt through replication of the current findings in independent samples and testing longitudinally for prognostic value.

Sex and Diffusion Tensor Imaging of White Matter in Schizophrenia: A Systematic Review Plus Meta-analysis of the Corpus Callosum.

Sex is considered an understudied variable in health research. Schizophrenia is a brain disorder with known sex differences in epidemiology and clinical presentation. We systematically reviewed the literature for sex-based differences of diffusion properties of white matter tracts in schizophrenia. We then conducted a meta-analysis examining sex-based differences in the genu and splenium of the corpus callosum in schizophrenia. Medline and Embase were searched to identify relevant papers. Studies fulfilling the following criteria were included: (1) included individuals with a diagnosis of schizophrenia, (2) included a control group of healthy individuals, (3) included both sexes in the patient and the control groups, (4) used diffusion tensor imaging, and (5) involved analyzing metrics of white matter microstructural integrity. Fractional anisotropy (FA) was used as the measure of interest in the meta-analysis. Of 730 studies reviewed, 75 met the inclusion criteria. Most showed no effect of sex, however, those that did found either that females have lower FA than males, or that the effect of disease in females is larger than that in males. The findings of the meta-analysis in the corpus callosum supported this result. There is a recognized need for studies on schizophrenia with a sufficient sample of female patients. Lack of power undermines the ability to detect sex-based differences. Understanding the sex-specific impact of illness on neural circuits may help inform development of new treatments, and improvement of existing interventions.

Brain-Behavior Participant Similarity Networks Among Youth and Emerging Adults with Schizophrenia Spectrum, Autism Spectrum, or Bipolar Disorder and Matched Controls.

There is considerable heterogeneity in social cognitive and neurocognitive performance among people with schizophrenia spectrum disorders (SSD), autism spectrum disorders (ASD), bipolar disorder (BD), and healthy individuals. This study used Similarity Network Fusion (SNF), a novel data-driven approach, to identify participant similarity networks based on relationships among demographic, brain imaging, and behavioral data. T1-weighted and diffusion-weighted magnetic resonance images were obtained for 174 adolescents and young adults (aged 16-35 years) with an SSD (n=51), an ASD without intellectual disability (n=38), euthymic BD (n=34), and healthy controls (n=51). A battery of social cognitive and neurocognitive tasks were administered. Data integration, cluster determination, and biological group formation were then obtained using SNF. We identified four new groups of individuals, each with distinct neural circuit-cognitive profiles. The most influential variables driving the formation of the new groups were robustly reliable across embedded resampling techniques. The data-driven groups showed considerably greater differentiation on key social and neurocognitive circuit nodes than groups generated by diagnostic analyses or dimensional social cognitive analyses. The data-driven groups were validated through functional outcome and brain network property measures not included in the SNF model. Cutting across diagnostic boundaries, our approach can effectively identify new groups of people based on a profile of neuroimaging and behavioral data. Our findings bring us closer to disease subtyping that can be leveraged toward the targeting of specific neural circuitry among participant subgroups to ameliorate social cognitive and neurocognitive deficits.

Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects.

BACKGROUND: Deficits in neurocognition and social cognition are drivers of reduced functioning in schizophrenia spectrum disorders, with potentially shared neurobiological underpinnings. Many studies have sought to identify brain-based biomarkers of these clinical variables using a priori dichotomies (e.g., good vs. poor cognition, deficit vs. nondeficit syndrome). METHODS: We evaluated a fully data-driven approach to do the same by building and validating a brain connectivity-based biomarker of social cognitive and neurocognitive performance in a sample using resting-state and task-based functional magnetic resonance imaging (n = 74 healthy control participants, n = 114 persons with schizophrenia spectrum disorder, 188 total). We used canonical correlation analysis followed by clustering to identify a functional connectivity signature of normal and poor social cognitive and neurocognitive performance. RESULTS: Persons with poor social cognitive and neurocognitive performance were differentiated from those with normal performance by greater resting-state connectivity in the mirror neuron and mentalizing systems. We validated our findings by showing that poor performers also scored lower on functional outcome measures not included in the original analysis and by demonstrating neuroanatomical differences between the normal and poorly performing groups. We used a support vector machine classifier to demonstrate that functional connectivity alone is enough to distinguish normal and poorly performing participants, and we replicated our findings in an independent sample (n = 75). CONCLUSIONS: A brief functional magnetic resonance imaging scan may ultimately be useful in future studies aimed at characterizing long-term illness trajectories and treatments that target specific brain circuitry in those with impaired cognition and function.

Gray Matter Neuritic Microstructure Deficits in Schizophrenia and Bipolar Disorder.

BACKGROUND: Postmortem studies have demonstrated considerable dendritic pathologies among persons with schizophrenia and to some extent among those with bipolar I disorder. Modeling gray matter (GM) microstructural properties is now possible with a recently proposed diffusion-weighted magnetic resonance imaging modeling technique: neurite orientation dispersion and density imaging. This technique may bridge the gap between neuroimaging and histopathological findings. METHODS: We performed an extended series of multishell diffusion-weighted imaging and other structural imaging series using 3T magnetic resonance imaging. Participants scanned included individuals with schizophrenia (n = 36), bipolar I disorder (n = 29), and healthy controls (n = 35). GM-based spatial statistics was used to compare neurite orientation dispersion and density imaging-driven microstructural measures (orientation dispersion index and neurite density index [NDI]) among groups and to assess their relationship with neurocognitive performance. We also investigated the accuracy of these measures in the prediction of group membership, and whether combining them with cortical thickness and white matter fractional anisotropy further improved accuracy. RESULTS: The GM-NDI was significantly lower in temporal pole, anterior parahippocampal gyrus, and hippocampus of the schizophrenia patients than the healthy controls. The GM-NDI of patients with bipolar I disorder did not differ significantly from either schizophrenia patients or healthy controls, and it was intermediate between the two groups in the post hoc analysis. Regardless of diagnosis, higher performance in spatial working memory was significantly associated with higher GM-NDI mainly in the frontotemporal areas. The addition of GM-NDI to cortical thickness resulted in higher accuracy to predict group membership. CONCLUSIONS: GM-NDI captures brain differences in the major psychoses that are not accessible with other structural magnetic resonance imaging methods. Given the strong association of GM-NDI with disease state and neurocognitive performance, its potential utility for biological subtyping should be further explored.

Neuroimaging predictors of functional outcomes in schizophrenia at baseline and 6-month follow-up.

PURPOSE: Studies show that deficit syndrome schizophrenia patients, characterized by primary negative symptoms and poor functional outcome, have impairment in specific neural circuits. We assessed whether these same neural circuits are directly linked to functional outcomes across schizophrenia patients. METHODS: T1- and diffusion-weighted MR images were obtained for schizophrenia (n=30) and matched healthy control participants (n=30). Negative symptoms and functional outcome were assessed at baseline and 6-month follow-up. Cortical thickness and tract-wise fractional anisotropy (FA) were compared between groups. To assess relationships of neuroimaging measures with functional outcome, principal component analysis (PCA) was performed on tract-wise FA values and components were entered into a multiple regression model for schizophrenia participants. RESULTS: Consistent with the literature, schizophrenia participants showed frontotemporal reductions in cortical thickness and tract-wise FA compared to controls. The top two components from PCA explained 71% of the variance in tract-wise FA values. The second component (associated with inferior longitudinal and arcuate fasciculus FA) was significantly correlated with functional outcome (baseline: ß=0.54, p=0.03; follow-up: ß=0.74, p=0.047); further analysis revealed this effect was mediated by negative symptoms. Post-hoc network analysis revealed increased cortical coupling between right inferior frontal and supramarginal gyri (connected by the arcuate fasciculus) in schizophrenia participants with poorer functional outcome. CONCLUSIONS: Our findings indicate that impairment in the same neural circuitry susceptible in deficit syndrome schizophrenia predicts functional outcome in a continuous manner in schizophrenia participants. This relationship was mediated by negative symptom burden. Our findings provide novel evidence for brain-based biomarkers of longitudinal functional outcome in people with schizophrenia.