Disrupted in schizophrenia 1 (DISC1), a genetic risk factor for multiple serious psychiatric diseases including schizophrenia, bipolar disorder and autism, is a key regulator of multiple neuronal ...functions linked to both normal development and disease processes. As these diseases are thought to share a common deficit in synaptic function and architecture, we have analyzed the role of DISC1 using an approach that focuses on understanding the protein-protein interactions of DISC1 specifically at synapses. We identify the Traf2 and Nck-interacting kinase (TNIK), an emerging risk factor itself for disease, as a key synaptic partner for DISC1, and provide evidence that the DISC1-TNIK interaction regulates synaptic composition and activity by stabilizing the levels of key postsynaptic density proteins. Understanding the novel DISC1-TNIK interaction is likely to provide insights into the etiology and underlying synaptic deficits found in major psychiatric diseases.
We identify galaxy groups and clusters in volume-limited samples of the Sloan Digital Sky Survey (SDSS) redshift survey, using a redshift-space friends-of-friends algorithm. We optimize the ...friends-of-friends linking lengths to recover galaxy systems that occupy the same dark matter halos, using a set of mock catalogs created by populating halos of N-body simulations with galaxies. Extensive tests with these mock catalogs show that no combination of perpendicular and line-of-sight linking lengths is able to yield groups and clusters that simultaneously recover the true halo multiplicity function, projected size distribution, and velocity dispersion. We adopt a linking length combination that yields, for galaxy groups with 10 or more members: a group multiplicity function that is unbiased with respect to the true halo multiplicity function; an unbiased median relation between the multiplicities of groups and their associated halos; a spurious group fraction of less than 61%; a halo completeness of more than 697%; the correct projected size distribution as a function of multiplicity; and a velocity dispersion distribution that is 620% too low at all multiplicities. These results hold over a range of mock catalogs that use different input recipes of populating halos with galaxies. We apply our group-finding algorithm to the SDSS data and obtain three group and cluster catalogs for three volume-limited samples that cover 3495.1 deg super(2) on the sky, go out to redshifts of 0.1, 0.068, and 0.045, and contain 57,138, 37,820, and 18,895 galaxies, respectively. We correct for incompleteness caused by fiber collisions and survey edges and obtain measurements of the group multiplicity function, with errors calculated from realistic mock catalogs. These multiplicity function measurements provide a key constraint on the relation between galaxy populations and dark matter halos.
Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, ...but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.
We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.
The random forest methodology estimated cellular density with
= 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (
= 0.52). Outputs were also reported as graphic maps.
Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.
Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest grade disease present. Imaging techniques for doing so are generally not validated ...against the histopathologic criterion standard. The purpose of this work was to estimate the local glioma grade using a machine learning model trained on preoperative image data and spatially specific tumor samples. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models.
Patients with gliomas were enrolled in a prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, followed by image-guided stereotactic biopsy before resection. An imaging description was developed for each biopsy, and multiclass machine learning models were built to predict the World Health Organization grade. Models were assessed on classification accuracy, Cohen κ, precision, and recall.
Twenty-three patients (with 7/9/7 grade II/III/IV gliomas) had analyzable imaging-pathologic pairs, yielding 52 biopsy sites. The random forest method was the best algorithm tested. Tumor grade was predicted at 96% accuracy (κ = 0.93) using 4 inputs (T2, ADC, CBV, and transfer constant from dynamic contrast-enhanced imaging). By means of the conventional imaging only, the overall accuracy decreased (89% overall, κ = 0.79) and 43% of high-grade samples were misclassified as lower-grade disease.
We found that local pathologic grade can be predicted with a high accuracy using clinical imaging data. Advanced imaging data improved this accuracy, adding value to conventional imaging. Confirmatory imaging trials are justified.
Various tumor characteristics have been associated with neurocognitive functioning (NCF), though the role of tumor grade has not been adequately examined.
Seventy-two patients with histologically ...confirmed grade IV glioma (n = 37), grade III glioma (n = 20), and grade II glioma (n = 15) in the left temporal lobe completed preoperative neuropsychological assessment. Rates of impairment and mean test performances were compared by tumor grade with follow-up analysis of the influence of other tumor- and patient-related characteristics on NCF.
NCF impairment was more frequent in patients with grade IV tumor compared with patients with lower-grade tumors in verbal learning, executive functioning, as well as language abilities. Mean performances significantly differed by tumor grade on measures of verbal learning, processing speed, executive functioning, and language, with the grade IV group exhibiting worse performances than patients with lower-grade tumors. Group differences in mean performances remained significant when controlling for T1-weighted and fluid attenuated inversion recovery MRI-based lesion volume. Performances did not differ by seizure status or antiepileptic and steroid use.
Compared with patients with grade II or III left temporal lobe glioma, patients with grade IV tumors exhibit greater difficulty with verbal learning, processing speed, executive functioning, and language. Differences in NCF associated with glioma grade were independent of lesion volume, seizure status, and antiepileptic or steroid use, lending support to the concept of "lesion momentum" as a primary contributor to deficits in NCF of newly diagnosed patients prior to surgery.
Recent evidence suggests that human cells require more genetic changes for neoplastic transformation than do their murine counterparts. However, a precise enumeration of these differences has never ...been undertaken. We have determined that perturbation of two signaling pathways—involving p53 and Raf—suffices for the tumorigenic conversion of normal murine fibroblasts, while perturbation of six pathways—involving p53, pRb, PP2A, telomerase, Raf, and Ral-GEFs—is needed for human fibroblasts. Cell type-specific differences also exist in the requirements for tumorigenic transformation: immortalized human fibroblasts require the activation of Raf and Ral-GEFs, human embryonic kidney cells require the activation of PI3K and Ral-GEFs, and human mammary epithelial cells require the activation of Raf, PI3K, and Ral-GEFs.
The rs1076560 polymorphism of DRD2 (encoding dopamine receptor D2) is associated with alternative splicing and cognitive functioning; however, a mechanistic relationship to schizophrenia has not been ...shown. Here, we demonstrate that rs1076560(T) imparts a small but reliable risk for schizophrenia in a sample of 616 affected families and five independent replication samples totaling 4017 affected and 4704 unaffected individuals (odds ratio=1.1; P=0.004). rs1076560(T) was associated with impaired verbal fluency and comprehension in schizophrenia but improved performance among healthy comparison subjects. rs1076560(T) also associated with lower D2 short isoform expression in postmortem brain. rs1076560(T) disrupted a binding site for the splicing factor ZRANB2, diminished binding affinity between DRD2 pre-mRNA and ZRANB2 and abolished the ability of ZRANB2 to modulate short:long isoform-expression ratios of DRD2 minigenes in cell culture. Collectively, this work implicates rs1076560(T) as one possible risk factor for schizophrenia in the Han Chinese population, and suggests molecular mechanisms by which it may exert such influence.
Recent advances in machine learning have enabled image-based prediction of local tissue pathology in gliomas, but the clinical usefulness of these predictions is unknown. We aimed to evaluate the ...prognostic ability of imaging-based estimates of cellular density for patients with gliomas, with comparison to the gold standard reference of World Health Organization grading.
Data from 1181 (207 grade II, 246 grade III, 728 grade IV) previously untreated patients with gliomas from a single institution were analyzed. A pretrained random forest model estimated voxelwise tumor cellularity using MR imaging data. Maximum cellular density was correlated with the World Health Organization grade and actual survival, correcting for covariates of age and performance status.
A maximum estimated cellular density of >7681 nuclei/mm
was associated with a worse prognosis and a univariate hazard ratio of 4.21 (
< .001); the multivariate hazard ratio after adjusting for covariates of age and performance status was 2.91 (
< .001). The concordance index between maximum cellular density (adjusted for covariates) and survival was 0.734. The hazard ratio for a high World Health Organization grade (IV) was 7.57 univariate (
< .001) and 5.25 multivariate (
< .001). The concordance index for World Health Organization grading (adjusted for covariates) was 0.761. The maximum cellular density was an independent predictor of overall survival, and a Cox model using World Health Organization grade, maximum cellular density, age, and Karnofsky performance status had a higher concordance (C = 0.764; range 0.748-0.781) than the component predictors.
Image-based estimation of glioma cellularity is a promising biomarker for predicting survival, approaching the prognostic power of World Health Organization grading, with added values of early availability, low risk, and low cost.
The hydraulic resistances of the intima and media determine water flux and the advection of macromolecules into and across the arterial wall. Despite several experimental and computational studies, ...these transport processes and their dependence on transmural pressure remain incompletely understood. Here, we use a combination of experimental and computational methods to ascertain how the hydraulic permeability of the rat abdominal aorta depends on these two layers and how it is affected by structural rearrangement of the media under pressure. Ex vivo experiments determined the conductance of the whole wall, the thickness of the media and the geometry of medial smooth muscle cells (SMCs) and extracellular matrix (ECM). Numerical methods were used to compute water flux through the media. Intimal values were obtained by subtraction. A mechanism was identified that modulates pressure-induced changes in medial transport properties: compaction of the ECM leading to spatial reorganization of SMCs. This is summarized in an empirical constitutive law for permeability and volumetric strain. It led to the physiologically interesting observation that, as a consequence of the changes in medial microstructure, the relative contributions of the intima and media to the hydraulic resistance of the wall depend on the applied pressure; medial resistance dominated at pressures above approximately 93 mmHg in this vessel.
Clinical brain MR imaging registration algorithms are often made available by commercial vendors without figures of merit. The purpose of this study was to suggest a rational performance comparison ...methodology for these products.
Twenty patients were imaged on clinical 3T scanners by using 4 sequences: T2-weighted, FLAIR, susceptibility-weighted angiography, and T1 postcontrast. Fiducial landmark sites (
= 1175) were specified throughout these image volumes to define identical anatomic locations across sequences. Multiple registration algorithms were applied by using the T2 sequence as a fixed reference. Euclidean error was calculated before and after each registration and compared with a criterion standard landmark registration. The Euclidean effectiveness ratio is the fraction of Euclidean error remaining after registration, and the statistical effectiveness ratio is similar, but accounts for dispersion and noise.
Before registration, error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 2.07 ± 0.55 mm, 2.63 ± 0.62 mm, and 3.65 ± 2.00 mm, respectively. Postregistration, the best error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 1.55 ± 0.46 mm, 1.34 ± 0.23 mm, and 1.06 ± 0.16 mm, with Euclidean effectiveness ratio values of 0.493, 0.181, and 0.096 and statistical effectiveness ratio values of 0.573, 0.352, and 0.929 for rigid mutual information, affine mutual information, and a commercial GE registration, respectively.
We demonstrate a method for comparing the performance of registration algorithms and suggest the Euclidean error, Euclidean effectiveness ratio, and statistical effectiveness ratio as performance metrics for clinical registration algorithms. These figures of merit allow registration algorithms to be rationally compared.
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