ChromSorter PC: A database of chromosomal regions associated with human prostate cancer

BACKGROUND: Our increasing use of genetic and genomic strategies to understand human prostate cancer means that we need access to simplified and integrated information present in the associated biomedical literature. In particular, microarray gene expression studies and associated genetic mapping studies in prostate cancer would benefit from a generalized understanding of the prior work associated with this disease. This would allow us to focus subsequent laboratory studies to genomic regions already related to prostate cancer by other scientific methods. We have developed a database of prostate cancer related chromosomal information from the existing biomedical literature. The input material was based on a broad literature search with subsequent hand annotation of information relevant to prostate cancer. DESCRIPTION: The database was then analyzed for identifiable trends in the whole scale literature. We have used this database, named ChromSorter PC, to present graphical summaries of chromosomal regions associated with prostate cancer broken down by age, ethnicity and experimental method. In addition we have placed the database information on the human genome using the Generic Genome Browser tool that allows the visualization of the data with respect to user generated datasets. CONCLUSIONS: We have used this database as an additional dataset for the filtering of genes identified through genetics and genomics studies as warranting follow-up validation studies. We would like to make this dataset publicly available for use by other groups. Using the Genome Browser allows for the graphical analysis of the associated data . Additional material from the database can be obtained by contacting the authors ([email protected])

Effects of Thresholding on Voxel-Wise Correspondence of Breath-Hold and Resting-State Maps of Cerebrovascular Reactivity

Functional magnetic resonance imaging for presurgical brain mapping enables neurosurgeons to identify viable tissue near a site of operable pathology which might be at risk of surgery-induced damage. However, focal brain pathology (e.g., tumors) may selectively disrupt neurovascular coupling while leaving the underlying neurons functionally intact. Such neurovascular uncoupling can result in false negatives on brain activation maps thereby compromising their use for surgical planning. One way to detect potential neurovascular uncoupling is to map cerebrovascular reactivity using either an active breath-hold challenge or a passive resting-state scan. The equivalence of these two methods has yet to be fully established, especially at a voxel level of resolution. To quantitatively compare breath-hold and resting-state maps of cerebrovascular reactivity, we first identified threshold settings that optimized coverage of gray matter while minimizing false responses in white matter. When so optimized, the resting-state metric had moderately better gray matter coverage and specificity. We then assessed the spatial correspondence between the two metrics within cortical gray matter, again, across a wide range of thresholds. Optimal spatial correspondence was strongly dependent on threshold settings which if improperly set tended to produce statistically biased maps. When optimized, the two CVR maps did have moderately good correspondence with each other (mean accuracy of 73.6%). Our results show that while the breath-hold and resting-state maps may appear qualitatively similar they are not quantitatively identical at a voxel level of resolution

Unified Topological Inference for Brain Networks in Temporal Lobe Epilepsy Using the Wasserstein Distance

Persistent homology can extract hidden topological signals present in brain networks. Persistent homology summarizes the changes of topological structures over multiple different scales called filtrations. Doing so detect hidden topological signals that persist over multiple scales. However, a key obstacle of applying persistent homology to brain network studies has always been the lack of coherent statistical inference framework. To address this problem, we present a unified topological inference framework based on the Wasserstein distance. Our approach has no explicit models and distributional assumptions. The inference is performed in a completely data driven fashion. The method is applied to the resting-state functional magnetic resonance images (rs-fMRI) of the temporal lobe epilepsy patients collected at two different sites: University of Wisconsin-Madison and the Medical College of Wisconsin. However, the topological method is robust to variations due to sex and acquisition, and thus there is no need to account for sex and site as categorical nuisance covariates. We are able to localize brain regions that contribute the most to topological differences. We made MATLAB package available at https://github.com/laplcebeltrami/dynamicTDA that was used to perform all the analysis in this study

The Rat Genome Database (RGD): developments towards a phenome database

The Rat Genome Database (RGD) (http://rgd.mcw.edu) aims to meet the needs of its community by providing genetic and genomic infrastructure while also annotating the strengths of rat research: biochemistry, nutrition, pharmacology and physiology. Here, we report on RGD's development towards creating a phenome database. Recent developments can be categorized into three groups. (i) Improved data collection and integration to match increased volume and biological scope of research. (ii) Knowledge representation augmented by the implementation of a new ontology and annotation system. (iii) The addition of quantitative trait loci data, from rat, mouse and human to our advanced comparative genomics tools, as well as the creation of new, and enhancement of existing, tools to enable users to efficiently browse and survey research data. The emphasis is on helping researchers find genes responsible for disease through the use of rat models. These improvements, combined with the genomic sequence of the rat, have led to a successful year at RGD with over two million page accesses that represent an over 4-fold increase in a year. Future plans call for increased annotation of biological information on the rat elucidated through its use as a model for human pathobiology. The continued development of toolsets will facilitate integration of these data into the context of rat genomic sequence, as well as allow comparisons of biological and genomic data with the human genomic sequence and of an increasing number of organisms

T1−/T2‐weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy

Abstract Short‐range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age‐matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group‐level aberrations in overall myelin content or myelin changes through time in TLE

Neuroticism in temporal lobe epilepsy is associated with altered limbic-frontal lobe resting-state functional connectivity

Neuroticism, a core personality trait characterized by a tendency towards experiencing negative affect, has been reported to be higher in people with temporal lobe epilepsy (TLE) compared with healthy individuals. Neuroticism is a known predictor of depression and anxiety, which also occur more frequently in people with TLE. The purpose of this study was to identify abnormalities in whole-brain resting-state functional connectivity in relation to neuroticism in people with TLE and to determine the degree of unique versus shared patterns of abnormal connectivity in relation to elevated symptoms of depression and anxiety. Ninety-three individuals with TLE (55 females) and 40 healthy controls (18 females) from the Epilepsy Connectome Project (ECP) completed measures of neuroticism, depression, and anxiety, which were all significantly higher in people with TLE compared with controls. Resting-state functional connectivity was compared between controls and groups with TLE with high and low neuroticism using analysis of variance (ANOVA) and t-test. In secondary analyses, the same analytics were performed using measures of depression and anxiety and the unique variance in resting-state connectivity associated with neuroticism independent of symptoms of depression and anxiety identified. Increased neuroticism was significantly associated with hyposynchrony between the right hippocampus and Brodmann area (BA) 9 (region of prefrontal cortex (PFC)) (p \u3c 0.005), representing a unique relationship independent of symptoms of depression and anxiety. Hyposynchrony of connection between the right hippocampus and BA47 (anterior frontal operculum) was associated with high neuroticism and with higher depression and anxiety scores (p \u3c 0.05), making it a shared abnormal connection for the three measures. In conclusion, increased neuroticism exhibits both unique and shared patterns of abnormal functional connectivity with depression and anxiety symptoms between regions of the mesial temporal and frontal lobe

Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy

This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P \u3c 0.05), fewer patient (P \u3c 0.001) and parental years of education (P \u3c 0.05), greater racial diversity (P \u3c 0.05), and greater number of lifetime generalized seizures (P \u3c 0.001). The three groups also differed in an orderly manner across total intracranial (P \u3c 0.001) and bilateral cerebellar cortex volumes (P \u3c 0.01), and rate of bilateral hippocampal atrophy (P \u3c 0.014), but minimally in regional measures of cortical volume or thickness. In contrast, large-scale patterns of cortical-subcortical covariance networks revealed significant differences across groups in global and local measures of community structure and distribution of hubs. Resting-state fMRI revealed stepwise anomalies as a function of cluster membership, with the most abnormal patterns of connectivity evident in the generalized impairment group and no significant differences from controls in the cognitively intact group. Overall, the distinct underlying cognitive phenotypes of temporal lobe epilepsy harbor systematic relationships with clinical, sociodemographic and neuroimaging correlates. Cognitive phenotype variations in patient and familial education and ethnicity, with linked variations in total intracranial volume, raise the question of an early and persisting socioeconomic-status related neurodevelopmental impact, with additional contributions of clinical epilepsy factors (e.g., lifetime generalized seizures). The neuroimaging features of cognitive phenotype membership are most notable for disrupted large scale cortical-subcortical networks and patterns of functional connectivity with bilateral hippocampal and cerebellar atrophy. The cognitive taxonomy of temporal lobe epilepsy appears influenced by features that reflect the combined influence of socioeconomic, neurodevelopmental and neurobiological risk factors

Neuroanatomical correlates of personality traits in temporal lobe epilepsy: findings from the Epilepsy Connectome Project

Behavioral and personality disorders in temporal lobe epilepsy (TLE) have been a topic of interest and controversy for decades, with less attention paid to alterations in normal personality structure and traits. In this investigation, core personality traits (the Big 5) and their neurobiological correlates in TLE were explored using the Neuroticism Extraversion Openness-Five Factor Inventory (NEO-FFI) and structural magnetic resonance imaging (MRI) through the Epilepsy Connectome Project (ECP). NEO-FFI scores from 67 individuals with TLE (34.6 ± 9.5 years; 67% women) were compared to 31 healthy controls (32.8 ± 8.9 years; 41% women) to assess differences in the Big 5 traits (agreeableness, openness, conscientiousness, neuroticism, and extraversion). Individuals with TLE showed significantly higher neuroticism, with no significant differences on the other traits. Neural correlates of neuroticism were then determined in participants with TLE including cortical and subcortical volumes. Distributed reductions in cortical gray matter volumes were associated with increased neuroticism. Subcortically, hippocampal and amygdala volumes were negatively associated with neuroticism. These results offer insight into alterations in the Big 5 personality traits in TLE and their brain-related correlates