scholarly journals Topological gene-expression networks recapitulate brain anatomy and function

2018 ◽  
Author(s):  
Alice Patania ◽  
Pierluigi Selvaggi ◽  
Mattia Veronese ◽  
Ottavia Dipasquale ◽  
Paul Expert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Expression Data ◽  
Pharmacological Challenge ◽  
Topological Network ◽  
Dopaminergic Pathway ◽  
And Function

AbstractUnderstanding how gene expression translates to and affects human behaviour is one of the ultimate aims of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to produce and analyze genes co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas, and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene-set and find that co-expression networks produced by Mapper returned a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that topological network descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenge.

scholarly journals Topological gene expression networks recapitulate brain anatomy and function

2019 ◽  
Vol 3 (3) ◽  
pp. 744-762 ◽  
Author(s):  
Alice Patania ◽  
Pierluigi Selvaggi ◽  
Mattia Veronese ◽  
Ottavia Dipasquale ◽  
Paul Expert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Expression Data ◽  
Related Gene ◽  
Genetic Pathways ◽  
Dopaminergic Pathway ◽  
And Function

Understanding how gene expression translates to and affects human behavior is one of the ultimate goals of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to analyze gene co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene set and find that co-expression networks produced by Mapper return a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that network based descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenges.

scholarly journals A practical guide to linking brain-wide gene expression and neuroimaging data

2018 ◽  
Author(s):  
Aurina Arnatkevičiūtė ◽  
Ben D. Fulcher ◽  
Alex Fornito
Keyword(s):  
Gene Expression ◽  
Brain Atlas ◽  
Expression Data ◽  
Analysis Pipeline ◽  
Atlas Data ◽  
Processing Pipeline ◽  
Neuroimaging Data ◽  
Brain Structure And Function ◽  
Expression Atlas ◽  
And Function

AbstractThe recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on the molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

scholarly journals Transcriptomic and Cellular Decoding of Regional Brain Vulnerability to Neurodevelopmental Disorders

2019 ◽  
Author(s):  
Jakob Seidlitz ◽  
Ajay Nadig ◽  
Siyuan Liu ◽  
Richard A.I. Bethlehem ◽  
Petra E. Vértes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Patterns ◽  
Neurodevelopmental Disorders ◽  
Copy Number Variations ◽  
Brain Anatomy ◽  
Specific Cell ◽  
Expression Data ◽  
Regional Brain ◽  
Genetic Risks ◽  
Neuroimaging Data

AbstractNeurodevelopmental disorders are highly heritable and associated with spatially-selective disruptions of brain anatomy. The logic that translates genetic risks into spatially patterned brain vulnerabilities remains unclear but is a fundamental question in disease pathogenesis. Here, we approach this question by integrating (i) in vivo neuroimaging data from patient subgroups with known causal genomic copy number variations (CNVs), and (ii) bulk and single-cell gene expression data from healthy cortex. First, for each of six different CNV disorders, we show that spatial patterns of cortical anatomy change in youth are correlated with spatial patterns of expression for CNV region genes in bulk cortical tissue from typically-developing adults. Next, by transforming normative bulk-tissue cortical expression data into cell-type expression maps, we further link each disorder’s anatomical change map to specific cell classes and specific CNV-region genes that these cells express. Finally, we establish convergent validity of this “transcriptional vulnerability model” by inter-relating patient neuroimaging data with measures of altered gene expression in both brain and blood-derived patient tissue. Our work clarifies general biological principles that govern the mapping of genetic risks onto regional brain disruption in neurodevelopmental disorders. We present new methods that can harness these principles to screen for potential cellular and molecular determinants of disease from readily available patient neuroimaging data.

scholarly journals A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lianxin Zhong ◽  
Qingfang Meng ◽  
Yuehui Chen ◽  
Lei Du ◽  
Peng Wu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Small Sample ◽  
Expression Data ◽  
Cancer Subtypes ◽  
Cancer Pathogenesis ◽  
Depth Study ◽  
And Function

Abstract Background Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. Results In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. Conclusion The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.

Magnetic Resonance Methodologies

2017 ◽  
Author(s):  
Peter A. Bandettini ◽  
Hanzhang Lu
Keyword(s):  
Magnetic Resonance ◽  
Brain Metabolism ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Cutting Edge ◽  
Brain Anatomy ◽  
Resonance Imaging ◽  
Specific Assessment ◽  
Structural Connections ◽  
And Function

Magnetic resonance imaging is a noninvasive tool for assessing brain anatomy, perfusion, metabolism, and function with precision. In this chapter, the basics and the most cutting edge examples of MRI-based measures are described. The first is measurement of cerebral perfusion, including the latest techniques involving spin-labelling as well as the tracking of exogenous contrast agents. Functional MRI is then discussed, along with some of the cutting edge methodology that has yet to make it into routine clinical practice. Next, resting state fMRI is described, a powerful technique whereby the entire brain connectivity can be established. Diffusion-based MRI techniques are useful for diagnosing brain trauma as well as understanding the structural connections in healthy and pathological brains. Spectroscopy is able to make spatially specific and metabolite-specific assessment of brain metabolism. The chapter ends with an overview of structural imaging with MRI, highlighting the developing field of morphometry and its potential for differentially assessing individual brains.

scholarly journals Patterns of Immune Infiltration in Endometriosis and Their Relationship to r-AFS Stages

2021 ◽  
Vol 12 ◽  
Author(s):  
Qiyu Zhong ◽  
Fan Yang ◽  
Xiaochuan Chen ◽  
Jinbo Li ◽  
Cailing Zhong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Plasma Cells ◽  
Immune Cell ◽  
M2 Macrophages ◽  
Expression Data ◽  
Gene Expression Microarray Data ◽  
And Function ◽  
Ovarian Endometriomas ◽  
Activation Status

Background: Endometriosis (EMS) is an estrogen-dependent disease in which endometrial glands and stroma arise outside the uterus. Current studies have suggested that the number and function of immune cells are abnormal in the abdominal fluid and ectopic lesion tissues of patients with EMS. The developed CIBERSORT method allows immune cell profiling by the deconvolution of gene expression microarray data.Methods: By applying CIBERSORT, we assessed the relative proportions of immune cells in 68 normal endometrial tissues (NO), 112 eutopic endometrial tissues (EU) and 24 ectopic endometrial tissues (EC). The obtained immune cell profiles provided enumeration and activation status of 22 immune cell subtypes. We obtained associations between the immune cell environment and EMS r-AFS stages. Macrophages were evaluated by immunohistochemistry (IHC) in 60 patients with ovarian endometriomas.Results: Total natural killer (NK) cells were significantly decreased in EC, while plasma cells and resting CD4 memory T cells were increased in EC. Total macrophages in EC were significantly increased compared to those of EU and NO, and M2 macrophages were the primary macrophages in EC. Compared to those of EC from patients with r-AFS stage I ~ II, M2 macrophages in EC from patients with stage III ~ IV were significantly increased. IHC experiments showed that total macrophages were increased in EC, with M2 macrophages being the primary subtype.Conclusions: Our data demonstrate that deconvolution of gene expression data by CIBERSORT provides valuable information about immune cell composition in EMS.

scholarly journals Splotch: Robust estimation of aligned spatial temporal gene expression data

2019 ◽  
Author(s):  
Tarmo Äijö ◽  
Silas Maniatis ◽  
Sanja Vickovic ◽  
Kristy Kang ◽  
Miguel Cuevas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Data ◽  
Rna Seq ◽  
Computational Framework ◽  
Temporal Gene Expression ◽  
Tissue Sections ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Approaches To Study ◽  
And Function

AbstractSpatial genomics technologies enable new approaches to study how cells interact and function in intact multicellular environments but present a host of technical and computational challenges. Here we describe Splotch, a novel computational framework for the analysis of spatially resolved transcriptomics data. Splotch aligns transcriptomics data from multiple tissue sections and timepoints to generate improved posterior estimates of gene expression. We demonstrate alignment of a large corpus of single-cell RNA-seq data into an automatically generated spatial-temporal coordinate and study optimal design for spatial transcriptomics experiments.

scholarly journals Imaging brain structure and function, infection and gene expression in the body using light

1997 ◽  
Vol 352 (1354) ◽  
pp. 755-761 ◽  
Author(s):  
David A. Benaron ◽  
Pamela R. Contag ◽  
Christopher H. Contag
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
The Body ◽  
Low Light ◽  
Non Invasive ◽  
Positron Emission ◽  
Brain Structure And Function ◽  
Imaging Of Infection ◽  
And Function

Light can be used to probe the function and structure of human tissues. We have been exploring two distinct methods: (i) externally emitting light into tissue and measuring the transmitted light to characterize a region through which the light has passed, and (ii) internally generating light within tissue and using the radiated light as a quantitative homing beacon. The emitted–light approach falls within the domain of spectroscopy, and has allowed for imaging of intracranial haemorrhage in newborns and of brain function in adults. The generated–light approach is conceptually parallel to positron emission tomography (PET) or nuclear medicine scanning, and has allowed for real–time, non–invasive monitoring and imaging of infection and gene expression in vivo using low–light cameras and ordinary lenses. In this paper, we discuss recent results and speculate on the applications of such techniques.

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