Biology subjects

Verma, R.

Publications and source records attributed to Verma, R..

7 recordsLinked to original sources

bioRxiv2018

Structural connectivity analysis using Finsler geometry

In this work we demonstrate how Finsler geometry--and specifically the related geodesic tractography-- can be levied to analyze structural connections between different brain regions. We present new theoretical developments which support the definition of a novel Finsler metric and associated con-nectivity measures, based on closely related works on the Riemannian framework for diffusion MRI. Using data from the Human Connectome Project, as well as population data from an autism spectrum disorder study, we demonstrate that this new Finsler metric, together with the new connectivity measures, results in connectivity maps that are much closer to known tract anatomy compared to previous geodesic connectivity methods. Our implementation can be used to compute geodesic distance and connectivity maps for segmented areas, and is publicly available.

neuroscience↗

bioRxiv2018

Limits to anatomical accuracy of diffusion tractography using modern approaches

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the ISBI 2018 3D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractographys inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

neuroscience↗

bioRxiv2018

Single-Cell Transcriptomic Analysis of Human Lung Reveals Complex Multicellular Changes During Pulmonary Fibrosis

Pulmonary fibrosis is a devastating disorder that results in the progressive replacement of normal lung tissue with fibrotic scar. Available therapies slow disease progression, but most patients go on to die or require lung transplantation. Single-cell RNA-seq is a powerful tool that can reveal cellular identity via analysis of the transcriptome, but its ability to provide biologically or clinically meaningful insights in a disease context is largely unexplored. Accordingly, we performed single-cell RNA-seq on lung tissue obtained from eight transplant donors and eight recipients with pulmonary fibrosis and one bronchoscopic cryobiospy sample. Integrated single-cell transcriptomic analysis of donors and patients with pulmonary fibrosis identified the emergence of distinct populations of epithelial cells and macrophages that were common to all patients with lung fibrosis. Analysis of transcripts in the Wnt pathway suggested that within the same cell type, Wnt secretion and response are restricted to distinct non-overlapping cells, which was confirmed using in situ RNA hybridization. Single-cell RNA-seq revealed heterogeneity within alveolar macrophages from individual patients, which was confirmed by immunohistochemistry. These results support the feasibility of discovery-based approaches applying next generation sequencing technologies to clinically obtained samples with a goal of developing personalized therapies.\n\nOne Sentence SummarySingle-cell RNA-seq applied to tissue from diseased and donor lungs and a living patient with pulmonary fibrosis identifies cell type-specific disease-associated molecular pathways.

systems biology↗

bioRxiv2017

A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

Whole blood transcriptional signatures distinguishing active tuberculosis patients from asymptomatic latently infected individuals exist. Consensus has not been achieved regarding the optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. Here we show a blood transcriptional signature of active tuberculosis using RNA-Seq, confirming microarray results, that discriminates active tuberculosis from latently infected and healthy individuals, validating this signature in an independent cohort. Using an advanced modular approach, we utilise information from the entire transcriptome, which includes over-abundance of type I interferon-inducible genes and under-abundance of IFNG and TBX21, to develop a signature that discriminates active tuberculosis patients from latently infected individuals, or those with acute viral and bacterial infections. We suggest methods targeting gene selection across multiple discriminant modules can improve development of diagnostic biomarkers with improved performance. Finally, utilising the modular approach we demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.

immunology↗

bioRxiv2017

Multifactorial Dynamics of White Matter Connectivity During Adolescence

Studying developmental changes in white matter connectivity is critical for understanding neurobiological substrates of cognition, learning, and neuropsychiatric disorders. This becomes especially important during adolescence when a rapid expansion of the behavioral repertoire occurs. Several factors such as brain geometry, genetic expression profiles, and higher level architectural specifications such as the presence of segregated modules have been associated with the observed organization of white matter connections. However, we lack understanding of the extent to which such factors jointly describe the brain network organization, nor have insights into how their contribution changes developmentally. We constructed a multifactorial model of white matter connectivity using Bayesian network analysis and tested it with diffusion imaging data from a large community sample. We investigated contributions of multiple factors in explaining observed connectivity, including architectural specifications, which promote a modular yet integrative organization, and brains geometric and genetic features. Our results demonstrated that the initially dominant geometric and genetic factors become less influential with age, whereas the effect of architectural specifications increases. The identified structural modules are associated with well-known functional systems, and the level of association increases with age. This integrative analysis provides a computational characterization of the normative evolution of structural connectivity during adolescence.

neuroscience↗

bioRxiv2017

The Impact of In-Scanner Head Motion on Structural Connectivity Derived from Diffusion Tensor Imaging

Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion tensor imaging (DTI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency-and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for high-consistency network edges, which included both short-and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.

neuroscience↗

bioRxiv2017

Harmonization Of Multi-Site Diffusion Tensor Imaging Data

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, is counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.

neuroscience↗