Biology subjects

Joshi, A. A.

Publications and source records attributed to Joshi, A. A..

11 recordsLinked to original sources

bioRxiv2025

sEEG-Suite: An Interactive Pipeline for Semi-Automated Contact Localization and Anatomical Labeling with Brainstorm

Stereoelectroencephalography (sEEG) is a critical tool for mapping epileptic networks in patients with drug-resistant epilepsy. Accurate localization and labeling of sEEG contacts are essential for identifying the seizure onset zone (SOZ) and ensuring optimal resective surgery. Traditional methods for localizing and labeling sEEG contacts rely on manual processing, which is prone to human error and variability. To address these challenges, we developed and integrated a semi-automatic sEEG contact localization and labeling pipeline within Brainstorm1, an open-source software platform for multimodal brain imaging analysis2-8, widely adopted in the neuroscience community with over 50,000 registered users and an active online user forum. The software has been supported by the National Institute of Health (NIH) for over two decades. The pipeline presented in this paper performs three key steps: (1) import and apply rigid co-registration of post-implantation Computed Tomography (CT) or post-CT with pre-implantation Magnetic Resonance Imaging (pre-MRI), (2) post-CT image segmentation and semi-automatic detection of sEEG contacts using GARDEL9, which has been integrated as a Brainstorm plugin, and (3) automatic anatomical labeling of contacts using standard and commonly used brain anatomy templates and atlases. Integrating this pipeline into Brainstorm brings the best of both worlds: GARDELs automation and Brainstorms multimodal data compatibility, and rich library of visualization and advanced analysis tools at both sensor and source level6. This sEEG-Suite tool facilitates reproducible research, supports clinical workflows, and accelerates sEEG-based investigations of invasive brain recordings.

neuroscience↗

bioRxiv2025

High-resolution taxonomic profiling and metatranscriptomics identify microbial, biochemical, host and ecological factors in peri-implant disease

Biofilm-associated diseases like peri-implant mucositis (PIM) and peri-implantitis (PI) are significant clinical challenges affecting millions of dental implant patients globally. Although studies have described the role of microbial, host, or environmental factors in disease development, their complex interplay, particularly during dysbiosis remains poorly understood. This cross-sectional study characterized the microbiome composition and metatranscriptomes of 125 peri-implant biofilms from 48 individuals uncovering molecular signatures linked to peri-implant health (PIH), PIM, and PI. Distinct variations were observed in biofilm amount, composition, activity, phage populations and host response. Biofilms were categorized into four community types (CTs) based on the bacterial transcriptional activity: one linked to PIH, one to PI, and two to PIM. PIH and PIM were primarily characterized by aerotolerant taxa with increased anabolic processes, while PI was dominated by obligate anaerobes with complex biofilm morphology, and heightened catabolic activity and virulence. PIM samples, relative to PIH were characterized by biofilm expansion with minimal functional changes, except for the Neisseria-rich PIM subtype showing higher pyruvate and lipoic acid metabolism. The phagome mirrored the bacterial compositional variations across disease states. Furthermore, human transcriptome responses varied indicating increased keratinization in PIH, enhanced expression of ribosome components in PIM, and inflammatory signaling and hypoxia in PI. Additionally, we identified complex species-enzyme, phage-bacteria, and host-microbe associations within the peri-implant ecosystem. Our integrative multi-omics approach provides a comprehensive view of microbial, biochemical, host, and ecological factors associated with dysbiosis, offering novel insights into peri-implant disease dynamics. ImportancePeri-implant mucositis and peri-implantitis are highly prevalent inflammatory conditions that compromise the long-term survival and success of dental implants, yet their underlying biological mechanisms are largely unresolved. While next-generation sequencing has advanced our understanding of microbial composition across health and peri-implant diseases, it falls short of capturing microbial activity and the broader molecular context of peri-implant dysbiosis. Metatranscriptomics overcomes this limitation by profiling actively transcribed genes within the biofilm, offering direct insights into microbial community functions. In this study, we integrated full-length 16S rRNA gene amplicon sequencing with metatranscriptomic profiling to simultaneously assess microbial taxonomy, functional activity, phage dynamics, and host gene expression in peri-implant biofilms. Importantly, we provide a systems-level view and report previously undescribed associations between different molecular signatures in peri-implant ecosystem.

microbiology↗

bioRxiv2025

Invasive species drive polymicrobial resistance to amoxicillin in oral biofilms through beta-lactamase release

Since bacterial biofilms often cause refractory infections, antimicrobial susceptibility testing (AST) is highly desirable even for oral peri-implant biofilms. However, characterization of polymicrobial drug resistance is challenging due to high diversity and complexity of these biofilms. In this work, we developed laser-assisted AST and detected polymicrobial amoxicillin resistance in peri-implantitis. TEM-1 {beta}-lactamase production enabled an Enterobacter sp. strain SPS_532 to protect its otherwise susceptible biofilm cohabitants. To understand the {beta}-lactamase driven cross-protection in the human microbiome we aggregated genomic (n = 200,000) and patient microbial data (n = 27,000), developed a cross-protection assay, studied a representative strain collection (n = 118) and established a complex biofilm in vitro model (with an average of 133 species from 164 found in dental plaque). Multiple oral allochthonous species, e.g., Enterobacter, Klebsiella, Escherichia, Staphylococcus, and only a single typical oral microorganism, Haemophilus, were able to cross-protect. Diverse bla genes conferred activity, via a high expression of chromosomal gene, e.g., blaAmpC gene or by the presence of plasmidic gene, e.g., blaTEM-1 gene. Invaders not only cross-protected the biofilm from the antibiotic, but also supported expansion of opportunistic pathogens like Fusobacterium species. Cross-protection in complex biofilms depended on the diffusion rate and population size of the invader, which could be bio-controlled with a phage. Deciphering polymicrobial resistance might support the development of diagnostic and therapeutic approaches to combat implant-associated biofilm infections in the human mouth.

microbiology↗

bioRxiv2025

Integrative microbiome- and metatranscriptome-based analyses reveal diagnostic biomarkers for peri-implantitis

Peri-implantitis is a severe biofilm-associated infection of the tissues around dental implants that increases the risk of implant failure. The prognosis of peri-implantitis treatment is compromised by the resistance of well-organized and mature bacterial biofilms. Thus, early diagnosis of a pathogenic biofilm would enable treatment at a prognostically favourable stage. However, relatively little is known about how the microbial constitution of the biofilm changes during disease development. The aim of this cross-sectional study was therefore to identify peri-implant taxonomic and functional biomarkers that reliably indicate peri-implantitis using paired data from full length 16S rRNA gene amplicon sequencing (full-16S) and metatranscriptomics (RNAseq). Disease signatures were identified using 24 healthy and 24 peri-implantitis-associated biofilm samples from 32 patients. The taxonomic measurements were validated with 68 additional full-16S samples from another 40 patients. Both full-16S and RNAseq revealed significant differences between healthy and peri-implantitis samples, with respect to both the microbiome and functional profiles. A shift from aerotolerant Gram-positive bacteria to anaerobic Gram-negative bacteria was observed in peri-implantitis. Distinct metabolic pathways were expressed in healthy and peri-implantitis samples. Our results, based on paired taxonomic and functional profiles, provide for the first time important insights into the complex peri-implant biofilm ecology related to amino acid metabolism. Integrating taxonomic and functional information improved the predictive ability (AUC = 0.85) of the machine learning models and revealed diagnostic biomarkers with large effect sizes (Cohens d > 0.8). Primary biomarkers included health-associated Streptococcus, Rothia species and enzymes associated with peri-implantitis (urocanate hydratase, tripeptide aminopeptidase, NADH:ubiquinone reductase, phosphoenolpyruvate carboxykinase and polyribonucleotide nucleotidyltransferase - mostly expressed by Fusobacteriia and Bacteroidia). Thus, biofilm profiling at these two molecular levels reveals highly predictive disease biomarkers and provide the basis for developing early diagnostics and individualized therapy approaches for peri-implant diseases.

microbiology↗

bioRxiv2025

Patterns of grass (Poaceae) species distribution and richness across India

Tropical grassy biomes (TGBs) cover a substantial portion of Earths land surface area and support a rich diversity of fauna and flora. Yet, despite their importance for biodiversity and human livelihoods, the TGBs of South Asia, including India, have historically been undervalued, and as result, understudied. Here, we address this gap with the first subcontinental-scale analyses of grass species richness and distribution across the Indian region. Using data on species occurrences collated from multiple regional floras, we describe the distribution of families, functional groups and species of Poaecea across this region, and their variation as a function of climate and geomorphology. India hosts >1100 grass species, comprising [~]10% of global grass richness. Over half of these were perennial C4 grasses, with Panicoideae being the most speciose subfamily in the country. Consistent with patterns observed globally, the PACMAD clade consisting of both C3 and C4 species was more speciose in warmer regions, while the C3 subfamilies of the BEP clade, Pooideae and Bambusoideae, were more abundant in cooler regions of the country. Both total and C4 species richness were greatest in warmer districts with high moisture availability, while cooler and wetter districts with low precipitation seasonality supported greater C3 species richness. Our results spotlight previously unrecognized areas of high grass species richness in the arid and semi-arid areas of North-West India and the Deccan Peninsula in India, and call for conservation attention to neglected regions that are outside known biodiversity hotspots and protected areas.

ecology↗

bioRxiv2025

Optimizing electrode placement and information capacity for local field potentials in cortex

Recent neurosurgery advancements include improved stereotactic targeting and increased density and specificity of electrophysiological evaluation. This study introduces a subject-specific, in silico modeling tool for optimizing electrode placement and maximizing coverage with a variety of devices. The basis for optimization is the Shannon-Hartley information capacity of field potentials derived from dipolar sources. The approach integrates subject-specific MRI data with finite element modeling (FEM) used to simulate the sensitivity of subdural and intracortical devices. Sensitivity maps, or lead fields, from these models enable the comparison of different electrode placements, contact sizes, contact configurations, and substrate properties, which are often overlooked factors. One key tool is a genetic algorithm that optimizes electrode placement by maximizing information capacity. Another is a sparse sensor method, Sparse Electrode Placement for Input Optimization (SEPIO), that selects the best sensor subsets for accurate source classification. We demonstrate several use cases for clinicians, engineers, and researchers. Overall, these open-source tools provide a quantitative framework to select devices from a neurosurgical armament and to optimize device and contact placement. Using these tools may help refine electrode coverage with low channel count devices while minimizing the burden of invasive surgery. The study demonstrates that optimized electrode placement significantly improves the information capacity and signal quality of local field potential (LFP) recordings. The tools developed offer a valuable approach for refining neurosurgical techniques and enhancing the design of neural implants. HighlightsO_LItool for simulating subject-specific local field potentials and electrode sensitivity. C_LIO_LIptimized electrode placement enhances ROI source coverage, and signal quality. C_LIO_LIsensor-based classification boosts data quality without extra electrode cost. C_LIO_LIcomparisons of devices and contact arrangements. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/650658v3_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1a3a7aaorg.highwire.dtl.DTLVardef@154a776org.highwire.dtl.DTLVardef@be4826org.highwire.dtl.DTLVardef@72caa9_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data

We present BundleCleaner, an unsupervised multi-step frame-work that can filter, denoise and subsample bundles derived from diffusion MRI-based whole-brain tractography. Our approach considers both the global bundle structure and local streamline-wise features. We apply BundleCleaner to bundles generated from single-shell diffusion MRI data in an independent clinical sample of older adults from India using probabilistic tractography and the resulting cleaned bundles can better align with the atlas bundles with reduced overreach. In a downstream tractometry analysis, we show that the cleaned bundles, represented with less than 20% of the original set of points, can robustly localize along-tract microstructural differences between 32 healthy controls and 34 participants with Alzheimers disease ranging in age from 55 to 84 years old. Our approach can help reduce memory burden and improving computational efficiency when working with tractography data, and shows promise for large-scale multi-site tractometry.

neuroscience↗

bioRxiv2023

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Projects grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collections Population Imaging of Psychology dataset.

neuroscience↗

bioRxiv2020

A Hybrid High-Resolution Anatomical MRI Atlas with Sub-parcellation of Cortical Gyri using Resting fMRI

We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent grey-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired at a resolution of 0.547mm x 0.547mm x 0.800mm five times and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing adjusted Rand indices between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subjects resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.

bioinformatics↗

bioRxiv2020

A Novel Standalone Microfluidic Device for Local Control of Oxygen Tension for Intestinal-Bacteria Interactions

The intestinal environment is unique because it supports the intestinal epithelial cells under a normal oxygen environment and the microbiota under an anoxic environment. Due to importance of understanding the interactions between the epithelium and the microbiota, there is a strong need for developing representative and simple experimental models. Current approaches do not capture the dual-oxygen environment, require external anaerobic chambers, or are complex. Another major limitation is that in the solutions that can mimic the dual-oxygen environment, the oxygenation level of the epithelial cells is not known, raising the question whether the cells are hypoxic. We report standalone microfluidic devices that form a dual-oxygen environment without the use of an external anaerobic chamber or oxygen scavengers to coculture intestinal epithelial and bacterial cells. By changing the thickness of the device cover, the oxygen tension in the chamber could be modulated. We verified the oxygen levels using several tests: microscale oxygen sensitive sensors incorporated within the devices, hypoxic immunostaining of Caco-2 cells, and genetically encoded bacteria. Collectively, these methods monitored oxygen concentrations in devices more comprehensively than previous reports and allowed for control of oxygen tension to match the requirements of both intestinal cells and anaerobic bacteria. Our experimental model is supported by the mathematical model that considers diffusion of oxygen into the top chamber and the cellular oxygen consumption rate. This allowed us to experimentally determine the oxygen consumption rate of the epithelial cells more precisely.

bioengineering↗

bioRxiv2018

Climate and vegetation collectively drive soil respiration in montane forest-grassland landscapes of the southern Western Ghats, India

Land-use conversion to non-native species plantations not only affects biodiversity but also alters important ecosystem functions including above- and below-ground carbon sequestration, and CO2 release rates from soils via soil respiration. Though the role of soil temperature and moisture on soil respiration is well recognized, little is known about how their effects vary across different land-use types. This study looked at the effects of land-cover change on temporal patterns of soil respiration in a montane forest-grassland-plantation matrix, a highly diverse but climatically sensitive ecosystem in the tropical Western Ghats of India. Among native vegetation types, soil respiration rates were higher in grassland compared to forest patches. Invasion of grassland by an exotic tree species - wattle (Acacia mearnsii) reduced soil respiration rates to levels similar to that of forests. However, conversion of native grasslands to non-native pine (Pinus patula) plantations led to the largest declines in soil respiration rates. In addition, the sensitivity of soil respiration to changes in temperature and moisture differed between different vegetation types. Across all vegetation types, respiration was largely insensitive to changes in soil temperature when moisture levels were low. However, when soil moisture levels were high, respiration increased with temperature in grassland and wattle patches, decreased in the case of pine plantations, and remained largely unchanged in shola forests. Our results suggest that changes in aboveground vegetation type can significantly affect soil C cycling even in the absence of any underlying differences in soil type.

ecology↗