Biology subjects

Ghosh, S. S.

Publications and source records attributed to Ghosh, S. S..

24 records · Page 2Linked to original sources

bioRxiv2019

Proximal and Distal Gut Mucosa Adapt Differently to Westernized Diet, Promoting an Insulin-Resistant Dysmetabolic State

ObjectiveCombinatorial gut hormone therapy is one of the more promising strategies for identifying improved treatments for metabolic disease. Many approaches combine the established benefits of glucagon-like peptide-1 (GLP-1) agonism with one or more additional molecules with the aim of improving metabolic outcomes. Recent attention has been drawn to the glucose-dependent insulinotropic polypeptide (GIP) system due to compelling pre-clinical evidence describing the metabolic benefits of antagonising the GIP receptor (GIPR). We rationalised that benefit might be accrued from combining GIPR antagonism with GLP-1 agonism. To this end we investigated the metabolic effects of co-administration of previously reported peptide-based GIPR antagonists with the GLP-1 agonist liraglutide. MethodsTwo GIPR peptide antagonists, GIPA-1 (mouse GIP(3-30)NH2) and GIPA-2 (NAc-K10[{gamma}E{gamma}E-C16]-Arg18-hGIP(5-42)), were pharmacologically characterised in vitro in an assay measuring cAMP production in CHO-K1 cells overexpressing the mouse GIPR. These peptides were then characterised in vivo in lean mice for their effect on oral glucose tolerance, as well as their ability to antagonize exogenous GIP action. Finally, a mouse model of diet-induced obesity (DIO) was used to investigate the potential metabolic benefits of chronic dosing of peptide-based GIPR antagonists, alone or in combination with liraglutide. ResultsIn vitro, both GIPR peptides exhibited potent antagonistic properties, with GIPA-2 being the more potent of the two. Acute in vivo administration of GIPA-1 during an oral glucose tolerance test (OGTT) had negligible effects on glucose tolerance and circulated insulin in lean mice. In contrast, GIPA-2 impaired glucose tolerance and attenuated circulating insulin levels, with offsetting effects on glycemia noted with co-administration with exogenous mouse GIP, suggesting true antagonism via GIPA-2 at the GIP receptor. Chronic administration studies in a DIO mouse model showed expected effects of GLP-1 agonism (via liraglutide), lowering food intake, body weight, fasting blood glucose and plasma insulin concentrations while improving glucose sensitivity, whereas delivery of either GIPR antagonist alone had negligible effects on these parameters. Interestingly, chronic dual therapy with the GIPR antagonists and GLP-1 showed separation from single intervention arms though augmented insulin sensitizing effects (modestly lowering insulin and HOMA-IR) and lowering plasmas triglycerides and free-fatty acids, with more notable effects observed with GIPA-1 compared to GIPA-2. ConclusionWe conclude that, in contrast to the well-documented effects of GLP-1R agonism, systemic administration of peptide-based GIPR antagonists demonstrate minimal benefit on metabolic parameters in DIO mice, exhibiting no major effects on body weight, food intake and glycaemic parameters. However, the co-administration of both a GIPR antagonist together with a GLP1 agonist uncovers interesting synergistic and beneficial effects on measures of insulin sensitivity, circulating lipids and certain adipose stores that seem influenced by the degree or nature of GIP receptor antagonism.

molecular biology↗

bioRxiv2019

Analysis of task-based functional MRI data preprocessed with fMRIPrep

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure (BIDS) to standardize both the input datasets --MRI data as stored by the scanner-- and the outputs --data ready for modeling and analysis--, fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

bioinformatics↗

bioRxiv2019

Mapping the human subcortical auditory system using histology, post mortem MRI and in vivo MRI at 7T

Studying the human subcortical auditory system non-invasively is challenging due to its small, densely packed structures deep within the brain. Additionally, the elaborate three-dimensional (3-D) structure of the system can be difficult to understand based on currently available 2-D schematics and animal models. We addressed these issues using a combination of histological data, post mortem magnetic resonance imaging (MRI), and in vivo MRI at 7 Tesla. We created anatomical atlases based on state-of-the-art human histology (BigBrain) and post mortem MRI (50 m). We measured functional MRI (fMRI) responses to natural sounds and demonstrate that the functional localization of subcortical structures is reliable within individual participants who were scanned in two different experiments. Further, a group functional atlas derived from the functional data locates these structures with a median distance below 2mm. Using diffusion MRI tractography, we revealed structural connectivity maps of the human subcortical auditory pathway both in vivo (1050 m isotropic resolution) and post mortem (200 m isotropic resolution). This work captures current MRI capabilities for investigating the human subcortical auditory system, describes challenges that remain, and contributes novel, openly available data, atlases, and tools for researching the human auditory system.

neuroscience↗

bioRxiv2018

LittleBrain: a gradient-based tool for the topographical interpretation of cerebellar neuroimaging findings

Gradient-based approaches to brain function have recently unmasked fundamental properties of brain organization. Diffusion map embedding analysis of resting-state fMRI data revealed a primary-to-transmodal axis of cerebral cortical macroscale functional organization. The same method was recently used to analyze resting-state data within the cerebellum, revealing for the first time a sensorimotor-fugal macroscale organization principle of cerebellar function. Cerebellar gradient 1 extended from motor to non-motor task-unfocused (default-mode network) areas, and cerebellar gradient 2 isolated task-focused processing regions. Here we present a freely available and easily accessible tool that applies this new knowledge to the topographical interpretation of cerebellar neuroimaging findings. LittleBrain generates scatterplots that illustrate the relationship between cerebellar data (e.g., volumetric patient study clusters, task activation maps, etc.) and cerebellar gradients 1 and 2. This novel method of data mapping provides alternative, gradual visualizations that complement discrete parcellation maps of cerebellar functional neuroanatomy. We present application examples to show that LittleBrain can also capture subtle, progressive aspects of cerebellar functional neuroanatomy that would be difficult to visualize using conventional mapping techniques. Download and use instructions can be found at https://xaviergp.github.io/littlebrain.

neuroscience↗

bioRxiv2018

Functional gradients of the cerebellum: a fundamental movement-to-thought principle

A central principle for understanding the cerebral cortex is that macroscale anatomy reflects a functional hierarchy from primary to transmodal processing. In contrast, the central axis of motor and nonmotor macroscale organization in the cerebellum remains unknown. Here we applied diffusion map embedding to resting-state data from the Human Connectome Project dataset (n=1003), and show for the first time that cerebellar functional regions follow a gradual organization which progresses from primary (motor) to transmodal (DMN, task-unfocused) regions. A secondary axis extends from task-unfocused to task-focused processing. Further, these two principal gradients reveal functional properties of the well-established cerebellar double motor representation, and its relationship with the recently described triple nonmotor representation. These interpretations are further supported by data-driven clustering and cerebello-cerebral functional connectivity analyses. Importantly, these descriptions remain observable at the individual subject level. These findings, from an exceptionally large and high-quality dataset, provide new and fundamental insights into the functional organization of the human cerebellum, unmask new testable hypotheses for future studies, and yield an unprecedented tool for the topographical, macroscale interpretation of cerebellar findings.

neuroscience↗

bioRxiv2016

Mindboggling morphometry of human brains

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains every conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, and more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggles algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.\n\nAuthor SummaryBrains vary in many ways, including their shape. Analysing differences in shape between brains or changes in brain shape over time has been used to characterize morphology of diseased brains, but these analyses conventionally rely on simple volumetric shape measures. We believe that access to a greater variety of shape measures could provide greater sensitivity and specificity to morphological disturbances, and could aid in diagnosis, tracking, and prediction of the progression of mental health disorders. Mindboggle is open source software that provides neuroscientists (and indeed, anyone interested in computing shapes) tools for computing a variety of shape measures, including area, volume, thickness, curvature, geodesic depth, travel depth, Laplace-Beltrami spectra, and Zernike moments. In addition to algorithmic contributions, we conducted evaluations and applied Mindboggle to conduct the most detailed shape analysis of human brains.

neuroscience↗