Biology subjects

Selim, M. K.

Publications and source records attributed to Selim, M. K..

4 recordsLinked to original sources

bioRxiv2026

Multimodal neuroimaging-microbiota integration identifies Akkermansia as a modulator of alcohol-induced gut-liver-brain pathology

Alcohol use disorder (AUD) disrupts the gut-liver-brain axis, yet mechanistically grounded and therapeutically actionable targets within this network remain poorly defined. To identify microbial modulators of alcohol-induced tissue pathology, longitudinal advanced diffusion MRI and fecal 16S rRNA profiling were integrated across Marchigian Sardinian alcohol-preferring rats evaluated at baseline, after four weeks of voluntary alcohol intake, and following six weeks of abstinence. Machine learning, specifically random forest models combining neuroimaging and microbiota data, improved phase classification and identified Akkermansia as the microbial feature most strongly associated with alcohol-related white matter microstructural abnormalities. Alcohol exposure induced widespread white matter alterations alongside gut dysbiosis characterized by reduced microbial diversity. To evaluate functional relevance, Akkermansia muciniphila was administered during the abstinence phase. Supplementation with A. muciniphila restored intestinal mucus, reduced liver injury markers, and elevated myelin basic protein levels within affected white matter regions. Collectively, these findings highlight Akkermansia as a critical modulator of alcohol-induced gut-liver-brain pathology and provide experimental support for a causal contribution of specific gut bacteria to persistent white matter damage in AUD. More broadly, this work establishes a robust multimodal framework for microbiome-based target discovery with clear translational relevance for disorders characterized by dysfunction along the gut-liver-brain axis. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAlcohol use disorder (AUD) is associated with gut dysbiosis, impaired intestinal barrier function, liver injury, and persistent white matter abnormalities. Previous studies in patients and animal models have linked alcohol exposure to reduced microbial diversity, altered gut permeability, and white matter microstructural damage, particularly during abstinence. Other work has shown that microbiota-derived interventions can ameliorate peripheral consequences of alcohol exposure, especially in the gut and liver. However, the specific microbial features linked to alcohol-induced brain pathology remain poorly defined, and no prior study has integrated longitudinal microbiota and neuroimaging data to identify candidate microbial modulators of alcohol-related white matter damage and then functionally test them in vivo across the gut-liver-brain axis. Added value of this studyWe developed a multimodal framework that integrates longitudinal advanced diffusion MRI with fecal microbiota profiling and machine learning in alcohol-preferring rats. This approach identified Akkermansia as the microbial feature most strongly associated with alcohol-induced white matter abnormalities. Guided by this result, we administered Akkermansia muciniphila during abstinence and observed coordinated beneficial effects across multiple organs, including restoration of intestinal mucus, reduction of liver injury markers, and recovery of myelin basic protein in affected white matter regions. To our knowledge, this is the first study to combine longitudinal microbiota-MRI integration with experimental validation of a microbiota-based intervention that mitigates alcohol-induced pathology across the gut-liver-brain axis while restoring central white matter integrity. Implications of all the available evidenceOur findings support a mechanistic contribution of specific gut bacteria to persistent alcohol-induced tissue damage and identify Akkermansia as a candidate modulator of gut-liver-brain axis dysfunction in AUD. More broadly, this study establishes a generalizable strategy for integrating microbiota and neuroimaging data to discover biologically meaningful and therapeutically actionable targets in complex disorders involving coordinated peripheral and central pathology.

neuroscience↗

bioRxiv2025

10.5 Tesla High-Resolution Macaque Brain MRI for Connectivity Studies

Mapping brain connectivity in primates remains a major challenge due to difficulties in resolving microscopic white matter architecture, while maintaining whole-brain coverage. Increasing imaging spatial resolution is key for disambiguating fibre configurations within smaller anatomical volumes. Here, we present novel developments that allow high-resolution diffusion MRI of the macaque brain using one of the world's highest-field human MRI scanners operating at 10.5 Tesla, allowing both in vivo and ex vivo macaque brain imaging. Our approach achieves very high spatial resolution across both tissue states, (up to 580 m)3 in vivo and (300 m)3 ex vivo, with diffusion weighting up to b = 6000 s/mm2. We detail methodological advances in data acquisition, image reconstruction, processing and whole-brain tractography that overcome critical challenges associated with ultra-high-field imaging. This work establishes a new framework for high-resolution in vivo and ex vivo neuroimaging of the NHP brain at 10.5 T using a human bore scanner, paving the way for subsequent analyses of brain connectivity across species and tissue states at unprecedented detail. The dataset, along with all processing pipelines, containerised workflows, and reusable web services, is openly shared to support reproducibility and future integration with microscopy for studying white matter microstructure and connections at the mesoscale.

neuroscience↗

bioRxiv2025

Diffusion-weighted steady-state free precession imaging in the ex vivo macaque brain on a 10.5T human MRI scanner

Diffusion MRI provides a non-invasive probe of local fibre bundles and long-range anatomical connections to characterise the structural connectome. One way to achieve very high spatial resolution diffusion MRI data for connectivity investigations is to scan ex-vivo brains over many hours or days, ideally at ultra-high field strength to boost signal levels. However, conventional diffusion MRI acquisition techniques do not generally deliver good data quality for the challenging conditions of ex-vivo tissue, characterised by reduced diffusivities and relaxation times when compared to in vivo. In this work, we investigate the potential of the diffusion-weighted steady-state free precession (DW-SSFP) sequence for ex vivo diffusion imaging of the macaque brain using a 10.5 T human MRI scanner with a conventional (Gmax = 70 mT/m) gradient set. SNR-efficiency optimisations incorporating experimental relaxation times demonstrate that the DW-SSFP sequence is predicted to achieve improved or similar SNR efficiency compared to a diffusion-weighted spin- and stimulated-echo sequence. Importantly, DW-SSFP can achieve this with the additional benefit of negligible geometric distortions, unlike conventional diffusion MRI using an echo-planar imaging readout. Using optimised DW-SSFP sequence parameters, we propose a protocol at 0.4 mm isotropic resolution using a two-shell multi-orientation protocol (effective b-values of 3200 s/mm2 and 5600 s/mm2). We fit the data using Tensor, Ball and 3-Sticks and Constrained Spherical Deconvolution signal representations. The results demonstrate high-quality diffusivity estimates across the entire brain with the ability to resolve multiple fibre populations in challenging crossing-fibre regions. The data will be made fully open source and multimodal as part of the Center for Mesoscale Connectomics, providing a resource for future connectivity investigations.

neuroscience↗

bioRxiv2024

SAMson: an automated brain extraction tool for rodents using SAM

Accurate brain extraction is a critical step in the analysis of rodent head magnetic resonance imaging (MRI) data. However, current methods often encounter difficulties in handling the diverse range of imaging setups, resolutions, and experimental conditions that are commonly found in this field. Based on the Segment Anything Model (SAM), we introduce here SAMson (SAM for Segmentation Of Neuroimages), an automated tool for robust rodent brain extraction. SAMson integrates a bounding box generator and a mask prediction pipeline, offering fully automated and semi-automated modes to address varying experimental complexities. The performance of SAMson was evaluated using three multi-centre rodent MRI datasets annotated at the pixel level, which differed in terms of acquisition parameters, resolution, and animal age groups. SAMson demonstrates superior performance to existing methods, including BET, RBM, and BEN, in terms of segmentation accuracy, with Jaccard indices exceeding 90% across datasets. The semi-automated mode demonstrates particular efficacy in challenging scenarios, including low-resolution images and cases requiring refined mask precision. In contrast to conventional volumetric techniques, SAMson identifies errors at the level of individual slices, thereby enabling rapid and targeted correction when needed. By providing open-source access, SAMson aims to support large-scale research workflows and advance translational neuroscience. The curated data can be downloaded from https://doi.org/10.20350/digitalCSIC/17000, and the code is available at https://github.com/CanalsLab/SAMson.

neuroscience↗