Aleman Gomez, Y. | Search bioRxiv

bioRxiv2024

Eye-Opening Advances: Automated 3D Segmentation, Key Biomarkers Extraction, and the First Large-Scale MRI Eye Atlas.

In this study we introduce an automated 3D segmentation of the healthy human adult eye and orbit from Magnetic Resonance Images, to improve ophthalmic diagnostics and treatments. Past efforts primarily focused on small sample sizes and varied imaging modalities. Here, we leverage a large-scale dataset of T1-weighted MRI of 1245 subjects and the use of the deep learning-based nnU-Net for MR-Eye segmentation tasks. The results showcase robust and accurate 3D segmentations of lens, globe, optic nerve, rectus muscles, and orbital fat. We also present the automated estimation of key ophthalmic morphometry biomarkers such as axial length and volumetry, while benchmarking correlations between body mass index and eye structure volumes. Quality control protocols are introduced through the pipeline to ensure the reliability of the segmented large-scale data, further enhancing the applicability of our algorithm in clinical research. As major outcome we provide the first large-scale unbiased eye atlases (female, male and combined) towards standardization of spatial normalization tools for MR-Eye.

bioengineering↗

bioRxiv2023

Brain network connectivity underlying remission in early psychosis: a whole brain model approach

BackgroundAlterations in brain connectivity occur early during psychosis and underlie the clinical manifestations of the illness as well as patient functioning and outcome. After a first episode of psychosis (FEP), different trajectories are possible and best described by the clinical-staging model that places the patient along a continuum of conditions: from non-remitting chronic symptoms to full-remission, often followed by relapses. However, little is known about the differences in brain connectivity that could underlie these differences in clinical outcome. MethodsIn this study, we included resting-state fMRI and DSI data from a cohort of 128 healthy controls (HC) and 88 patients with early psychosis (EP) stratified based on their ability to remit after the FEP. In particular we focused on differences between stage IIIb,c remitting-relapsing (EP3R) and stage IIIa non-remitting (EP3NR) patients. We investigated alterations in resting-state functional connectivity (FC), and combined information derived from fMRI and DSI into generative whole-brain models of each condition to explore the underlying mechanisms. ResultsOpposite alterations in FC could be found in patients as compared to HC, depending on their stage. In non-remitting patients (EP3NR), we observed a reduction of FC, aligned with the reduced structural connectivity found in previous studies, while remitting-relapsing patients (EP3R) showed increased FC, potentially indicating a relevant compensatory mechanism. By means of a whole-brain network model, we showed that in HC a subset of areas is characterized by increased stability to prevent an oversynchronisation of the network, while in EP3 patients such property is lost. This alteration was more relevant in the EP3R than in EP3NR patients, probably indicating a compensatory response to the reduced effective conductivity (global coupling) highlighted by the model in both EP3 conditions as compared to controls. ConclusionsThese findings highlight the significance of categorizing patients into subgroups based on the progression of their psychotic disorders, providing insights into the factors contributing to heterogeneity in functional alterations. They enhance our understanding of the interplay between structural and functional properties, shedding light on the mechanisms of psychosis emergence, remission and progression, with potential implications for future therapeutic advancements.

neuroscience↗

bioRxiv2022

Connectome spectrum electromagnetic tomography: a method to reconstruct electrical brain sources at high-spatial resolution

Connectome Spectrum Electromagnetic Tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

neuroscience↗