Frontiers in Neuroinformatics

CycleGAN models show consistent brain MRI synthesis across datasets supporting downstream tissue characterization in multiple sclerosis

2026-03-12

Shayan Shahrokhi, Olayinka Oladosu, Rehman Tariq, Yunyan Zhang

BackgroundSecondary quantitative analysis of brain magnetic resonance imaging (MRI) can provide valuable information for many neurological diseases, including multiple sclerosis (MS), but it demands complete datasets that are often unavailable clinically. We investigated how image synthesis via deep learning using cycle-consistent generative adversarial networks (CycleGANs) compared with Pix2Pix as a related method, based on T1-weighted and T2-weighted brain MRI in MS, following verification on two streamlined datasets. The synthesized images were also evaluated against the source data.MethodsThe streamlined datasets involved 1,113 healthy participants from the Human Connectome Project (HCP) and 318 participants from the Parkinson’s Progression Markers Initiative (PPMI). The MS cohort in this study included 105 participants scanned with different protocols. Image synthesis was bidirectional between T1- and T2-weighted MRI using CycleGAN with and without spectral normalization, as well as Pix2Pix. Utility testing focused on T1-weighted MRI that was most often unavailable in MS, and that involved lesion detection, brain volumetry, and lesion texture analysis.ResultsAll CycleGAN models performed competitively, while Pix2Pix performed better, mostly with streamlined datasets (p < 0.001). The average peak signal-to-noise ratio ranged from 24.860–28.570 versus 28.520–31.100, and the structural similarity index ranged from 0.838–0.901 versus 0.924–0.943. With spectral normalization, CycleGAN improved in PPMI but not in HCP and generally not in MS (p < 0.001). Furthermore, the synthesized images showed high similarity to the source data in utility tests, although Pix2Pix T1 images appeared more heterogeneous in lesion texture than source T1 images.ConclusionCycleGAN without spectral normalization appeared feasible for synthesizing common clinical brain MRI, including T1-weighted images usable for subsequent quantitative analysis in MS.

On the need for abstract, deep reinforcement learning models in neuroscience

2026-01-27

Santina Duarte, Xena Al-Hejji, Edgar Bermudez Contreras, Eric Chalmers

Assessing the eligibility of Brainomix e-ASPECTS for acute stroke imaging

2025-12-11

Mateusz Dorochowicz, Arkadiusz Kacała, Michał Puła, Adrian Korbecki, Aleksandra Kosikowska, Aleksandra Tołkacz, Anna Zimny, Maciej Guziński

BackgroundTimely and accurate assessment of acute ischemic stroke is crucial for determining eligibility for mechanical thrombectomy. The Alberta Stroke Program Early CT Score (ASPECTS) is a widely used tool for evaluating early ischemic changes on non-contrast CT (NCCT), but its interpretation is subject to interobserver variability. Brainomix e-ASPECTS is an automated software designed to standardize and expedite this assessment. We aimed to evaluate the clinical utility and diagnostic performance of the Brainomix e-ASPECTS software in an unselected, real-world cohort of patients undergoing NCCT for suspected acute ischemic stroke.MethodsWe retrospectively analyzed 1,029 NCCT studies from 954 patients between March 2020 and December 2024. e-ASPECTS scores were compared to radiologist-assigned ASPECTS, which served as the reference standard. Diagnostic accuracy, sensitivity, specificity, and correlation between scoring methods were assessed.ResultsThere was a strong correlation between e-ASPECTS and radiologist ASPECTS (ρ = 0.953, p < 0.001). For detecting acute ischemia, sensitivity was 95.8% (95% CI, 93.6–97.3%), specificity 96.9% (95% CI, 94.7–98.2%), and overall accuracy 96.3% (95% CI, 95.1–97.5%). The positive predictive value was 97.2% (95% CI, 95.3–98.4%), and the negative predictive value was 95.3% (95% CI, 92.8–96.9%). Score concordance was high, with exact matches in 92.3% of cases and a ≤ 1-point difference in 97.7%. Misclassification for thrombectomy eligibility (ASPECTS < 6) occurred in four cases (0.4%). The software achieved a processing success rate of 91.9%.ConclusionE-ASPECTS demonstrates high diagnostic accuracy and strong agreement with expert radiological assessment, supporting its role as a valuable decision support tool in acute stroke imaging. However, its use should complement, not replace, expert interpretation, particularly in patients with low ASPECTS scores, where treatment decisions are most sensitive.

Information-theoretic gradient flows in mouse visual cortex

2025-10-30

Erik D. Fagerholm, Hirokazu Tanaka, Milan Brázdil

IntroductionNeural activity can be described in terms of probability distributions that are continuously evolving in time. Characterizing how these distributions are reshaped as they pass between cortical regions is key to understanding how information is organized in the brain.MethodsWe developed a mathematical framework that represents these transformations as information-theoretic gradient flows — dynamical trajectories that follow the steepest ascent of entropy and expectation. The relative strengths of these two functionals provide interpretable measures of how neural probability distributions change as they propagate within neural systems. Following construct validation in silico, we applied the framework to publicly available continuous ΔF/F two-photon calcium recordings from the mouse visual cortex.ResultsThe analysis revealed consistent bi-directional transformations between the rostrolateral area and the primary visual cortex across all five mice. These findings demonstrate that the relative contributions of entropy and expectation can be disambiguated and used to describe information flow within cortical networks.DiscussionWe introduce a framework for decomposing neural signal transformations into interpretable information-theoretic components. Beyond the mouse visual cortex, the method can be applied to diverse neuroimaging modalities and scales, thereby providing a generalizable approach for quantifying how information geometry shapes cortical communication.

Software and pipelines for registration and analyses of rodent brain image data in reference atlas space

2025-09-24

Maja A. Puchades, Sharon C. Yates, Gergely Csucs, Harry Carey, Arda Balkir, Trygve B. Leergaard, Jan G. Bjaalie

Advancements in methodologies for efficient large-scale acquisition of high-resolution serial microscopy image data have opened new possibilities for experimental studies of cellular and subcellular features across whole brains in animal models. There is a high demand for open-source software and workflows for automated or semi-automated analysis of such data, facilitating anatomical, functional, and molecular mapping in healthy and diseased brains. These studies share a common need to consistently identify, visualize, and quantify the location of observations within anatomically defined regions, ensuring reproducible interpretation of anatomical locations, and thereby allowing meaningful comparisons of results across multiple independent studies. Addressing this need, we have developed a suite of desktop and web-applications for registration of serial brain section images to three-dimensional brain reference atlases (QuickNII, VisuAlign, WebAlign, WebWarp, and DeepSlice) and for performing data analysis in a spatial context provided by an atlas (Nutil, QCAlign, SeriesZoom, LocaliZoom, and MeshView). The software can be utilized in various combinations, creating customized analytical pipelines suited to specific research needs. The web-applications are integrated in the EBRAINS research infrastructure and coupled to the EBRAINS data platform, establishing the foundation for an online analytical workbench. We here present our software ecosystem, exemplify its use by the research community, and discuss possible directions for future developments.