Biology subjects

Sibon, I.

Publications and source records attributed to Sibon, I..

5 recordsLinked to original sources

bioRxiv2025

Generating Synthetic MR Perfusion Maps from DWI and FLAIR in Acute Ischemic Stroke using Deep Learning

BackgroundMagnetic resonance imaging (MRI) is critical for acute stroke triage, but time-consuming, and often requires contrast injection for perfusion imaging. This study aimed to synthesize T-map perfusion maps from routinely available, non-contrast DWI and FLAIR using deep generative models. We hypothesized that relevant perfusion information could be inferred from these modalities to streamline imaging and reduce reliance on dynamic susceptibility contrast perfusion. MethodsAcute MRI data from 355 patients with anterior circulation stroke, including dynamic susceptibility contrast perfusion, were retrospectively collected from two European centers (Heidelberg: 2010-2018; Bordeaux: 2021-2022). Six versions of a denoising diffusion probabilistic model (DDPM) and a GAN architecture were trained to generate synthetic T-max perfusion maps from DWI, FLAIR, and infarct core mask as inputs. Performance was assessed by comparing synthetic and ground truth T-max maps using image similarity metrics. Regions with T-max >6s were compared using Dice coefficients, and mismatch volume distributions were analyzed. An ablation study quantified the contribution of each input. ResultsThe best performance was achieved by a DDPM with a 2.5D architecture using DWI, FLAIR, infarct core mask, and a perfusion-weighted loss function. It produced synthetic perfusion T-max maps with high similarity to ground truth under 110 seconds. The model showed strong spatial overlap for T-max >6s regions in internal validation (average Dice = 0.82, SD = 0.08), and external validation average (Dice 0.59, SD = 0.13), respectively. Synthetic maps closely matched ground-truth mismatch distributions, capturing key perfusion patterns. The infarct core mask played a critical role in model performance, alongside DWI and FLAIR inputs. ConclusionsWe propose a non-invasive, scalable framework to generate synthetic T-max perfusion maps from non-contrast MRI. This approach could expand access to perfusion data in acute stroke, shorten imaging protocols, and accelerate treatment decisions by eliminating the need for contrast-enhanced acquisition. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/684079v2_ufig1.gif" ALT="Figure 1"> View larger version (94K): org.highwire.dtl.DTLVardef@164235forg.highwire.dtl.DTLVardef@14e5489org.highwire.dtl.DTLVardef@190214eorg.highwire.dtl.DTLVardef@17a9e3a_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Insights from multidimensional analyses of post-stroke fatigue

BackgroundPost-stroke fatigue (PSF) is an overlooked and debilitating condition. As a multidimensional construct, fatigue encompasses physical, cognitive, and emotional components, complicating efforts to understand PSF pathophysiological mechanisms and identify key predictors. ObjectivesWe aimed to investigate the impact of lesion characteristics on the different facets of PSF while accounting for socio-demographic, psychological, and neurological factors. Methods231 first-ever ischemic stroke patients from a prospective hospital-based cohort were assessed using the Multidimensional Fatigue Inventory (MFI) and the Hospital Anxiety and Depression Scale (HAD) alongside routine clinical evaluations. Lesion analysis was done through two approaches: a voxel-based method using support vector regression-based multivariate lesion-symptom mapping (SVR-LSM), and a network-based method using principal component analysis (PCA) of lesioned gray and white matter regions. ResultsThe overall prevalence of PSF was 20.8%. PSF was more frequent among women and younger patients and strongly associated with HAD scores. SVR-LSM identified an association between lesions in the right corona radiata and external capsule and total MFI scores but none with HAD scores. The network-based approach showed associations between mental fatigue and reduced activity subdimensions and brain components involving cerebro-cerebellar tracts. ConclusionsOur findings suggest that PSF arises from an interplay of socio-demographic, emotional, and cerebral risk factors, accounting for its heterogeneous presentation. Regarding the associations with the lesioned regions, the involvement of motor pathways raises the possibility that neuronal overactivity, compensating for disrupted networks, may contribute to long-term fatigue. Further whole-brain analyses are warranted to confirm and extend these observations.

neuroscience↗

bioRxiv2024

MRI R2* captures inflammation in disconnected brain structures after stroke: a translational study

Ischemic strokes disrupt brain networks, leading to remote effects in key regions like the thalamus, a critical hub for brain functions. However, non-invasive methods to quantify these remote consequences still need to be explored. This study aimed to demonstrate that MRI-derived R2* changes can capture iron accumulation linked with inflammation secondary to stroke-induced disconnection. In order to link remote R2* changes to stroke-induced disconnection, we first conducted a secondary analysis of 156 prospectively included stroke patients who underwent MRI at baseline and 1-year follow-up. We mapped fibers disconnected by baseline infarcts to compare the R2* changes over 1 year according to the disconnectivity status in specific thalamic nuclei groups. We also identified the predictors of elevated R2* at 1 year in a multivariate context through linear regressions. In parallel, to understand the biological underpinning of the remote R2* changes, we set up a translational mouse model through photothrombotic induction of focal cortical infarcts or sham procedures in 110 C57BL/6J mice. We explored the mice through combinations of in vivo MRI at 72h, 2-, 4-, and 8-weeks, histology, qPCR for gene expression, mass spectrometry for iron concentration quantification, and additional ex vivo high-resolution diffusion tensor imaging. In stroke patients, we found a significant increase of R2* within severely disconnected medial and lateral thalamic nuclei groups from baseline to 1 year. At the same time, no change occurred if these structures were not disconnected. We also showed that the disconnectivity status at baseline was a significant predictor of R2* at follow-up, independently from confounders, establishing a direct and independent relationship between baseline disconnection and the subsequent R2* increase within the associated locations. In mice, we recapitulated the patients conditions by observing increased R2* in the stroke groups, specifically within the disconnected thalamic nuclei. Such remote and focal R2* changes peaked at 2 weeks, preceding and correlating with longer-term atrophy at 8 weeks. We established that the remote R2* increase was spatially and temporally correlated with a significant increase of chemically determined iron load bound to ferritin within activated microglial cells. This study provides critical evidence that R2* is a sensitive marker of inflammation secondary to network disconnection, potentially informing future neuroprotective strategies targeting remote brain regions after stroke.

neuroscience↗

bioRxiv2024

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover optimal recipes for predicting each domain. The differences in these optimal recipes reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com

neuroscience↗

bioRxiv2023

Dynamic evolution of infarct volumes at MRI in ischemic stroke due to large vessel occlusion

Background and ObjectivesThe typical infarct volume trajectories in stroke patients, categorized as slow or fast progressors, remain largely unknown. This study aimed to reveal the characteristic spatiotemporal evolutions of infarct volumes caused by large vessel occlusion (LVO) and show that such growth charts help anticipate clinical outcomes. MethodsWe conducted a secondary analysis from prospectively collected databases (FRAME, 2017- 2019; ETIS, 2015-2022). We selected acute MRI data from anterior LVO stroke patients with witnessed onset which were divided into training- and independent validation-datasets. In the training-dataset, using Gaussian mixture analysis, we classified the patients into three growth groups based on their rate of infarct growth (diffusion volume / time-to-imaging). Subsequently, we extrapolated pseudo-longitudinal models of infarct growth for each group and generated sequential frequency maps to highlight the spatial distribution of infarct growth. We used these charts to attribute a growth group to the independent patients from the validation-dataset. We compared their 3-month modified Rankin scale (mRS) with the predicted values based on a multivariable regression model from the training-dataset that used growth group as independent variable. ResultsWe included 804 patients (median age, 73.0 years [IQR, 61.2-82.0 years]; 409 men). The training-dataset revealed non-supervised clustering into 11% (74/703) slow, 62% (437/703) intermediate, and 27% (192/703) fast progressors. Infarct volume evolutions were best fitted with a linear (r=0.809; P<.001), cubic (r=0.471; P<.001), and power (r=0.63; P<.001) functions for the slow, intermediate and fast progressors, respectively. Notably, the deep nuclei and insular cortex were rapidly affected in the intermediate and fast groups with further cortical involvement in the fast group. The variable "growth group" significantly predicted 3-month mRS (multivariate OR, 0.51; 95% CI: 0.37-0.72, P<.0001) in the training-dataset, yielding a mean AUC of 0.78 (95% CI: 0.66-0.88) in the independent validation-dataset. ConclusionsWe revealed spatiotemporal archetype dynamic evolutions following large vessel occlusion stroke according to three growth phenotypes called slow, intermediate and fast progressors, providing insight into anticipating clinical outcome. We expect this could help in designing neuroprotective trials aiming at modulating infarct growth prior EVT.

pathology↗