Biology subjects

Gauthier, S. A.

Publications and source records attributed to Gauthier, S. A..

5 recordsLinked to original sources

bioRxiv2025

Predicting cognition using estimated structural and functional connectivity networks and artificial intelligence in multiple sclerosis

BackgroundOur prior work demonstrated that estimated structural and functional connectomes (eSC and eFC) generated using multiple sclerosis (MS) lesion masks and artificial intelligence (AI) models can predict disability as effectively as SC and FC derived from diffusion and functional MRI in MS. The goal of this study was to assess the ability of eSC and eFC in predicting baseline and 4-year follow-up cognition in MS patients. MethodsOne hundred seventy-one MS patients (age: 42.67{+/-}10.41, 74% females) were included. The Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT), and Brief Visuospatial Memory Test (BVMT) were used to assess cognition. The Network Modification tool was performed to estimate SC, which was then used as an input to Krakencoder, an encoder-decoder model, to estimate FC. Ridge regression was performed to predict cognition using regional eSC and eFC, along with demographics and clinical information as well as conventional MRI metrics. Baseline cognition was added to the models that were used to predict the follow-up cognition. Spearmans correlation (r) was used to assess the prediction accuracy. ResultsThe highest accuracy was obtained when predicting follow-up SDMT using regional eSC or eFC (median r=0.58 for eSC and r=0.56 for eFC). Decreased eSC and eFC in the cerebellum and increased eFC in the default mode network were associated with lower follow-up SDMT scores. Baseline SDMT, clinical subtype, and age were the most important non-connectome metrics in predicting follow-up SDMT. ConclusionsOur findings demonstrate that eSC and eFC derived from clinically acquired MRI and AI models can effectively predict cognition. The use of lesion-based estimates of connectome disruptions may potentially improve cognition-related individualized treatment planning.

neuroscience↗

bioRxiv2025

TSPO-PET Reveals Higher Inflammation in White Matter Disrupted by Paramagnetic Rim Lesions in Multiple Sclerosis

ObjectiveTo explore whether the inflammatory activity is higher in white matter (WM) tracts disrupted by paramagnetic rim lesions (PRLs) and if inflammation in PRL-disrupted WM tracts is associated with disability in people with multiple sclerosis (MS). MethodsForty-four MS patients and 16 healthy controls were included. 18 kDa-translocator protein positron emission tomography (TSPO-PET) with the 11C-PK11195 radioligand was used to measure the neuroinflammatory activity. The Network Modification Tool was used to identify WM tracts disrupted by PRLs and non-PRLs that were delineated on MRI. The Expanded Disability Status Scale was used to measure disability. ResultsMS patients had higher inflammatory activity in whole brain WM compared to healthy controls (p=0.001). Compared to patients without PRLs, patients with PRLs exhibited higher levels of inflammatory activity in the WM tracts disrupted by any type of lesions (p=0.02) or PRLs (p=0.004). In patients with at least one PRL, inflammatory activity was higher in WM tracts highly disrupted by PRLs compared to WM tracts highly disrupted by non-PRLs (p=0.009). Elevated inflammatory activity in highly disrupted WM tracts was associated with increased disability in patients with PRL (p=0.03), but not in patients without PRL (p=0.2). InterpretationThis study suggests that patients with PRLs may exhibit more diffuse WM inflammation in addition to higher inflammation along WM tracts disrupted by PRLs compared to non-PRLs, which could contribute to larger lesion volumes and faster disability progression. Imaging PRLs may serve to identify patients with both focal and diffuse inflammation, guiding therapeutic interventions aimed at reducing inflammation and preventing progressive disability in MS.

neuroscience↗

bioRxiv2023

Brain tissue cerebrospinal fluid fraction increases quadratically in normal aging

Background and PurposeOur objective was to apply multi-compartment T2 relaxometry in cognitively normal individuals aged 20-80 years to study the effect of aging on the parenchymal cerebrospinal fluid fraction (CSFF), a measure of the microscopic-scale CSF space. Materials and MethodsA total of 66 volunteers (age range, 22-80 years) were enrolled. Voxel-wise maps of short-T2 myelin water fraction (MWF), intermediate-T2 intra/extra-cellular water fraction (IEWF), and long-T2 CSFF were obtained using fast acquisition with spiral trajectory and adiabatic T2prep (FAST-T2) sequence and three-pool non-linear least squares fitting. Multiple linear regression analysis with correction for multiple comparisons was performed to study the association between age and regional MWF, IEWF, and CSFF measurements, adjusting for sex and region of interest (ROI) volume. The cerebral white matter (WM), cerebral cortex, and subcortical deep gray matter (GM) were considered as ROIs. In each model, a quadratic term for age was tested using an ANOVA test. A Spearmans correlation between the normalized lateral ventricle volume, a measure of organ-level CSF space, and the regional CSFF, a measure of tissue-level CSF space, was computed. ResultsIn the multiple regression analysis, we found a statistically significant quadratic relationship between age and regional CSFF for all three ROIs (all p-values < 0.001). A statistically significant quadratic relationship with age was also found for MWF in the deep GM (p = 0.004) and IEWF in the cortex (p = 0.012). There was a statistically significant linear relationship between age and regional IEWF in the cerebral WM (p = 0.006) and deep GM (p = 0.002). In the univariate correlation analysis, the normalized lateral ventricle volume was found to correlate moderately with the regional CSFF measurement in the cerebral WM ({rho} = 0.43, p < 0.001), cortex ({rho} = 0.43, p < 0.001), and deep GM ({rho} = 0.49, p < 0.001). ConclusionBrain tissue water residing in different water compartments shows complex changing patterns with age. Parenchymal CSFF, a biomarker of microscopic-scale CSF-like water, shows a quadratic increase in both GM and WM, starting approximately at the age of 50.

neuroscience↗

bioRxiv2022

QSMRim-Net: Imbalance-Aware Learning for Identification of Chronic Active Multiple Sclerosis Lesions on Quantitative Susceptibility Maps

Background and PurposeChronic active multiple sclerosis (MS) lesions are characterized by a paramagnetic rim at the edge of the lesion and are associated with increased disability in patients. Quantitative susceptibility mapping (QSM) is an MRI technique that is sensitive to chronic active lesions, termed rim+ lesions on the QSM. We present QSMRim-Net, a data imbalance-aware deep neural network that fuses lesion-level radiomic and convolutional image features for automated identification of rim+ lesions on QSM. MethodsQSM and T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI of the brain were collected at 3T for 172 MS patients. Rim+ lesions were manually annotated by two human experts, followed by consensus from a third expert, for a total of 177 rim+ and 3986 rim negative (rim-) lesions. Our automated rim+ detection algorithm, QSMRim-Net, consists of a two-branch feature extraction network and a synthetic minority oversampling network to classify rim+ lesions. The first network branch is for image feature extraction from the QSM and T2-FLAIR, and the second network branch is a fully connected network for QSM lesion-level radiomic feature extraction. The oversampling network is designed to increase classification performance with imbalanced data. ResultsOn a lesion-level, in a five-fold cross validation framework, the proposed QSMRim-Net detected rim+ lesions with a partial area under the receiver operating characteristic curve (pROC AUC) of 0.760, where clinically relevant false positive rates of less than 0.1 were considered. The method attained an area under the precision recall curve (PR AUC) of 0.704. QSMRim-Net out-performed other state-of-the-art methods applied to the QSM on both pROC AUC and PR AUC. On a subject-level, comparing the predicted rim+ lesion count and the human expert annotated count, QSMRim-Net achieved the lowest mean square error of 0.98 and the highest correlation of 0.89 (95% CI: 0.86, 0.92). ConclusionThis study develops a novel automated deep neural network for rim+ MS lesion identification using T2-FLAIR and QSM images.

bioinformatics↗

bioRxiv2021

Increasing age is independently associated with higher free water in non-active MS brain - A multi-compartment analysis using FAST-T2

PurposeTo explore the relationship between the cerebral cortical perivascular space (PVS) and aging in non-active MS subjects by using the multi-echo T2 relaxometry based cerebrospinal fluid fraction (CSFF) map. MethodsMulti-echo spiral T2 data from 111 subjects with non-active multiple sclerosis (MS) were retrospectively investigated by fitting the T2 data into a three-compartment model, the three water compartments including myelin water, intra-extracellular water, and cerebrospinal fluid. Segmentation of T1w image was performed to get the region of interest (ROI) in cerebral cortical regions. The white matter lesion segmentation was conducted using a convolutional neural network (CNN) based segmentation tool. The CSFF in the ROIs were correlated with age by controlling the gender, white matter hyperintensity lesion burden, and MS disease duration. Multiple linear models were created for the analysis of aging effect on the CSFF. ResultsThe ROI analysis shows that the CSFF in the cerebral cortical regions (temporal, occipital, parietal, front, hippo, and mtl) are significantly linear increasing with age (p<0.01). The intra-extracellular water fraction (IEWF) in the ROIs are significantly linear decreasing (p<0.01). ConclusionThe multi-echo T2 based three-compartment model can be used to quantify the CSFF. The linear increase of CSF water contents in the cerebral cortical regions indicates increased perivascular space load in cortex with aging. The quantification of CSFF may provide a way to understand the glymphatic clearance function in aging and neurodegenerations. HighlightsO_LIMR T2 relaxometry is a valid method to quantify the cerebrospinal fluid fraction (CSFF) in cerebral cortical regions C_LIO_LIThe CSFF in the cerebral cortical regions are positively correlated with age by controlling the white matter lesion load in non-active MS subjects. C_LIO_LIQuantification of cerebral CSFF may reflect the perivascular space load in cortex and better interpret the disease progression in neurodegenerative disease, such as MS. C_LI

neuroscience↗