Biology subjects

Sweeney, E. M.

Publications and source records attributed to Sweeney, E. M..

3 recordsLinked to original sources

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↗

bioRxiv2020

Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging

Background and PurposeThe presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis (MS) lesion. Increased prevalence of these paramagnetic rim lesions (PRLs) is associated with a more severe disease course in MS. The identification of these lesions is time-consuming to perform manually. We present a method to automatically detect PRLs on 3T T2*-phase images. MethodsT1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. The images were then processed with lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We divided our data into a training set (15 patients, 673 lesions) and a testing set (4 patients, 204 lesions). We fit a random forest classification model on the training set and assessed our ability to classify lesions as PRL on the test set. ResultsThe number of PRLs per subject identified via our automated lesion labelling method was highly correlated with the gold standard count of PRLs per subject, r = 0.91 (95% CI [0.79, 0.97]). The classification algorithm using radiomic features can classify a lesion as PRL or not with an area under the curve of 0.80 (95% CI [0.67, 0.86]). ConclusionThis study develops a fully automated technique for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images. HighlightsO_LIA fully automated method for both the identification and classification of paramagnetic rim lesions is proposed. C_LIO_LIRadiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions. C_LIO_LIChallenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location. C_LI

neuroscience↗