Biology subjects

Moqadam, R.

Publications and source records attributed to Moqadam, R..

10 recordsLinked to original sources

bioRxiv2026

T2 relaxometry for myelin water fraction: an ex vivo brain imaging study

Abstract Introduction: Myelin water imaging provides a non-invasive approach for indirectly investigating myelin content of the brain by magnetic resonance Imaging (MRI). Myelin water fraction (MWF) reflects the fraction of water signal associated with water trapped between myelin layers. Postmortem imaging provides a unique opportunity to validate MWF metrics against gold-standard postmortem neuropathology. However, there is a need for validated sequences that would be applicable in postmortem settings. In this study, we validated and compared the applicability of turbo spin echo (TSE) and gradient-and-spin-echo (GRASE) techniques for myelin water imaging in formaldehyde fixed postmortem human brains. Methods: 40 postmortem human brain hemispheres were scanned with TSE and GRASE sequences. Acquired data were reconstructed using a non-parametric multicomponent T2 relaxometry approach using a consistent framework to support comparison between GRASE- and TSE-derived measures. Agreement between derived measures was quantified at both voxel and regional level. Maps derived from the full 32-echo GRASE reconstruction were compared against a truncated 14-echo reconstruction to examine the contribution of the longer echoes to the derived maps. Finally, a random forest regression model was trained based on TSE data to predict the GRASE-derived MWF maps. Results: Both sequences provided robust multicomponent T2 characterization across the brain, and the derived measures showed anatomical patterns consistent with expected differences across tissue types. TSE and GRASE derived maps had moderate to strong agreement at voxel (0.41< {rho} < 0.82, all pFDR<0.001) and regional levels (0.87< {rho} < 0.98, all pFDR<0.001), with consistently stronger agreement observed for regional metrics. Echo reduction comparisons between the full 32-echo GRASE reconstruction and a truncated 14-echo reconstruction showed minimal impact of echo truncation (all {rho} > 0.98, pFDR < 0.001). The nonlinear random forest regression model trained on TSE maps was able to accurately reproduce the MWF maps derived from GRASE (r = 0.91, RMSE = 8.87). Discussion: TSE and GRASE32 capture strongly related multicomponent T2 information in fixed postmortem human brain tissue, but with systematic and tissue-dependent quantitative differences across derived measures. These differences suggest that direct interchangeability should not be assumed and that cross-sequence mapping or calibration is required when quantitative equivalence is desired.

neuroscience↗

bioRxiv2026

Automatic Quality Control and Error Correction in MRI linear registration via a Residual Parameter Prediction Network for T1w MRI

Errors in linear registration can propagate to downstream nonlinear registration and bias volumetric estimations, deformation-based morphometry (DBM) and voxel-based morphometry (VBM) analyses. Subtle linear registration errors are particularly challenging as they are difficult to detect and may not result in obvious failures in nonlinear registration but still affect downstream results. Therefore, accurate identification and correction of these errors are critical. In this study, we present the Residual Affine COefficient Optimization Network (RACOON), a framework designed to identify and correct linear registration errors in T1w MRI scans registered to the MNI-ICBM152 space. RACOON's correction module achieved a residual misalignment RMSE of 0.778 mm on synthetic dataset, comparable to the variability observed among repeated QC-passed registrations using the same pipeline. For the classification module, RACOON achieved a balanced accuracy of 76.8% and a precision of 74.4%, outperforming existing state-of-the-art methods. RACOON is open source and publicly available at https://github.com/ZhaojinChen/RACOON.

neuroscience↗

bioRxiv2026

Spatiotemporal trajectories of formaldehyde fixation effects on quantitative MRI in postmortem human brains

Introduction: Postmortem human brain magnetic resonance imaging (MRI) offers a unique opportunity to study finer neuroanatomical details and enables direct correlations with gold standard histological and immunohistochemical assessments. However, to prevent tissue decay, postmortem brains are preserved in fixative solutions which can alter tissue properties and exert substantial impacts on the MRI signals. The present study investigates the impact of formalin fixation, the most commonly used solution for postmortem human brain preservation, on different quantitative MRI contrasts. Methods: 142 intact human brain hemispheres immersed in 10% formalin for a range of fixation durations (between 0 days and 20 years) were imaged in a 3T MRI scanner. A subset of 10 brains were further scanned repeatedly at days 0, 3, 10, 20, 30, 60, 90, and 120 to allow for better characterization of the initial transient effects of fixation. Voxel-wise T1 and T2* relaxation, T1/T2 ratio, and myelin water fraction (MWF) maps were generated for each specimen and timepoint, and linear and nonlinear models were used to examine the spatiotemporal changes associated with progressive fixation. Results: All investigated metrics were significantly impacted by formalin fixation, albeit at different rates and with differing regional patterns. T1 and T2* relaxation time decreased as a result of progressive fixation, whereas T1/T2 ratio and MWF measures increased. T1 relaxation and T1/T2 ratio showed nonlinear patterns with initially accelerated changes that decelerate in the first few months, whereas T2* relaxation and MWF changes followed a more linear trend. Conclusion: Formaldehyde fixation exerts systematic changes on quantitative MRI signals that can be modeled and adjusted for to allow for harmonized comparisons of MRI metrics across brains fixed for differing durations. The distinct temporal trajectories observed across metrics highlight the need to account for fixation duration in study design and downstream analyses, particularly when integrating datasets acquired under heterogeneous conditions. Our findings provide a quantitative framework for correcting fixation-induced biases, thereby improving the interpretability and reproducibility of postmortem MRI studies.

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bioRxiv2025

Comparison of T1- and T2-weighted MRI contrasts of ex vivo ex situ brains fixed with solutions used in gross anatomy laboratories

Post-mortem magnetic resonance imaging (MRI) offers high resolution and histological correlation, so protocols have been developed by brain banks using hemispheres fixed by immersion in Neutral-buffered formalin (NBF), but they provide limited tissue samples. Conversely, anatomy laboratories could supply complete brains perfused either with a salt-saturated (SSS) or an alcohol-formaldehyde (AFS) solution. These fixation methods alter the brains molecular properties, potentially affecting MRI quality and structural characteristics. T1- and T2-weighted (T1w, T2w) contrasts change with NBF fixation, but the effects of SSS or AFS remain unknown. We compared T1w and T2w intensities of different regions of interest (ROIs), including subcortical white matter (WM), cortical and deep gray matter (GM), in brains fixed with NBF, SSS and AFS. We scanned 20 ex situ hemispheres (NBF-immersed=7; SSS-perfused=7; AFS-perfused=6) in a 3T MRI scanner using T1w (0.7mm3) and T2w (0.64mm3) sequences overnight. Mean intensities of 29 ROIs in T1w and T2w MRIs and GM-WM ratios were calculated and compared in brains fixed with the three solutions. We found that T1w images were more affected by the fixation process, inverting the contrast of in vivo T1w and reducing the GM-WM contrast in AFS-fixed brains. T2w images resembled in vivo scans and maintained a sharp contrast in brains fixed with the three solutions, although the GM-WM intensity ratios were lowered in SSS-fixed brains. In conclusion, brains fixed with SSS and AFS from anatomy laboratories could be used for MRI studies, especially with the T2w sequence that seems more appropriate for structural analyses in different ROIs.

neuroscience↗

bioRxiv2025

Joint multi-omics profiling of brain and body health in aging

The human brain and peripheral systems undergo coordinated changes throughout the lifespan, yet studies of aging have traditionally examined these systems as separate entities. Here we ask how brain health relates to peripheral biomarkers of bodily health including body mass index, blood pressure, and blood biochemistry results. We use partial least squares analysis to identify generalizable patterns of covariance between multi-modal neuroimaging data (structural, functional, diffusion, and arterial spin labeling MRI), demographic, and peripheral physiological markers in two large-scale deeply phenotyped datasets: the Human Connectome Project-Aging and UK Biobank. This data-driven pattern learning approach identifies two principal axes of brain-body associations in both biological sex groups. The first axis is driven by the dominant contribution of age. Across multiple brain measures, aging is associated with loss of brain structural integrity and cerebral vascular dysfunction. The second axis is driven by metabolic features, characterized by low high-density lipoprotein cholesterol, elevated body mass index, blood pressure, glycosylated hemoglobin, insulin, glucose, and alanine aminotransferase, and reduced cerebral blood perfusion. Finally, we show that deviations from a healthy metabolic profile are linked to cognitive deficits, particularly in females. Our study contributes to development of comprehensive translatable biomarkers for brain health assessment, and highlights the importance of metabolic health as a determinant of brain health.

neuroscience↗

bioRxiv2025

RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using Autoencoders

Due to their high inter-tissue contrast, Magnetic resonance images (MRIs) can reflect neuroanatomical changes related to healthy aging and pathological processes. However, standard brain MRI acquisition resolutions hinder the ability to measure the more subtle changes that occur in early disease stages. Increasing the resolution during acquisition poses multiple challenges, including increased noise, higher acquisition times and cost, and discomfort of the scanned individual. In this work, we propose a robust, generalizable single-image super-resolution network for brain MRIs named Resolution Augmentation with Variational auto-Encoder Networks (RAVEN) with generative adversarial networks (GANs). We show RAVEN is capable of upsampling in-vivo and ex-vivo MRIs of diverse modalities (e.g. T1-weighted, T2-weighted, and T2*) and varying field strengths (3T to 7T) to target voxel sizes as small as 0.5mm isotropic using arbitrary upsampling factors. RAVEN achieved state-of-the-art performance against deep learning and non-deep learning methods, best preserving true anatomical information. We have also made RAVEN open access, with the source code as well as training and evaluation scripts available and ready to use at: https://github.com/waadgo/raven.

neuroscience↗

bioRxiv2025

PELICAN: a Longitudinal Image Processing Pipeline for Analyzing Structural Magnetic Resonance Images in Aging and Neurodegenerative Disease Populations

Structural magnetic resonance imaging (MRI) allows for accurate non-invasive assessment of the brains structure and its longitudinal changes. Availability of large scale longitudinal MRI datasets enables us to probe brain changes in health and disease, and derive longitudinal trajectories of brain morphometry based measures to estimate brain atrophy and other disease-related abnormalities. In contrast to their cross-sectional counterparts, image processing pipelines that have been designed for longitudinal data can reduce noise in the derived measurements by disentangling the within and between subject variabilities, improving the sensitivity of the downstream models in detecting more subtle longitudinal changes. Here we present PELICAN, our open source multi-contrast longitudinal image processing pipeline, that has been designed and extensively validated for use in longitudinal settings and populations with neurodegenerative disorders. PELICAN can use population specific average templates as intermediate targets to derive accurate nonlinear registrations for cases with substantial levels of atrophy, which commonly used pipelines struggle to process. We evaluated PELICANs performance across over 34,000 MRIs from multiple aging and neurodegenerative disorder cohorts, and compared its reliability and failure rates against FreeSurfer as a widely used image processing tool, showing superior performance of PELICAN compared to FreeSurfer, both in terms of failure rate and reliability. Our results demonstrate that PELICAN can be used to accurately process MRIs of individuals with neurodegenerative disease who present with greater levels of atrophy and white matter lesion burden.

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bioRxiv2024

Beyond brain size: disentangling the effect of sex and brain size on brain morphometry and cognitive functioning

It is imperative to study sex differences in brain morphology and function. However, there are major observable and unobservable confounding factors that can contribute to the estimated differences. Males have larger head sizes than females. Head size differences not only act as a confounding factor in studying sex differences in the brain, but also impact its anatomy and functioning. In this work, we seek to disentangle the effect of head size from sex in studying sex differentiated aging trajectories, its relation to canonical functional networks and cytoarchitectural classes, brain allometry, cognition. Using the UK Biobank (UKBB) neuroimaging data (N = 35,732 participants, 19,281 females, 44-82 years of age), we created a subsample (N = 11,294) where females (N = 5,657) and males were matched by their total intracranial volume (TIV) and age, a subsample that maintains the UKBB sample distribution, one matched only by age, and one that exaggerated the TIV difference between sexes. We then modeled the aging trajectories at both regional and vertex-wise levels in the four subsamples, and compared the estimations of the models. Our results show that when females and males have the same head size, the overall sex estimations tend towards zero, suggesting that most of the variability results from head size differences. Our approach also revealed bidirectional sex differences in brain neuroanatomy previously masked by the effect of head size. Further, the scaling relationship between regional and total brain volume remains fairly consistent across the lifespan and is not sex differentiated overall. We evaluated how the results of cognitive tests with perceived sex differences are influenced and explained by head size and found that although the correlation between TIV and cognitive scores is low, the matching process changes the direction of the effect sizes of differences between sexes in "verbal and numerical reasoning" and "working memory" cognitive domains. Taken together, employing a matching approach that is widely used in causal modeling studies, we provide new evidence for disentanglement of sex differences in the brain from head size as a biological confound.

neuroscience↗

bioRxiv2024

The Douglas Bell Canada Brain Bank Post-mortem Brain Imaging Protocol

Magnetic resonance imaging (MRI) is a valuable non-invasive tool that has been widely used for in vivo investigations of brain morphometry and microstructural characteristics. Postmortem MRIs can provide complementary anatomical and microstructural information to in vivo imaging and ex vivo neuropathological assessments without compromising the sample for future investigations. We have developed a postmortem MRI protocol for the brain specimens of the Douglas-Bell Canada Brain Bank (DBCBB), the largest brain bank in Canada housing over 3000 neurotypical and diseased brain specimens, that allows for acquisition of high-resolution 3T and 7T MRIs. Our protocol can be used to scan DBCBB specimens with minimal tissue manipulation, allowing for feasibly scanning large numbers of postmortem specimens while retaining the quality of the tissue for downstream histology and immunohistochemistry assessments. We demonstrate the robustness of this protocol in spite of the dependency of image quality on fixation by acquiring data on the first day of extraction and fixation, to over twenty years post fixation. The acquired images can be used to perform volumetric segmentations, cortical thickness measurements, and quantitative analyses which can be potentially used to link MRI-derived and ex vivo histological measures, assaying both the normative organization of the brain and ex vivo measures of pathology.

neuroscience↗

bioRxiv2023

Investigating the impact of motion in the scanner on brain age predictions

IntroductionBrain Age Gap (BAG) is defined as the difference between the brains predicted age and the chronological age of an individual. Magnetic resonance imaging (MRI)-based BAG can quantify acceleration of brain aging, and is used to infer brain health as aging and disease interact. Motion in the scanner is a common occurrence that can affect the acquired MRI data and act as a major confound in the derived models. As such, age-related changes in head motion may impact the observed age-related differences. However, the relationship between head motion and BAG as estimated by structural MRI has not been systematically examined. The aim of this study is to assess the impact of motion on voxel-based morphometry (VBM) based BAG. MethodsData were obtained from two sources: i) T1-weighted (T1w) MRIs from the Cambridge Centre for Ageing and Neuroscience (CamCAN) were used to train the brain age prediction model, and ii) T1w MRIs from the Movement-related artifacts (MR-ART) dataset were used to assess the impact of motion on BAG. MR-ART includes one motion-free and two motion-affected (one low and one high) 3D T1w MRIs. We also visually rated the motion levels of the MR-ART MRIs from 0 to 5, with 0 meaning no motion and 5 high motion levels. All images were pre-processed through a standard VBM pipeline. GM density across cortical and subcortical regions were then used to train the brain age prediction model and assess the relationship between BAG and MRI motion. Principal component analysis was used to perform dimension reduction and extract the VBM-based features. BAG was estimated by regressing out the portion of delta age explained by chronological age. Linear mixed effects models were used to investigate the relationship between BAG and motion session as well as motion severity, including participant IDs as random effects. We repeated the same analysis using cortical thickness based on FreeSurfer 7.4.1 and to compare the results for volumetric versus surface-based measures of brain morphometry. ResultsIn contrast with the session with no induced motion, predicted delta age was significantly higher for high motion sessions 2.35 years (t = 5.17, p < 0.0001), with marginal effect for low motion sessions 0.95 years (t = 2.11, p=0.035) for VBM analysis as well as 3.46 years (t = 11.45, p < 0.0001) for high motion and 2.28 years (t = 7.54, p<0.0001) for low motion based on cortical thickness. In addition, delta age was significantly associated with motion severity as evaluated by visual rating 0.45 years per rating level (t = 4.59, p < 0.0001) for VBM analysis and 0.83 years per motion level (t = 12.89, p<0.0001) for cortical thickness analysis. ConclusionMotion in the scanner can significantly impact brain age estimates, and needs to be accounted for as a confound, particularly when studying populations that are known to have higher levels of motion in the scanner. These results have significant implications for brain age studies in aging and neurodegeneration. Based on these findings, we recommend assessment and inclusion of visual motion ratings in such studies. In cases that the visual rating proves prohibitive, we recommend the inclusion of normalized Euler number from FreeSurfer as defined in the manuscript as a covariate in the models.

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