Biology subjects

Sepehrband, F.

Publications and source records attributed to Sepehrband, F..

11 recordsLinked to original sources

bioRxiv2024

Perivascular and parenchymal fluid characteristics are related to age and cognitive performance across the lifespan

Perivascular spaces (PVS) play a critical role in fluid transfer and waste clearance in the brain, but few studies have explored how alterations to perivascular fluid flow may impact brain maturation and behavior across the lifespan. This study aims to characterize age-related alterations to perivascular and parenchymal fluid flow characteristics across the lifespan in typically developing children (8-21 years) and aging adults (35-90 years) and assess their contribution to cognition. In this study, we employ multi-compartment diffusion models, neurite orientation dispersion and density imaging (NODDI) and tissue tensor imaging (TTI), to quantify free water diffusion characteristics within automatically defined perivascular spaces, the surrounding parenchyma, and at variable distances from the PVS. Our findings show free water diffusion characteristics within the PVS and surrounding parenchyma are associated with age in both developing children and in aging adults. Additionally, age was associated with accelerated change in free water diffusion measures with distance from the PVS. There was no direct effect of free water diffusion measures on cognitive scores across subjects; however, a more complex relationship emerged such that age modified the relationship between free water diffusion measures and cognition. Together, these findings provide evidence of age-associated alterations to fluid flow dynamics and cognition that may be related to the waste clearance system.

neuroscience↗

bioRxiv2022

Preserved cognition in elderly with intact rhinal cortex

Alzheimers disease pathology leads to neurodegeneration within the memory-related structures of the medial temporal cortex and hippocampus. Neurodegeneration also occurs as a part of normative aging and it is unclear whether medial temporal lobe subregions are selectively intact in older adults with preserved cognitive function in comparison to adults who are cognitively impaired. In this study, we used T1-weighted and high-resolution T2-weighted magnetic resonance images to assess age-related volumetric changes to medial temporal lobe regions, including the hippocampal formation and rhinal cortex, in patients with mild cognitive impairment and cognitively normal controls in two independent cohorts. Our results show age was significantly associated with regional atrophy in the hippocampus, but not the rhinal cortex. Additionally, variability in regional medial temporal lobe volume was associated with tau uptake in the rhinal cortex, but not the hippocampus. Together, these results suggest that the rhinal cortex may be more indicative of Alzheimers disease pathology and can help differentiate from age-related neurodegeneration.

neuroscience↗

bioRxiv2022

Brain perivascular space imaging across the human lifespan

Enlarged perivascular spaces (PVS) are considered a biomarker for vascular pathology and are observed in normal aging and neurological conditions; however, research on the role of PVS in health and disease are hindered by the lack of knowledge regarding the normative time course of PVS alterations with age. To this end, we characterized the influence of age, sex and cognitive performance on PVS anatomical characteristics in a large cross-sectional cohort ([~]1400) of healthy subjects between 8 and 90 years of age using multimodal structural MRI data. Our results show age is associated with wider and more numerous MRI-visible PVS over the course of the lifetime with spatially-varying patterns of PVS enlargement trajectories. In particular, regions with low PVS volume fraction in childhood are associated with rapid age-related PVS enlargement (e.g., temporal regions), while regions with high PVS volume fraction in childhood are associated with minimal age-related PVS alterations (e.g., limbic regions). PVS burden was significantly elevated in males compared to females with differing morphological time courses with age. Together, these findings contribute to our understanding of perivascular physiology across the healthy lifespan and provide a normative reference for the spatial distribution of PVS enlargement patterns to which pathological alterations can be compared.

neuroscience↗

bioRxiv2021

NiftyTorch: A Deep Learning framework for NeuroImaging

We present NiftyTorch a Deep Learning Framework for NeuroImaging. The motivation behind the development of such a library is that there are scant amount of centralized tool for deploying 3D deep learning for NeuroImaging. In addition, most of the existing tools require expert technical knowledge in Deep Learning or programming, creating a barrier for entry. The goal is to provide a one stop package using which the users can perform classification tasks, Segmentation tasks and Image Transformation tasks. The intended audience are the members of NeuroImaging who would like to explore deep learning but have no background in coding. In this article we explore the capabilities of the framework, the performance of the framework and the future work for the framework.

neuroscience↗

bioRxiv2020

Body mass index, time of day, and genetics affect perivascular spaces in the white matter

The analysis of cerebral perivascular spaces (PVS) using magnetic resonance imaging (MRI) allows to explore in vivo their contributions to neurological disorders. To date the normal amount and distribution of PVS in healthy human brains are not known, thus hampering our ability to define with confidence pathogenic alterations. Furthermore, it is unclear which biological factors can influence the presence and size of PVS on MRI. We performed exploratory data analysis of PVS volume and distribution in a large population of healthy individuals (n = 897, age = 28.8 {+/-} 3.7). Here we describe the global and regional amount of PVS in the white matter, which can be used as a reference for clinicians and researchers investigating PVS and may help the interpretation of the structural changes affecting PVS in pathological states. We found a relatively high inter-subject variability in the PVS amount in this population of healthy adults (range: 1.31-14.49 cm3). We then identified body mass index, time of day, and genetics as new elements significantly affecting PVS in vivo under physiological conditions, offering a valuable foundation to future studies aimed at understanding the physiology of perivascular flow.

neuroscience↗

bioRxiv2020

SC-GAN: 3D self-attention conditional GAN with spectral normalization for multi-modal neuroimaging synthesis

Image synthesis is one of the key applications of deep learning in neuroimaging, which enables shortening of the scan time and/or improve image quality; therefore, reducing the imaging cost and improving patient experience. Given the multi-modal and large-scale nature of neuroimaging data, the synthesis task is computationally challenging. 2D image synthesis networks do not take advantage of multi-dimensional spatial information and the 3D implementation has dimensionality problem, negatively affecting the network reliability. These limitations hinder the research and clinical applicability of deep learning-based neuroimaging synthesis. In this paper, we proposed a new network that is designed and optimized for the application of multi-modal 3D synthesis of neuroimaging data. The network is based on 3D conditional generative adversarial network (GAN), and employs spectral normalization and feature matching to stabilize the training process and ensure optimization convergence. We also added a self-attention module to model relationships between widely separated voxels. The performance of the network was evaluated by predicting positron emission tomography (PET) images, Fractional anisotropy (FA) and mean diffusivity (MD) maps from multi-modal magnetic resonance images (MRI) of 265 and 497 individuals correspondingly. The proposed network, called self-attention conditional GAN (SC-GAN), significantly outperformed conventional 2D conditional GAN and the 3D implementation, enabling robust 3D deep learning-based neuroimaging synthesis.

bioinformatics↗

bioRxiv2020

Alteration of perivascular spaces in early cognitive decline

Vascular contributions to early cognitive decline are increasingly recognized, prompting further investigation into the nature of related changes in perivascular space. Using magnetic resonance imaging, we show that, compared to a cognitively normal sample, individuals with early cognitive dysfunction have altered perivascular space presence and distribution, irrespective of Amyloid-{beta}. Surprisingly, we noted decreased perivascular space presence in the anterosuperior medial temporal lobe, which was associated with neurofibrillary tau tangle deposition in the entorhinal cortex, one of the hallmarks of early Alzheimers disease pathology. Our results suggest that anatomically-specific alteration of the perivascular spaces may provide an early biomarker of cognitive impairment in aging adults.

neuroscience↗

bioRxiv2019

Homologous laminar organization of the mouse and human subiculum

The subiculum is the major output structure of the hippocampal formation and one of the brain regions most affected by Alzheimers disease. Our previous work revealed a hidden laminar architecture within the mouse subiculum. However, the rotation of the hippocampal longitudinal axis across species makes it unclear how the laminar organization is represented in human subiculum. Using in situ hybridization data from the Allen Human Brain Atlas, we demonstrate that the human subiculum also contains complementary laminar gene expression patterns similar to the mouse. In addition, we provide evidence that the molecular domain boundaries in human subiculum correspond to microstructural differences observed in high resolution MRI and fiber density imaging. Finally, we show both similarities and differences in the gene expression profile of subiculum pyramidal cells within homologous lamina. Overall, we present a new 3D model of the anatomical organization of human subiculum and its evolution from the mouse.

neuroscience↗

bioRxiv2019

Image processing approaches to enhance perivascular space visibility and quantification using MRI

Imaging the perivascular spaces (PVS), also known as Virchow-Robin space, has significant clinical value, but there remains a need for neuroimaging techniques to improve mapping and quantification of the PVS. Current technique for PVS evaluation is a scoring system based on visual reading of visible PVS in regions of interest, and often limited to large caliber PVS. Enhancing the visibility of the PVS could support medical diagnosis and enable novel neuroscientific investigations. Increasing the MRI resolution is one approach to enhance the visibility of PVS but is limited by acquisition time and physical constraints. Alternatively, image processing approaches can be utilized to improve the contrast ratio between PVS and surrounding tissue. Here we combine T1- and T2-weighted images to enhance PVS contrast, intensifying the visibility of PVS. The Enhanced PVS Contrast (EPC) was achieved by combining T1- and T2-weighted images that were adaptively filtered to remove non-structured high-frequency spatial noise. EPC was evaluated on healthy young adults by presenting them to two expert readers and also through automated quantification. We found that EPC improves the conspicuity of the PVS and aid resolving a larger number of PVS. We also present a highly reliable automated PVS quantification approach, which was optimized using expert readings.

neuroscience↗

bioRxiv2018

A systematic bias in DTI findings

Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.\n\nHighlightsO_LIPerivascular space (PVS) fluid significantly contributes to diffusion tensor imaging metrics\nC_LIO_LIIncreased PVS fluid results in increased mean diffusivity and decreased fractional anisotropy\nC_LIO_LIPVS contribution to diffusion signal is overlooked and demands further investigation\nC_LI

neuroscience↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗