Biology subjects

Shirzadi, Z.

Publications and source records attributed to Shirzadi, Z..

3 recordsLinked to original sources

bioRxiv2023

Functional variations in gamma-secretase activity are critical determinants of the clinical, biomarker, and cognitive progression of autosomal dominant Alzheimer's disease

BackgroundThe balance between production, clearance, and toxicity of A{beta} peptides is central to Alzheimers disease (AD) pathobiology. Though highly variable in terms of age at symptom onset (AAO), hundreds of variants in PSEN1 cause autosomal dominant forms of AD (ADAD) with nearly complete penetrance. PSEN1 forms the catalytic core of the {gamma}-secretase complex and thereby directly mediates the production of longer, aggregation-prone A{beta} peptides relative to shorter, non-aggregating peptides. We hypothesized that the broad AAO and biomarker heterogeneity seen across ADAD would be predictable based on mutation-specific differences in the production of A{beta} species. MethodsA{beta}-37, 38, 40, 42, and 43 production was quantified from 161 unique PSEN1 variants expressed in HEK293 cells. Prediction of AAO was carried out in 106 variants with available AAO and then replicated in 55 variants represented across 190 PSEN1 mutation carriers who have detailed cognitive and biomarker data from the Dominantly Inherited Alzheimers Network (DIAN). ResultsVariations in A{beta} production across the 161 mutations examined in cell-based models were highly predictive of AAO. In those with corresponding in vivo data from the DIAN study, our cell-based {gamma}-secretase composite was strongly associated with biomarker and cognitive trajectories. ConclusionsThese findings elucidate the critical link between {gamma}-secretase function, A{beta} production, and AD progression and offer mechanistic support for the amyloid hypothesis. The approach used here represents a powerful tool to account for heterogeneity in disease progression in ADAD clinical trials and to assess the pathogenicity of variants of unknown significance or with limited family history.

neuroscience↗

bioRxiv2021

Automated Generation of Cerebral Blood Flow Maps Using Deep Learning and Multiple Delay Arterial Spin-Labelled MRI

The purpose of this work was to develop and evaluate a deep learning approach for estimation of cerebral blood flow (CBF) and arterial transit time (ATT) from multiple post-label delay (PLD) arterial spin-labelled (ASL) MRI. Six-PLD ASL MRI was acquired on a 1.5T or 3T system among 99 older males and females with and without cognitive impairment. We trained and compared two network architectures: standard feed-forward convolutional neural network (CNN) and U-Net. Mean absolute error (MAE) was evaluated between model estimates and ground truth obtained through conventional processing. The best-performing model was re-trained on inputs with missing PLDs to investigate generalizability to different PLD schedules. Relative to the CNN, the U-Net yielded lower MAE on training data. On test data, the U-Net MAE was 8.4{+/-}1.4 ml/100g/min for CBF and 0.22{+/-}0.09 s for ATT. Model uncertainty, estimated with Monte Carlo dropout, was associated with model error. Network estimates remained stable when tested on inputs with up to three missing PLD images. Mean processing times were: U-Net pipeline = 10.77s; ground truth pipeline = 10min 41s. These results suggest hemodynamic parameter estimation from 1.5T and 3T multi-PLD ASL MRI is feasible and fast with a deep learning image-generation approach.

bioengineering↗

bioRxiv2019

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. The toolbox adheres to previously defined international standards for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts to increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

neuroscience↗