Biology subjects

Shahbazi, A.

Publications and source records attributed to Shahbazi, A..

3 recordsLinked to original sources

bioRxiv2026

Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain

Resting-state functional Magnetic Resonance Imaging (fMRI) is widely used to infer the intrinsic functional organization of the brain, yet it remains unclear how well this approach can predict the structure of brain activity observed across a diverse set of mental states. Here we compare resting-state to task-based fMRI using diverse task batteries within the same individuals. We find that multi-task fMRI data consistently outperform resting-state estimates in predicting functional organization during novel tasks. This advantage persists across preprocessing strategies, brain regions, and independent datasets. While task activation estimates do show task-dependency when using only few tasks, increasing task diversity reduced task-specific bias, with convergence achieved using modest task sets. These improvements translate into superior individual parcellations and connectivity models. Together, our results dissociate reliability from validity in neuroimaging and challenge the prevailing assumption that rest provides a privileged window into intrinsic brain organization. Instead, functional architecture appears most faithfully revealed when the brain is actively driven through diverse task states.

neuroscience↗

bioRxiv2023

Analysis of lncRNA, miRNA, mRNA-associated ceRNA networks in Autism

Autism is a multifactorial behavioral disease, which is caused by different environmental and genetic alterations. In this disease, various molecular pathways such as inflammation and cell-cell connections are affected. In this study, by analyzing RNA-seq data and bioinformatics approaches, candidate genes were selected for further analysis, and the expression of candidate genes in the blood samples of autistic people was compared to normal. Finally, the candidate genes showed potential as biological markers for prognosis in autism. Through the analysis of these candidate genes, the researchers were able to identify changes in the expression of certain genes that were associated with the disease. By understanding how these genes are involved in the disease, researchers can develop better treatments and diagnose the disease earlier.

bioinformatics↗

bioRxiv2018

Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (CT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipelines reconstructions, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

neuroscience↗