Biology subjects

Baratz, Z.

Publications and source records attributed to Baratz, Z..

4 recordsLinked to original sources

bioRxiv2023

Predicting Individual Traits From T1-weighted Anatomical MRI Using the Xception CNN Architecture

Modeling individual traits is a long-standing goal of neuroscientific research, as it allows us to gain a more profound understanding of the relationship between brain structure and individual variability. In this article, we used the Keras-Tuner library to evaluate the performance of a tuned Xception convolutional neural network (CNN) architecture in predicting sex and age from a sample of 4,049 T1-weighted anatomical MRI scans originating from 1,594 participants. In addition, we used the same tuning procedure to predict the big five inventory (BFI) personality traits for 415 participants (represented by 1,253 scans), and compared the results with those generated by applying transfer learning (TL) based on the models for sex and age. To minimize the effects of preprocessing procedures, scans were subjected exclusively to brain extraction and linear registration with the 2 mm MNI152 template. Our results suggest that CNNs trained with hyperparameter optimization could be used as an effective and accessible tool for predicting subject traits from anatomical MRI scans, and that TL shows potential for application across target domains. While BFI scores were not found not be predictable from T1-weighted scans, further research is required to assess other preprocessing and prediction workflows.

neuroscience↗

bioRxiv2023

The Labbing Project: A database management application for neuroimaging research

The goal of this article is to present "The Labbing Project"; a novel neuroimaging data aggregation and preprocessing web application built with Django and VueJS. Neuroimaging data can be complex and time-consuming to work with, especially for researchers with limited programming experience. This web application aims to streamline the process of aggregating and preprocessing neuroimaging data by providing an intuitive, user-friendly interface that allows researchers to upload, organize, and preprocess their data with minimal programming requirements. The application utilizes Django, a popular Python web framework, to create a robust and scalable platform that can handle large volumes of data and accommodate the needs of a diverse user base. This robust infrastructure is complemented by a user-friendly VueJS frontend application, supporting commonplace data querying and extraction tasks. By automating common data processing tasks, this web application aims to save researchers time and resources, enabling them to focus on their research rather than data management.

bioinformatics↗

bioRxiv2023

Optimizing FreeSurfer's Surface Reconstruction Parameters for Anatomical Feature Estimation

Magnetic resonance imaging (MRI) is a powerful tool for non-invasive imaging of the human body. However, the quality and reliability of MRI data can be influenced by various factors, such as hardware and software configurations, image acquisition protocols, and preprocessing techniques. In recent years, the introduction of large-scale neuroimaging datasets has taken an increasingly prominent role in neuroscientific research. The advent of publicly available and standardized repositories has enabled researchers to combine data from multiple sources to explore a wide range of scientific inquiries. This increase in scale allows the study of phenomena with smaller effect sizes over a more diverse sample and with greater statistical power. Other than the variability inherent to the acquisition of the data across sites, preprocessing and feature generation steps implemented in different labs introduce an additional layer of variability which may influence consecutive statistical procedures. In this study, we show that differences in the configuration of surface reconstruction from anatomical MRI using FreeSurfer results in considerable changes to the estimated anatomical features. In addition, we demonstrate the effect these differences have on within-subject similarity and the performance of basic prediction tasks based on the derived anatomical features. Our results show that although FreeSurfer may be provided with either a T2w or a FLAIR scan for the same purpose of improving pial surface estimation (relative to based on the mandatory T1w scan alone), the two configurations have a distinctly different effect. In addition, our findings indicate that the similarity of within-subject scans and performance of a range of models for the prediction of sex and age are significantly effected, they are not significantly improved by either of the enhanced configurations. These results demonstrate the large extent to which elementary and sparsely reported differences in preprocessing workflow configurations influence the derived brain features. The results of this study are meant to underline the importance of optimizing preprocessing procedures based on experimental results prior to their distribution and consecutive standardization and harmonization efforts across public datasets. In addition, preprocessing configurations should be carefully reported and included in any following analytical workflows, to account for any variation originating from such differences. Finally, other representations of the raw data should be explored and studied to provide a more robust framework for data aggregation and sharing.

neuroscience↗

bioRxiv2018

A Framework for Cortical Layer Composition Analysis using Low Resolution T1 MRI Images

The layer composition of the cerebral cortex represents a unique anatomical fingerprint of brain development, function, connectivity and pathology. Historically the cortical layers were investigated solely ex-vivo using histological means, but recent magnetic resonance imaging (MRI) studies suggest that T1 relaxation images can be utilized to separate the layers. Despite technological advancements in the field of high resolution MRI, accurate estimation of whole brain layer composition has remained limited due to partial volume effects, leaving some layers far beyond the image resolution. In this study we offer a simple and accurate method for layer composition analysis, resolving partial volume effects and cortical curvature heterogeneity. We use a low resolution echo planar imaging inversion recovery (EPI IR) MRI scan protocol that provides fast acquisition (~12 minutes) and enables extraction of multiple T1 relaxation time components per voxel, which are assigned to types of brain tissue and utilized to extract the subvoxel composition of each T1 layer. While previous investigation of the layers required the estimation of cortical normals or smoothing of layer widths (similar to VBM), here we developed a sphere-based approach to explore the inner mesoscale architecture of the cortex. Our novel algorithm conducts spatial analysis using volumetric sampling of a system of virtual spheres dispersed throughout the entire cortical space. The methodology offers a robust and powerful framework for quantification and visualization of the layers on the cortical surface, providing a basis for quantitative investigation of their role in cognition, physiology and pathology.

neuroscience↗