Biology subjects

Assaf, Y.

Publications and source records attributed to Assaf, Y..

16 recordsLinked to original sources

bioRxiv2024

Laminar RNNs: using biologically-inspired network topology on the cortical laminar level in memory tasks

Advancements in neuroscience and artificial intelligence have been fueling one another for decades. In this study, we integrate a neuroimaging model of laminar-level connectomics into a biologically-inspired deep learning model of recurrent neural networks (RNNs) for working memory tasks. The resulting model offers a way to incorporate a more comprehensive representation of brain topology into artificial intelligence without diminishing the performance of the network compared to previous models.

neuroscience↗

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↗

bioRxiv2022

Clustering multilayered cytoarchitectonic surface-based data in the brain

The laminar microstructure of the cerebral cortex is considered a unique anatomical mark of the development, function, connectivity, and even various pathologies of the brain. In recent years, multiple neuroimaging studies have utilized magnetic resonance imaging (MRI) relaxometry to visualize and explore this intricate microstructure. By successfully delineating the cortical laminar components, the applicability of T1 relaxometry has been expanded beyond solely a direct measure of myeline content. However, validating its applicability as a measure of cortical cytoarchitecture demands analyzing the complex resulting laminar datasets. In this study, we adapt and implement an algorithm for clustering cell omics profiles to cluster these complex microstructural cortical datasets. The resulting clusters correspond with an established atlas of cytoarchitectonic features, providing robust validation of T1 imaging as a tool for exploring cortical laminar composition. Lastly, we demonstrate the applicability of this framework in the exploration of the cytoarchitectonic features behind various unique skillsets.

neuroscience↗

bioRxiv2022

A connectomics-based taxonomy of mammals

Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle for the comparison of neural architectures have been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyze the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion magnetic resonance imaging (MRI) scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a single protocol on a single scanner. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting the connectome organization recapitulates traditional taxonomies defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.

neuroscience↗

bioRxiv2020

Distributional changes in myelin-specific MRI markers uncover dynamics in the fornix following spatial navigation training

Increasing evidence implicates white matter (WM) dynamics supporting learning in the mature brain. Recent MRI studies, mostly using diffusion tensor MRI (DT-MRI), have demonstrated learning-induced WM changes at the microstructural level. However, while DT-MRI-derived measures have sensitivity to general WM microstructural changes, they lack compartmental specificity, making them difficult to relate to underlying cellular mechanisms, stymying deeper understanding of mechanisms supporting training-induced gains in performance. Gaining a deeper understanding demands a more detailed characterization of changes in specific WM sub-components. To this end, four microstructural MRI techniques were employed to study alterations in rat brains after 5-days of water maze training: DT-MRI; Composite Hindered and Restricted Model of Diffusion (CHARMED); magnetization transfer (MT) imaging; quantitative susceptibility mapping and R2*. The hypothesis tested here was that microstructural changes would be: (i) observed in tracts supporting spatial navigation, i.e., fornix and corpus callosum (CC); and (ii) more pronounced in the myelin-specific measures. Medians and distributions of microstructural parameters were derived along the fornix, CC and cingulum (as a comparison tract) using the tractometry approach. Summary measures were derived from different metrics using unsupervised data reduction. Significant pre-vs-post training differences were found in the medians of two principal components loading on: (i) anisotropy indices; and (ii) MT ratio. The most striking effect, however, was seen in the distributions of pre-vs-post training MT ratio in the fornix, consistent with the primary hypothesis, and highlighting the value of this alternative to the standard approach (i.e., comparing means/medians of DT-MRI parameters) for studying neuroplasticity in vivo. Significance statementRecent MRI-studies have demonstrated that white matter (WM) dynamics support learning, even in the mature brain. However, most studies are based on diffusion tensor MRI (DT-MRI) measures, which although providing sensitivity to WM microstructural changes, do not provide a direct translation to the underlying cellular mechanisms making interpretation difficult. Using alternative quantitative MRI approaches, this study provided more specific subcomponent microstructural insights into the brains WM response to water-maze training. We show that myelin-specific MR measures show more marked changes than axonal-specific and DT-MRI measures, in WM tracts responsible for spatial navigation. The results promote adoption of these alternative approaches to DT-MRI for studying neuroplasticity in vivo.

neuroscience↗

bioRxiv2020

Brain volumetric changes in the general population following the COVID-19 outbreak and lockdown

The COVID-19 outbreak introduced unprecedented health-risks, as well as pressure on the financial, social, and psychological well-being due to the response to the outbreak. Here, we examined the manifestations of the COVID-19 outbreak on the brain structure in the healthy population, following the initial phase of the pandemic in Israel. We pre-registered our hypothesis that the intense experience of the outbreak potentially induced stress-related brain modifications. Volumetric changes in n = 50 participants who were scanned before and after the COVID-19 outbreak and lockdown, were compared with n = 50 control participants who were scanned twice prior to the pandemic. The pandemic provided a rare opportunity to examine brain plasticity in a natural experiment. We found volumetric increases in bilateral amygdalae, putamen, and the anterior temporal cortices. Changes in the amygdalae diminished as time elapsed from lockdown relief, suggesting that the intense experience associated with the pandemic outbreak induced transient volumetric changes in brain regions commonly associated with stress and anxiety.

neuroscience↗

bioRxiv2020

Modelling cortical laminar connectivity in the macaque brain

In 1991, Felleman and Van Essen published their seminal study regarding hierarchical processing in the primate cerebral cortex. Their work encompassed a widescale analysis of connections reported through tracing between 35 regions in the macaque visual cortex, extending from cortical regions to the laminar level. In this work, we revisit laminar-level connectivity in the macaque brain using a whole-brain MRI-based approach. We use multi-modal ex-vivo MRI imaging of the macaque brain in both white and grey matter, which are then integrated via a simple model of laminar connectivity. This model uses a granularity-based approach to define a set of rules that expands cortical connections to the laminar level. Different fiber tracking routines are then examined in order to explore the ability of our model to infer laminar connectivity. The network of macaque cortical laminar connectivity resulting from the chosen routine is then validated in the visual cortex by comparison to findings from Felleman and Van Essen with an 83% accuracy level. By using a more comprehensive definition of the cortex that addresses its heterogenous laminar composition, we can explore a new avenue of structural connectivity on the laminar level.

neuroscience↗

bioRxiv2019

Structural integrity following focused ultrasound thalamotomy and its correlation with tremor relief

BackgroundMagnetic-resonance-guided focused ultrasound ablation of ventral intermediate nucleus of the thalamus is a new treatment for tremor disorders.\n\nObjectivesWe sought to evaluate the white matter integrity prior to and following focused ultrasound ablation and its correlation with clinical outcome.\n\nMethods22 patients with essential tremor and 17 patients with Parkinsons disease underwent tremor and quality-of-life assessments prior to and at one and six months following focused ultrasound ablation. All patients underwent MRI including T1, T2-FLAIR and diffusion weighted imaging before treatment and at one day, 7-10 days, 1-3 months, and 6 months or more following it. Diffusivity parameters were calculated and fiber tractography measures were extracted. Change in diffusivity parameters were assessed inside the ablated core, in the motor thalamus and throughout the dentato-rubro-thalamo-cortical tract.\n\nResultsWe found short-term changes in the motor thalamus and in the tract between the thalamus and regions within the dentato-rubro-thalamo-cortical tract. Long term damage was found in the ablated core and in the tract connecting the thalamus and red-nucleus. Inverse correlation was found between fractional anisotropy in the motor thalamus one day following ablation and tremor improvement in both patient groups, with lower values before treatment associated with better outcome (tremor relief) in essential tremor patients.\n\nConclusionsShort-and long-term changes in white matter integrity are present following focused ultrasound thalamotomy. Regions demonstrating long-term white matter changes may be responsible for the tremor relief seen in patients, implicating these regions in the disorders pathogenesis.

neuroscience↗

bioRxiv2019

Mammillothalamic disconnection alters hippocampo-cortical oscillatory activity and microstructure: Implications for diencephalic amnesia

Diencephalic amnesia can be as disruptive as the more commonly known temporal lobe amnesia, yet the precise contribution of diencephalic structures to memory processes remains elusive. We used discrete lesions of the mammillothalamic tract to model aspects of diencephalic amnesia and assessed the impact of these lesions on multiple measures of activity and plasticity within the hippocampus and retrosplenial cortex. Lesions of the mammillothalamic tract had widespread indirect effects on hippocampo-cortical oscillatory activity within both theta and gamma bands. Both within-region oscillatory activity and cross-regional synchrony were altered. The network changes were state-dependent, displaying different profiles during locomotion and paradoxical sleep. Consistent with the associations between oscillatory activity and plasticity, complementary analyses using several convergent approaches revealed microstructural changes, which appeared to reflect a suppression of learning-induced plasticity in lesioned animals. Together, these combined findings suggest a mechanism by which damage to the medial diencephalon can impact upon learning and memory processes, highlighting important role for the mammillary bodies in the co-ordination of hippocampo-cortical activity.

neuroscience↗

bioRxiv2019

Short-Term Plasticity Following Motor Sequence Learning Revealed by Diffusion MRI

Current non-invasive methods to detect structural plasticity in humans are mainly used to study long-term changes. Diffusion MRI was recently proposed as a novel approach to reveal gray matter changes following spatial navigation learning and object-location memory tasks. In the present work we used diffusion MRI to investigate the short-term neuroplasticity that accompanies sequential motor learning. Following a 45-minutes training session in which participants learned to accurately play a short sequence on a piano keyboard, changes in diffusion properties were revealed mainly in motor system regions such as the premotor cortex and cerebellum. A second learning session taking place immediately afterwards shifted the attention of participants onto timing of key pressing instead of accuracy. This second session induced a different plasticity pattern, demonstrating the dynamic nature of this phenomenon, formerly thought to require months of training in order to be detectable. These results provide us with an important reminder that the brain is an extremely dynamic structure. Furthermore, diffusion MRI offers a novel measure to follow tissue plasticity particularly over short timescales, allowing new insights into the dynamics of structural brain plasticity.

neuroscience↗

bioRxiv2018

Cyfip1 haploinsufficiency is associated with white matter changes, myelin thinning, reduction of mature oligodendrocytes and behavioural inflexibility

The biological basis of the increased risk for psychiatric disorders due to the pathogenic 15q11.2 copy number deletion is unknown. Previous work has shown disturbances in white matter tracts in human carriers of the deletion at this locus. Using a novel rat model, we have recapitulated low dosage of the candidate risk gene CYFIP1 present within the 15q11.2 interval to reveal alterations in white matter microstructure. Using diffusion tensor imaging, we first showed extensive white matter changes in Cyfip1 mutants that were most pronounced in the corpus callosum and external capsule. Transmission electron microscopy showed that these changes were associated with thinning of the myelin sheath in the corpus callosum. Myelin thinning was independent of changes in axon number or diameter but was associated with a reduction in the number of mature oligodendrocytes. Finally, we demonstrated functional effects on cognitive phenotypes sensitive to both disruptions in myelin and callosal circuitry.

neuroscience↗

bioRxiv2018

Cortical microinfarcts caused by single penetrating vessel occlusion lead to widespread reorganization across the entire brain in a CX3CR1 dependent manner

1. Introduction 1. Introduction 2. Results 3. Discussion 4. Methods and Materials 6. Author Contribution 7. Competing interests References Dementia, and the associated cognitive impairment that goes along with it, have been attributed to a plethora of different mechanisms. As proper brain function is tightly coupled with the on-demand supply of oxygen and nutrients to active areas, perturbation to blood flow and the resulting neurovas-cular uncoupling play a crucial role in the ethology of these pathologies [1, 2, 3], even before the onset of the clinical symptoms [4]. Although research has traditionally focused on systemic and large-scale perturbations such as hypertension and stroke, cortical microinfarcts emerge nowaday ...

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↗

bioRxiv2017

The Role Of Diffusion MRI In Neuroscience

Diffusion weighted imaging has further pushed the boundaries of neuroscience by allowing us to peer farther into the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We will briefly summarise key questions, that have historically been raised in neuroscience, concerning the brains white matter. We will then expand on the benefits of diffusion weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review will highlight the invaluable contribution of diffusion weighted imaging in neuroscience, present its limitations and put forth new challenges for the future generations who may wish to exploit this powerful technology to gain novel insights.

neuroscience↗