Biology subjects

Dahan, S.

Publications and source records attributed to Dahan, S..

9 recordsLinked to original sources

bioRxiv2026

From topography to connectome: Towards an integrated understanding of the resting brain

As the field expands from early research into the human connectome, there has been a fast expansion in the number of analytical approaches to study resting state functional MRI (rsfMRI) data. With increasing focus on individual differences, topographical brain maps of spatial organization have emerged in addition to traditional functional connectomes. Here, we developed a deep-learning model to embed maps of network topography and faithfully translate to individualized connectomes. Results confirmed the validity of the surface vision transformer based on reconstruction accuracy (0.73{+/-}0.09) and accurate topography-to-connectome translation (0.43{+/-}0.08). Importantly, translated connectomes retained identifiability and brain-cognition associations. These findings establish a direct mapping from spatial topography to connectomes that can be used to integrate scientific insights across rsfMRI sub-fields. This is an important step towards broadening our conceptualization of the connectome and supporting a broader integration of findings to inform a complete understanding of the human connectome. TeaserTranslating from spatial maps of brain organization to region-to-region connectomes retains shared individual differences.

bioinformatics↗

bioRxiv2025

Altered Frontoparietal, Temporal and Sensorimotor Structure-Function Coupling and Its Genetic Underpinnings in Bipolar Disorder

BackgroundStructure-function coupling quantifies how strongly structural connectivity supports functional communication across brain regions. Investigating structure-function coupling in bipolar disorder, a condition marked by dysconnectivity, may elucidate underlying neural mechanisms. We examined regional structure-function coupling in bipolar disorder and its genetic underpinnings to characterize network-level disruptions. MethodsRegional structure-function coupling was estimated in UK Biobank participants using edge-wise regression between measures of structural connectivity and functional connectivity. Bipolar disorder (n=163) and controls (n=326) were age and sex-matched and compared using general linear models. To explore the genetic basis of these structure-function coupling alterations, genome-wide association studies (GWAS) were conducted in an independent UK Biobank sample (n=38,190) for regions showing significant group differences. ResultsStructure-function coupling demonstrated a unimodal-to-transmodal gradient with highest coupling evident in visual regions (R2=0.29) and lowest in the insula (R2=0.03). The Bipolar disorder group showed higher structure-function coupling in the temporal pole and superior frontal gyrus ({beta}=0.269; {beta}=0.211) and lower coupling in the supramarginal, precentral, and postcentral gyri and the frontal pole ({beta}=-0.206 to -0.275). GWAS identified seven significant loci, with mapped genes (e.g., KAT6B, INPP5A, PLCE1) involved in neuronal development and cellular signaling. ConclusionBipolar disorder showed altered structure-function coupling across regions implicating multiple networks. These alterations suggest stronger structure-function alignment in limbic and attention networks and weaker alignment in sensorimotor, executive, frontoparietal, and salience regions relative to controls, potentially disrupting flexible polysynaptic communication. Altered coupling may relate to genetic variation affecting neurodevelopment, neuronal signaling, and synaptic plasticity. Together, these findings offer novel insight into the architectural contributions to the pathophysiology underlying bipolar disorder.

neuroscience↗

bioRxiv2025

Motifs of brain cortical folding from birth to adulthood: structural asymmetry and folding-functional links

Cortical folding of brain is widely regarded as an interplay between genetic programming and biomechanical forces, closely linked to cytoarchitectonic regionalisation. Abnormal folding patterns are frequently observed in neurodevelopmental conditions and psychiatric disorders. However, significant inter-individual variability of secondary and tertiary folds obscures detection of shape biomarkers and confounds investigation of folding-functional relationships. Here we investigate cortical folding heterogeneity at fine scale, using novel hierarchical surface registration (MSM-HT) to parse cortical folding patterns into a representative family of distinct anatomical templates. By applying this technique both to young adults from the Human Connectome Project (HCP) and neonates in the Developing HCP and Brain Imaging in Babies (BIBS) cohorts, we identify and characterize common lobe-wise folding patterns: observing consistency across both age groups, with neonatal samples showing less variation. Crucially, we highlight significant hemispheric asymmetry within the temporal lobe for both adults and neonates, and show that improved correspondence of shape does not translate to improved areal correspondence, affirming previous studies that have pointed to dissociation of folding and functional organisation. This study provides a critical step towards understanding brain asymmetry and complex relationships between folding and function, offering a robust framework to generalise the uncovered cortical folding motifs across datasets and developmental stages.

neuroscience↗

bioRxiv2023

Surface Generative Modelling of Neurodevelopmental Trajectories

Cortical neurodevelopment is sensitive to disruption following preterm birth, with lasting impact on cognitive outcomes. The creation of generative models of neurodevelopment could aid clinicians in identifying atrisk subjects but is complicated by the degree of subject variability in cortical folding, and significant heterogeneity in the effect of preterm birth. In this work, we propose a graph convolutional generative adversarial network (GAN) and a training scheme to simulate neonatal cortical surface developmental trajectories. The proposed model is used to smoothly modify two cortical phenotypes: post-menstrual age at scan (PMA) and gestational age at birth (GA) on data from the developing Human Connectome Project (dHCP) [1]. The synthetic images were validated with an independently trained regression network, and compared against follow up scans, indicating that the model can realistically age individuals whilst preserving subject-specific cortical morphology. Deviation between simulated healthy scans, and preterm follow up scans generated a metric of individual atypicality, which improved prediction of 18-month cognitive outcome over GA alone.

neuroscience↗

bioRxiv2023

Geneneralising the HCP multimodal cortical parcellation to UK Biobank

The Human Connectome Project Multimodal Parcellation (HCP_MMP1.0) provides a robust in vivo map of the cerebral cortex, which demonstrates variability in structure and function that cannot be captured through diffeomorphic image registration alone. The HCP successfully employed a fully-connected neural network architecture to capture this variation, however it is unclear whether this approach generalises to other datasets with less rich imaging protocols. In this paper we propose and validate a novel geometric deep learning framework for generating individualised HCP_MMP1.0 parcellations in UK Biobank (UKB), an extremely rich resource that has led to numerous breakthroughs in neuroscience. To address substantial differences in image acquisition (for example, 6 minutes of resting-state fMRI per subject for UKB vs. 60 minutes per subject for HCP), we introduce a multi-step learning procedure including pretraining with a convolutional autoencoder. Compared to a fully-connected baseline, our proposed framework improved average detection rate across all areas by 10.4% (99.1% vs 88.7%), and detection of the worst performing area by 51.0% (80.9% vs. 29.9%). Importantly, this was not a result of the framework predicting one consistent parcellation across subjects, as visual inspection indicated that our method was sensitive to atypical cortical topographies. Code and trained models will be made available at https://www.github.com.

neuroscience↗

bioRxiv2022

Hematopoiesis and cell growth are differentially regulated by TAL1 isoforms

T-cell acute lymphoblastic leukemia (T-ALL) protein 1 (TAL1) is a central transcription factor in hematopoiesis. The timing and level of TAL1 expression orchestrate the differentiation to specialized blood cells and its overexpression is a common cause of T-ALL. Here we studied the two protein isoforms of TAL1, short and long, that are generated by the use of alternative promoters as well as by alternative splicing. We analyzed the expression of each isoform by deleting an enhancer or insulator, or by opening chromatin at the enhancer location. Our results show that each enhancer promotes expression from a specific TAL1 promoter. Expression from a specific promoter gives rise to a unique 5 UTR with differential regulation of translation. Moreover, our study suggests that the enhancers regulate TAL1 exon 3 alternative splicing by inducing changes in the chromatin at the splice site, which we demonstrate is mediated by KMT2B. Furthermore, our results indicate that TAL1-short binds more strongly to TAL1 E-protein partners and functions as a stronger transcription factor than TAL1-long. Specifically TAL1-short has a unique transcription signature promoting apoptosis. Finally, when we expressed both isoforms in mice bone-marrow, we found that while overexpression of both isoforms prevents lymphoid differentiation, expression of TAL-short alone leads to hematopoietic stem cell exhaustion. Furthermore, we found that TAL1-short promoted erythropoiesis and reduced cell survival in the CML cell line K562. While TAL1 and its partners are considered promising therapeutic targets in the treatment of T-ALL, our results show that TAL1-short could act as a tumor suppressor and suggest that altering TAL1 isoforms ratio could be a preferred therapeutic approach.

molecular biology↗

bioRxiv2021

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

The emerging field of geometric deep learning extends the application of convolutional neural networks to irregular domains such as graphs, meshes and surfaces. Several recent studies have explored the potential for using these techniques to analyse and segment the cortical surface. However, there has been no comprehensive comparison of these approaches to one another, nor to existing Euclidean methods, to date. This paper benchmarks a collection of geometric and traditional deep learning models on phenotype prediction and segmentation of sphericalised neonatal cortical surface data, from the publicly available Developing Human Connectome Project (dHCP). Tasks include prediction of postmenstrual age at scan, gestational age at birth and segmentation of the cortical surface into anatomical regions defined by the M-CRIB-S atlas. Performance was assessed not only in terms of model precision, but also in terms of network dependence on image registration, and model interpretation via occlusion. Networks were trained both on sphericalised and anatomical cortical meshes. Findings suggest that the utility of geometric deep learning over traditional deep learning is highly task-specific, which has implications for the design of future deep learning models on the cortical surface. The code, and instructions for data access, are available from https://github.com/Abdulah-Fawaz/Benchmarking-Surface-DL.

neuroscience↗

bioRxiv2021

VEGFA's distal enhancer regulates its alternative splicing in CML

Enhancer demethylation in leukemia and lymphoma was shown to lead to overexpression of genes which promote cancer characteristics. The vascular endothelial growth factor A (VEGFA) enhancer, located 157 Kb downstream of its promoter, is demethylated in chronic myeloid leukemia (CML). VEGFA has several alternative splicing isoforms with different roles in vascularization and cancer progression. Since transcription and splicing are coupled in space and time, we hypothesized that the VEGFA enhancer can also regulate the genes alternative splicing to contribute to the pathology of CML. Our results show that mutating the VEGFA +157 enhancer promotes exclusion of exons 6b and 7 and activating the enhancer by tethering a chromatin activator has the opposite effect. In line with these results, CML patients display high enhancer activity and inclusion of VEGFA exons 6b and 7. To search for a key protein connecting transcription with alternative splicing, we analyzed 161 chromatin immunoprecipitation (ChIP)-seq experiments for DNA binding proteins and found that PML and CCNT2 bind VEGFAs promoter and enhancer. CCNT2 is a positive regulator of RNA polymerase II (RNAPII) transcription elongation and indeed its silencing promotes exclusion of exons 6b and 7. Slowing down RNAPII elongation promotes exclusion of exons 6b and 7. Thus our results suggest that VEGFAs +157 enhancer regulates its alternative splicing by increasing RNAPII elongation rate via CCNT2. Our work demonstrates the importance of the interplay between transcription and alternative splicing.

cancer biology↗

bioRxiv2020

KDM3A regulates alternative splicing of cell-cycle genes following DNA damage

Changes in the cellular environment result in chromatin structure alteration, which in turn regulates gene expression. To learn about the effect of the cellular environment on the transcriptome, we studied the H3K9 de-methylase KDM3A. Using RNA-seq, we found that KDM3A regulates the transcription and alternative splicing of genes associated with cell cycle and DNA damage. We showed that KDM3A undergoes phosphorylation by PKA at serine 265 following DNA damage, and that the phosphorylation is important for a proper cell cycle regulation. We demonstrated that SAT1 alternative splicing, regulated by KDM3A, plays a role in cell cycle regulation. Furthermore we found that KDM3As demethylase activity is not needed for SAT1 alternative splicing regulation. In addition, we identified KDM3As protein partner ARID1A, the SWI/SNF subunit, and SRSF3 as regulators of SAT1 alternative splicing and showed that KDM3A is essential for SRSF3 binding to SAT1 pre-mRNA. These results suggest that KDM3A serves as a sensor of the environment and an adaptor for splicing factor binding. Our work reveals chromatin sensing of the environment in the regulation of alternative splicing.

molecular biology↗