Biology subjects

Asadi, S.

Publications and source records attributed to Asadi, S..

6 recordsLinked to original sources

bioRxiv2026

Substrate biasing in UCHL5 proteoforms

Proteolytic deubiquitinating enzymes bridge a gap in substrate recognition through complex regulatory mechanisms. A growing portion of these are accomplished through proteoforms that uniquely control association and diverse sets of cleavage capabilities that relay distinct physiological outcomes. This study describes substrate biasing governed by UCHL5 proteoforms. It demonstrates that N-terminal ubiquitination activates the enzyme towards monoubiquitin substrates, a feature that is conserved across UCHL5 homologs. Crystallographic and spectroscopic data suggest that the N-terminal ubiquitin binds intramolecularly in an allosteric binding site and inhibits branched chain substrate cleavage. Association with Rpn13/Adrm1 relieves this inhibition and reestablishes its ability to debranch, potentially controlling nonspecific debranching compared to retention of needed activity on the 26S proteasome. Collectively, this study describes the molecular basis for substrate selectivity in a deubiquitinating enzyme, an unexplored area in the enzymes that counteract ubiquitin E3 ligases.

biochemistry↗

bioRxiv2026

A Comprehensive Analysis Comparing Isotropic ADC to BOLD-fMRI: Sensitivity to Resting State Networks and Grey to White Matter Functional Connectivity

While Blood Oxygenation Level-Dependent (BOLD) fMRI remains the gold standard for mapping functional brain networks with MRI, its vascular origins inherently conflate haemodynamic effects with neural activity, limiting its sensitivity in white matter (WM) or its interpretation in neurovascular diseases. Apparent Diffusion Coefficient (ADC) fMRI offers an alternative, diffusion-based contrast that is theoretically more sensitive to neuromorphological coupling and therefore more specific to neuronal activation, though investigated primarily during task-based conditions. This study aimed to comprehensively evaluate the efficacy of isotropic ADC-fMRI in detecting established resting-state networks (RSNs) and to extend this methodology to the investigation of grey-to-white matter (GM-WM) functional connectivity. Our analyses revealed a gradient of ADC detectability shaped by the degree of static functional cohesion and structural tethering of each network. The visual and somatomotor networks, being both highly segregated and strongly anchored to underlying structural pathways, yielded the most robust detection. The default mode network (DMN) and dorsal attention network (DAN) reached group-level significance but with lower effect sizes, and their detection proved fragile across analytical approaches. The frontoparietal network (FPN) and salience network (SAN), whose functional identity is defined by dynamic cross-network reconfiguration, did not reach significance. This gradient partially mirrors the established hierarchy of network segregation observed in BOLD, while further suggesting that ADC sensitivity depends on the structural grounding of each network. Furthermore, ADC demonstrated superior sensitivity to GM-WM functional coupling compared to BOLD. GM-WM functional connectivity profiles derived from ADC were significantly more aligned with underlying structural WM architecture across subjects. Taken together, these findings position isotropic ADC-fMRI as a viable complementary modality to BOLD, offering a more direct window into the neural and structural foundations of brain connectivity.

neuroscience↗

bioRxiv2026

The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales

Conventional connectome edge weights, such as number of streamlines (NOS) or diffusion tensor imaging (DTI) metrics, lack specificity to microstructural details which may hold relevance for macroscale brain organisation. Since biophysical diffusion modelling offers greater specificity to microstructure, we investigated whether parameters from the Standard Model of white matter diffusion provide informative alternatives for connectome weights - namely the intra-axonal signal fraction (f) and perpendicular extra-axonal diffusivity (DePerp). Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, DePerp, NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties and hub structure, only DePerp and normalised NOS captured non-random properties of local organisation at all spatial scales. We then correlated each weighted connectome with resting-state fMRI functional connectivity and intracranial measurements of conduction velocity. At both the whole-brain and regional level, NOS gave strong coupling with fMRI functional connectivity. Connectomes weighted by DePerp, FA and RD gave weak but significant coupling with fMRI functional connectivity and conduction velocity. Notably, DePerp and RD gave highest consistency in regional structure-function coupling across spatial scales. Thus, connectome weights derived from DePerp capture meaningful aspects of brain network organisation with functional relevance.

neuroscience↗

bioRxiv2026

Directed Brain Connectomics Revealed by Bicommunity Structure

The brain is a complex, interconnected system in which structural wiring underpins the flow of information between functional units. After two decades of advances in connectomics, the asymmetry of edge structures in large-scale brain networks remains largely unexplored. Here, we build a directed connectome combining structure and function, and dissect its organization into bicommunities where sending and associated receiving sets of nodes, through clustering of directed-edge information, are fully acknowledged. Our findings reveal a primary directional axis from sensory to association cortices, indicative of bottom-up information flow. Validation with invasive electrophysiological measures demonstrates that these bicommunities capture an intermediate organizational scale at which connection asymmetry aligns across modalities. Finally, sets of distinct known anatomical fiber bundles and their functional mapping are recovered as specific bicommunities. This work identifies asymmetric pathways onto which neural computation can be expressed, and establishes directed connectomics as a novel framework for understanding brain organization.

neuroscience↗

bioRxiv2025

Non-invasive prediction of conduction velocities in the human brain from MRI-derived microstructure features at 7 Tesla

The conduction velocity of neuronal signals along axons is a key neurophysiological property that can be altered in various disease processes. While cortico-cortical evoked potentials (CCEPs) can be measured in presurgical assessment to provide information about conduction delay between a subset of brain regions, it is currently not possible to efficiently and systematically estimate conduction velocity in vivo across the whole brain. Given the established link between conduction velocity and axon morphology (most notably axon diameter but also myelination), mapping a reliable and quantitative metric linked to axon properties could fill the gap of inferring conduction velocity across the entire human brain. By integrating multiple MRI-derived microstructural measures - including axon radius, axonal water fraction, extra-axonal perpendicular diffusivity, and longitudinal relaxation time - and conduction velocity estimates obtained from a large database of CCEPs, we developed a whole-brain prediction model of conduction velocity. Our multivariate MRI-based model explained 29% of variance in neurophysiological conduction velocity, making it possible to partially predict whole-brain conduction velocity and delay matrices along connections for which no direct measurement is commonly available from epilepsy surgery investigations. This integrative MRI-based approach could provide a non-invasive framework for comprehensively characterising conduction delays in vivo across the human brain white matter.

neuroscience↗

bioRxiv2022

Fingerprinting of brain disease: Connectome identifiability in cognitive decline and neurodegeneration

In analogy to the friction ridges of a human finger, the functional connectivity patterns of the human brain can be used to identify a given individual from a population. In other words, functional connectivity patterns constitute a marker of human identity, or a brain fingerprint. Yet remarkably, very little is known about whether brain fingerprints are preserved in brain ageing and in the presence of cognitive decline due to Alzheimers disease (AD). Using fMRI data from two independent datasets of healthy and pathologically ageing subjects, here we show that individual functional connectivity profiles remain unique and highly heterogeneous across early and late stages of cognitive decline due to AD. Yet, the patterns of functional connectivity making subjects identifiable, change across health and disease, revealing a functional reconfiguration of the brain fingerprint. We observed a fingerprint change towards between-functional system connections when transitioning from healthy to dementia, and to lower-order cognitive functions in the earliest stages of the disease. These findings show that functional connectivity carries important individualised information to evaluate regional and network dysfunction in cognitive impairment and highlight the importance of switching the focus from group differences to individual variability when studying functional alterations in AD. The present data establish the foundation for clinical fingerprinting of brain diseases by showing that functional connectivity profiles maintain their uniqueness, yet go through functional reconfiguration, during cognitive decline. These results pave the way for a more personalised understanding of functional alterations during cognitive decline, moving towards brain fingerprinting in personalised medicine and treatment optimization during cognitive decline.

neuroscience↗