Biology subjects

Razban, R. M.

Publications and source records attributed to Razban, R. M..

3 recordsLinked to original sources

bioRxiv2023

Brain signaling becomes less integrated and more segregated with age

The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, Pint and Pseg, from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data. AUTHOR SUMMARYThe integration-segregation framework succinctly captures the tradeoff brains face between seamless function (more integration) in light of energetic constrains (more segregation). Despite its ubiquitous use in the field, there is no consensus on its definition with various graph theoretical properties being proposed. Here, we define the two states based on the underlying mechanism of neuronal coupling strength to provide a physical foundation for the framework. We find that younger adults brains are close to perfectly balancing between integration and segregation, while older adults brains veer off towards random signaling.

neuroscience↗

bioRxiv2022

Percolation Theory Identifies Critical Constraints on White Matter Tract Development

We perform targeted attack, a systematic computational unlinking of the network, to analyze its effects on global communication across the network through its giant cluster. Across diffusion magnetic resonance images from individuals in the UK Biobank, Adolescent Brain Cognitive Development Study and Developing Human Connectome Project, we find that targeted attack procedures on increasing white matter tract lengths and densities are remarkably invariant to aging and disease. Time-reversing the attack computation suggests a mechanism for how brains develop, for which we derive an analytical equation using percolation theory. Based on a close match between theory and experiment, our results demonstrate that tracts are limited to emanate from regions already in the giant cluster and tracts that appear earliest in neurodevelopment are those that become the longest and densest. SignificanceAs brains develop through neural growth and specialization, what mechanism ensures that new neurons are integrated into a fully connected brain, avoiding "bridges to nowhere"? Here, we study brain structure development from the perspective of percolation, a global measure of communication. Analyzing over 35,000 diffusion MRI scans on human individuals, from newborns to adults, we identify the following rules of brain neurogenesis through percolation theory: earlier tracts become longer and denser while maintaining a giant cluster. This signature, invariant to age or mental health, suggests a fundamental condition for the brain to function as an emergent whole.

neuroscience↗

bioRxiv2019

Effects of single mutations on protein stability are Gaussian distributed

AbstractThe distribution of protein stability effects is known to be well-approximated by a Gaussian distribution from previous empirical fits. Starting from first-principles statistical mechanics, we more rigorously motivate this empirical observation by deriving per residue protein stability effects to be Gaussian. Our derivation requires the number of amino acids to be large, which is satisfied by the standard set of 20 amino acids found in nature. No assumption is needed on the protein length or the number of residues in close proximity in space, in contrast to previous applications of the central limit theorem to protein energetics. We support our derivation results with computational and experimental data on mutant protein stabilities across all types of protein residues. Statement of SignificanceDefining the distribution of single mutant stability effects ({Delta}{Delta}Gs) is the first step in modeling the role protein stability plays in evolution. Although empirical fits have been made to elucidate its form, a complete theoretical understanding of {Delta}{Delta}G distributions is lacking. Here, we derive how a simple Gaussian form can arise, while still including the intricacies of protein sequence and structure. We backup our derivation with previously released computational and experimental {Delta}{Delta}Gs.

biophysics↗