Biology subjects

Minati, L.

Publications and source records attributed to Minati, L..

6 recordsLinked to original sources

bioRxiv2025

Whole brain resting-state EEG dynamic: A mixture of linear aperiodic and nonlinear resonant stochastic processes

A longstanding debate in neuroscience concerns whether macroscale brain signals can be described as purely linear Gaussian processes or they harbor the more complex statistics of nonlinear dynamics. We introduce BiSpectral EEG Component Analysis (BiSCA), a framework unifying power spectral and bispectral analysis to test for nonlinearity by identifying inter-frequency harmonic relationships and disambiguating them from superimposed components. In particular, simulations confirm that it is able to separate valid nonlinearity from non-Gaussianity, which is a common confound. Applying this test to two large human brain recordings datasets (1,771 intracranial channels and 960 individuals scalp EEG), we find the brains broadband, aperiodic background behaves as a linear, Gaussian process, while narrowband Rho oscillatory --including the Alpha and Mu rhythms --are the primary source of cortical nonlinearity, exhibiting significant quadratic cross-frequency coupling. Both recordings show significant departures from linear Gaussian behavior. We observe a clear dissociation between signal power and nonlinearity. While the occipital Alpha rhythm dominates the power spectrum, the strongest nonlinear signatures arise from the less dominant parietal Mu rhythm. These findings suggest that nonlinear resonance is pervasive in cortical signals, primarily expressed through resonant oscillations rather than aperiodic activity. Without accounting for these intrinsic nonlinear interactions, a principled understanding of neural activity will be incomplete.

neuroscience↗

bioRxiv2025

Lifespan Mapping of EEG Source Spectral Dynamics with Xi-AlphaNET

We introduce{xi} - Net, a mesoscale generative model of cortical activity that models the EEG aperiodic ({xi}) and -rhythm () components via Hida-Matern processes constrained by anatomical connectivity and interareal delays. This framework integrates fractional differential equations (modeling damping/resonance), decomposition of Spectral Granger Causality, and lifespan quantification. Bayesian inversion of the model, on cross-spectral rsEEG data from 1,965 participants aged 5-100 years (HarMNqEEG dataset), allows us to estimate cortical activity and effective connectivity patterns at 8,003-voxel resolution.{xi} processes showed extended cortical networks, with occurrence probability increasing with age but amplitude peaking midlife (inverted-U trajectory). processes exhibited a localization in the posterior areas of the cortex and two specialized networks (parieto-occipital, sensorimotor), with increasing localization probability and peak amplitude/frequency showing age-linked inverted-U trajectory. The model uniquely estimates global conduction delays, which are negatively correlated with frequency and independent myelin concentration profiles, mechanistically linking neural propagation times to regulation.

neuroscience↗

bioRxiv2020

Phospho-RNA sequencing with CircAID-p-seq

Accurate positional information concerning ribosomes and RNA binding proteins with respect to their transcripts is important to understand the global regulatory network underlying protein and RNA fate in living cells. Most footprinting approaches generate RNA fragments bearing a phosphate or cyclic phosphate groups at their 3' end. Unfortunately, all current protocols for library preparation rely only on the presence of a 3' hydroxyl group. Here, we developed circAID-p-seq, a PCR-free library preparation for 3' phospho-RNA sequencing. We applied circAID-p-seq to ribosome profiling, which produces fragments protected by ribosomes after endonuclease digestion. CircAID-p-seq, combined with the dedicated computational pipeline circAidMe, facilitates accurate, fast, highly efficient and low-cost sequencing of phospho-RNA fragments from eukaryotic cells and tissues. While assessing circAID-p-seq to portray ribosomes engaged with transcripts, we provide a versatile tool to unravel any 3'-phospho RNA molecules.

molecular biology↗

bioRxiv2020

One-shot analysis of translated mammalian lncRNAs with AHARIBO

A vast portion of the mammalian genome is transcribed as long non-coding RNAs (lncRNAs) acting in the cytoplasm with largely unknown functions. Surprisingly, lncRNAs have been shown to interact with ribosomes, encode uncharacterized proteins, or act as ribosome sponges. These functions still remain mostly undetected and understudied owing to the lack of efficient tools for genome-wide simultaneous identification of ribosome-associated lncRNAs and peptide-producing lncRNAs. Here we present AHARIBO, a method for the detection of lncRNAs either untranslated, but associated with ribosomes, or encoding small peptides. Using AHARIBO in mouse embryonic stem cells during neuronal differentiation, we isolated ribosome-protected RNA fragments, translated RNAs and corresponding de novo synthesized polypeptides. Besides identifying mRNAs under active translation and associated ribosomes, we found and distinguished lncRNAs acting as ribosome sponges or encoding micropeptides, laying the ground for a better functional understanding of hundreds lncRNAs.

cell biology↗

bioRxiv2019

Scale-resolved analysis of brain functional connectivity networks with spectral entropy

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between an empirical functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure.\n\nWe apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks.

neuroscience↗

bioRxiv2018

Targeting translation activity at the ribosome interface with UV-active small molecules

Puromycin is a well-known antibiotic that is used to study the mechanism of protein synthesis and to monitor translation efficiency due to its incorporation into nascent peptide chains. However, puromycin effects outside the ribo-some catalytic core remain unexplored. Here, we developed two puromycin analogues (3PB and 3PC) that can efficiently interact with several proteins involved in translation, ribosome function and RNA processing. We biochemically characterized the binding of these analogues and globally mapped the direct small molecule-protein interactions in living cells using clickable and photoreactive puromycin-like probes in combination with in-depth mass spectrometry. We identified a list of proteins that interact with ribosomes during translation (e.g. eEF1A, ENO1 and GRP78) and we addressed possible uses of the probes to sense the activity of protein synthesis and to capture associated RNA. By coupling genome-wide RNA sequencing methods with these molecules, the characterization of unexplored translational control mechanisms will be feasible.

biochemistry↗