Biology subjects

Torabi, M.

Publications and source records attributed to Torabi, M..

8 recordsLinked to original sources

bioRxiv2026

Denoising the central autonomic network: characterizing processes jointly associated with arousal and non-neuronal physiological artefacts in dynamic fMRI analyses

Using a multimodal dataset including fMRI, EEG-fMRI and concurrent physiological recordings, we investigated the effect of denoising systemic low frequency oscillations (sLFOs) on the characterization of dynamic signatures of central autonomic regulation and their relation to ongoing physiological states. We demonstrated that the time-frequency profiles of couplings between BOLD time series and cardiac and respiratory processes were statistically comparable between regions of the brain associated with autonomic function and non-autonomic motor regions, suggesting that these couplings largely do not reflect neuronal activation related to autonomic activity. We further showed that model-based (via physiological response functions) and data-driven (via CompCor nuisance regressors extracted from cerebrospinal fluid) methods of sLFO denoising had a statistically similar effect on these frequency profiles. We novelly applied co-activation pattern analysis to assess state dynamics of autonomic-associated regions of the brain, finding that such brain states interrelate decreases in vigilance and increases in heart rate, respiratory flow, and head motion, thus providing evidence for global arousal processes affecting BOLD signal in autonomic-associated regions. Lastly, we modelled sliding-window dynamic functional connectivity within the central autonomic network (CAN) as modulated by heart rate variability, showing significant differences in model outcomes linked to each denoising pipeline. With these findings, we provide a comprehensive discussion of the implications for the application of denoising techniques to the CAN and comment on the origins of dynamic components of the BOLD signal linked to autonomic regulation, highlighting the global role played by arousal.

neuroscience↗

bioRxiv2026

Phosphoproteomic Insights into TRPV4 - AMPK Signaling Axis in the Choroid Plexus Epithelium: Implications for Therapeutic Targeting in Hydrocephalus

BackgroundHydrocephalus is characterized by abnormal accumulation of cerebrospinal fluid (CSF) due to disrupted secretion, circulation, or reabsorption. CSF homeostasis is regulated by ion and water channels on choroid plexus epithelial (CPe) cells, including the mechanosensitive cation channel, transient receptor potential vanilloid 4 (TRPV4). Although TRPV4 antagonism prevents hydrocephalus progression in rats, how TRPV4 activity modulates CSF production remains unclear. MethodsBecause TRPV4 function is phosphorylation-dependent, we examined its activation (GSK1016790A) and inhibition (RN1734) and the downstream signaling alterations in human choroid plexus papilloma cells (HIBCPP) using phosphoproteomic mass spectrometry. ResultsOur phosphoproteomic analysis revealed significant changes in kinases and tight junction (TJ) proteins regulating epithelial barrier integrity. TRPV4 activation altered TJ proteins such as Zonula Occludens-1 (ZO-1), and Claudin-7 (CLDN7) and AMP-activated protein kinase (AMPK), phosphorylation, with a notable foldchange increase of AMPK inhibitory phospho form (Ser496). Further our electrophysiological and biochemical analyses demonstrated that AMPK activation followed by TRPV4 stimulation increased AMPK Ser496 phosphorylation, and epithelial permeability was subsequently elevated, although it was statistically not significant. In contrast, AMPK inhibition prior to TRPV4 activation resulted in the opposite effect with substantial epithelial barrier tightness which was evident from the ZO-1 expression. Moreover, a pharmacologically available anti-diabetic drug, metformin exhibited a similar trend like Compound C in reducing the barrier epithelial permeability after TRPV4 agonist treatment, highlighting that metformin promotes barrier tightness via an AMPK-dependent pathway coupled to TRPV4 signaling. ConclusionsOverall, these findings identify an AMPK-TRPV4 signaling axis that modulates epithelial permeability and may influence CSF regulation, highlighting a potential therapeutic target for hydrocephalus.

neuroscience↗

bioRxiv2026

When Can Brain Connectivity Track the Working Mind? A Large-Scale Benchmark of Dynamic Functional Connectivity Across Cognitive Paradigms

Dynamic functional connectivity (dFC) -- the time-varying re-configuration of brain network interactions -- has become a widely adopted method for studying how neural dynamics reflect ongoing cognition. Yet a fundamental question remains unresolved: can dFC reliably track when a person is cognitively engaged, and if not, why does it fail? Here, we address this question through a large-scale benchmark of seven widely used dFC methods, evaluating how well each predicts task presence across 16 fMRI datasets encompassing over 1,500 participants and 28 distinct experimental settings, complemented by realistic simulated data. Across experimental data, dFC-based tracking of cognitive engagement was unreliable in many cases: most method-experiment combinations performed near chance, and no single method succeeded across all contexts. This failure, however, was not uniform. Both experimental and simulated data showed that decoding performance varied systematically with three interacting factors -- experimental design, data quality, and the choice of dFC method -- rather than depending on dFC features alone. Critically, we identify specific experimental design conditions associated with more reliable tracking: paradigms with longer, more regular task blocks and fewer task-rest transitions were substantially more decodable, while data quality independently influenced performance across methods. These findings offer actionable principles for when dFC can -- and cannot -- be expected to serve as a reliable marker of underlying cognitive states.

neuroscience↗

bioRxiv2026

Nipoppy: A framework for standardizing neuroimaging studies to facilitate international derived-data sharing

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

bioinformatics↗

bioRxiv2026

Molecular and Energetic Basis of Histidine Switch Dynamics in Respiratory Complex I

The respiratory complex I in mitochondria and bacteria drives the two-electron reduction of quinone to pump protons across the membrane. The molecular basis of this catalytic reaction remains enigmatic despite significant progress in structural characterization of the complex. A highly conserved histidine residue in the distal antiporter-like subunit of its membrane domain has been shown to undergo conformational changes in molecular simulations and cryo-EM structures. However, the function of histidine switch dynamics, and energetics of its conformational transitions remain unclear. Here, by applying enhanced sampling simulations, we evaluate the energetics of the histidine switch dynamics and demonstrate that it is coupled to the charge state of lysine residues ca. 10 [A] apart, which cause hydrogen bond restructuring and stabilize histidine in specific conformations. Hybrid QM/MM metadynamics simulations show that the histidine participates in gated proton transfer and may function as a proton confurcation device in complex I and related proteins. Importance of workProton pumping by the mitochondrial respiratory chain enzymes needs to be highly directional so that the ATP production occurs as efficiently as possible during high energy demands. With the protons being hard to detect by most methods of structural biology, computer simulation approaches as applied in this work offer critical information on how molecular gates within respiratory complexes can function and help elucidate the challenging questions in the field of mitochondrial bioenergetics.

biophysics↗

bioRxiv2025

Deep Learning for Cognitive Task Presence Prediction from Dynamic Functional Connectivity

Dynamic functional connectivity (dFC) studies the time-varying coordination between brain regions measured with fMRI and is a potential biomarker for understanding cognitive dynamics and tracking the development of neurological disorders. However, a critical methodological challenge lies in the variability of dFC estimates across different dFC assessment methods, raising concerns about the reliability and interpretation of downstream findings. While deep learning (DL) models have demonstrated the ability to capture traditionally inaccessible data patterns in many disciplines, they encounter challenges when applied to neuroimaging data. For instance, the high dimensionality, noise, and temporal complexity inherent in dFC makes it challenging for DL models to extract meaningful and interpretable insights. In this study, we investigated how DL architectures can be developed and adapted to predict task presence over time from task-based dFC data, and additionally, how the choice of dFC assessment method influences the predictive performance of DL models. We developed and compared a convolutional neural network (CNN), a node-level classification graph convolutional network (GCN), and a graph-level classification GCN based on their ability to predict time points at which subjects were performing a cognitive task or at rest. In our study, the results indicate that both DL model architecture and dFC estimation methodology significantly impact task presence prediction capacity, while the specific task paradigm had minimal influence out of the limited types that were explored. This work offers a powerful benchmark for understanding the dynamics of underlying task-driven cognitive state transitions and the analytical flexibility limitations of dFC estimation methods and DL architectures.

neuroscience↗

bioRxiv2025

Meta all the way down: An overview of neuroimaging meta-analyses

Meta-analyses are invaluable tools for navigating the rapidly expanding scientific literature. Given their high value, ensuring the quality of meta-analyses is paramount. We conducted a multifaceted overview, examining each step in a manual neuroimaging meta-analysis on a large scale. We used four novel datasets comprising over 14,000 papers, including fMRI meta-analyses, fMRI studies, studies included in meta-analyses, and studies associated with image data on NeuroVault. Regarding successes, two-thirds of meta-analyses stated that they followed PRISMA guidelines, and 65% included a flowchart describing their inclusion process. We point out several areas for improvement. Pre-registration was fairly rare (20%), and only half listed their exact search strategy. There could be a location bias in which papers are included, and many did not include enough studies to be robust against publication bias (68% of meta analyses have less than 30 studies included). We also offer ideas for future directions. As image based meta-analysis is the gold standard, we have indicated which topics have the most image data available. The potential redundancy of topics can be visualized in our paper, and we recommend future meta-analyses be in conversation with past ones by citing and discussing previous similar work. By addressing these findings, the neuroimaging community can collectively improve the field of neuroimaging meta-analyses.

neuroscience↗

bioRxiv2023

On the variability of dynamic functional connectivity assessment methods

Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly-used dFC methods. We implemented seven dFC assessment methods in Python and used them to analyze fMRI data of 395 subjects from the Human Connectome Project. We measured the pairwise similarity of dFC results using several similarity metrics in terms of overall, temporal, spatial, and inter-subject similarity. Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Surprisingly, the observed variability in dFC estimates was comparable to the expected natural variation over time, emphasizing the impact of methodological choices on the results. Our findings revealed three distinct groups of methods with significant inter-group variability, each exhibiting distinct assumptions and advantages. These findings highlight the need for multi-analysis approaches to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds, and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox that enables multi-analysis dFC assessment. This study sheds light on the impact of dFC assessment analytical flexibility, emphasizing the need for careful method selection and validation, and promoting the use of multi-analysis approaches to enhance reliability and interpretability of dFC studies.

neuroscience↗