Biology subjects

Galati, G.

Publications and source records attributed to Galati, G..

3 recordsLinked to original sources

bioRxiv2025

MOTUM: a system for Motion Online Tracking Under MRI

Attempts to implement realistic body-environment interactions during functional magnetic resonance imaging (fMRI) experiments have developed expensive, hardly reproducible, and task-specific setups. Here, we introduce MOTUM (Motion Online Tracking Under MRI), a novel system that combines real-time kinematic tracking with immersive virtual reality, enabling participants to perform naturalistic movements inside the scanner. As a proof-of-concept, we tested MOTUM during a reach-to-grasp task with and without visual feedback of ones hand (N = 7). The system achieved high-fidelity motion tracking, induced an intense immersive experience, evoked expected sensorimotor brain activations, and maintained high fMRI data quality. Standard fMRI control metrics were below the critical threshold in 99% of volumes, indicating that participants arm movements had minimal impact on head motion and data quality. While hand movements had little to no effect on brain activity, arm movements resulted in sparse spurious correlations that were easily controlled for. Critically, MOTUM allowed us to extract rich kinematic indices and link them directly to brain activity on a trial-by-trial basis. Parametric modulation analyses revealed that natural variations in movement dynamics significantly influenced neural responses in parietal, frontal, and occipital regions. In sum, MOTUM is a robust method to study motor control and beyond, enabling a new class of fMRI experiments that bridge ecological realism and experimental control, pushing current neuroimaging research towards real-life neuroscience.

neuroscience↗

bioRxiv2025

Introducing furanocoumarin biosynthetic genes in tomato results in coumarins accumulation and impacted growth

Over the past three decades, eeorts to decipher plant metabolism have shed light on key enzymes driving specialized metabolite biosynthesis. Although only few pathways have been completely investigated to date, their characterization paves the way for exploring the potential eeects of specialized metabolites on plant physiology. Among them is the linear furanocoumarin pathway, which was recently completed to produce up to psoralen. In this study, we report the first metabolic engineering of the linear furanocoumarin pathway to enable artificial psoralen production in tomato, through the integration of four genes coding for the enzymes: Umbelliferone Synthase, Demethylsuberosin Synthase, Marmesin Synthase and Psoralen Synthase. Interestingly, coumarins were produced instead of furanocoumarins. Using morphophysiological, metabolomic, and transcriptomic analyses, we suggest how coumarins, particularly scopoletin, can impact growth and aeect plant physiology, even at low concentrations. As coumarins have increasingly attracted interest for agricultural applications due to their minimal environmental impact, this work both expands and challenges their potential by highlighting the physiological costs and benefits they may impose on tomato. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/663522v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1df0bdeorg.highwire.dtl.DTLVardef@843d9borg.highwire.dtl.DTLVardef@1e898b6org.highwire.dtl.DTLVardef@18205eb_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

plant biology↗

bioRxiv2023

Learning to segment self-generated from externally caused optic flow through sensorimotor mismatch circuits

Efficient sensory detection requires the capacity to ignore task-irrelevant information, for example when optic flow patterns created by egomotion need to be disentangled from object perception. To investigate how this is achieved in the visual system, predictive coding with sensorimotor mismatch detection is an attractive starting point. Indeed, experimental evidence for sensorimotor mismatch signals in early visual areas exists, but it is not understood how they are integrated into cortical networks that perform input segmentation and categorization. Our model advances a biologically plausible solution by extending predictive coding models with the ability to distinguish self-generated from externally caused optic flow. We first show that a simple three neuron circuit produces experience-dependent sensorimotor mismatch responses, in agreement with calcium imaging data from mice. This microcircuit is then integrated into a neural network with two generative streams. The motor-to-visual stream consists of parallel microcircuits between motor and visual areas and learns to spatially predict optic flow resulting from self-motion. The second stream bidirectionally connects a motion-selective higher visual area (mHVA) to V1, assigning a crucial role to the abundant feedback connections: the maintenance of a generative model of externally caused optic flow. In the model, area mHVA learns to segment moving objects from the background, and facilitates object categorization. Based on shared neurocomputational principles across species, the model also maps onto primate vision. Our work extends the Hebbian predictive coding to sensorimotor settings, in which the agent actively moves - and learns to predict the consequences of its own movements. Significance statementThis research addresses a fundamental challenge in sensory perception: how the brain distinguishes between self-generated and externally caused visual motion. Using a computational model inspired by predictive coding and sensorimotor mismatch detection, the study proposes a biologically plausible solution. The model incorporates a neural microcircuit that generates sensorimotor mismatch responses, aligning with experimental data from mice. This microcircuit is integrated into a neural network with two streams: one predicting self-motion-induced optic flow and another maintaining a generative model for externally caused optic flow. The research advances our understanding of how the brain segments visual input into object and background, shedding light on the neural mechanisms underlying perception and categorization not only in rodents, but also in primates.

neuroscience↗