Biology subjects

Negrello, T.

Publications and source records attributed to Negrello, T..

2 recordsLinked to original sources

bioRxiv2025

Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia.

Anesthesia recovery is critical for resuming normal physiological and neuronal functions; however, the mechanisms involved remain elusive. Here, we identify a female-selective corticosterone-mediated microglia-neuron interaction in vivo during ketamine anesthesia recovery, absent in males. This microglia-neuron interaction induces plastic and functional neuronal changes, as evidenced by increased spine density and mEPSC frequency, which is occluded upon microglia depletion. We show that this process is driven through upregulation of the stress-responsive co-chaperone Fkbp5 mRNA and its protein, FKBP51, in female microglia. Fkbp5/FKBP51 is a key intermediary in a corticosteroid-induced stress response, and its involvement points towards a critical interface between endocrine signaling and microglia. Thus, to counteract the observed KXA-mediated corticosterone increase in the blood, we remove the primary source of corticosterone through adrenalectomy. Close microglia-neuron interaction was absent, but was reinstated after corticosterone injection. Our findings offer a new mechanism of microglia-mediated neuronal plasticity during anesthesia recovery, which is mediated through corticosterone, enhancing our understanding of sex differences in brain function.

neuroscience↗

bioRxiv2024

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

Neuronal shape determines how neurons process and integrate information, yet a consistent and objective classification of neuronal morphologies remains elusive. Current approaches rely heavily on subjective expert views or on predefined features, limiting reproducibility and interpretability. Here, we present an interpretable deep learning framework that unifies topological data analysis, graph neural networks, and traditional morphometrics to classify neuronal morphologies objectively and transparently. Our framework compares complementary mathematical representations of neurons to capture geometric, topological, and graph-structural information. Then it benchmarks their performance against expert-labeled datasets. We show that topology- and graph-based models achieve accuracies comparable to human experts, revealing that both global branching invariants and local connectivity patterns are essential to define morphological cell types. Using explainable artificial intelligence methods, we identify structural features driving each classification decision, bridging computational and neuroanatomical interpretations. This open source and reproducible approach provides a foundation for scalable, interpretable and biologically meaningful neuronal taxonomy, enabling consistent comparisons between data sets and species.

neuroscience↗