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Bevilacqua, M.

Publications and source records attributed to Bevilacqua, M..

2 recordsLinked to original sources

Pathway and directional specificity of Hebbian plasticity induction in the cortical visual motion processing network

Cortico-cortical paired associative stimulation (ccPAS), which repeatedly pairs single pulse TMS over two distant brain regions with a specific time interval, is thought to modulate synaptic plasticity. Applied to the motion cortical pathway, ccPAS has been shown to improve motion discrimination when specifically targeting backward projections, stimulating the medio-temporal area (MT) followed by the primary visual cortex (V1). However, there is no direct neuroimaging evidence of the spatial selectivity of the ccPAS effects (i.e., pathway or direction specificity) or detailing the exact nature of the ccPAS effects (i.e., the oscillatory signature, timing...). In this study, we applied ccPAS along the motion discrimination pathway, in the top-down direction (MT-to-V1: "Backward ccPAS") and in the bottom-up direction (V1-to-MT: "Forward ccPAS") in sixteen healthy volunteers and compared changes in visual network activity in response to single pulse TMS over V1 and MT using spectral granger causality (sGC). The sGC results showed common increases in direct V1-to-MT and V1-to-IPS bottom-up inputs in the high Beta/low Gamma band (25-40 Hz) for both ccPAS, probably reflecting task exposure. However, a clear distinction in information transfer occurred in the re-entrant MT-to-V1 signals, which were only modulated by Backward ccPAS. This difference was predictive of the behavioural improvements at the motion discrimination task. Our results support the view of the possibility to specifically enhance re-entrant Alpha oscillatory signals from MT-to-V1 to promote motion discrimination performance through Backward ccPAS. These findings contribute to better understanding visual processing in healthy subjects and how it can be modulated to pave the way to clinical translation in vision handicapped patients. The changes in re-entrant MT-to-V1 inputs could help to provide single-subject prediction scenarios in patients suffering from a visual system stroke, in whom visual recovery might partly rely on the top-down inputs to the spared V1 neurons.

neuroscience↗

Deciphering Tumour Tissue Organization by 3D Electron Microscopy and machine learning

Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organization of patient-derived xenograft tissues generated from hepatoblastoma, the most frequent childhood liver tumour, by serial block-face scanning electron microscopy using an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentations, mathematics and infographics. By digitally reconstituting an entire hepatoblastoma sample with a blood capillary, a bile canaliculus-like structure, hundreds of tumour cells and their main organelles (e.g. cytoplasm, nucleus, mitochondria), we report unique 3D ultrastructural data about the organization of tumoral tissue. We found that the size of hepatoblastoma cells correlates with the size of their nucleus, cytoplasm and mitochondrial mass. We also discovered that the blood capillary controls the planar alignment and size of tumour cells in their 3D milieu. Finally, a set of tumour cells polarized in the direction of a hot spot corresponding to a bile canaliculus-like structure. In conclusion, this pilot study allowed the identification of bioarchitectural parameters that shape the internal and spatial organization of tumours, thus paving the way for new investigations in an emerging field that we call onconanotomy.

cancer biology↗