Antonio Giuliano Zippo

My academic and research journey has been driven by a profound passion for understanding the intricate complexities of neural systems, with a growing focus on neurodevelopmental research. As a computational neuroscientist with a multidisciplinary background in computer science and applied mathematics, I have consistently sought to integrate advanced computational methodologies with cutting-edge neuroscientific investigations. My methodological approach combines computational modeling, advanced imaging techniques, and molecular analysis. By leveraging technologies like synchrotron-based phase-contrast tomographic microscopy, high-density in vivo electrophysiology and developing sophisticated machine learning algorithms for neural network analysis, I aim to provide comprehensive, multi-scale insights into neural system development and potential pathological mechanisms. The interdisciplinary nature of my research is reflected in my academic training and professional trajectory. With a PhD in Applied Mathematics and extensive experience in computational methods, I bring a rigorous analytical perspective to neuroscientific investigations. My ongoing research at the Consiglio Nazionale delle Ricerche and collaborative work with international institutions like the University of Manchester highlight my commitment to pushing the boundaries of our understanding of neural development. Looking forward, I am dedicated to continuing my research at the intersection of neurophysiology, computational neuroscience and developmental biology. My goal is to develop innovative methodological approaches that can provide deeper insights into the complex mechanisms underlying neurodevelopmental disorders, ultimately contributing to more targeted therapeutic strategies.

SCIENTIFIC INTERESTS 

Neuronal Network Dynamics and Computational Neuroscience

Computational Modeling of Neural Connectivity

My research has made significant contributions to understanding neural network dynamics through advanced in vivo electrophysiological and computational approaches. In the seminal paper “Neuronal functional connectivity among multiple brain areas during spontaneous and evoked activities” (PLoS Computational Biology, 2013), we developed innovative techniques for analyzing neural connectivity across diverse brain regions. This work demonstrated the potential of computational methods to unravel the information representation within neural populations of sensory stimuli. A critical advancement came with the publication “Small-world networks in neuronal populations: a computational perspective” (Neural Networks, 2013), which introduced novel mathematical models for characterizing neuronal network architectures. By applying graph theory and network science principles, we provided insights into how neural networks self-organize and maintain functional efficiency.

Predictive Modeling of Neural Activity

Building on similar foundations, our research in “Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex” (PLoS ONE, 2012) developed sophisticated algorithms for anticipating neuronal firing patterns. This work demonstrated the potential of machine learning techniques in decoding neural signal dynamics, with significant implications for understanding neural information processing.

Chronic Pain and Neurological Mechanisms

In vivo electrophysiological Investigations

A significant milestone was the publication “The thalamo-cortical complex network correlates of chronic pain”

(Scientific Reports, 2016), which provided a detailed mapping of neural network alterations associated with chronic pain conditions. This work demonstrated the complex interplay between neural circuits and pain perception.

 

Neurovascular interactions

Our comprehensive approach to chronic pain research has yielded groundbreaking insights into the underlying neurological mechanisms. The publication “Paclitaxel alters the microvascular network in the central and peripheral nervous system of rats with chemotherapy-induced painful peripheral neuropathy” (Journal of Peripheral Nervous System, 2024) showed remarkable angiogenetic correlations in a chemotherapy-induced peripheral neuropathy.

Innovative Pain Management Approaches

Our research extended beyond mere description to exploring potential interventional strategies. The study “Removal of behavioural and electrophysiological signs of chronic pain by in vivo modulation of brain cortical sensory circuits” (bioarxiv, 2021) proposed novel approaches to pain management through targeted neural circuit modulation by means of radiation therapy similar to these used in oncological radiotherapy. The project is under development and follow-up experiment are planned for the summer 2025 at the Australian synchrotron facility in Melbourne.

Neurodevelopmental Disorder Research

Recent publications have significantly advanced our understanding of neurodevelopmental disorders. The study “Disruption of autism-associated Pcdh9 gene leads to transcriptional alterations, synapses overgrowth and aberrant excitatory transmission in the CA1” (Journal of Neuroscience, 2024) provided critical insights into the molecular mechanisms underlying synaptic development. Another pivotal contribution is the manuscript “Shank3 modulates Rpl3 expression and protein synthesis via mGlu5: implications for Phelan McDermid syndrome” (Molecular Psychiatry, 2025), which explored the intricate relationship between genetic variations and neurological phenotypes. Another study investigates the role of the transcription factor TCF20 in the development of dendrites and dendritic spines, which are crucial for synaptic connectivity and neural circuit function (Journal of Neurochemistry, 2025). Eventually, significant contributions to the understanding of the role of PCDH19 in epilepsy and autism has been presented in 2024 (Molecular Psychiatry). The work has shown that PCDH19 plays a critical role in the development of inhibitory synapses, and that mutations in PCDH19 can lead to an imbalance of excitation and inhibition in the brain. This imbalance can lead to seizures and other neurological problems. In addition, the work has also shown that PCDH19 is involved in the development of autism and that mutations in PCDH19 can lead to abnormal development of social brain regions. This abnormal development can lead to social impairments, such as those seen in autism.

Computational Approaches in Neurological Research

Machine Learning and Neural Network Analysis

The publication “From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks” (Computer Methods and Programs in Biomedicine, 2023) represented a significant advancement in applying machine learning techniques to neural network analysis.

Pose Reconstruction and Advanced Behavioral Analysis

Our work on “Three-Dimensional Unsupervised Probabilistic Pose Reconstruction (3D-UPPER) for Freely Moving Animals” (Scientific Reports, 2023) introduced innovative methodologies for analyzing animal behavior, demonstrating the potential of computational approaches in understanding neural-behavioral interactions. Dr. Zippo also made significant methodological and analytical contributions to both studies. In the Current Biology papers (2020, 2022), he co-developed computational tools, behavioral setups and algorithms for 3D reconstruction of mouse behavior, contributed to data analysis, and supported the quantification of behavioral and neuronal dynamics.

Interdisciplinary Methodological Innovations

Advanced Recording and Stimulation Technologies

The publication “A novel wireless recording and stimulating multichannel epicortical grid for supplementing or enhancing the sensory-motor functions in monkey (Macaca fascicularis)” (Frontiers in System Neuroscience, 2015) highlighted our commitment to developing innovative technological solutions for neural research.

FURTHER INFO

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