Research Lines for prospective PhD Students (Cohort 42: 2026-2029)

Research Line #: 1

Supervisor: Antonino Vallesi

Potential Co-Supervisor/s: Simone Messerotti Benvenuti, Ettore Ambrosini, Arianna Menardi, Giorgia Cona, Mario Bonato, Maurizio Corbetta, Enrico Collantoni, Fabio Sambataro

Other potential Collaborators: TBA

Short abstract: This research lines aims to examine how life experience, training and specialized expertise shape and enhance cognitive processes, and whether these effects can be amplified through targeted laboratory interventions. Adopting an integrative approach, the project will compare individuals with structured, long-term experience to identify cognitive and neural patterns linked to enhanced cognition, with a special focus on executive abilities. Key components of such expertise will then be translated into controlled training programs for the general population, and combined with laboratory techniques (e.g., neurofeedback or cognitive training) to test potential synergistic effects. By bridging real-world experience with experimental cognitive neuroscience, this work seeks to develop scalable, personalized interventions to strengthen executive functioning in both healthy (young and older) adults and clinical populations, using behavioral, electrophysiological, and/or neuroimaging methods.

Research Line #: 2

Supervisor: Antonino Vallesi

Potential Co-Supervisor/s: Arianna Menardi, Annachiara Cagnin, Ettore Ambrosini, Diego Cecchin, Marco Zorzi, Samir Suweis, Maurizio Corbetta, Alessandra Bertoldo, Michele Allegra, Fabio Sambataro, Enrico Collantoni

Other potential Collaborators: TBA

Short abstract: The relationship between brain connectivity and cognitive performance is essential for understanding how brain regions coordinate to support functions like executive functions, attention, decision-making and memory. In healthy individuals, optimal connectivity patterns enable efficient cognitive functioning. However, in pathological conditions, such as neurodegenerative diseases or psychiatric disorders, disrupted connectivity may impair cognitive abilities. Studying these changes provides insights into brain function and dysfunction, helping to better understand diagnosis and prognosis, and to develop targeted interventions aimed at improving cognitive performance, or at least at minimizing loss of function in case of neurodegeneration.

Research Line #: 3

Supervisor: Alessandra Bertoldo

Potential Co-Supervisor/s: Maurizio Corbetta, Samir Suweis, Michele Allegra, Marco Zorzi, Antonino Vallesi, Stefano De Marchi, Mattia Veronese

Other potential Collaborators: TBA

Short abstract: This research project aims to generate an integrated framework to understand the brain as a multi-layer, energy-constrained system in which cognitive processes emerge from the dynamic interplay between structure, function, electrophysiology, and metabolism. Moving beyond pairwise connectivity, this research project combines EEG dynamics, fMRI functional organization, structural connectomics, and FDG-PET metabolic measures within a unified neuroenergetic model. It is hypothesized that cognitive brain state transitions are governed by hierarchical energetic constraints: structural architecture defines baseline energy limits, electrophysiological dynamics modulate demand, and functional connectivity adapts to optimize network efficiency under metabolic cost. By incorporating effective connectivity, causal interactions, metastability, and controllability across scales are captured. This approach aims to reveal how energy shapes cognitive brain organization, drives state switching, and evolves across the lifespan.

Research Line #: 4

Supervisor: Alessandra Bertoldo

Potential Co-Supervisor/s: Alessandro Salvalaggio, Maurizio Corbetta, Diego Cecchin, Renzo Manara, other co-supervisors with expertise relevant to the defined project.

Other potential Collaborators: TBA

Short abstract: Gliomas, particularly glioblastoma (GBM), induce widespread alterations in brain networks extending beyond the visible lesion, limiting current imaging-based clinical decision-making. The research project aims to develop a connectome-based framework integrating structural MRI, diffusion MRI, and resting-state fMRI to quantify, at the individual level, the impact of glioma on brain organization. By combining structural, functional, and effective connectivity, network-level disruptions associated with tumor progression and infiltration will be identified. These multimodal features will be linked to histo-molecular markers (e.g., MGMT, IDH) and survival outcomes to derive novel prognostic biomarkers. The project will further enable prediction of disease impact and support clinical translation through tools for personalized surgical planning, radiotherapy optimization, and neurorehabilitation. An automated platform will facilitate integration into routine clinical workflows, including settings with limited imaging data. This approach aims to advance precision neuro-oncology by bridging brain connectivity and clinical practice.

Research Line #: 5

Supervisor: Cristina Scarpazza

Potential Co-Supervisor/s: Gentili Claudio

Other potential Collaborators: TBA

Short abstract: This research line seeks to test whether adding non‑invasive brain stimulation (NIBS) techniques to standard treatments can improve their effectiveness in reducing violent behavior in a defined group of offenders. The main goal is to examine whether increasing excitability and activity of the prefrontal cortex (PFC) via transcranial direct current stimulation (tDCS) can modulate aggression in male violent offendersThe project also aims to assess whether combining tDCS with standard treatment—especially psychoeducational interventions for IPV offenders—produces stronger and more long‑lasting improvements than standard treatment alone.

Research Line #: 6

Supervisor: Cristina Scarpazza

Potential Co-Supervisor/s: Gentili Claudio

Other potential Collaborators: TBA

Short abstract: This research line aims to investigate how neuroscientific evidence and cognitive biases influence human decision‑making in forensic contexts. The project seeks to identify systematic biases, improve the integration of neuroscience in court, and develop guidelines to support more accurate, transparent, and fair forensic decision‑making.

Research Line #: 7

Supervisor: Manfredo Atzori

Potential Co-Supervisor/s: Gianmaria Silvello, Maurizio Corbetta. Additional co-supervisors related to the considered use cases.

Other potential Collaborators: TBA

Short abstract: Exascale volumes of multimodal data are continuously produced in neurosciences and the biomedical domain, including for instance patient information, clinical data, biological laboratory data, bio-images, bio-signals, instrumental examinations and genetic data.
Fully exploiting them in order to extract knowledge and patterns is still an open challenge, due to the heterogeneity of data and state of the art limitations in machine learning.
This research line aims at developing machine learning methods to extract knowledge from multimodal biomedical data, in order to characterize patients with variable spatial and temporal resolution, leading to multimodal knowledge exploration and, eventually, precision medicine applications.

Research Line #: 8

Supervisor: Manfredo Atzori

Potential Co-Supervisor/s: Maurizio Corbetta, Alessandra Del Felice.

Other potential Collaborators: TBA

Short abstract: The recent advancements of machine learning and, particularly, of deep learning paved the way to unprecedented opportunities in many domains, including neuroscience and rehabilitation.
This research line targets the proposal of innovative ideas leveraging the capabilities of modern deep learning approaches for research applications in neurosciences and assistive robotics targeting real time rehabilitation of stroke patients using EEG and EMG data.

Research Line #: 9

Supervisor: Samir Suweis

Potential Co-Supervisor/s: Michele Allegra, Marco Dal Maschio, Stefano Vassanelli, Aram Megighian.

Other potential Collaborators: TBA

Short abstract: The brain is a dynamical system whose activity continuously evolves through a high-dimensional space of possible trajectories. Understanding whether, and how, these trajectories can be steered in a controlled manner is a fundamental challenge at the interface of neuroscience, physics, and machine learning. Animal models now provide an unprecedented opportunity to address this question, thanks to large-scale recordings and precise electrical or optogenetic perturbations. This project aims to develop a closed-loop control framework to steer pathological brain activity toward healthier target dynamics. We will combine functional connectivity, stochastic control, and recurrent neural networks to infer neural dynamics and design adaptive stimulation strategies based on the current brain state. The goal is to identify interventions that optimally move activity closer to a desired target pattern. The approach will be tested on large-scale neuronal recordings from zebrafish, Drosophila, and neuronal cultures interfaced with electrical or optogenetic control. By integrating cpmputational neuroscience, machine learning, and control theory, the project seeks to establish a principled framework for rational brain stimulation in disease recovery.

Research Line #: 10

Supervisor: Samir Suweis

Potential Co-Supervisor/s: Michele Allegra, Marco Zorzi, Antonino Vallesi, Stefano De Marchi, Alessandra Bertoldo, Maurizio Corbetta

Other potential Collaborators: TBA

Short abstract: Brain activity appears to be organized into dynamical modes that alternate over time, including global modes and states dominated by specific resting state networks such as the default mode network. These modes may be altered in neurological and psychiatric disorders. Experimental evidence shows that brain stimulation, delivered electrically or through transcranial magnetic stimulation, can support recovery of such disorders. Yet, how to identify optimal stimulation targets remains largely unknown. This project aims to understand the generative principles of brain eigenmodes and to develop methods to control them in order to restore healthy dynamics. We will build a generative model of mode emergence, interaction, and switching, and use it to design interventions that steer pathological dynamics toward healthy configurations. The framework will combine latent dynamical systems, network models, and control theory. Applications will focus on fMRI, TMS, EEG, and MEG data, linking brain dynamics across temporal and spatial scales. The project will provide a quantitative basis for understanding large-scale brain organization and for designing control strategies to recover healthy brain states.

Research Line #: 11

Supervisor: Camillo Porcaro

Potential Co-Supervisor/s: Alessandra Bertoldo, Maurizio Corbetta, other co-supervisors with expertise relevant to the defined project

Other potential Collaborators: TBA

Short abstract: The human brain operates at the ‘edge of chaos’, dynamically balancing an aperiodic energetic baseline (1/f noise), transient periodic oscillations, and critical network branching to optimize cognition. Current neuroscientist largely treats these computational features in isolation, failing to capture how neurological pathologies, such as epilepsy or dementia, collapse this delicate interdependency into rigid, inescapable states. This PhD project pioneers a unified ‘Triadic Phase Space’ framework. By synthesizing multiscale electrophysiology with statistical physics, we will mathematically map the geometry of healthy and pathological brain dynamics. By formalising the causal mechanics between baseline excitation/inhibition and network criticality, this research will develop personalized, Physics-Informed Digital Twins. Ultimately, this framework provides the algorithmic blueprint for next-generation, closed-loop Brain-Computer Interfaces capable of moving beyond symptom suppression to actively steer a patient’s neural manifold back to optimal cognitive fluidity.

Research Line #: 12

Supervisor: Camillo Porcaro

Potential Co-Supervisor/s: Alessandro Salvalaggio, Maurizio Corbetta, other co-supervisors with expertise relevant to the defined project

Other potential Collaborators: TBA

Short abstract: Brain tumors exhibit profound structural and microenvironmental chaos, creating a “complexity gap” where standard Euclidean imaging measurements consistently fail to capture true tumor infiltration and biological aggressiveness. This PhD project investigates the “Fractal Paradox” in neuro-oncology by applying advanced non-linear mathematical modeling to multimodal magnetic resonance imaging (MRI). By extracting high-order complexity metrics, specifically fractal dimension, lacunarity, and thermodynamic entropy, the project aims to decode the chaotic architecture of gliomas. The research will systematically evaluate these biophysical markers to perform non-invasive “virtual biopsies” (e.g., predicting IDH1 status), resolve critical diagnostic dilemmas such as distinguishing true tumor progression from treatment-induced pseudoprogression, and accurately predict patient overall survival. Ultimately, this work seeks to translate fractal geometry and non-linear dynamics from theoretical biophysics into robust, standardized clinical biomarkers, fundamentally redefining how tumor heterogeneity is quantified in precision neuro-oncology.

Research Line #: 13

Supervisor: Giorgia Cona

Potential Co-Supervisor/s: Jeff Kiesner, Camillo Porcaro, Giorgio Arcara, other co-supervisors with expertise relevant to the defined project

Other potential Collaborators: TBA

Short abstract: The menstrual cycle is a natural part of life for many women, yet its effects on mental well-being remain poorly understood and often stigmatized. While all women experience hormonal fluctuations, only a minority show meaningful changes in mood, cognition, or behavior. Evidence suggests these differences reflect increased brain sensitivity to typical hormonal changes rather than abnormal hormone levels. This project investigates the psychological, cognitive and neural features of two symptom profiles. One profile is characterized by irritability and anxiety, which emerges after ovulation during the mid-luteal phase. The other profile involves depressed mood and reduced interest or pleasure, which are more common in the days immediately preceding menstruation. The probject will involve cognitive assessment and recordings of brain electrical activity, allowing researchers to observe how brain and cognitive functioning change in relation to the mentrual-related symtoms.

Research Line #: 14

Supervisor: Giorgia Cona

Potential Co-Supervisor/s: Camillo Porcaro, Giorgio Arcara, Enrico Collantoni, Angelo Antonini

Other potential Collaborators: TBA

Short abstract: The aim of this project is exploring brain dynamics associated with time-related processes, such as delay discounting and mental time travel, by using EEG and brain stimulation techniques.Furthermore, the project will explore how such processes change in clinical population, such as in patiens with anorexia or bulimia and patients with Parkinson’s disease.

Research Line #: 15

Supervisor: Filippo Pisano

Potential Co-Supervisor/s: Judit Gervain, Marco Dal Maschio, Manuela Allegra

Other potential Collaborators: TBA

Short abstract: In the past few decades, optical approaches to control and monitor neural activity have revolutionized neuroscience research. Recently, photonic strategies have opened promising directions towards next-generation pre-clinical and clinical tools. This research line aims to explore innovative approaches exploiting light-brain interactions to extract information on the functional and biomolecular state of neural tissues, with high sensitivity and high spatio-temporal resolution, in situ. The activities will entail interdisciplinary approaches bridging the disciplines of optics and photonics (in particular, the development of advanced optical systems), data analysis and neuro-biology.

Research Line #: 16

Supervisor: Emanuela Formaggio

Potential Co-Supervisor/s: Maria Rubega, Michela Agostini

Other potential Collaborators: TBA

Short abstract: There is very limited research into the effectiveness of conventional and bimanual robot-based upper limb (UL) rehabilitation for people with neurological disorders. In recent years, conventional UL rehabilitation has been enhanced by the development of robots, which can provide high-intensity, repetitive, interactive and task-specific exercises. This project aims to explore the underlying neuronal mechanisms of UL recovery using neurophysiological investigations and the innovative robotic equipment available at the Department of Neuroscience and Padova University Hospital. The project involves a multidisciplinary team of physiatrists, physiotherapists, neuropsychologists, and bioengineers specialising in advanced biological signal processing and biomechanics.

Research Line #: 17

Supervisor: Emanuela Formaggio

Potential Co-Supervisor/s: Edoardo Passarotto, Maria Rubega

Other potential Collaborators: TBA

Short abstract: Healthy aging is one of the greatest challenges of our time, and it is important to identify cost-effective activities to counteract cognitive decline. Meta-analyses suggest that listening to music can improve health, well-being, and cognitive function in older adults. However, results are inconsistent and the process by which music affects cognitive function is largely unexplained. This project aims to identify aspects of music that improve cognitive performance in older adults and to develop personalized music listening interventions to counteract cognitive decline based on electroencephalography (EEG) activity. Effectiveness will be evaluated on behavioral and EEG parameters.

Research Line #: 18

Supervisor: Stefano Masiero

Potential Co-Supervisor/s: Gianluca Regazzo

Other potential Collaborators: TBA

Short abstract: This project brings together experts in rehabilitation medicine, neurology, psychology and data analytics to investigate the neurophysiological factors that influence how patients respond to treatment for nociplastic pain conditions, such as fibromyalgia. The central objective is to identify mechanistic and clinical predictors that can inform the personalisation of rehabilitation pathways. This project aims to stratify patients according to their neurophysiological, functional and psychosocial profiles to optimise therapeutic matching and improve clinical outcomes. Key pathophysiological domains include central sensitisation, altered descending pain modulation, autonomic dysregulation and maladaptive sensorimotor integration. These mechanisms will be analysed in relation to differential responses to rehabilitation interventions such as graded exercise therapy, therapeutic exercise, cognitive behavioural strategies, education-based interventions and integrative approaches (e.g. spa-based or environmental rehabilitation models). The PhD student will be involved in acquiring, analysing and interpreting multimodal datasets, including neurophysiological measures, quantitative sensory testing, biomechanical and kinematic assessments, and patient-reported outcomes.

Research Line #: 19

Supervisor: Stefano Masiero

Potential Co-Supervisor/s: Maria Rubega, Emanuela Formaggio, Paola Contessa

Other potential Collaborators: TBA

Short abstract: The pathophysiology of Adolescent Idiopathic Scoliosis (AIS) is not yet completely understood, but multi-factorial hypotheses have been proposed including defective central nervous system control of posture, biomechanics alterations and alterations of body schema. AIS could be the expression of a sub-clinical nervous system disorder. The aim of our study was to compare the electroencephalography activity in adolescents with AIS and controls, to examine the brain oscillatory changes related to balance control and inquire possibly related body schema representational alterations. Results may represent a valuable biomarker of AIS disease progression and offers novel therapeutic targets according to the identified pathophysiological patterns.

Research Line #: 20

Supervisor: Marco Zorzi

Potential Co-Supervisor/s: Andea Zangrossi; additional co-supervisors: Maurizio Corbetta, Giovanni Pezzulo (CNR-ISTC)

Other potential Collaborators: TBA

Short abstract: This project investigates eye movements during free-viewing tasks to uncover individual differences and identify novel biomarkers for neurological disorders. Building upon foundational work establishing idiosyncratic “viewing styles”, this research line aims to decode the complex dynamics of visual exploration. The primary objective is the unsupervised discovery of oculomotor endophenotypes that map onto specific neuro-cognitive profiles. The project will leverage advanced computational approaches, such as generative modeling and active inference frameworks, to model the underlying computational mechanisms of active vision and gaze allocation, moving beyond standard descriptive metrics. Ultimately, this research seeks to translate spontaneous viewing behavior into powerful, non-invasive computational phenotypes, offering critical insights into healthy cognitive diversity and the mechanistic disruptions characterizing neurological disease.

Research Line #: 21

Supervisor: Marco Zorzi

Potential Co-Supervisor/s: Michele Allegra, Maurizio Corbetta, Luca Pasa, Samir Suweis

Other potential Collaborators: TBA

Short abstract: This project focuses on developing explainable machine and/or deep learning models for the long-term prediction of clinical outcomes across diverse neurological disorders. A core objective is to rigorously assess the predictive contribution of multimodal data using a hierarchical framework that mirrors real-world clinical availability. Models will progressively integrate baseline demographics, neurological/neuropsychological information, routine structural neuroimaging, and ultimately, advanced neuroimaging (i.e., connectomics). Crucially, the project emphasizes Explainable AI (XAI) techniques to ensure the driving factors behind long-term predictions remain transparent, clinically interpretable, and biologically plausible. By mapping the specific added value of each clinical and neuroimaging tier, this research aims to deliver robust, trustworthy computational tools that optimize personalized patient care and resource allocation.

Research Line #: 22

Supervisor: Miryam Carecchio

Potential Co-Supervisor/s: Alessandra Bertoldo, Renzo Manara

Other potential Collaborators: TBA

Short abstract: Primary Brain Calcification (PBC) is a genetic neurodegenerative disorder characterized by bilateral basal ganglia calcification, with possible additional calcium deposition in cerebellar dentate nuclei, subcortical white matter, cerebral cortex, brainstam and thalami. MRI has been scarcely used in PBC diagnosis, given that CT is the gold standard to visualize brain calcification. This research line aims to investigate PBC disease using a multimodal MRI neuroimaging approach to better characterize its underlying pathophysiology. By integrating complementary imaging techniques, including structural MRI, diffusion imaging, functional MRI, and metabolic imaging, we seek to capture alterations across different levels of brain organization. The study will focus on identifying patterns of brain calcification and their relationship with microstructural integrity, functional connectivity, and metabolic changes, correlating these features with genetic mutations, clinical and neuropsychological scales.
Particular attention will be given to the interplay between structural damage and functional reorganization, with the goal of understanding how these changes contribute to clinical symptoms. Advanced data integration methods will be applied to combine information across modalities and improve sensitivity to subtle disease-related alterations.
Overall, this approach aims to provide a more comprehensive characterization of PBC, potentially contributing to the identification of imaging biomarkers and improving our understanding of disease mechanisms and disease trajectories across its heterogeneous clinical and genetic spectrum.

Research Line #: 23

Supervisor: Onelia Gagliano

Potential Co-Supervisor/s: TBA

Other potential Collaborators: TBA

Short abstract: Engineering organoid models represents a powerful strategy to investigate the cellular and molecular basis of human neurodevelopment. In this project, human pluripotent stem cell–derived organoids will be developed and refined to faithfully recapitulate early stages of neural tube formation. By integrating bioengineering approaches and controlled microenvironmental cues, these models will enable precise manipulation of developmental processes.
The platform will be used to dissect key signaling pathways, morphogenetic events, and gene regulatory networks involved in neural tube development. Particular emphasis will be placed on modeling the pathogenesis of neural tube defects, including spina bifida. Advanced imaging and transcriptomic analyses will provide high-resolution insights into developmental dynamics. Ultimately, this work aims to uncover fundamental mechanisms and identify novel targets for early diagnosis and therapeutic intervention.