Welcome
Marcella Montagnese
Research Fellow
University of Cambridge
Biography
I am Postdoctoral Researcher in Clinical Neuroinformatics at the Department of Clinical Neurosciences (University of Cambridge). I work in a team led by Dr Rittman, Professor Kourtzi, and Professor Williams as part of a Wellcome Trust funded Innovator Award.
My current work centers around utilizing real-world memory clinic data from the NHS and combine it with machine learning and network neuroscience methods to develop early markers for dementia detection and prognosis. By developing a low-cost, non-invasive, and integrated AI tool for the NHS, we can help doctors detect and diagnose dementia early, ultimately leading to better treatment and management options for patients. BBC news coverage of the project can be found here. I am also involved in the PASSIAN Project, which aims to implement federated learning in the NHS.
I recently completed my PhD in Neuroimaging at King’s College London working with Prof Mehta and Prof ffytche on a project exploring the neural and cognitive correlates of Parkinson’s Disease psychosis. In my doctoral work, I employed multimodal imaging techniques (resting state fMRI, structural and diffusion imaging) and combined them with network neuroscience, transcriptomics, and receptor maps approaches. I have also gained further expertise on graph theoretical and machine learning analyses as part of my NIHR SPARC Award at the University of Cambridge under the amazing supervision of Prof Ed Bullmore and Dr Sarah Morgan.
My other projects include computational modelling of cognitive performance and cognitive decline, the investigation of psychotic symptoms across diagnostic categories, and the exploration and classification of multimodal hallucinations (i.e. hallucinations occurring across multiple sensory modalities).
Beyond my research, I am passionate about promoting open access to coding and AI resources. Recently, I was selected as an ambassador for the Women Techmakers Initiative powered by Google, where I work to promote diversity and inclusion in the tech industry.
- Visual hallucinations and psychosis
- Neuroimaging (diffusion, fMRI, EEG, T1)
- Computational Psychiatry
- Brain networks
- Machine learning
- Open Science
- Federated Learning
PhD in Neuroimaging, 2018-2022
King's College London
MPhil in Computational Psychiatry, 2017-2018
University of Cambridge
BSc in Psychology and Neuroscience, 2014-2017
University of Cambridge
Skills
- Advanced programming
- Computational Modelling
- hBayesDM
- Data analysis and preprocessing
- Advanced illustrations and plotting
- Traditional & multilevel meta-analysis methods
- Linear Mixed Modelling
- Bayesian statistics
- Synthetic data simulation for Null hyp. testing
- Partial Least Square (PLS) Analyses;
- SPLINE Longitudinal Modelling
- fMRI, structural, and diffusion weighted imaging
- Preprocessing and analysis softwares (Freesurfer, SPM, FSL, AFNI, fMRIprep)
- EEG data processing and ERP analyses
- Docker & Reproducible Analysis Pipelines
- Graph theory analyses
- Wavelet Despiking methods
- High-performance computing (Sun Grid Engines & Slurm)
- Data science skills with Python;
- Machine Learning libraries (TensorFlow, PyTorch, Scikit-learn, Numpy, Keras)
- Pycharm, JupyterNotebook, Visual Studio
- Nilearn and Nipype
- Transcriptomic and receptor maps analyses
- Drift diffusion modelling
- Phlebotomy certificate
- Clinical training in electrocardiography (ECG)
- Administering neuropsychological tests (cognition, psychosis,dementia)
- Protocol development for pharmaceutical interventions
Experience
All Publications
Projects
Longitudinal Morphometric Similarity Networks
Constructing and investigating Morphometric Similarity Networks from structural and diffusion weighted imaging data in a large, longitudinal cohort of patients with Parkinson’s Disease Psychosis.
Cognitive Decline in Parkinson’s Psychosis
Using multi-level meta-analytic tools to investigate if a specific profile of impaired cognition and visual function is linked to vulnerability to visual hallucinations in Parkinson’s Disease. The overall aim is to better understand the complex relationship between psychosis and cognitive decline in Parkinson’s patients.
Brain networks in Parkinson’s Disease Psychosis
Using graph theoretical approaches and resting fMRI data to better characterise the neural fingerprints of visual hallucinations in Parkinson’s Disease. This includes (i) evaluating group differences in FC in terms of both Von Economo cytoarchitectonic principles and well-established functional connectivity networks, (ii) NBS analyses, and (iii) machine learning approaches to identify patterns of covariance between rsfMRI networks and cognitive and clinical biomarkers of interest (cognitive tasks, MCI tests, cerebrospinal fluid biomarkers such as β-Amyloid, T-Tau and α-Synuclein)
The neural basis of psychosis in Parkinson’s disease.
Developing targets for understanding current treatments and developing novel treatments The aim of this pharmachological intervention is to implement and enhance a neuroimaging protocol to test for whole brain impairment in PD patients with and without psychosis to (i) enhance understanding of the neural basis of PD psychosis, (ii) estimate the magnitude of impairment both in predefined brain regions and across brain networks and (iii) test for drug effects (5-HT2a inverse agonism) in these networks.
Computational Modelling of RL in Schizophrenia
This work focuses on the application of computational models of reinforcement learning (RL) in patients with schizophrenia, in addition to investigating the link between RL task performance and molecular genetic risk for the disorder.
Multimodal Hallucinations
Hallucinations can occur in different sensory modalities, both simultaneously and serially in time. Hallucinatory experiences occurring in multiple sensory systems—multimodal hallucinations (MMHs)—are more prevalent than previously thought and may have greater adverse impact than unimodal ones, but they remain relatively underresearched.
Grants & Awards
Training & Accomplishments
