Welcome

Marcella Montagnese

Marcella Montagnese

Neuroimaging PhD Student

King's College London

University of Cambridge

Biography

I am currently an PhD student in Neuroimaging at King’s College London working with Prof Mitul Mehta and Prof Dominic ffytche on a project exploring the neural and cognitive correlates of Parkinson’s Disease psychosis. I employ multimodal imaging techniques (resting state fMRI, structural and diffusion imaging) and combine 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 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).

Interests
  • Visual hallucinations and psychosis
  • Neuroimaging (fMRI, EEG)
  • Computational Psychiatry
  • Brain networks
  • Machine learning
  • Open Science
Education
  • 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

R

  • Advanced programming
  • Computational Modelling
  • hBayesDM
  • Data analysis and preprocessing
  • Advanced illustrations and plotting

Statistics

  • 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

neuroimaging
Neuroimaging

  • 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)

python
Python

  • 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

health
Clinical Skills

  • Phlebotomy certificate
  • Clinical training in electrocardiography (ECG)
  • Administering neuropsychological tests (cognition, psychosis,dementia)
  • Protocol development for pharmaceutical interventions

Experience

 
 
 
 
 
University of Cambridge (Psychiatry Department)
NIHR SPARC Award Internship
University of Cambridge (Psychiatry Department)
May 2021 – Present Cambridge (UK)
Working on graph-theoretical and Machine Learning analyses of brain imaging data under the supervision of Professor Ed Bullmore and Dr. Sarah Morgan
 
 
 
 
 
University of Cambridge (Genetics Department)
Summer Research Fellowship
University of Cambridge (Genetics Department)
Jun 2016 – Aug 2016 Cambridge (UK)
Running optogenetic testing of the role of modulatory neurons in larval Drosophila. Funded by the Genetics Society UK Summer Studentship Award
 
 
 
 
 
Harvard University (Psychology Department)
Summer Research Fellowship
Harvard University (Psychology Department)
Jul 2015 – Sep 2015 Cambridge (US)
Running eye-tracking and behavioural experiments on patients with Prader-Willi syndrome. Working on natural language coding for the Natural History of Song Project (NHS).

All Publications

Quickly discover relevant content by filtering publications.
Cognition, hallucination severity and hallucination-specific insight in neurodegenerative disorders and eye disease
Metacognition and hallucinations: novel methods and approaches
Octopaminergic neurons have multiple targets in Drosophila larval mushroom body calyx and can modulate behavioral odor discrimination
A Review of Multimodal Hallucinations: Categorization, Assessment, Theoretical Perspectives, and Clinical Recommendations
Reinforcement learning as an intermediate phenotype in psychosis? Deficits sensitive to illness stage but not associated with polygenic risk of schizophrenia in the general population

Projects

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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

Selected for Oxford Machine Learning Summer School - 10% success rate
NIHR SPARC Scheme Award for placement at the University of Cambridge - 5% success rate
British Neuropsychiatry Association Conference Award
British Neuroscience Association Conference Travel Grant (£300)
4 year NIHR Maudsley Biomedical Research Centre (BRC) PhD Studentship (>£60,000)
King’s College Cambridge Graduate Student Fund (GSF) grant (£305)
King’s College Cambridge HE Durham Fund for vacation research (£700)
Bedford fund for research on animal biology (£250) and Undergraduate Travel Grant (£500)
Genetics Society UK- Summer Studentship Award (£1,600)
Cambridge University European Bursary (£5,400)
King’s College Cambridge Ernest Gellner’s Scholarship (£4,500)
St Clare’s Oxford full Academic scholarship (£56,000) & Principal’s Award for academic excellence

Training & Accomplish­ments

Coursera
Neural Networks and Deep Learning
DataCamp
Object-Oriented Programming in R

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