Ongoing Projects
Test-retest Analysis and Robustness of Regional Architectural Graphs using Open Neuroimaging
This project is financed by Xjenza.
The TARRAGON project aims to develop and refine MRI data analysis methods by leveraging secondary published data sources and incorporating data science techniques. Specifically, this project will focus on validating the VB Index by systematically evaluating its robustness across varying scan durations, cognitive states, preprocessing methods, and graph construction parameters. To achieve this, we will utilise robust, open-access neuroimaging datasets renowned for their large-scale individual subject data and suitability for test-retest reliability assessments. By doing so, the project seeks to improve the detection and understanding of neurological and psychiatric disorders and contribute to the advancement of neuroimaging techniques.
Synthetic Anatomy for Radiological Applications
This project is financed by Xjenza.
Functional magnetic resonance imaging (fMRI) is a crucial tool for understanding human brain function, offering non-invasive insights into cognition and clinical conditions like schizophrenia, Alzheimer’s, and epilepsy. However, technical challenges have impeded its clinical utility, particularly in data processing and quality assurance. To address this, Our project SARA aims to develop of brain-mimicking phantoms capable of generating and modifying T2*-based signals, enhancing fMRI reliability. Our pioneering approach involves incorporating signal-producing biomolecules into anthropomorphic brain phantoms, a concept absent in current research. By employing innovative data analysis techniques, we aim to improve signal-to-noise ratios and facilitate early and reliable diagnoses. Through these efforts, we seek to advance fMRI methodologies and provide robust tools for future research and clinical applications.
Brain Research through Imaging Analysis for Neuro-oncology
This project is financed by RIDT cancer.
The BRIAN project aims to develop predictive models for tumour failure locations. Our goal extends beyond understanding current tumour behaviour. We aim to improve the effectiveness of treatment by helping clinicians anticipate future tumour spread. This means refining surgical planning to ensure that resection margins not only cover the existing tumour but also areas likely to be involved in future propagation. We aim to accomplish this by integrating data from longitudinal MRI scans to map potential progression paths of these deadly tumours along neural fibre tracts. This capability could significantly improve the precision of surgical resections, not merely removing the tumour present but also targeting areas likely to develop future growth.
By doing so, we aim to reduce instances of tumour treatment failure, which are often due to undetected tumour spread during initial treatment.By enhancing the precision of tumour resections and the effectiveness of glioblastoma treatment strategies, our project holds promise to improve life expectancy for glioblastoma patients and reduce the morbidity associated with tumour recurrence. This represents a major advancement in the field of neuro-oncology and a significant stride towards our ultimate goal: a world where high grade gliomas are no longer a death sentence, but a condition that can be effectively managed and treated.
Past Projects
Operation TOM
This project is financed by Xjenza.
The primary objective of this project is to gain further understanding into the effects of spaceflight on cortical organisation. The VB toolbox, in its current state, may not consistently capture the true underlying biological mechanisms of local brain function due to limitations arising from the preprocessing of fMRI data, leading to inadvertent amplification of homogeneity at a small scale. It’s important to note that these limitations are not a result of the toolbox’s construction but are inherent in the fMRI data preprocessing. The suggested scientific approach involves precisely quantifying the impact of resulting artefacts and then developing mitigation measures. These measures may include exploring adjustments to standard preprocessing methods and utilising data reconstruction techniques.
Boundaries of the Brain
This project was financed by Xjenza.
The concept of a “brain region” has become a fundamental tenet of many types of neuroscientific analyses. It is now common to think of the brain as a “connectome.” A connectome describes the physical or functional interaction between various brain regions. As such, how one defines a “brain region” has the potential to completely change the results of a particular analysis. While approaches exist to easily “parcellate” the brain into an arbitrary number of brain regions, very few of these approaches are anatomically principled. Most, make three strong, and not necessarily plausible, assumptions. The first is that these regions are static, the second is that these regions have sharp boundaries, the third is that these boundaries fully circumscribe a region.These misconceptions drove our lab to develop the Vogt-Bailey Toolbox. The VB Toolbox is based on spectral graph theory and is used to quantify boundaries in the human cerebral cortex in a biologically meaningful fashion that relies on biologically plausible assumptions. The code for this software is openly available on GitHub.
The BOB project had three specific objectives:
- Optimise the software to decrease computational time;
- Test the software by using data from healthy individuals;
- Apply the technique on clinical data in order to evaluate the possibility of using such maps to identify clinical disorders.
Our project BOB was successful in achieving all its objectives which allowed the extension of the project, now named BE-BOB.
Beyond the Boundaries of the Brain
This project was financed by the University of Malta’s Research Excellence Fund.
Beyond the Boundaries of the Brain, or Be-BOB, was a continuation of the work done during the BOB project. This project had 3 specific objectives:
- To test our algorithms against competing approaches and include the most promising candidate algorithms into our software;
- To test and validate the novel algorithms and statistical approaches on a cohort of brain scans with specific diseases, with an initial focus on autism;
- To quantify the statistical certainty of the boundary maps we produce. Our VB toolbox will help answer both basic scientific questions about the nature of the brain as well as be a stepping-stone for mental health diagnostic and prognostic solutions.
Measuring the Architecture of Consciousness
This project was financed by a European Cooperation of Science and Technology Action Grant.
MARC’s objective was to substantially increase the predictive accuracy of prognoses for disorders of consciousness. The main aim of the project was to develop a data analysis pipeline to process substantial amounts of Magnetic Resonance to develop a neural architecture map that is associated with different metrics of consciousness.
The study investigated inter-site and inter-scanner variability in a “travelling heads” type of study. Using scanners by the same manufacturer (but two different models), the study investigated qMRI metrics for inter-site and inter-scanner differences and parcel-level effects. Thus, the study eliminated potential differences in MR images emanating from equipment being created by different manufacturers or from differences in the individuals’ brains under study. By doing so, we hoped to harmonise our measurements across countries and scanners and achieve identical results within acceptable tolerance limits.
The BOB Lab 