Dr Vesna Vuksanovic

Senior Lecturer - Health Data Science

Health Data Science

+44 (0) 1792 602212

vesna.vuksanovic@swansea.ac.uk

Office - 104
First Floor
Data Science Building
Singleton Campus

Available For Postgraduate Supervision

Research Links

https://orcid.org/0000-0003-4655-698X

About

Dr Vesna Vuksanovic is a Senior Lecturer in Health Data Science whose research combines computational modelling, network science and interpretable AI to understand heterogeneity in brain ageing and neurodegeneration. Her group integrates multimodal neuroimaging with genetic, transcriptomic and physiological data to investigate brain trajectories and their underlying biological mechanisms.

She leads an interdisciplinary research group and is Programme Director of the MSc Health Data Science. Her previous industry-linked research in dementia clinical trials and neuroimaging biomarker development led to three international patents.

Before joining Swansea University, she held research positions at the University of Aberdeen and Technical University of Berlin/Bernstein Center for Computational Neuroscience, following a Royal Society Fellowship at the Lancaster University Physics Department and an earlier academic appointment at the University of Belgrade.

Vesna's research has also been featured in public media, helping to communicate scientific findings to wider audiences (watch: BRACE Academic Conference Talk).

Areas Of Expertise

Computational & Network Neuroscience
Multimodal Neuroimaging
Interpretable AI & Machine Learning
Imaging Genetics & Transcriptomics
Complex Systems & Dynamical Modelling
Brain Ageing & Neurodegeneration

Career Highlights

Teaching Interests

Programme Director, MSc Health Data Science. Teaching interests include medical statistics, computational modelling, network science and neuroimaging. contribution to chapters for a handbook in medical biophysics.

Research supervision of MSc and PhD students across computational and health-data sciences.

Research Collaborations

Publications

Research Highlights

Batziou, V., Young, A., Rittman, T., & Vuksanovic, V. (n.d.) Network subtypes of cortical similarity reveal molecular correlates of normative and compensatory ageing associated with longevity genes expression. medRxiv
https://doi.org/10.1101/2025.10.19.25337876, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa70902
Rubido, N., Riedel, G., & Vuksanovic, V. (2023). Genetic basis of anatomical asymmetry and aberrant dynamic functional networks in Alzheimer’s disease. Brain Communications, 6(1)
https://doi.org/10.1093/braincomms/fcad320, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65252
Vuksanovic, V. (2022). Brain morphometric similarity and flexibility. Cerebral Cortex Communications, 3(3)
https://doi.org/10.1093/texcom/tgac024, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60224
Vuksanovic, V., Staff, R., Morson, S., Ahearn, T., Bracoud, L., Murray, A., Bentham, P., Kipps, C., Harrington, C., & Wischik, C. (2021). Degeneration of basal and limbic networks is a core feature of behavioural variant frontotemporal dementia. Brain Communications, 3(4)
https://doi.org/10.1093/braincomms/fcab241, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56976
Vuksanovic, V. (2021). Brain Morphological and Functional Networks: Implications for Neurodegeneration. In Stefanovska A., McClintock P.V.E. (eds). (Ed.), Understanding Complex Systems (pp. 329-342). Springer International Publishing
https://doi.org/10.1007/978-3-030-59805-1_21, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56977

All Research

Journal Articles

Rubido, N., Riedel, G., & Vuksanovic, V. (2023). Genetic basis of anatomical asymmetry and aberrant dynamic functional networks in Alzheimer’s disease. Brain Communications, 6(1)
https://doi.org/10.1093/braincomms/fcad320, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65252
Vuksanovic, V. (2022). Brain morphometric similarity and flexibility. Cerebral Cortex Communications, 3(3)
https://doi.org/10.1093/texcom/tgac024, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60224
Vuksanovic, V., Staff, R., Morson, S., Ahearn, T., Bracoud, L., Murray, A., Bentham, P., Kipps, C., Harrington, C., & Wischik, C. (2021). Degeneration of basal and limbic networks is a core feature of behavioural variant frontotemporal dementia. Brain Communications, 3(4)
https://doi.org/10.1093/braincomms/fcab241, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56976
Viol, A., Vuksanovic, V., & Hövel, P. (2021). Information parity in complex networks. Physica A: Statistical Mechanics and its Applications, 561, 125233
https://doi.org/10.1016/j.physa.2020.125233, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60498
Shiells, H., Schelter, B., Bentham, P., Baddeley, T., Rubino, C., Ganesan, H., Hammel, J., Vuksanovic, V., Staff, R., Murray, A., Bracoud, L., Wischik, D., Riedel, G., Gauthier, S., Jia, J., Moebius, H., Hardlund, J., Kipps, C., Kook, K...., & Wischik, C. (2020). Concentration-Dependent Activity of Hydromethylthionine on Clinical Decline and Brain Atrophy in a Randomized Controlled Trial in Behavioral Variant Frontotemporal Dementia. Journal of Alzheimer's Disease, 75(2), 501-519.
https://doi.org/10.3233/jad-191173, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60497
Salokhiddinov, M. & Vuksanovic, V. (2020). Layer –specific Map of the Cortical Surface: the Differences in Cytoarchitecture Properties Across Lobes (275). Neurology, 94(15_supplement)
https://doi.org/10.1212/wnl.94.15_supplement.275, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa69529
Vuksanovic, V. (2019). Cortical Thickness and Functional Networks Modules by Cortical Lobes. Neuroscience, 423, 172-176.
https://doi.org/10.1016/j.neuroscience.2019.10.038, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60501
Schelter, B., Shiells, H., Baddeley, T., Rubino, C., Ganesan, H., Hammel, J., Vuksanovic, V., Staff, R., Murray, A., Bracoud, L., Riedel, G., Gauthier, S., Jia, J., Bentham, P., Kook, K., Storey, J., Harrington, C., & Wischik, C. (2019). Concentration-Dependent Activity of Hydromethylthionine on Cognitive Decline and Brain Atrophy in Mild to Moderate Alzheimer’s Disease. Journal of Alzheimer's Disease, 72(3), 931-946.
https://doi.org/10.3233/jad-190772, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60500
Vuksanovic, V., Staff, R., Ahearn, T., Murray, A., & Wischik, C. (2019). Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer’s Disease and Behavioral Variant Fronto-Temporal Dementia. International Journal of Neural Systems, 29(06), 1850055
https://doi.org/10.1142/s0129065718500557, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60502
Vuksanovic, V. & Hövel, P. (2015). Dynamic changes in network synchrony reveal resting-state functional networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(2), 023116
https://doi.org/10.1063/1.4913526, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60503
Vuksanovic, V. & Hövel, P. (2014). Functional connectivity of distant cortical regions: Role of remote synchronization and symmetry in interactions. NeuroImage, 97, 1-8.
https://doi.org/10.1016/j.neuroimage.2014.04.039, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60504
Batziou, V., Young, A., Rittman, T., & Vuksanovic, V. (n.d.) Network subtypes of cortical similarity reveal molecular correlates of normative and compensatory ageing associated with longevity genes expression. medRxiv
https://doi.org/10.1101/2025.10.19.25337876, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa70902

Book Chapters

Vuksanovic, V. (2021). Brain Morphological and Functional Networks: Implications for Neurodegeneration. In Stefanovska A., McClintock P.V.E. (eds). (Ed.), Understanding Complex Systems (pp. 329-342). Springer International Publishing
https://doi.org/10.1007/978-3-030-59805-1_21, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56977

Other Research Outputs

Rubido, N., Batziou, V., Fuad, M., & Vuksanovic, V. (n.d.) arXiv
https://doi.org/10.48550/arXiv.2505.03458, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa69526

Supervision

Precision Profiling of Myelin Maps Through Multimodal Integration of Imaging and Genetic (current)

PhD

Other supervisor: Dr Owain Howell

Taught Modules

PM-344 Capstone Project

The aim of this module is to provide a capstone experience to students¿ learning, through participating in their own enquiry-based research project, with guidance from an academic supervisor. The project may be laboratory or non-laboratory based, but it will always involve a research question that is drawn from the literature, and focused on a topic relevant to the life sciences. It will ask a research question and involve the critical analysis of research findings. Students will refine their oral and written communication skills to a graduate level through an oral presentation and dissertation on their research findings and conclusions.
PMIM202 Statistics for Health Data Science

This module is designed for a diverse group of graduates and professionals with backgrounds in quantitative sciences, computer science, medicine, or related fields. The module is particularly well-suited to computer science graduates moving into, or looking to apply their skills in health-related profession, as well as healthcare professionals interested in improving their data-related skills to get to the next stage of their career or apply for their first job in this field. The module is taught through a combination of lectures and hands-on computer lab sessions. The focus is on statistical methods and programming tools used in the analysis of health-related data. While prior experience in statistical programming is helpful, it is not essential. Students should either have a basic foundation or be motivated to develop these skills as part of the module. What students will learn: Lectures provide a strong theoretical background in statistical methods, including: 1. Study design and research methodology 2. Population and sampling methods, including random sampling and bootstrapping 3. Core statistical concepts: variable types, descriptive statistics, and basic data visualisation (e.g. histograms) 4. Statistical tests for comparisons and associations between variables. 5. Introduction to advanced methods or related concepts such as, e.g., statistics of graphs, hypergraphs, Bayesian priors. 6. Scientific writing and preparation of research manuscripts, skills required also for thesis writing. Hands-On Learning: Practical sessions are delivered through interactive online exercises on Canvas and are designed to strengthen concepts taught in lectures. Students will work directly with statistical programming tools to analyse real-world health datasets. These exercises are designed for individual, self-paced work on the datasets and tasks provided, preparing students to apply their newly acquired skills in research, clinical, or industry settings.
PMIM702 Health Data Dissertation

Students will work independently on a relevant health data science project, including (but not restricted to): analysis of a large scale dataset, systematic review (with or without meta analysis), surveys/questionnaires, use of trusted research environments, pedagogical research. They will develop and answer a specific research question, in collaboration, and under the guidance of their supervisor provided in the form of regular supervisory sessions.
PMPM05 Research Methods and the Medical Physics Specialties

This module provides healthcare professionals and clinical scientists with essential skills in research methods and evidence-based practice, alongside the opportunity to gain specialised knowledge in a chosen clinical domain. The first component introduces key aspects of research design, statistical analysis, critical appraisal of scientific literature, and the integration of evidence-based practice into clinical decision-making. Learners will explore research ethics, governance frameworks, and public and patient involvement (PPI), alongside practical sessions focused on statistical techniques and effective communication of findings to diverse audiences The second component offers a flexible, tailored learning experience. Students will have the opportunity to choose one of up to four (between two and four in any one year) specialist streams aligned to their area of interest and/or practice: Stream 1: Radiotherapy Physics ¿ Brachytherapy and other specialised radiotherapy techniques. Stream 2: Nuclear Medicine ¿ Quality control of nuclear medicine equipment. Stream 3: Radiation Safety and Diagnostic Radiology ¿ Handling and safety of radioactive materials. Stream 4: Imaging with Non-Ionising Radiation ¿ Advanced ultrasound techniques and optical radiation safety. This flexible approach ensures that participants gain both a solid foundation in generic research methods and an in-depth understanding of a specific area relevant to their professional practice or interests.

Key Grants and Projects

Brain ageing model: identifying neuroimaging patterns with relevance to neurodegeneration. PI 2023
Brain Flexibility: a potential non-invasive biomarker for dementia. PI 2020
A scalable resource leveraging anonymised population data, natural language processing and machine learning for epilepsy research. co-I 2022
Using brain network modules to improve diagnostic accuracy of behavioural variant of frontotemporal dementia and Alzheimer’s disease and predicting determinants of treatment response to LMTM. PI 2018
Temporal Fluctuations in Functional Networks of the Human Brain: Modelling Brain Cognitive States. co-PI 2016

Invited Presentations, Lectures and Conferences

Data driven approaches to neuroimaging analysis to identify dementia sub-types. Computational Neuroscience Society.

2021

Leipzig, Germany
Organization of cortical morphometric similarity networks in health and neurodegenerative disorders.

2019

Institute for Mathematical Biology and Complex System, Aberdeen, Scotland.
From structure to activity: the relationship between brain morphological and functional modularity.

2018

The Kavli Royal Society Centre, Chicheley Hall, Buckinghamshire, England.

Dr Vesna Vuksanovic

Telephone number

Email address

Research Links

About

Areas Of Expertise

Career Highlights

Research Highlights

All Research

Precision Profiling of Myelin Maps Through Multimodal Integration of Imaging and Genetic (current)

PM-344 Capstone Project

PMIM202 Statistics for Health Data Science

PMIM702 Health Data Dissertation

PMPM05 Research Methods and the Medical Physics Specialties

Brain ageing model: identifying neuroimaging patterns with relevance to neurodegeneration. PI 2023

Brain Flexibility: a potential non-invasive biomarker for dementia. PI 2020

A scalable resource leveraging anonymised population data, natural language processing and machine learning for epilepsy research. co-I 2022

Using brain network modules to improve diagnostic accuracy of behavioural variant of frontotemporal dementia and Alzheimer’s disease and predicting determinants of treatment response to LMTM. PI 2018

Temporal Fluctuations in Functional Networks of the Human Brain: Modelling Brain Cognitive States. co-PI 2016

Data driven approaches to neuroimaging analysis to identify dementia sub-types. Computational Neuroscience Society.

Organization of cortical morphometric similarity networks in health and neurodegenerative disorders.

From structure to activity: the relationship between brain morphological and functional modularity.