Dr Vesna Vuksanovic

Senior Lecturer – Health Data Science

Health Data Science

+44 (0) 1792 602212

vesna.vuksanovic@swansea.ac.uk

Cellular Office - 104
First Floor
Data Science Building
Singleton Campus

Available For Postgraduate Supervision

Research Links

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

ResearchGate

About

Vesna’s research for the past two+ decades has focused on understanding complex information derived from multimodal health-related datasets, including imaging, physiological signals, genetic data, and clinical and cognitive characteristics.

Before joining Swansea University, she held research positions at the Technical University of Berlin and the University of Aberdeen, where she currently holds an Honorary position. Vesna started her research and academic career at the Institute of Biophysics, University of Belgrade Medical School, where she was a faculty member after earning her PhD in Biophysics. At the same time she was awarded a Royal Society Visiting Research Fellowship at the Department of Physics, Lancaster University.

Vesna is currently Programme Director for the MSc in Health Data Science, where she also leads the Health Data Modelling module. Vesna also lectures on the Research Methods in Medical Physics module.

Vesna is a Fellow of the Higher Education Academy (HEA).

Areas Of Expertise

Data Analysis
Multimodal Neuroimaging
Brain Network Analysis
Neurodegenerative Diseases
Clinical Applications
Complex Systems

Career Highlights

Teaching Interests

Vesna's teaching interests include topics from medical statistics, network science, MRI analysis and biophysics. Vesna has authored chapters for a textbook in Medical Biophysics.

Vesna has extensive experience in postgraduate curriculum development, academic lecturing, and research supervision of MSc and PhD students across computational and health-data sciences.

Research Collaborations

Publications

Research Highlights

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
Rubido, N., Batziou, V., Fuad, M., & Vuksanovic, V. (n.d.) The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis. arXiv
https://doi.org/https://doi.org/10.48550/arXiv.2505.03458, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa69526
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
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., 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. (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
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

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
Rubido, N., Batziou, V., Fuad, M., & Vuksanovic, V. (n.d.) The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis. arXiv
https://doi.org/https://doi.org/10.48550/arXiv.2505.03458, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa69526

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

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.
PMIM102 Scientific Computing and Health Care

The module aims to raise the awareness of students about scientific computing in the field of health data science. It focuses on basic software development workflows, tools, and skills that health data scientists most often employ. Students will also learn about the professional context within which health data scientists operate.
PMIM102J Scientific Computing and Health Care

The module aims to raise the awareness of students about scientific computing in the field of health data science. It focuses on basic software development workflows, tools, and skills that health data scientists most often employ. Students will also learn about the professional context within which health data scientists operate.
PMIM202 Health Data Modelling

Health data scientists are expected to work with diverse data sources. Modelling data encompasses setting up database models and analysing the data using statistical models. The objective of this module is to raise the awareness of students about the various processes of data modelling and the key operations involved in the data processing of large and diverse datasets.
PMIM202J Health Data Modelling

Health data scientists are expected to work with diverse data sources. Modelling data encompasses setting up database models and analysing the data using statistical models. The objective of this module is to raise the awareness of students about the various processes of data modelling and the key operations involved in the data processing of large and diverse datasets.
PMIM302 Introductory Analysis of Linked Health Data

This module introduces the topic of linked health data analysis at an introductory to intermediate level. It fills a gap in research training opportunities by combining the principles of health care epidemiology with hands-on practical exercises in the implementation of computing solutions. The module provides students with a theoretical grounding in the classroom on each topic, followed by a training session on the corresponding computing solutions. Students use anonymised linked health data files in the hands-on exercises. The computing component of the module assumes a basic familiarity with computing syntax used in programs such as SPSS and/or R and methods of basic statistical analysis.
PMIM402J Machine Learning in Healthcare

Data scientists working in healthcare are called to deal with problems involving classification and pattern recognition. The objective of this module is to provide the essential theory and practical aspects of widely used machine learning software.
PMIM602 Advanced Analysis of Linked Health Data

This module is taught at an intermediate to advanced level and assumes that students have completed PMIM302 Introductory Analysis of Linked Health Data or have equivalent knowledge. Advanced principles of health care epidemiology are combined with hands-on practical exercises in the implementation of computing solutions. The module provides students with a theoretical grounding on each topic, followed by a training session on the corresponding computing solutions. Students use de-identified data files in the hands-on exercises. The computing component of the module assumes a basic competence in the preparation of computing syntax for programs such as SPSS, SAS, STATA or R and familiarity with the statistical analysis of linked data files at an introductory to intermediate level. This CPD course will be jointly delivered by Professor David Preen from the University of Western Australia and Dr Pete Arnold from Swansea University.
PMPM02 Research Methods in Medical Physics and Clinical Engineering

Generic teaching on research methodology in medical physics and clinical engineering clinical science specialism.
PMPM05 Research Methods in Medical Physics and Clinical Engineering

The overall aim of this module is to ensure that the trainee in medical physics or clinical engineering has the underpinning knowledge of the importance of research, development and innovation across the NHS and in healthcare science in particular and to provide the underpinning knowledge for the research project.

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

Chicheley Hall, Buckinghamshire, England, UK.

Dr Vesna Vuksanovic

Telephone number

Email address

Research Links

About

Areas Of Expertise

Career Highlights

Publications

Research Highlights

All Research

Journal Articles

Book Chapters

Taught Modules

PM-344 Capstone Project

PMIM102 Scientific Computing and Health Care

PMIM102J Scientific Computing and Health Care

PMIM202 Health Data Modelling

PMIM202J Health Data Modelling

PMIM302 Introductory Analysis of Linked Health Data

PMIM402J Machine Learning in Healthcare

PMIM602 Advanced Analysis of Linked Health Data

PMPM02 Research Methods in Medical Physics and Clinical Engineering

PMPM05 Research Methods in Medical Physics and Clinical Engineering

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.

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

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