Dr Vesna Vuksanovic

Uwch Ddarlithydd – Gwyddor Data Iechyd

Health Data Science

+44 (0) 1792 602212

vesna.vuksanovic@abertawe.ac.uk

104
Llawr Cyntaf
Yr Adeilad Gwyddor Data
Campws Singleton

Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig

Dolenni Ymchwil

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

ResearchGate

Trosolwg

Ymunodd Vesna â Phrifysgol Abertawe yn 2021 ac mae'n Uwch Ddarlithydd yn yr adran Gwyddor Data Iechyd. Mae hi hefyd yn Uwch Ddarlithydd Anrhydeddus ym Mhrifysgol Aberdeen. Cyn cymryd ei phenodiad ym Mhrifysgol Abertawe, roedd wedi'i lleoli ym Mhrifysgol Aberdeen ac ym Mhrifysgol Dechnegol Berlin, yr Almaen.

Meysydd Arbenigedd

Dadansoddi data
Niwroddelweddu Amlfodd
Dadansoddiad Connectome yr Ymennydd
Clefydau Niwroddirywiol

Uchafbwyntiau Gyrfa

Ymchwil

Prif ddiddordeb Vesna yw delweddu amlfodd a datblygu modelau cyfrifiannol o heneiddio’n iach a niwroddirywiad mewn clefydau ymennydd sy’n achosi dementia.

Mae prosiectau cyfredol yn cynnwys (i) mapio newidiadau heterogenaidd ar draws rhanbarthau'r ymennydd mewn heneiddio'n iach ac mewn anhwylderau niwroddirywiol a (ii) astudio dilyniant y clefyd a'r ymateb i driniaeth feddygol mewn cleifion dementia sy'n cymryd rhan mewn treial clinigol.

Mae Vesna hefyd yn gyd-ddyfeisiwr ar dri patent rhyngwladol, e.e., “Dulliau rhwydwaith ar gyfer clefydau niwroddirywiol” (US17/272885)

Cydweithrediadau

Cyhoeddiadau

Uchafbwyntiau Ymchwil

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. (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. (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

Holl Ymchwil

Erthyglau Cylchgronau

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
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

Penodau Llyfrau

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

Modiwlau a Addysgir

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.
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.

Grantiau a Phrosiectau Allweddol

Model heneiddio'r ymennydd: adnabod patrymau niwroddelweddu sy'n berthnasol i niwroddirywiad. 2023 - 2024

, £67,939:PI
PI: Hyblygrwydd yr Ymennydd: biomarcwr anfewnwthiol posibl ar gyfer dementia. 2020 - 2024

, £111,918:PI
Adnodd graddadwy sy'n ysgogi poblogaeth ddienw data, prosesu iaith naturiol a dysgu peirianyddol ar gyfer ail-epilepsi chwilio. 2022 - 2023

, £50,000: cyd-I
Defnyddio modiwlau rhwydwaith yr ymennydd i wella cywirdeb diagnostig amrywiad ymddygiadol dementia arlaisblaen a chlefyd Alzheimer a rhagfynegi penderfynyddion ymateb triniaeth i LMTM. 2018 - 2022

Efrydiaeth PhD, £103,912:PI
Amrywiadau Amserol yn Rhwydweithiau Swyddogaethol yr Ymennydd Dynol: Modelu Cyflyrau Gwybyddol yr Ymennydd. 2016 - 2018

, EUR174,000: cyd-I

Cyflwyniadau, Darlithoedd a Chynadleddau a wahoddwyd

Dulliau dadansoddi niwroddelweddu a yrrir gan ddata i nodi is-fathau dementia. Cymdeithas Niwrowyddoniaeth Gyfrifiadurol.

2021

Leipzig, yr Almaen
Trefniadaeth rhwydweithiau tebygrwydd morffometrig cortigol mewn anhwylderau iechyd a niwroddirywiol.

2019

Sefydliad ar gyfer Bioleg Mathemategol a System Cymhleth, Aberdeen, yr Alban
O strwythur i weithgaredd: y berthynas rhwng modiwlaredd morffolegol a swyddogaethol yr ymennydd.

2018

Chicheley Hall, Swydd Buckingham, Lloegr, Y Deyrnas Unedig.

Dr Vesna Vuksanovic

Rhif ffôn

Cyfeiriad ebost

LinkedIn Account

Dolenni Ymchwil

Trosolwg

Meysydd Arbenigedd

Uchafbwyntiau Gyrfa

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Holl Ymchwil

Erthyglau Cylchgronau

Penodau Llyfrau

Modiwlau a Addysgir

PM-344 Capstone Project

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

Grantiau a Phrosiectau Allweddol

Model heneiddio'r ymennydd: adnabod patrymau niwroddelweddu sy'n berthnasol i niwroddirywiad. 2023 - 2024

PI: Hyblygrwydd yr Ymennydd: biomarcwr anfewnwthiol posibl ar gyfer dementia. 2020 - 2024

Adnodd graddadwy sy'n ysgogi poblogaeth ddienw data, prosesu iaith naturiol a dysgu peirianyddol ar gyfer ail-epilepsi chwilio. 2022 - 2023

Defnyddio modiwlau rhwydwaith yr ymennydd i wella cywirdeb diagnostig amrywiad ymddygiadol dementia arlaisblaen a chlefyd Alzheimer a rhagfynegi penderfynyddion ymateb triniaeth i LMTM. 2018 - 2022

Amrywiadau Amserol yn Rhwydweithiau Swyddogaethol yr Ymennydd Dynol: Modelu Cyflyrau Gwybyddol yr Ymennydd. 2016 - 2018

Cyflwyniadau, Darlithoedd a Chynadleddau a wahoddwyd

Dulliau dadansoddi niwroddelweddu a yrrir gan ddata i nodi is-fathau dementia. Cymdeithas Niwrowyddoniaeth Gyfrifiadurol.

Trefniadaeth rhwydweithiau tebygrwydd morffometrig cortigol mewn anhwylderau iechyd a niwroddirywiol.

O strwythur i weithgaredd: y berthynas rhwng modiwlaredd morffolegol a swyddogaethol yr ymennydd.