Neural Substrates of Cognitive Changes in Healthy Ageing Using Whole Brain MRI Magnetic Susceptibility Mapping of Tissue Iron

Dr Karin Shmueli (primary)
Department of Medical Physics and Biomedical Engineering
Dr Rimona Weil (secondary)
Dementia Research Centre


Healthy older adults show wide variation in cognitive performance that does not relate directly to measures of underlying neuropathological burden. Iron-mediated oxidative stress is a potential mechanism for the neuronal vulnerability in healthy ageing. MRI Quantitative Susceptibility Mapping (QSM) measures tissue composition and is dominated by iron content in the brain. This project will use QSM to measure brain iron changes in healthy older individuals and investigate the relationship between these and performance across cognitive domains at baseline and after longitudinal follow-up. This will provide new and important insights into the neural substrates of early cognitive changes in healthy ageing.


1. J.H. Lanskey, P. McColgan, A.E. Schrag, J. Acosta-Cabronero, G. Rees, H.R. Morris, and R.S. Weil, Can neuroimaging predict dementia in Parkinson’s disease? Brain, 2018. 141(9): 2545-2560.
2. R.S. Weil, J.S. Winston, L.A. Leyland, K. Pappa, R.B. Mahmood, H.R. Morris, and G. Rees, Neural correlates of early cognitive dysfunction in Parkinson’s disease. Ann Clin Transl Neurol, 2019. 6(5): 902-912.
3. W. Li, H. Jiang, N. Song, and J. Xie, Oxidative stress partially contributes to iron-induced alpha-synuclein aggregation in SK-N-SH cells. Neurotox Res, 2011. 19(3): 435-42.
4. R. Ward, F.A. Zucca, J.H. Duyn, R.R. Crichton, and L. Zecca, The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurology, 2014. 13(10): 1045-1060.
5. K. Shmueli, J.A. de Zwart, P. van Gelderen, T.Q. Li, S.J. Dodd, and J.H. Duyn, Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data. Magn Reson Med, 2009. 62(6): 1510-22.
6. J. Acosta-Cabronero, M.J. Betts, A. Cardenas-Blanco, S. Yang, and P.J. Nestor, In Vivo MRI Mapping of Brain Iron Deposition across the Adult Lifespan. J Neurosci, 2016. 36(2): 364-74.
7. K.L.H. Carpenter, W. Li, H.J. Wei, B. Wu, X. Xiao, C.L. Liu, . . . H.L. Egger, Magnetic susceptibility of brain iron is associated with childhood spatial IQ. Neuroimage, 2016. 132: 167-174.
8. F. Darki, F. Nemmi, A. Moller, R. Sitnikov, and T. Klingberg, Quantitative susceptibility mapping of striatum in children and adults, and its association with working memory performance. Neuroimage, 2016. 136: 208-14.
9. Y.W. Sun, X. Ge, X. Han, W.W. Cao, Y. Wang, W.N. Ding, . . . J.R. Xu, Characterizing Brain Iron Deposition in Patients with Subcortical Vascular Mild Cognitive Impairment Using Quantitative Susceptibility Mapping: A Potential Biomarker. Frontiers in Aging Neuroscience, 2017. 9.
10. J.M. van Bergen, X. Li, J. Hua, S.J. Schreiner, S.C. Steininger, F.C. Quevenco, . . . P.G. Unschuld, Colocalization of cerebral iron with Amyloid beta in Mild Cognitive Impairment. Sci Rep, 2016. 6: 35514.
11. S. Ayton, A. Fazlollahi, P. Bourgeat, P. Raniga, A. Ng, Y.Y. Lim, . . . A.I. Bush, Cerebral quantitative susceptibility mapping predicts amyloid-beta-related cognitive decline. Brain, 2017. 140(8): 2112-2119.
12. J.M.G. van Bergen, X. Li, F.C. Quevenco, A.F. Gietl, V. Treyer, S.E. Leh, . . . P.G. Unschuld, Low cortical iron and high entorhinal cortex volume promote cognitive functioning in the oldest-old. Neurobiol Aging, 2018. 64: 68-75.
13. J.M.G. van Bergen, X. Li, F.C. Quevenco, A.F. Gietl, V. Treyer, R. Meyer, . . . P.G. Unschuld, Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and beta-amyloid as an indicator of cognitive performance at high age. Neuroimage, 2018. 174: 308-316.
14. A. Babayan, M. Erbey, D. Kumral, J.D. Reinelt, A.M.F. Reiter, J. Robbig, . . . A. Villringer, A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data, 2019. 6: 180308.
15. C. Langkammer, F. Schweser, K. Shmueli, C. Kames, X. Li, L. Guo, . . . B. Bilgic, Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge. Magn Reson Med, 2018. 79(3): 1661-1673.
16. J. Yoon, E. Gong, I. Chatnuntawech, B. Bilgic, J. Lee, W. Jung, . . . J. Lee, Quantitative susceptibility mapping using deep neural network: QSMnet. Neuroimage, 2018. 179: 199-206.
17. S. Bollmann, K.G.B. Rasmussen, M. Kristensen, R.G. Blendal, L.R. Ostergaard, M. Plocharski, . . . M. Barth, DeepQSM – using deep learning to solve the dipole inversion for quantitative susceptibility mapping. Neuroimage, 2019. 195: 373-383.

Animal disease, health and welfare
Area of Biology
Techniques & Approaches
Image Processing