Dr Jiaxiang Zhang
Reader
- zhangj73@cardiff.ac.uk
- +44 (0)29 2087 0471
- Cardiff University Brain Research Imaging Centre, Maindy Road, Cardiff, CF24 4HQ
- Available for postgraduate supervision
Overview
My research involves the investigation of neural and computational mechanisms of decision-making, learning and action. A central theme is to understand how the human brain integrates cognitive and perceptual processes to regulate behaviour in a dynamic, changing environment. The new understanding of these basic cognitive operations is then used for the examination of cognitive deficits in neurodegeneration and dementia. I use a combination of multimodal neuroimaging, behavioural measures, and computational modelling.
For more information, please visit https://ccbrain.org
Please contact me for research fellow or PhD studentship opportunities.
Biography
Education
- 2009 PhD, Department of Computer Sciences, University of Bristol
- 2005 MSc in Advanced Computing (distinction), Department of Computer Sciences, University of Bristol
- 2003 BEng in Computer Networking, Northwestern Polytechnical University, China
Honours and awards
- Trainee travel award, Organization for Human Brain Mapping (2014, 2011)
- Junior Research Fellow (elected), Wolfson College, University of Cambridge (2011-2014)
- Overseas Research Student Award (2006-2008)
Academic positions
2017 – present Senior Lecturer, School of Psychology, Cardiff University, UK
2015 – 2017 Lecturer, School of Psychology, Cardiff University, UK
2010 – 2014 Investigator scientist, MRC Cognition and Brain Sciences Unit, Cambridge, UK
2009 – 2010 Postdoctoral research fellow, School of Psychology, University of Birmingham, UK
Committees and reviewing
- Grant review for ESRC, BBSRC, MRC, Royal Society, Wellcome Trust, ANR (France), FWO (Belgium), Irish Research Council and Research Promotion Foundation (Cyprus)
- Journal review for Advances in Cognitive Psychology, Behavior Research Methods, Brain, Cerebral Cortex, Cognitive Neurodynamics, Current Biology, eLife, European Journal of Neuroscience, Experimental Brain Research, Frontiers (in Decision Neuroscience, Human Neuroscience and Psychology), Journal of Mathematical Psychology, Journal of Neuroscience, Journal of Experimental Psychology: applied, Journal of Pain Research, Knowledge-Based Systems, Neurocomputing, Neuroimage, Neuropsychologia, Psychological Review, Psychonomic Bulletin & Review, Psychopharmacology, Psychological Science, Plos One, Scientific Reports
Publications
2024
- Read, M. et al. 2024. Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T. Neuropsychologia 194, article number: 108783. (10.1016/j.neuropsychologia.2023.108783)
2023
- Lopes, M. A., Hamandi, K., Zhang, J. and Creaser, J. L. 2023. The role of additive and diffusive coupling on the dynamics of neural populations. Scientific Reports 13, article number: 4115. (10.1038/s41598-023-30172-3)
2022
- Zhang, J. and Tait, L. 2022. +microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG. NeuroImage 258, pp. 119346. (10.1016/j.neuroimage.2022.119346)
- Karahan, E. et al. 2022. The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure. Communications Biology 5, article number: 1007. (10.1038/s42003-022-03974-w)
- Sicurella, E. and Zhang, J. 2022. Deep learning for parameter recovery from a neural mass model of perceptual decision-making. Presented at: Conference on Cognitive Computational Neuroscience, San Francisco, 25-28 August 20222022 Conference on Cognitive Computational Neuroscience Proceedings. CCN, (10.32470/CCN.2022.1095-0)
- Krzeminski, D. and Zhang, J. 2022. Imperfect integration: congruency between multiple sensory sources modulates decision-making processes. Attention, Perception, and Psychophysics 84(5), pp. 1566-1582. (10.3758/s13414-021-02434-7)
- Wolpe, N., Hezemans, F. H., Rae, C. L., Zhang, J. and Rowe, J. B. 2022. The pre-supplementary motor area achieves inhibitory control by modulating response thresholds. Cortex 152, pp. 98-108. (10.1016/j.cortex.2022.03.018)
- Lopes, M. A., Bhatia, S., Brimble, G., Zhang, J. and Hamandi, K. 2022. A computational biomarker of photosensitive epilepsy from interictal EEG. eNeuro 9(3) (10.1523/eneuro.0486-21.2022)
- Tait, L. and Zhang, J. 2022. MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses. NeuroImage 251, article number: 119006. (10.1016/j.neuroimage.2022.119006)
2021
- Si, R., Rowe, J. B. and Zhang, J. 2021. Functional localization and categorization of intentional decisions in humans: a meta-analysis of brain imaging studies. NeuroImage 242, article number: 118468. (10.1016/j.neuroimage.2021.118468)
- Tait, L., Ozkan, A., Szul, M. J. and Zhang, J. 2021. A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high-resolution atlas: performance, precision, and parcellation. Human Brain Mapping 42(14), pp. 4685-4707. (10.1002/hbm.25578)
- Tait, L., Lopes, M. A., Stothart, G., Baker, J., Kazanina, N., Zhang, J. and Goodfellow, M. 2021. A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease. PLoS Computational Biology 17(8), article number: e1009252. (10.1371/journal.pcbi.1009252)
- Zajkowski, W., Krzeminski, D., Barone, J., Evans, L. and Zhang, J. 2021. Breaking deadlocks: reward probability and spontaneous preference shape voluntary decisions and electrophysiological signals in humans. Computational Brain & Behavior 4, pp. 191-212. (10.1007/s42113-020-00096-6)
- Antunes Lopes, M., Krzemiński, D., Khalid, H., Singh, K. D., Masuda, N., Terry, J. R. and Zhang, J. 2021. A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG. Clinical Neurophysiology 132(4), pp. 922-927. (10.1016/j.clinph.2020.12.021)
- Lopes, M. A., Zhang, J., Krzeminski, D., Hamandi, K., Chen, Q., Livi, L. and Masuda, N. 2021. Recurrence quantification analysis of dynamic brain networks. European Journal of Neuroscience 53(4), pp. 1040-1059. (10.1111/ejn.14960)
2020
- Szul, M. J., Bompas, A., Sumner, P. and Zhang, J. 2020. The validity and consistency of continuous joystick response in perceptual decision-making. Behavior Research Methods 52, pp. 681-693. (10.3758/s13428-019-01269-3)
- Krzeminski, D., Masuda, N., Hamandi, K., Singh, K. D., Routley, B. and Zhang, J. 2020. Energy landscape of resting magnetoencephalography reveals frontoparietal network impairments in epilepsy. Network Neuroscience 4(2), pp. 374-396. (10.1162/netn_a_00125)
- Hodgetts, C. J. et al. 2020. The role of the fornix in human navigational learning. Cortex 124, pp. 97-110. (10.1016/j.cortex.2019.10.017)
2019
- Karahan, E., Costigan, A., Graham, K., Lawrence, A. and Zhang, J. 2019. Cognitive and white-matter compartment models reveal selective relations 1 between corticospinal tract microstructure and simple reaction time. Journal of Neuroscience 39(30), pp. 5910-5921. (10.1523/JNEUROSCI.2954-18.2019)
2018
- Jia, K., Xue, X., Lee, J., Fang, F., Zhang, J. and Li, S. 2018. Visual perceptual learning modulates decision network in the human brain: the evidence from psychophysics, modeling, and functional magnetic resonance imaging. Journal of Vision 18(12), article number: 9. (10.1167/18.12.9)
- Dima, D. C., Perry, G., Messaritaki, E., Zhang, J. and Singh, K. D. 2018. Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces. Human Brain Mapping 39(10), pp. 3993-4006. (10.1002/hbm.24226)
- Wolpe, N., Zhang, J., Nombela, C., Ingram, J. N., Wolpert, D. M., CAN, C. and Rowe, J. B. 2018. Sensory attenuation in Parkinson's disease is related to disease severity and dopamine dose. Scientific Reports 8, article number: 15643. (10.1038/s41598-018-33678-3)
- Phillips, H. N., Cope, T. E., Hughes, L. E., Zhang, J. and Rowe, J. B. 2018. Monitoring the past and choosing the future: the prefrontal cortical influences on voluntary action. Scientific Reports 8, article number: 7247. (10.1038/s41598-018-25127-y)
2016
- Zhang, J., Nobela, C., Wolpe, N., Barker, R. and Rowe, J. 2016. Time on timing: dissociating premature responding from interval sensitivity in Parkinson's disease. Movement Disorders -New York- 31(8), pp. 1163-1172. (10.1002/mds.26631)
- Zhang, J. et al. 2016. Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson's disease. Brain 139(1), pp. 161-173. (10.1093/brain/awv331)
- Song, Y. and Zhang, J. 2016. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine. Journal of Neuroscience Methods 257, pp. 45-54. (10.1016/j.jneumeth.2015.08.026)
2015
- Mason, S. et al. 2015. The role of the amygdala during emotional processing in Huntington's disease: From pre-manifest to late stage disease. Neuropsychologia 70, pp. 80-89. (10.1016/j.neuropsychologia.2015.02.017)
- Zhang, J. and Rowe, J. B. 2015. The neural signature of information regularity in temporally extended event sequences. NeuroImage 107, pp. 266-276. (10.1016/j.neuroimage.2014.12.021)
2014
- Zhang, J. and Rowe, J. B. 2014. Dissociable mechanisms of speed-accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift-diffusion model. Frontiers in Neuroscience 8, article number: 2014. (10.3389/fnins.2014.00069)
2013
- Song, Y. and Zhang, J. 2013. Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction. Expert Systems with Applications 40(14), pp. 5477-5489. (10.1016/j.eswa.2013.04.025)
- Zhang, J., Kriegeskorte, N., Carlin, J. D. and Rowe, J. B. 2013. Choosing the rules: Distinct and overlapping frontoparietal representations of task rules for perceptual decisions. Journal of Neuroscience 33(29), pp. 11852-11862. (10.1523/JNEUROSCI.5193-12.2013)
2012
- Zhang, J., Hughes, L. E. and Rowe, J. B. 2012. Selection and inhibition mechanisms for human voluntary action decisions. NeuroImage 63(1), pp. 392-402. (10.1016/j.neuroimage.2012.06.058)
- Song, Y., Crowcroft, J. and Zhang, J. 2012. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. Journal of Neuroscience Methods 210(2), pp. 132-146. (10.1016/j.jneumeth.2012.07.003)
- Zhang, J. 2012. The effects of evidence bounds on decision-making: theoretical and empirical developments. Frontiers in Psychology 3, article number: 263. (10.3389/fpsyg.2012.00263)
2011
- Bogacz, R., Usher, M., Zhang, J. and McClelland, J. L. 2011. Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice. In: Seth, A. K., Prescott, T. J. and Bryson, J. J. eds. Modeling Natural Action Selection. Cambridge University Press, pp. 91-119., (10.1017/CBO9780511731525.009)
2010
- Zhang, J., Meeson, A., Welchman, A. E. and Kourtzi, Z. 2010. Learning alters the tuning of functional magnetic resonance imaging patterns for visual forms. Journal of Neuroscience 30(42), pp. 14127-14133. (10.1523/JNEUROSCI.2204-10.2010)
- Zhang, J. and Kourtzi, Z. 2010. Learning-dependent plasticity with and without training in the human brain. Proceedings of the National Academy of Sciences of the United States of America 107(30), pp. 13503-13508. (10.1073/pnas.1002506107)
- Zhang, J. and Bogacz, R. 2010. Bounded Ornstein-Uhlenbeck models for two-choice time controlled tasks. Journal of Mathematical Psychology 54(3), pp. 322-333. (10.1016/j.jmp.2010.03.001)
- Zhang, J. and Bogacz, R. 2010. Optimal decision making on the basis of evidence represented in spike trains. Neural Computation 22(5), pp. 1113-1148. (10.1162/neco.2009.05-09-1025)
2009
- Schwarzkopf, D. S., Zhang, J. and Kourtzi, Z. 2009. Flexible learning of natural statistics in the human brain. Journal of Neurophysiology 102(3), pp. 1854-1867. (10.1152/jn.00028.2009)
- Zhang, J., Bogacz, R. and Holmes, P. 2009. A comparison of bounded diffusion models for choice in time controlled tasks. Journal of Mathematical Psychology 53(4), pp. 231-241. (10.1016/j.jmp.2009.03.001)
2008
- Zhang, J. and Bogacz, R. 2008. Superior Colliculus and Basal Ganglia control the saccadic response in motion discrimination tasks. In: Wang, R., Shen, E. and Gu, F. eds. Advances in Cognitive Neurodynamics. Springer Netherlands, pp. 475-479., (10.1007/978-1-4020-8387-7_82)
2007
- Bogacz, R., Usher, M., Zhang, J. and McClelland, J. L. 2007. Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice. Philosophical Transactions of the Royal Society of London Series B - Biological Sciences 362(1485), pp. 1655-1670. (10.1098/rstb.2007.2059)
Teaching
- UG Year 1 – PS1018 Research Methods in Psychology (practicals, 2015-16)
- UG Year 2 – PS2017 Biological Psychology (2015-16), PS2023 Thinking, Emotion and Consciousness (2016-present)
- UG Final Year – PS3000 Final year research projects (2015-present)
- MSc – PTS507 Neuroimaging of Perception and Action (2015-17)
- MSc – Neuroimaging Research Project (2016-present)
Research topics
For more information, please visit my lab website: https://ccbrain.org
1. How do we make rapid decisions? – The deciding brain
On a foggy night, you drive up to a traffic light. Visibility is poor and you can hardly see if the signal is green or red. On such a scenario, making the right decision is critical.
Our brain has an amazing ability to conduct such tasks rapidly and accurately. We examine how the brain integrates information over a short time period during decision-making. The integration of information is an essential process. It reduces the noise in sensory systems and thereby facilitates more accurate choices. We use computational models at different levels of complexity to account for behavioural measures and neuroimaging data (fMRI and MEG/EEG). This work helps us understand different roles of brain regions during the decision processes, and reveals the information flow from perception to action.
2. How do we choose between equal options? – The volitional brain
Apple or orange, cash or card: we can intentionally choose between these options in order to fulfil our goals and desires, even when all the options are similar and not associated with explicit rewards. The notion of intentional decision covers this fundamental, yet poorly understood ability to our lives: acting voluntarily based on internal intention.
How do we make decisions based on internal intention? How do internal and external factors influence intentional decisions? Can we ever predict one’s intention? We study the computational, neuroanatomical and neurochemical mechanisms of intentional decisions.
3. When and how can things go wrong? – The diseased brain
We often take our basic cognitive capacities for granted until diseases take it away from us. In patients with neurodegenerative diseases, the inability of appropriate behaviour can manifest as impulsivity, apathy, and perseveration that affects patients’ quality of life severely, and exacerbate carer burden.
In collaboration with clinical scientists, we use multimodal brain imaging and computational simulation as tools, to provide a mechanistic understanding of brain alternations and cognitive decline in patients with Alzheimer’s and Parkinson’s diseases. We also examine healthy young adults with heightened genetic risks of age-related cognitive decline, decades before any symptom becomes clinically apparent.
Funding
- PI, "Characterizing the cohort-based individual variability of whole-brain multimodal connectome", Welcome Trust ISSF (£46k). 2021.
- PI, “Free the mind: the neurocognitive determinants of intentional decision”, European Research Council starting grant (€1.48m). 2017-23.
- PI, “Characterising alternations of neural dynamics in intractable epilepsy using neurophysiologically-informed models”, Wellcome Trust ISSF (£44k). 2017-18.
- Lead Supervisor, “Optimal decoding of spatiotemporal patterns in Magnetoencephalography (MEG)”, EPSRC DTP PhD studentship (~£80k). 2017-21.
- Co-I, "The subiculum: a key interface between scene representation and event memory?", BBSRC (£572k). PIs: Carl Hodgetts and Andrew Lawrence. 2021-24.
- Co-I, "Recurrence analysis for the characterisation and classification of epileptic patients", GW4 Accelerator award (£34k). PI: Naoki Masuda. 2019-20.
- Co-I, “Recurrence analysis for time-varying networks and its application to brain dynamics”, GW4 Data Science Seed Corn Funding (£5k). PI: Naoki Masuda. 2018.
- Co-I, “A new noradrenergic strategy to treat Impulsivity in Progressive Supranuclear Palsy”, Medical Research Council (£805k FEC). PI: James Rowe. 2017-20.
- Co-I, “Characterising brain network differences during scene perception and memory in young adult APOE-e4 carriers: multi-modal imaging in ALSPAC”. Medical Research Council (£1.8M FEC). PIs: Kim Graham and Andrew Lawrence. 2016-20.
- Co-I, “Neuro-physiologically informed models and machine learning classification of task-driven and resting-state oscillatory dynamics in Schizophrenia”. Wellcome Trust ISSF (£38k). PI: Krish Singh. 2015-16.
Research group
Supervision
If you are interested in applying for a PhD, please contact me. I supervise PhD students in the areas of:
- Decision making and learning
- Voluntary behaviour and volition
- Brain imaging and human electrophysiology
For more information, please visit https://ccbrain.org
Current supervision
Past projects
- Dominik Krzeminski at School of Psychology, Cardiff University
- Wojciech Zajkowski at School of Psychology, Cardiff University
- Maciej Szul at School of Psychology, Cardiff University
- Yuedong Song (Co-supervised) at Computer Laboratory, University of Cambridge