Dr Jiaxiang Zhang

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)