Our research is theoretically driven by ideas of Predictive Coding, a computational framework that posits the brain is a predictive, efficient and adaptive machine. The main goal of the group is to understand how the brain’s circuitry implements these mechanisms, which enable us to make predictions about future events as well as learn about, and adapt to, the contingencies of a novel environment. Along with our work on typical cognition in healthy human individuals, our mission is to contribute to the understanding of mental illness, in particular to those conditions where predictive processes and brain circuitry are disrupted such as in schizophrenia and anxiety. To pursue this endeavour we use a combination of computational modelling, machine learning and brain imaging techniques such as magnetoencephalography (MEG), electroencephalography (EEG), and magnetic resonance imaging (MRI).
Marta Garrido | Laboratory Head
Associate Professor Marta Garrido leads the Cognitive Neuroscience and Computational Psychiatry Laboratory at the Melbourne School of Psychological Sciences at the University of Melbourne and is Chief Investigator in the Australian Research Council Centre of Excellence for Integrative Brain Function. Marta received her PhD in 2008 from University College London. She then completed postdocs at University California Los Angeles and back at University College London. In 2013 she moved to the Queensland Brain Institute on a Discovery Early Career Researcher Award and later established her independent laboratory. In mid 2019 the lab moved to the University of Melbourne.
Jeremy TaylorI am a Senior Research Assistant in Dr Marta Garrido’s lab, where I use computation to understand prediction generation and how the brain responds to surprise. My primary focus is developing tools for visualising dynamic spatiotemporal M/EEG statistics and using machine learning techniques to classify psychiatric disorders. I graduated in electrical and biomedical engineering in 2017.
Dr Ilvana Dzafic
Dr Ilvana DzaficI am a cognitive neuroscientist using neuroimaging techniques to study cognition and reward processing in the healthy population and in schizophrenia. This research is driven by the ideas behind 'predictive coding', a theoretical framework for how the brain continually generates and updates predictive models of the world based on prior expectations and sensory experience.
Talina BayelevaI have joined the Garrido lab as a MPhil in Neuroscience student after graduating from Griffith University with a Bachelor of Biomedical Science. My project involves analysing electroencephalography (EEG) data using machine learning in order to predict treatment response in people with schizophrenia.
Shivam KalhanFor me it starts with my curiosity on the fundamental nature of the brain and how it works to shape our reality. I have completed a BSc (Hons) in neuroscience from the University of Otago, New Zealand. Currently, I'm a PhD student in the Garrido lab investigating the neurocircuitry underpinning belief updating in addiction. When I'm not in the lab, you will most likely find me on the tennis court!
Roshini RandeniyaMy PhD research takes a Bayesian approach to understanding sensory learning with respect to Autistic Traits and Sensory Sensitivities. I currently use neuroimaging methods such as fMRI and computational methods such as Dynamic Causal Modelling to understand the brain pathways underlying sensory learning.
Dr Kelly Garner
Dr Kelly GarnerI aim to understand how the brain instantiates the influence of expectation and reward upon the operations of selective-attention, and the subsequent consequences for sensory experience. I have joined the Garrido group on a Marie-Curie fellowship, in collaboration with Ole Jensen at the University of Birmingham, where we will combine computational neuro/magnetic imaging and DBS approaches to address this question. The rest of the time, you'll most likely find me outdoors, preferably on a rock face or upon some sand grains.
Chrys ZantisI am a textile based mid career Australian artist. As part of my ongoing exhibition Internal landscapes, I am a textile based mid career Australian artist. As part of my ongoing exhibition Internal landscapes, I am conducting my own research in the field of neuroscience, anatomy, and biotechnology interventions only through visual perspectives and for cultural perspectives for cultural outcomes, rather than scientific endeavour. I have given myself the task of looking for the poetry within the science. Even within my short time of working with Marta’s team I'm finding this poetry glimmering and glistening like a grains of gold in a swirling gold pan.
University of Melbourne Based
University of Queensland Based
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- K. Garner, M.I. Garrido*, P.E. Dux*. Cognitive capacity limits are remediated by pratice-induced plasticity in a striatal-cortical network. https://www.biorxiv.org/content/10.1101/564450v1
- C. Pernet, M.I. Garrido, A Gramfort, .... A. Puce. Best Practices in Data Analysis and Sharing in Neuroimaging using MEEG. https://osf.io/a8dhx/
- Taylor, J., Garrido, M.I. (2019). Porthole and Stormcloud: Tools for visualisation of spatiotemporal M/EEG statistics. bioRxiv doi:org/10.1101/534784
- McFadyen, J., Smout, C., Tsuchiya, N., Mattingley, J., Garrido, M.I. (2019). Surprising threats accelerate evidence accumulation for conscious perception. bioRxiv dio.org/10.1101/525519
- Dietz, M., Nielsen, J., Roepstorff, A., Garrido, M.I. (2017). Dysconnection of right parietal and frontal cortex in neglect syndrome. bioRxiv doi: 10.1101/192583
- Dzafic, I., Randeniya, R., Garrido, M.I. (2018). Reduced top-down connectivity as an underlying mechanism for psychotic experiences in healthy people. bioRxiv doi: 10.1101/296988
- J.A. Taylor, K.M. Larsen, I. Dzafic, M.I. Garrido. Predicting Individual Psychotic Experiences on a Continuum using machine learning. https://www.biorxiv.org/content/early/2018/07/30/380162
- L.K.L. Oestreich*, R. Randeniya*, M.I. Garrido. Auditory prediction errors and auditory white matter microstructure predict psychotic experiences in the healthy population. Brain Structure and Function (accepted 24/09/2019) https://www.biorxiv.org/content/10.1101/544452v1
- L.K.L Oestreich, R. Randenya, M.I. Garrido. (2019) Structural connectivity facilitates functional connectivity of auditory prediction error generation within a fronto-temporal network. NeuroImage 195:454-462. Best paper of the month awarded by University of Queensland Centre for Clinical Research.
- Larsen, K.M., Morup, M., Birknow, M.R., Fischer, E., Olsen, L., Didriksen, M., Baare, W.F.C., Werge, T.M.,Garrido, M.I*., Siebner, H.R*. Individuals with 22q11.2 deletion syndrome show intact prediction but reduced adaptation in responses to repeated sounds: evidence from Bayesian mapping. Neuroimage: Clinical (accepted 13/02/2019) *equal contribution
- Smout, C., Tang, M., Garrido, M.I., Mattingley, M. (2019). Attention Promotes the Neural Encoding of Prediction Errors. PLOS Biology (Accepted)
- McFadyen, J., Mattingley, J.,Garrido, M.I. (2019). An afferent white matter pathway from the pulvinar to the amygdala facilitates fear recognition. eLife 8:e40766
- Harris, C.D., Rowe, E.G.,Randeniya, R., Garrido, M.I. (2018). Bayesian Model Selection Maps for group studies using M/EEG data.Frontiers in Neuroscience. 12: Article 598
- Oestreich, L.K.L., Randenya, R., Garrido, M.I. (2018). White matter connectivity distruptions in the pre-clinical continuum of psychosis: A connectome study. Hum Brain Mapp. 40(2): 529-537
- van Heusden, E., Harris, A., Garrido, M., Hogendoorn, H.(2019).Predictive coding of visual motion in both monocular and binocular visual processing. Journal of Vision. 19 (1):3, 1-12
- Larsen, K.M., Dzafic, I., Siebner, H.R., Garrido, M. (2018). Alteration of functional brain architecture in 22q11.2 deletion syndrome - Insights into susceptibility for psychosis. NeuroImage 197: 328-336
- Oestreich, L., Whitford, T., Garrido, M.I. (2018). Prediction of speech sounds is facilitated by a functional fronto-temporal network. frontiers in Neural Circuits. 12:43
- Larsen, K.M., Morup, M., Birknow, M., Fischer, E., Hulme, O., Vangkilde, A., Schmock., H., Baare, W.F.C., Didriksen, M., Olsen, L., Werge, T., Siebner, H.R., Garrido, M.I. (2018). Altered auditory processing and effective connectivity in 22q 11.2 deletion syndrome. Schizophrenia Research. 197: 328-336
- Dzafic, I., Burianova, H., Periyasamy, S., Mowry, B. (2018). Association between schizophrenia polygenic risk and neural correlates of emotional perception. Psychiatry Research: Neuroimaging. 276:33-40
- Cornwell, B., Garrido, M.I. Overstreet, C., Pine, D., Grillon, C.(2017). The un-predictive brain under threat: a neuro-computational account of anxious hypervigilance. Biological Psychiatry. 82: 447-454
- Randeniya, R., Oestreich, L., Garrido, M.I. (2018). Sensory prediction errors in the continuum of psychosis. Schizophrenia Research 191: 109-122
- Garrido, M.I., Rowe, E., Halasz, V., Mattingley, J. (2017). Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli. Cerebral Cortex. 1-12
- McFayden, J., Mermillod, M., Mattingley, J., Halasz. V., Garrido, M.I. (2017). A rapid subcortical amygdala route for faces irrespective of spatial frequency and emotion. J. Neurosci 37 (14) 3864-3874
- Oestreich, L., Lyall, A., Pasternak, O., Kikinis, Z., Newell., Savadjiev, P., Bouix, S., Shenton, M., Kubicki, M. (2017). Characterizing white matter changes in chronic schizophrenia: A free-water imaging multi-site study. Schizophrenia Research, 189pp:153-161
- Taylor, J.A., Matthews, N., Michie, P.T., Rosa, M.J., Garrido, M.I. (2017). Auditory prediction errors as individual biomarkers of schizophrenia. Neuroimage: Clinical. 15:264-273
- Sherwell, C., Garrido, M.I. , Cunnington, R. (2016). Timing in predictive coding: The role of task relevance and global probability. Journal of Cognitive Neuroscience. 29 (5): 780-792
- Oestreich,L.K.L., Pasternak,O., Shenton, M.E., Kubicki, M., Gong, X., Australian Schizophrenia Research Bank, McCarthy-Jones, S., Whitford, T.J. (2016). Abnormal white matter microstructure and increased extracellular free-water in the cingulul bundle associated with delusions in chronic schizophrenia. NeuroImage: Clinical, 12, pp: 405-414
- Roberts, G., Lord, A.,Frankland, A., Wright, A., Lau, P., Levy, F., Lenroot, R.K., Mitchell, P.B., Breakspear, M. (2016). Functional dysconnection of the inferior frontal gyrus in young people with bipolar disorder or at genetic high risk. Biological Psychiatry.
- Kaunitz, L.N., Rowe, E.G., Tsuchiya, N. (2016). Large Capacity of Conscious Access for Incidental Memories in Natural Scenes. PsychSci, 27(9) 1266-1277
- Garrido, M.I., Teng, C.L.J., Taylor, J., Rowe, E.G. and Mattingley. J.B. (2016). Surprise responses in the human brain demonstrate statistical learning under high concurrent cognitive demand. npj Science of Learning. 1, 16006
- Roberts, J.A., Perry, A., Lord, A.R., Roberts, G., Mitchell, P.B., Smith, R.E., Calamante, F. and Breakspear, M. (2016). The contribution of geometry to the human connectome. NeuroImage, 124, pp: 379-393.
- Zavitz, E., Yu, H.H., Rowe, E.G., Rosa, M.G. and Price, N.S. (2016). Rapid Adaptation Induces Persistent Biases in Population Codes for Visual Motion. The Journal of Neuroscience, 36(16), pp: 4579-4590.
- Cooray.G., Garrido, M.I., Brismar, T., Hyllienmark, L. (2016). The maturation of mismatch negativity networks in normal adolescence. Clinical Neurophysiology 127(1) pp: 520-529
- Dürschmid, S., Zaehle, T., Hinrichs, H., Heinze, H., Voges, J., Garrido, M.I. Dolan, R.J., Knight, R. (2016). Sensory deviancy detection measured directly within the Human Nucleus Accumbens. Cerebral Cortex 26 pp: 1168-1175
- Litvak, V., Garrido, M.I., Zeidman, P., Friston, K. (2015). Empirical Bayes for Group (DCM) Studies: A Reproducibility Study. Frontiers in Human Neuroscience.
- Garrido, M.I. Barnes, G.R., Kumaran, D., Maguire, E.A., Dolan, R.J. (2015). Ventromedial prefrontal cortex drives hippocampal theta oscillations induced by mismatch computations. NeuroImage 120 pp: 362-370.
- Poch, C., Garrido, M.I. Igoa, J.M., Belinchon, M., Morales-Morales,I., Campo, P. (2015).Time-varying effective connectivity during visual object naming as a function of semantic demands. Journal of Neuroscience 35(23) pp: 8768-8776
- He, W., Garrido, M.I. Sowman, P.F, Brock, J., Johnson, B.W. (2015). Development of effective connectivity in the core network for face perception. Hum. Brain Mapp., 36 pp: 2161–2173
- Rosa, M.J., Portugal, L., Hahn, T., Fallgatter, A.J., Garrido, M.I. Shawe-Taylor, J., Mourao-Miranda, J. (2015). Sparse network-based models for patient classification using fMRI. Neuroimage 105 pp: 493 506.
- Dietz, M.J., Friston, J.B., Mattingley, J.B., Roepstorff, A., Garrido, M.I. (2014). Effective connectivity reveals right-hemisphere dominance in audiospatial perception: Implications for models of spatial neglect. The Journal of Neuroscience 34 pp: 5003 - 5011. Selected by Australian Life Scientist as some of the best Australian research published in May/June 2014.
- Cooray, G.K., Garrido, M.I. Hyllienmark, L., Brismar, T. (2014). A mechaistic model of mismatch negativity in the aging brain. Clinical Neurophysiology 125 (9) pp: 1774 - 1782.
- Garvert, M.M., Friston, K.J., Dolan R.J., Garrido, M.I. (2014). Subcortical amygdala pathways enable rapid face processing. Neuroimage 102 Pt 2 pp: 309 -316.
- Lieder, F., Stephan, K.E., Daunizeau, J., Garrido, M.I. Friston. (2013). A neurocomputational model of the mismatch negativity. PLoS Computational Biology 9 (11):e1003288.
- Garrido, M.I. Sahani, M., Dolan, R.J. (2013). Outlier responses reflect sensitivity to statistical structure in the human brain. PLoS Computational Biology 9 (3) e1002999
- Lieder, F., Daunizeau, J., Garrido, M.I. Friston, K.J., Stephan, K.E. (2013). Modelling trial-by-trial changes in the mismatch negativity. PLoS Computational Biology 9(2): e1002911
- Campo, P., Garrido, M.I. Moran, R.J., Garcia-Morales, I., Poch, C., Toledano, R., Gil-Nagel, A., Dolan, R., Friston, K. (2013). Network configuration and working memory impairment in mesial temporal lobe epilepsy. NeuroImage 72 pp:48-54
- Boly, M., Massimini, M., Garrido, M.I. Gosseries, O., Noirhomme, Q., Laureys, S., Soddu, A. (2012). Brain connectivity in disorders of consciousness. Brain Connectivity 2 pp: 1 - 10.
- Garrido, M.I. Barnes,G., Sahani, M., Dolan, R. (2012). Functional evidence for a dual route to amygdala. Current Biology 22 pp: 129 - 134.
- Garrido, M.I. Brain connectivity: the feeling of blindsight. Current Biology 22: R599 - R600. Invited Dispatch.
- Murta, T., Leal, A., Garrido, M.I. Figueiredo, P. (2012). Dynamic causal modelling of epileptic seizure propagation pathways: a combined EEG-fMRI study. NeuroImage 62 pp: 1634 - 1642. Awarded 2nd prize, "Liga Portuguesa contra a Epilepsia" 2013.
- Campo, P., Garrido, M.I. Moran, R., Maestu, F., Garcia-Morales, I., Gil-Nagel., A., del Pozo, F., Dolan, R., Friston, K. (2012) Remote effects of hippocampal sclerosis on effective connectivity during working memory encoding: a case of connectional diaschisis? Cerebral Cortex 22 pp: 1225 -1235.
- Garrido, M.I. Dolan, R., Sahani, M. (2011). Surprise leads to noiser perceptual decisions. iPerception 2 pp: 112 - 120.
- Boly, M., Garrido, M.I. Gosseries, O., Bruno, M.A., Schnakers, C., Massimini, M., Litvak, V., Laureys, S., Friston, K. (2011). Preserved feedforward but impaired top-down processes in the vegetative state. Science 332 pp: 858 - 862. (Recommended in Faculty of 1000, article factor of 8 (must read) and 6 (recommended)). (see also: G. Miller (2011) Feedback From Frontal Cortex May Be a Signature of Consciousness. Science 332:779; J.R.King, T. Bekinschtein, S.M.Dehaene (2011) Comment on "Preserved feedforward but impaired top-down processesin the vegetative state". Science 334: 1203; L. Welberg (2011). Auditory processing: sounding out consciousness. Nat Rev Neurosci 12: 369)
- Garrido, M.I. Kilner, J.M., Kiebel, S.J., Friston, K.J. (2009). Dynamic causal modelling of the response to frequency deviants. Journal of Neurophysiology 101 pp: 2620- 2631.
- Garrido, M.I. Kilner, J.M., Stephan, K.E., Friston, K.J. (2009). The mismatch negativity: a review of the underlying mechanisms. Invited review. Clinical Neurophysiology 120 pp: 453 - 463. Issue cover.
- Kiebel, S.J., Garrido, M.I. Moran, R.J., Chen, C.C., Friston, K.J. (2009). Dynamic causal modelling for EEG and MEG. Review. Human Brain Mapping 30 pp: 1866 - 1876.
- Garrido, M.I. Kilner, J.M., Kiebel, S.J., Stephan, K.E., Baldeweg, T., Friston, K.J. (2009). Repetition suppression and cortico-cortical plasticity in the human brain. NeuroImage 48 pp: 269 - 279.
- Garrido, M.I. Friston, K.J, Kiebel, S.J., Stephan, K.E., Baldeweg, T., Kilner, J.M. (2008). The functional anatomy of the MMN: A DCM study of the roving paradigm. NeuroImage 42 pp: 936 - 944.
- Kiebel, S.J., Garrido, M.I. Moran, R.J., Friston, K.J. (2008). Dynamic causal modelling for EEG and MEG. Cogn Neurodyn 2 pp: 121 - 136.
- Garrido, M.I. Kilner, J.M., Kiebel, S.J., Friston, K.J. (2007). Evoked brain responses are generated by feedback loops. Proceedings of the National Academy of Sciences of the USA 104 pp: 20961 - 20966.
- Garrido, M.I. Kilner, J.M., Kiebel, S.J., Stephan, K.E., Friston, K.J. (2007). Dynamic causal modelling of evoked potentials: A reproducibility study. NeuroImage 36 pp: 571 - 580.
- Kiebel, S.J., Garrido, M.I. Friston, K.J. (2007). Dynamic causal modelling of evoked responses: The role of intrinsic connections. NeuroImage 36 pp: 332 -345.
- Kiebel, S.J., Garrido, M.I. Friston, K.J. (2010). Analysing functional and effective connectivity with EEG and MEG. In: Simultaneous EEG and fMRI: Recording, Analysis and Application. Markus Ullsperger & Stefan Deneber. Oxford University Press. Chapter 3.8 pp: 235 - 249.
- Kiebel, S.J., Garrido, M.I. Friston, K.J. (2009). Dynamic causal modelling for evoked responses. In: Brain signal analysis: Advances in neurelectric and neuromagnetic methods. Todd Handy, The MIT Press. Chapter 6 pp: 141 - 169.
- Friston, K., Kiebel, S., Garrido, M.I. David, O. (2007). Dynamic casual models for EEG. In: Statistical Parametric Mapping 2007. Eds: K. Friston, J. Ashburner, S. Kiebel, T. Nichols, W. Penny. Academic Press. Chapter 42 pp: 561 - 576.
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