Forgotten Memories Brought Back in Mice? Hold Up...

A study published yesterday in Cell[1] has purportedly found that memories formed as infants may be able to be retrieved as adults using optogenetic techniques, and various media outlets have enthusiastically inferred that this suggests we may be able to remember the events from our infancy, some even suggesting we may be able to remember our own birth. 

The study, led by psychologist Paul Frankland, was based on previous research by the group which found that the “forgetting” of memories during infancy may be a result of high levels of hippocampal neurogenesis at this age – i.e. the neurons representing the memories are replaced by new neurons, thus erasing the memories. 

The aim of the current study was to determine if the memories formed during infancy are permanently lost due to a failure in encoding during infancy, or become progressively inaccessible over time due to a progressive loss in the ability to retrieve them as the mice age. 

The researchers placed young mice into a box and gave them a foot-shock, so that when they are placed back into the box they freeze in anticipation of the shock - a classic fear-based training paradigm. The mice had been engineered to contain a specific set of light-sensitive neurons in a particular region of the hippocampus involved in the formation of memories - the dentate gyrus - which allowed the researchers to activate these neurons by firing a laser at them. They then placed the same mice back into the box as adults and activated this set of neurons, thus reinstating the memory and causing the mice to freeze in anticipation of the shock. 

The authors seem to suggest that this means that some hidden traces of the memories created during infancy were retained and were able to be recalled by the researchers by optogenetically activating the specific set of hippocampal neurons which were observed to be activated during the contextual fear encoding (the “dentate gyrus encoding ensemble”), positing that the reactivation of these ensembles was sufficient for “memory recovery” in adulthood. 

They then quantified the activity of an activity-regulated gene, c-Fos, in cortical and subcortical brain regions following the fear learning event. The purpose of this was to determine whether fluorescently-tagged neurons activated during the memory encoding event were preferentially reactivated during the “memory recall” in adulthood – which of course they were, implying successful recall of the memory. 

However, while they may have been able to reactivate a neural pathway they have essentially programmed into the mice’s brains during infancy, I’m not so sure it was the actual memories themselves which were “recovered”. 

It’s worth nothing that these are memories which the researchers have created in the mice by giving them foot-shocks within a particular environmental context; they are not naturally formed memories. 

“When the infant mice were placed in the box and the laser was turned on, the animals’ memories of the electric shock returned and they froze in place.” 

The fact that the mice freeze when they are placed into the same environment in which they were given a foot-shock during infancy does not necessarily mean that they remember receiving the foot-shock. It simply means that a particular behaviour (freezing) has been programmed into their brains and artificially re-instated by firing lasers at the neurons underlying this behaviour. It means that the same neural pathways resulting in a fear-based freezing response were activated – but these may be totally separate from the pathways containing the actual memory of the event (if they even exist). Besides, we are talking about a simple, conditioned response here; an instinctual behaviour in response to pain – much like you learn to quickly move your hand away from a hot plate – not an actual subjective, detailed memory of an event. It’s possible that the mice would freeze if placed in an entirely different context and the same light-sensitive neurons artificially reactivated.

“To first induce memory formation in the animals, the scientists placed the mice in a box and gave them a mild foot shock. While young adult mice retained this memory and froze when put in the box a second time, infant mice forgot this fear-related memory after a day and behaved normally when they encountered the box again” 

Percent freezing levels declined with retention delay in P17, but
not P60, mice. From Guskjolen et al., 2018, Figure 1(B).

So the infant mice were not forming the memories? This appears to contradict the conclusions of the study – i.e. that those memories are simply hard-to-retrieve; hidden deep within the brain, unable to be recovered by natural cues, and that direct stimulation of the engram (in combination with re-exposure to the training context) may reinstate the connections, leading to memory recovery. How can we say that the memories are simply difficult to retrieve when we are unsure if they were ever formed in the first place? 

“However, we found that opto-stimulation of neural ensembles that were engaged during training was sufficient to induce conditioned freezing at the same retention delays. These results suggest that the underlying engram corresponding to the fear conditioning event is not completely overwritten. Rather, this engram presumably exists in an otherwise inaccessible, dormant state, in which “natural” reminders (such as exposure to the training context) most often do not induce successful reactivation(...) This pattern of results is reminiscent of other amnestic states, including mouse models of retrograde amnesia and Alzheimer’s disease, in which opto-stimulation of tagged encoding ensembles (but not presentation of natural cues alone) permits memory recovery." 

Again, the conclusions drawn assume that the artificially activated ensembles encode the actual memory of the event itself – which is not only not confirmed, but hard to believe considering when the mice were put back in the box the second-time as infants, they had not remembered the fear-related memory supposedly created the day before. How, then, do we know that the memory was encoded at all? How do we know it is the memory that is recalled, and not simply a programmed, artificially instated fear-response? This is too simplistic of a model to draw such far-fetched conclusions, and we certainly can’t say that this type of “forgetting” in infancy is akin to other types of amnesia such as that in Alzheimer’s disease. They are completely different processes, at completely different ages. 

Furthermore, less cortical “re-engagement” was observed following optogenetic stimulation of the dentate gyrus engrams in mice trained as infants compared to those trained as adults, further highlighting the possibility that the memories which were purportedly retrieved may not have been formed at all in the infants. 

“Indeed, whereas adult contextual fear memories are successfully consolidated over the course of weeks, equivalent infant memories are being actively forgotten during this period and therefore perhaps not successfully consolidated in the cortex (…) opto-stimulation of tagged dentate gyrus ensembles leads to recovery of an engram that is qualitatively different (and likely impoverished) compared to the equivalent representation in adult animals.” 

The authors even concede that the “memory recovery” did not persist into the light OFF periods – i.e. when the trained mice were placed into the box as adults, they did not freeze unless the hippocampal engrams engineered to be light-sensitive were activated by the researchers – a pattern which has been observed in similar studies involving reactivation of tagged engram cells in the dentate gyrus [2–7]. 

While the study further adds weight to the idea that infantile forgetting is likely due to a failure of memory encoding in the infant brain (something which we knew anyway), the methods used are simply insufficient to be able to draw some of the conclusions the authors propose, and the study certainly does not suggest that we may be able to recover our infantile memories anytime soon.

 References: 

[1] A. Guskjolen, J.W. Kenney, J. de la Parra, B.A. Yeung, S.A. Josselyn, P.W. Frankland, Recovery of “Lost” Infant Memories in Mice, Current Biology. 0 (2018). doi:10.1016/j.cub.2018.05.059.

[2] X. Liu, S. Ramirez, P.T. Pang, C.B. Puryear, A. Govindarajan, K. Deisseroth, S. Tonegawa, Optogenetic stimulation of a hippocampal engram activates fear memory recall, Nature. 484 (2012) 381–385. doi:10.1038/nature11028.

[3] T. Kitamura, S.K. Ogawa, D.S. Roy, T. Okuyama, M.D. Morrissey, L.M. Smith, R.L. Redondo, S. Tonegawa, Engrams and circuits crucial for systems consolidation of a memory, Science. 356 (2017) 73–78. doi:10.1126/science.aam6808.

[4] D.S. Roy, S. Muralidhar, L.M. Smith, S. Tonegawa, Silent memory engrams as the basis for retrograde amnesia, Proc. Natl. Acad. Sci. U.S.A. 114 (2017) E9972–E9979. doi:10.1073/pnas.1714248114.

[5] T.J. Ryan, D.S. Roy, M. Pignatelli, A. Arons, S. Tonegawa, Memory. Engram cells retain memory under retrograde amnesia, Science. 348 (2015) 1007–1013. doi:10.1126/science.aaa5542.

[6] D.S. Roy, A. Arons, T.I. Mitchell, M. Pignatelli, T.J. Ryan, S. Tonegawa, Memory retrieval by activating engram cells in mouse models of early Alzheimer’s disease, Nature. 531 (2016) 508–512. doi:10.1038/nature17172.

[7] S. Ramirez, X. Liu, P.-A. Lin, J. Suh, M. Pignatelli, R.L. Redondo, T.J. Ryan, S. Tonegawa, Creating a false memory in the hippocampus, Science. 341 (2013) 387–391. doi:10.1126/science.1239073.

Advances in Brain Imaging: A Shift Towards Functional Connectivity

Numerous imaging techniques have been utilised to study the structure and function of the brain, including functional magnetic resonance imaging (fMRI), positron-emission tomography (PET) and X-ray computed tomography (CT). fMRI is a form of blood-oxygen-level dependent (BOLD) contrast imaging which measures the relative proportions of oxyhemoglobin and deoxyhemoglobin – based on the assumption that oxygenated blood flows to active neurons at a greater rate than inactive neurons (the hemodynamic response) – while PET detects gamma rays emitted by a positron-emitting radiotracer introduced into the body and CT combines many X-ray images to construct a 3D representation of the brain. fMRI has typically been used to map brain regions which become activated while the subject is engaged in a particular task, however more recently it has been realised that since brain regions often work in networks (e.g. the default mode network), understanding cognitive processes and behaviours at a neurobiological level requires analysis of the functional connectivity between brain regions constituting functional networks (Menon, 2011). The brain is a unique organ in that, despite its fixed anatomy, local interactions are dynamically modulated to allow a vast functional repertoire (Park & Friston, 2013). Thus, projects such as the Human Connectome Project have begun to map the functional connectivity of the brain using resting-state and task-based fMRI, while recent advances in neuroimaging technology have enabled mental processes and behaviours to be correlated with specific brain structures with greater accuracy and reliability. 

Tong & Pratte (2012) discuss the advances in brain imaging, presenting a wealth of information gathered on a range of mental processes using novel fMRI techniques such as multivoxel pattern analysis. The authors discuss how new techniques allow (to some extent) the ability to “mind read” which of two previously viewed images a subject was imagining, and go as far as to say: “As these methodologies continue to advance, it will become increasingly important to consider the ethical implications of this technology”. This is a bold statement emphasising the fast-evolving nature of brain imaging technology. The authors also review how numerous studies over the past decade have succeeded in decoding various top-down mental processes, such as feature-based attention (Kamitani & Tong, 2005), imagination (Reddy et al., 2010), episodic memory (Rissman & Wagner, 2012) and numerical processing (Knops et al., 2009). For example, Kamitano & Tong (2005) found, using statistical algorithms on fMRI data, that specific fMRI signals in the visual cortex (V1) could reliably predict which of eight stimulus orientations the subject was attending to, indicating that the visual cortex encodes detailed orientation information which can reliably predict subjective perception and suggesting direct orientation mapping at a neurobiological level. Later neuroimaging research by Tong et al. (2012) found that the correlation between such orientation-selective activity patterns and the quality of the sensory input could be directly predicted by the average BOLD amplitude in the brain region of interest using multivariate pattern analysis, offering a reliable model of fMRI pattern classification. This is a prime example of how both brain imaging technology and the way in which researchers use it has advanced over recent years, allowing unprecedented revelations in the neurobiology of specific brain functions.

Tong & Pratte (2012) also highlight methodological issues encountered in brain imaging research. For example, when analysing fMRI data collected while subjects watched humorous events in a video, it transpired that the ventricles were the most statistically informative brain region in predicting a subjects’ urge to laugh – even though it is highly unlikely that the ventricles themselves play a functional role in the cognitive processing of humorous events (Tong & Pratte, 2012). This error is dubbed the “fallacy of reverse inference” (Poldrack, 2006), and is eloquently demonstrated by neuroscientist James Fallon’s conclusion that he must be a psychopath based on PET images of his own brain (Fallon, 2013). Another problem faced is that brain regions are often associated with multiple mental processes, and regions implicated in particular functions tend to vary with experimental variables such as task demands and the specific baseline condition used to identify them (Rabinovich et al., 2012), and even between individual subjects (Kelly et al., 2012). For instance, the hippocampus is known to be involved in the recall of episodic memories, but has also been implicated in the imagining of future events; however, patients with hippocampal damage in some conditions retain unimpaired imagination, suggesting that the hippocampus may not be necessary for it (Martin et al., 2011). Furthermore, there is the issue of causation versus correlation. For example, a famous MRI study by Maguire et al. (2006) found that London taxi drivers had greater grey matter volume in the mid-posterior (but not anterior) hippocampus compared to London bus drivers, who are not required to learn the colossal amount of information which constitutes “the knowledge”. However, the results do not indicate whether these differences are a direct result of the learning of “the knowledge”, or whether subjects showing this particular neuroanatomy are predisposed to becoming London taxi drivers.

Bennett et al. (2009) highlighted some of the common issues faced when analysing fMRI data (e.g. the multiple comparisons problem) by providing evidence that fMRI scans of a dead salmon apparently showed statistically significant brain activity according to commonly-used statistical tests. Subsequent fMRI studies swiftly adopted the corrected comparison methods proposed (Bennett et al., 2009). Additionally, while identifying functional connectivity has recently been recognised as more important than identifying individual brain regions (Menon, 2011), there are limits to how much fMRI can reveal about connectivity. For example, fMRI studies demonstrate that activation of the prefrontal cortex during cognitive evaluation of threatening facial expressions is associated with an attenuated response of the amygdala, apparently indicating a functional network for emotional regulation (Hariri et al., 2003). However, fMRI data does not necessarily show that the amygdala was inhibited by the prefrontal cortex; this is merely inferred. In fact, such negative correlations are often incorrectly interpreted as “inhibitory interactions” (Kelly et al., 2012). Thus, novel techniques / computational models have been developed attempting to overcome these issues.

One such model is the state-space multivariate dynamical systems (MDS) model (Ryali et al., 2011), which takes into account inherent regional differences in the hemodynamic response and focuses on changes in latent signals rather than BOLD signals – which themselves do not necessarily measure the underlying neural activity. More recently optogenetic fMRI (ofMRI) – which combines optogenetic control of neural circuits with fMRI, enabling more direct investigation of connectivity in vivo (Lee, 2012) – when combined with the MDS model was found to be reliable method for identifying functional interactions between brain regions (Ryali et al., 2016).

Figure 1: ofMRI: optically-driven local excitation in defined rodent neocortical cells drives positive BOLD. a. Experimental schematic: transduced cells (triangles) and blue light delivery shown in M1 motor cortex. b. ChR2-EYFP expression in M1. c. ofMRI hemodynamic response during 6 consecutive epochs of optical stimulation. d. BOLD activation is observed at the site of optical stimulation. Adapted from Lee (2012).

Another advancement in statistical analysis being increasingly employed in fMRI studies is the shift from the identification of regions of interest (ROI) to the “parcellation” of whole brain resting-state fMRI data into spatially coherent regions of homogenous functional connectivity (e.g. by cluster analysis) (Craddock et al., 2012). This type of analysis offers several advantages, including providing a measure of the stability of resting-state functional networks between individual subject data and across grouped subjects data (Bellec et al., 2010). Variations between subjects in these intrinsic functional connectivity based “parcellations” does not appear to be correlated with structural variations; rather, they follow intrinsic variations in functional connectivity evoked by specific tasks (Mennes et al., 2010). Thus, studies are beginning to examine links between these task-evoked variations in resting-state functional connectivity and specific behaviours/mental processes (Kelly et al., 2012; Adelstein et al., 2011). Such novel methods of statistical analysis hold promise in elucidating stable functional networks across individuals, with the aim of explaining cognitive functions at a neurobiological level.

Furthermore, recent advances in MRI imaging techniques allow full brain scans to be completed up to 2-3 times faster. This is a significant advantage since it allows for greater statistical definition of neuronal networks (i.e clearer identification of functionally relevant networks), as well as enhanced visualisations of structural connections in the brain such as white matter tracts (Feinberg & Setsompop, 2013). Similarly, the development of high-field strength MRI scanners is expected to dramatically advance our understanding of the pathology of multiple sclerosis (MS) (Filippi, 2014). Advances in brain imaging techniques and how they are used is also benefitting the study of many other pathological behaviours such as those of autism or schizophrenia. Rather than focusing on pathology of individual brain regions, studies are increasingly focusing on aberrant interactions between specific distributed neural networks as well as more general disturbances in functional connectivity (Menon, 2011). For example, one study found that schizophrenic subjects show less integrated but more diverse functional connectivity during task-based behavioural measures compared to controls, even suggesting a possible advantage of the “schizophrenia connectome” (Lynall et al., 2010). Recent neuroimaging studies of autism spectrum disorder (ASD) have also shifted the focus to identifying abnormal connectivity (Vissers et al., 2012), with some studies reporting “underconnectivity” in frontal regions and others reporting “overconnectivity” – likely as a result of the specific methodological variables/analysis type in each study (Nair et al., 2014). Studies are beginning to examine the development of normal functional connectivity in the brain throughout adolescence and the effect of genes and the environment in the development of abnormal connectivity (Blakemore, 2012). However, while these advances are promising, there remain some limitations; for example, different mental diseases often affect the same resting-state networks, bringing into question the specificity of the findings of neuroimaging studies (Barkhof et al., 2014).

Figure 2: EEG waveform representations to spatial/map representations (and analyses).
From Michel & Murray (2012)

Recent advances in signal analysis have also allowed electroencephalography (EEG) to be used as an efficient brain imaging technique, particularly useful in the investigation of abnormal temporal dynamics in functional networks at the millisecond range. Michel & Murray (2012) argue that the full potential of EEG has been underestimated, proposing that proper analysis of the electric field potentials from each electrode can provide spatio-temporal information which may be useful as an adjunct to fMRI studies. Diffusion tensor imaging (DTI) – a form of MRI which tracks the diffusion of water molecules along white matter tracts – has also been used effectively in a recent connectome analysis study of major depressive disorder (MDD), finding reduced structural connectivity in regions constituting the default mode network as well as the frontal cortex, thalamus and caudate regions, thought to be involved in emotional and cognitive processing (Korgaonkar et al., 2014). However, structural connectivity does not necessarily predict functional connectivity.

With regard to connectome projects, Ohno et al. (2016) review recent advances in the acquisition and analysis of large “connectomic” data sets, while concluding that the combination of different imaging modalities and further advances in the (automated) analysis of data may revolutionise our understanding of the structural and functional connectome, elucidating neural mechanisms underlying not only basic sensory/motor functions and disease but also higher mental processes such as consciousness and intelligence.

Recent brain imaging studies have shown a marked paradigm shift from attempting to localise mental processes/behaviours to individual brain regions to identifying whole-brain structural and functional connectivity patterns which may become aberrant in various neurological/psychiatric disorders. As brain imaging technologies continue to advance, researchers continue to develop novel methods to exploit them to their full potential, allowing unprecedented advances in the neurobiology of specific cognitive functions and behaviours.

References: 

Adelstein, J. S., Shehzad, Z., Mennes, M., DeYoung, C. G., Zuo, X.-N., Kelly, C., Margulies, D. S., Bloomfield, A., Gray, J. R., Castellanos, F. X. and Milham, M. P. (2011) ‘Personality Is Reflected in the Brain’s Intrinsic Functional Architecture’, Valdes-Sosa, M. (ed.), PLoS ONE, 6(11), p. e27633.

Barkhof, F., Haller, S. and Rombouts, S. A. R. B. (2014) ‘Resting-State Functional MR Imaging: A New Window to the Brain’, Radiology, 272(1), pp. 29–49.

Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H. and Evans, A. C. (2010) ‘Multi-level bootstrap analysis of stable clusters in resting-state fMRI’, NeuroImage, 51(3), pp. 1126–1139.

Bennett, C., Miller, M. and Wolford, G. (2009) ‘Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction’, NeuroImage (Organization for Human Brain Mapping 2009 Annual Meeting), 47, Supplement 1, p. S125.

Blakemore, S.-J. (2012) ‘Imaging brain development: The adolescent brain’, NeuroImage, 61(2), pp. 397–406.

Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P. and Mayberg, H. S. (2012) ‘A whole brain fMRI atlas generated via spatially constrained spectral clustering’, Human brain mapping, 33(8), p. 10.1002/hbm.21333.

Fallon, J. (2013) The Psychopath Inside: A Neuroscientist’s Personal Journey into the Dark Side of the Brain, New York, Penguin Publishing Group.

Feinberg, D. A. and Setsompop, K. (2013) ‘Ultra-fast MRI of the human brain with simultaneous multi-slice imaging’, Frontiers of In Vivo and Materials MRI Research, 229, pp. 90–100.

Filippi, M. (2014) ‘Recent advances in MS neuroimaging’, Multiple Sclerosis and Related Disorders, 3(6), p. 767.

Hariri, A. R., Mattay, V. S., Tessitore, A., Fera, F. and Weinberger, D. R. (2003) ‘Neocortical modulation of the amygdala response to fearful stimuli’, Biological Psychiatry, 53(6), pp. 494–501.

Kamitani, Y. and Tong, F. (2005) ‘Decoding the visual and subjective contents of the human brain’, Nat Neurosci, 8(5), pp. 679–685.

Kelly, C., Biswal, B., Craddock, R. C., Castellanos, F. X. and Milham, M. P. (2012) ‘Characterizing variation in the functional connectome: promise and pitfalls’, Trends in cognitive sciences, 16(3), p. 10.1016/j.tics.2012.02.001.

Knops, A., Thirion, B., Hubbard, E. M., Michel, V. and Dehaene, S. (2009) ‘Recruitment of an Area Involved in Eye Movements During Mental Arithmetic’, Sciencexpress, pp. 1–4.

Korgaonkar, M. S., Fornito, A., Williams, L. M. and Grieve, S. M. (2014) ‘Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis’, Molecular and Neural Systems In Depression, 76(7), pp. 567–574.

Lee, J. H. (2012) ‘Informing brain connectivity with optogenetic functional magnetic resonance imaging’, NeuroImage, 62(4), pp. 2244–2249.

Lynall, M.-E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Müller, U. and Bullmore, E. (2010) ‘Functional connectivity and brain networks in schizophrenia’, The Journal of neuroscience : the official journal of the Society for Neuroscience, 30(28), pp. 9477–9487.

Maguire, E. A., Woollett, K. and Spiers, H. J. (2006) ‘London taxi drivers and bus drivers: A structural MRI and neuropsychological analysis’, Hippocampus, 16(12), pp. 1091–1101.

Martin, V. C., Schacter, D. L., Corballis, M. C. and Addis, D. R. (2011) ‘A role for the hippocampus in encoding simulations of future events’, Proceedings of the National Academy of Sciences of the United States of America, 108(33), pp. 13858–13863.

Mennes, M., Kelly, C., Zuo, X.-N., Di Martino, A., Biswal, B. B., Castellanos, F. X. and Milham, M. P. (2010) ‘Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity’, NeuroImage, 50(4), pp. 1690–1701.

Menon, V. (2011) ‘Large-scale brain networks and psychopathology: a unifying triple network model’, Trends in Cognitive Sciences, 15(10), pp. 483–506.

Michel, C. M. and Murray, M. M. (2012) ‘Towards the utilization of EEG as a brain imaging tool’, NeuroImage, 61(2), pp. 371–385.

Nair, A., Keown, C. L., Datko, M., Shih, P., Keehn, B. and Müller, R.-A. (2014) ‘Impact of methodological variables on functional connectivity findings in autism spectrum disorders’, Human Brain Mapping, 35(8), pp. 4035–4048.

Ohno, N., Katoh, M., Saitoh, Y. and Saitoh, S. (2016) ‘Recent advancement in the challenges to connectomics’, Microscopy, 65(2), pp. 97–107.

Park, H.-J. and Friston, K. (2013) ‘Structural and Functional Brain Networks: From Connections to Cognition’, Science, 342(6158), [online] Available from: http://science.sciencemag.org/content/342/6158/1238411.abstract.

Poldrack, R. A. (2006) ‘Can cognitive processes be inferred from neuroimaging data?’, Trends in Cognitive Sciences, 10(2), pp. 59–63.

Rabinovich, M. I., Friston, K. J. and Varona, P. (2012) Principles of Brain Dynamics: Global State Interactions, Cambridge, Massachusetts, MIT Press.

Reddy, L., Tsuchiya, N. and Serre, T. (2010) ‘Reading the mind’s eye: Decoding category information during mental imagery’, NeuroImage, 50(2), pp. 818–825.

Rissman, J. and Wagner, A. D. (2012) ‘Distributed Representations in Memory: Insights from Functional Brain Imaging’, Annual Review of Psychology, 63, pp. 101–28.

Ryali, S., Shih, Y.-Y. I., Chen, T., Kochalka, J., Albaugh, D., Fang, Z., Supekar, K., Lee, J. H. and Menon, V. (2016) ‘Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions’, NeuroImage, 132, pp. 398–405.

Ryali, S., Supekar, K., Chen, T. and Menon, V. (2011) ‘Multivariate dynamical systems models for estimating causal interactions in fMRI’, NeuroImage, 54(2), pp. 807–823.

Tong, F., Harrison, S. A., Dewey, J. A. and Kamitani, Y. (2012) ‘Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex’, NeuroImage, 63(3), pp. 1212–1222.

Tong, F. and Pratte, M. S. (2012) ‘Decoding Patterns of Human Brain Activity’, Annual Review of Psychology, 63(1), pp. 483–509.

Vissers, M. E., X Cohen, M. and Geurts, H. M. (2012) ‘Brain connectivity and high functioning autism: A promising path of research that needs refined models, methodological convergence, and stronger behavioral links’, Neuroscience & Biobehavioral Reviews, 36(1), pp. 604–625.