My core research concerns the cognitive neuroscience of human memory. One of the most important developments during the last two decades of cognitive neuroscience has been the realisation that human memory is best viewed as a collection of distinct types of memory. Examples include the distinction between short-term memory (or "working memory") and long-term memory, and the distinction between explicit ("conscious") and implicit ("unconscious") expressions of long-term memory.

I have concentrated in particular on three hypothetical types of long-term memory associated with repetition of a stimulus: recollection, familiarity and priming. Recollection and familiarity are examples of explicit memory, and are differentiated primarily by whether the repetition is accompanied by retrieval of contextual detail associated with the initial stimulus presentation (recollection), or simply by a feeling attributed to recent exposure to the stimulus, in the absence of contextual information (familiarity). Priming is an example of implicit memory, reflecting facilitated processing of a stimulus within a given task, following its repetition, in the absence of conscious associations of that stimulus with the past.

Nonetheless, there is still much debate about the precise fractionation of human memory. The distinctions between recollection, familiarity and priming receive some support from the different types of memory deficit in patients with brain damage, and from functional dissociations between various experimental manipulations in memory tests on healthy individuals. However, clear dissociations are often difficult to demonstrate with behavioural measures alone [1]. Moreover, some researchers argue that the functional dissociations held to support the distinction between recollection, familiarity and priming can in fact be explained by quantitative differences along a continuum of memory "strength" within a single memory system.

Functional imaging, by which I include both haemodynamic techniques like functional magnetic resonance imaging (fMRI) and electrophysiological techniques like magneto- and electro-encephalography (MEG/EEG), offers a relatively new means of addressing these questions [1]. Clear dissociations between the pattern of brain activity associated with recollection and familiarity, for example, would support a qualitative rather than simply quantitative difference between them. In addition to providing further evidence for different types of memory, imaging data can also feedback into psychological theorising, helping to elucidate, for example, how different types of memory interact to allow normal mnemonic functioning. Such theoretical development is a necessary prerequisite for a full understanding of the memory impairments associated with neurological damage or disease, or with "healthy" ageing, and the rehabilitation thereof.

Within this framework, my research involves five main projects.

1. The neural correlates of different expressions of long-term memory.

This strand of research initially concentrated on event-related fMRI experiments of visual recognition memory, in which the participant must distinguish stimuli seen previously in the experiment ("old items") from those not seen in the experiment ("new items"). This simple paradigm has two main advantages. Foremost, it has a long history of laboratory investigation, which has resulted in a number of robust empirical findings and a large body of theory. Secondly, its procedural simplicity facilitates its adaptation to the scanning environment. This simplicity also affords a high level of experimental control that is difficult to achieve with more naturalistic tests of memory.

The main aim has been to determine whether the contributions of familiarity and recollection to recognition memory are associated with activation of different brain regions. In collaboration with Mick Rugg, we have used several experimental manipulations to attempt to separate familiarity and recollection, including Remember/Know judgments [2], confidence judgments [3], study task [4] and modified source memory tasks [5], including memory for emotional context [6,7]. In general, these experiments have supported the recollection/familiarity distinction, though the experiments are far from conclusive. Important findings include the association of posterior cingulate and inferior parietal cortex with recollection, and the association of anterior medial temporal cortex (outside the hippocampus) with familiarity (for meta-analysis and reviews, see [8, 9, 10, 11]).

A further common finding has been the activation of prefrontal cortex during recognition memory. After considering a large number of similar findings in our laboratories, Tim Shallice, Paul Fletcher and I hypothesised that these activations reflect a number of distinct control processes that work to optimise memory performance; for example, in monitoring retrieved information in relation to the task goals [12,13,14,15,22]. More recently, we found that a manipulation as simple as the ratio of old to new items dissociated prefrontal activations, which we attributed to control processes, from parietal activations, which we attributed to "true" explicit memory retrieval [16].

The above experiments concentrated on the test phase of the recognition memory task. I have also conducted fMRI experiments focused on the study phase, in collaboration with Leun Otten. These studies use the "subsequent memory" paradigm, in which participants perform a simple task on a series of items while in the scanner (but are not told that this is part of a memory test). They are then given a surprise recognition memory test, data from which are used to "back-sort" items from the study phase into those that were remembered and those that were forgotten. This allows one to look at the neural correlates of successful encoding (i.e, that predict later explicit memory). With this paradigm, we have shown that the hippocampus appears to predict subsequent memory regardless of the study task [17]. Other regions, like anterior left inferior prefrontal cortex, appear to predict subsequent memory (for words) only during tasks that require some semantic elaboration of the words [9]. We have also been able to distinguish subsequent memory effects that occur on an item-by-item level from those that reflect longer-lasting "states" (e.g, periods of sustained attention) [18].

In addition to the explicit memory processes of familiarity and recollection, recognition memory tasks may include contributions of repetition priming, a form of implicit memory (see above; for review see [23, 24]). Even if such priming does not affect behaviour in the task, its neural correlates may still exist in the fMRI data. Indeed, no imaging study, to my knowledge, has achieved clear separation of recollection, familiarity and priming within the same paradigm. One recent attempt of ours for example could not dissociate priming from explicit memory [4].

In collaboration with Bjoern Schott, Alan Richardson-Klavehn and Emrah Duzel however, we have had more success in dissociating implicit and explicit memory by using a modified word-stem completion task [19]. Moreover, we were able to dissociate neural correlates at encoding that lead to subsequent implicit memory from those that lead to subsequent explicit memory [20].

Another method to attempt to separate implicit and explicit memory is to use pharmacological agents that impair explicit memory. In collaboration with Christiane Thiel, we used fMRI to contrast the effects of lorezapam and scopolamine (versus placebo) on the word-stem completion task mentioned above [25], and also on an artificial grammar learning task [26]. The results of the former were consistent with those from [19] in demonstrating priming-related haemodynamic decreases in certain brain regions even with minimal explicit memory contamination.

Another extension of the recognition memory task is to tests of memory for associations between items, such as face-names pairs, successful memory for which has been associated with recollection. Andrew Mayes in Liverpool has investigated an amnesic patient who is particularly impaired at such associative tasks, despite having what appears to be normal familiarity for the individual items. We are currently running a version of this task to run in healthy volunteers using fMRI.

The recognition memory paradigm has limitations however. Firstly, the test may only tax a subset of memory retrieval processes. The provision of a strong retrieval cue (a so-called "copy cue") minimises the need for participants to engage in elaborative search strategies (as is required, for example, when freely recalling studied items without external cues). Unfortunately, recall paradigms are more difficult to adapt to the scanning environment, since they normally require spoken responses, which can cause movement-related artefacts in fMRI data. I have developed novel methods to minimise such artefacts [21], and used them successfully to investigate associative (proactive) interference in a cued recall paradigm [22] (in addition to the word-stem completion paradigm mentioned above [19,20]).

2. The neural mechanisms of face processing and their priming.

This strand of research focuses on perceptual priming of faces, using a variety of techniques in addition to fMRI, namely EEG, MEG and computational modelling. The goal is to develop detailed models that relate these spatial and temporal data in the human to the data recorded from single neurons in the nonhuman primate. I believe such models will facilitate formal comparisons between these different levels of neuroscience (comparisons that are currently made informally). The appeal of priming is that the simplicity of the paradigms used means that comparable experiments can be performed in both species. The appeal of faces is that much is already known about the manner in which faces are processed in the visual system of human and nonhuman primates. The appeal of collecting both fMRI and EEG/MEG data in humans is to estimate how activity in the human brain develops over both space and time.

This project is in its initial stages. I have performed a number of fMRI studies of face repetition priming that explore various parameters of interest. These include effects of pre-experimental familiarity [27], task [28] and lag between repetitions [29]. These parameters help define the domain of interest, and will provide constraints on later modelling. I have shown that they modulate activity in several regions of lateral occipitotemporal and mid-fusiform cortex that are particularly responsive to faces. Given the limited temporal resolution of fMRI (which averages over several seconds of neural/synaptic activity), I have performed similar experiments using EEG [29,30] and, more recently, using MEG with Krish Singh and colleagues. Indeed, for one experiment in particular, which separates face perception (intact vs. scrambled faces), face recognition (familiar vs. unfamiliar faces) and face priming (initial vs. repeated presentations of familiar and unfamiliar faces), we have data from all three modalities - fMRI, EEG and MEG - which I am currently trying to integrate together with Jeremie Mattout and Karl Friston (see below).

The data currently suggest that, for long-lag repetition at least, priming effects emerge relatively late (onsetting approx 300-400ms). This suggests that the modulation of haemodynamic responses (which integrate over several seconds of neural activity) following repetition do not reflect rapid "bottom-up" facilitation [23]. Rather, informal considerations suggest that repetition priming reflects re-entrant interactions between anterior and posterior parts of inferior temporal cortex [32]. However, the next step is to integrate the fMRI, EEG and MEG data more formally. This is a difficult problem, and will be attempted by using fMRI and anatomical MRI to constrain the cortical localisation of the EEG and MEG priming effects (see Project 5 below). The final step will be replace these "inverse solutions" with "forward models". Forward models map from a hypothetical neural cause to the observed data (rather than mapping from the data to an inferred cause). These models will be based on simple neural networks, and allow more formal (though model-dependent) comparisons between human fMRI and EEG data and neural signals recorded in the nonhuman primate. These networks may even be extended to effects of pharmacological agents that we have shown to modulate the fMRI correlates of face priming [31].

The neural network models will probably be based on "predictive coding" ideas, in which the "backward" connections from neurons within a layer of a hierarchical system (e.g, for visual object processing) predict the input to lower layers in the hierarchy. Synaptic plasticity in these connections (e.g, following stimulus presentation) can reduce the prediction error, producing faster convergence of the system on an interpretation of a repeated stimulus, haemodynamic response reductions and ultimately behavioural priming. This work is in collaboration with Karl Friston.

3. The use of repetition suppression to investigate object perception / language.

The most common finding in fMRI/PET studies of priming is a reduced haemodynamic response for primed versus unprimed stimuli (for review, see [26]). This reduction has been termed "repetition suppression". It is often assumed that repetition suppression reflects facilitation of the processes performed by a set of neurons owing to performance of the same processes in the recent past. If so, repetition suppression can be used as a tool to map the brain regions associated with different processes. For example, if a brain region shows a reduced response when a particular view of an object is repeated, but not when the same object is repeated from a different view, then it can be inferred that the processes performed by that brain region are view-specific, i.e, reflect relatively "low-level" properties of the picture, rather than true "object-based" representations. Note however that the sensitivity and interpretation of this use of repetition suppression (or "fMR adaptation") depends on the repetition lag [36] and effects of attention [34] (with no repetition suppression observed when the prime was unattended in the latter study). Note also that, if repetition suppression reflects reduced prediction error in a model like that outlined above, the locus of repetition suppression may not, in fact, correspond in any simple way to the location of stimulus representations of interest.

This technique has already been used extensively in fMRI studies of visual object processing (in a manner analogous to that with which behavioural priming has been used to test theories of object perception). For example, in collaboration with Patrik Vuilleumeir, we demonstrated not only increasing levels of visual object abstraction from posterior to anterior occipital/temporal cortices, but also an interesting lateralisation of view-dependence in inferior temporal cortex [33]. Volker Thoma, Evelyn Eger and I are further examining how these effects generalise across split-views.

In collaboration with Jon Driver, Ray Dolan and colleagues, we have also applied the technique to different stages of face processing. With Joel Winston, for example, we used repetition suppression to support the proposal of separate neural pathways for processing of facial identity and facial expression [35]. With Pia Rotsthein, we used repetition suppression to examine the neural correlates of categorical perception of faces that are morphs between two famous faces [37]. With Stefan Schweinberger and Evelyn Eger, we used repetition suppression to examine the generalisation of inferior temporal responses over different views of famous and unfamiliar faces [38]. In general, the results support the "box-and-arrow" model of Vicki Bruce, Andy Young and colleagues.

The same use of repetition suppression can be applied to language. In a preliminary fMRI study [39], I identified a region in anterior inferior temporal cortex that showed repetition suppression for words but not pseudowords (suggesting a role for this region in lexical or semantic access). Matt Davis and I have also performed an fMRI study of repetition priming of words in which some of the prime words were subliminal (i.e, presented very briefly and backward masked). Subliminal primes are attractive because they minimise possible confounds of haemodynamic repetition effects, such as explicit memory of the prime for example (see Project 1), though in this case we failed to find repetition suppression for subliminal primes (despite finding clear effects for supraliminal primes). If subliminal primes can be combined successfully with imaging, their use is likely to further contribute to brain mapping studies of language.

4. Short-term memory.

My past research, stemming from my PhD [40,41], concerns short-term memory, in particular memory for serial order (e.g, the order of digits in a novel telephone number). This involved a computational model [42,43,44] and empirical tests of its predictions [45,46,47,53], together with related work concerning effects of ageing [48] and development [49] on short-term memory. More recently, I have attempted, in collaboration with Neil Burgess and Graham Hitch, to map similar models onto the brain using fMRI [50,51], while continuing behavioural experiments to test for a timing signal, a critical component of these models [52].

During my MSc, I also briefly examined the short-term memory capacities of auto-associative neural networks together with David Willshaw [54,55].

5. Methodological developments in functional imaging.

[1] Henson, R. N. A. (in press). What can functional imaging tell the experimental psychologist? Quarterly Journal of Experimental Psychology.

[2] Henson, R.N.A., Rugg, M.D., Shallice, T., Josephs, O. & Dolan, R.J. (1999). Recollection and familarity in recognition memory: an event-related fMRI study. Journal of Neuroscience, 19, 3962-3972.

[3] Henson, R.N.A., Rugg, M. D., Shallice, T., & Dolan, R.J. (2000) Confidence in recognition memory for words: dissociating right prefrontal roles in episodic retrieval. Journal of Cognitive Neuroscience, 12, 913-923.

[4] Henson, R.N.A., Hornberger, M. & Rugg, M.D. (in press). Further dissociating processes in recognition memory using fMRI. Journal of Cognitive Neuroscience..

[5] Rugg, M.D., Henson, R.N. & Robb, W. (2002). Neural correlates of retrieval processing in the prefrontal cortex during recognition and exclusion tasks. Neuropsychologia, 41, 40-52.

[6] Maratos, E. J., Dolan, R. J., Morris, J. D., Henson, R.N.A. & Rugg, M. D. (2001). Neural activity associated with episodic memory for emotional context. Neuropsychologia, 39, 910-920.

[7] Smith, A.P.R., Henson, R.N.A., Dolan, R.J., & Rugg, M.D. (2004). fMRI correlates of the episodic retrieval of emotional contexts. Neuroimage, 22, 868-878.

[8] Henson, R.N.A., Cansino, S., Herron, J.E., Robb, W.G.K. & Rugg, M.D. (2003). A familiarity signal in human anterior medial temporal cortex. Hippocampus, 13, 259-262.

[9] Rugg, M.D., Otten, L.J., & Henson, R.N.A. (2002). The neural basis of episodic memory: evidence from functional neuroimaging. Philosophical Transactions of the Royal Society, B, 357, 1097-1110.

[10] Henson, R.N.A. (2004). Explicit memory. Human Brain Function, 2^nd Edition. Frackowiak, Friston, Frith, Dolan & Price (Eds.), pp. 487-498. Elsevier, London.

[11] Rugg, M. D. & Henson, R.N.A. (2002). Episodic memory retrieval: an (event-related) functional neuroimaging perspective. In A. Parker, E. Wilding and T. Bussey (Eds.) The cognitive neuroscience of memory: encoding and retrieval. pp. 3-37. Hove: Psychology Press

[12] Henson, R.N.A., Shallice, T. & Dolan, R.J. (1999) The role of right prefrontal cortex in episodic retrieval: an fMRI test of the monitoring hypothesis. Brain, 122, 1367-1381.

[13] Henson, R., Shallice, T., Rugg, M., Fletcher, P. & Dolan, R. (2001). Functional imaging dissociations within right prefrontal cortex during episodic memory retrieval. Brain & Cognition, 47, 79-81.

[14] Fletcher, P.C. & Henson, R.N.A. (2001). Frontal lobes and human memory - insights from functional imaging. Brain, 124, 849-881.

[15] Fletcher, P. & Henson, R.N.A. (2004). Prefrontal cortex and long-term memory retrieval. Human Brain Function, 2^nd Edition. Frackowiak, Friston, Frith, Dolan & Price (Eds.), pp. 499-514. Elsevier, London.

[16] Herron, J.E., Henson, R.N.A. & Rugg, M.D. (2004). Probability effects on the neural correlates of retrieval success: an fMRI study. Neuroimage, 21, 302-310.

[17] Otten, L., Henson, R.N.A. & Rugg, M.D. (2001). Depth of processing effects on neural correlates of memory encoding: relationship between findings from across- and within-task comparisons. Brain, 124, 399-412.

[18] Otten, L.J., Henson, R.N. & Rugg, M.D. (2002). State- and item-related neural correlates of successful memory encoding. Nature Neuroscience, 5, 1339-1344.

[19] Schott et al (sub).

[20] Schott et al (sub2)

[22] Henson, R., Shallice, T., Josephs, O. & Dolan, R. (2002). Functional magnetic resonance imaging of proactive interference during spoken cued recall. Neuroimage, 17, 543-558.

[23] Henson, R.N.A. (2003). Neuroimaging studies of priming. Progress in Neurobiology, 70, 53-81.

[24] Henson, R.N.A. (2004). Implicit memory. Human Brain Function, 2^nd Edition. Frackowiak, Friston, Frith, Dolan & Price (Eds.), pp. 471-486. Elsevier, London.

[25] Thiel, C.M., Henson, R.N.A., Morris, J.S., Friston, K.J. & Dolan, R.J. (2001). Pharmacological modulation of behavioural and neuronal correlates of repetition priming. Journal of Neuroscience, 21, 6846-6852.

[26] Thiel, C.M., Shanks, D.R., Henson, R.N. & Dolan, R.J. (2003). Neuronal correlates of familiarity-driven decisions in artificial grammar learning. Neuroreport, 14, 131-136.

[27] Henson, R.N.A., Shallice, T. & Dolan, R.J. (2000). Neuroimaging evidence for dissociable forms of repetition priming. Science, 287, 1269-1272.

[28] Henson, R.N.A, Shallice, T., Gorno-Tempini, M.-L. & Dolan, R.J (2002). Face repetition effects in implicit and explicit memory tests as measured by fMRI. Cerebral Cortex, 12, 178-186.

[29] Henson, R., Ross, E., Rylands, A., Vuilleumier, P. & Rugg, M. (2004). ERP and fMRI effects of lag on priming for familiar and unfamiliar faces. Neuroimage (HBM04 abstract).

[30] Henson, R N.A., Goshen-Gottstein, Y., Ganel, T., Otten, L.J., Quayle, A. & Rugg, M.D. (2003). Electrophysiological and haemodynamic correlates of face perception, recognition and priming. Cerebral Cortex, 13, 793-805.

[31] Thiel, C.M., Henson, R.N. & Dolan, R.J. (2002). Scopolamine but not lorazepam modulates face repetition priming: a psychopharmacological fMRI study. Neuropsychopharmacology, 27, 282-292.

[32] Henson, R.N.A. & Rugg, M.D. (2003). Neural response suppression, haemodynamic repetition effects and behavioural priming. Neuropsychologia, 41, 263-270.

[33] Vuilleumier, P., Henson, R.N., Driver, J. & Dolan, R.J. (2002). Multiple levels of visual object constancy revealed by event-related fMRI of repetition priming. Nature Neuroscience, 5, 491-499.

[34] Eger, E., Henson, R.N., Driver, J. & Dolan, R.J. (2004). BOLD repetition decreases in object-responsive ventral visual areas depend on spatial attention. Journal of Neurophysiology, 92, 1241-1247.

[35] Winston, J.S., Henson,R.N., Fine-Goulden, M.R. & Dolan, R.J (2004). fMRI-adaptation reveals dissociable neural representations of identity and expression in face perception. Journal of Neurophysiology, 92, 1830-1839.

[36] Henson, R. N., Rylands, A., Ross, E., Vuilleumeir, P. & Rugg, M. D. (2004). The effect of repetition lag on electrophysiological and haemodynamic correlates of visual object priming. Neuroimage, 21, 1674-1689.

[37] Rotsthein, P., Henson, R.N., Treves, A., Driver, J. & Dolan, R. (in press). Morphing Marilyn into Maggie dissociates physical and identity face-representations in the brain. Nature Neuroscience.

[38] Eger et al (sub).

[39] Henson, R.N.A. (2001). Repetition effects for words and nonwords as indexed by event-related fMRI: A preliminary study. Scandinavian Journal of Psychology, special issue, 42, 179-186.

[40] Henson, R.N.A. (1996). Short-term memory for serial order. Unpublished doctoral thesis, University of Cambridge. · Click HERE for more information.

[41] Henson, R.N.A. (2001). Short-term memory for serial order. The Psychologist, 14, 70-73.

[42] Henson, R.N.A. (1998). Short-term memory for serial order: the Start-End Model. Cognitive Psychology, 36, 73-137.

[43] Henson, R.N.A. & Burgess, N. (1997). Representations of serial order. In Bullinaria, J.A., Glasspool, D.W. & Houghton, G. (Eds.), 4th Neural Computation and Psychology Workshop (pp. 283-300), London: Springer.

[44] Page, M. & Henson, R.N.A., (2001). Models of short-term memory: modelling immediate serial recall of verbal material. In Andrade, J. (Ed.), Working Memory: a work in progress (pp. 177-198). London: Routledge.

[45] Henson, R.N.A. (1999). Coding position in short-term memory. International Journal of Psychology, 34, 403-409.

[46] Henson, R.N.A. (1999). Positional information in short-term memory: relative or absolute? Memory & Cognition, 27, 915-927.

[47] Henson, R.N.A. (1998). Item repetition in short-term memory: Ranschburg repeated. Journal of Experimental Psychology: Learning, Memory and Cognition, 24, 1162-1181.

[48] Maylor, E. A. & Henson, R.N.A. (2000). Aging and the Ranschburg effect: No evidence of reduced response suppression in old age. Psychology and Aging, 15, 657-670.

[49] McCormack, T., Brown, G.D.A., Vousden, J.I. & Henson, R.N.A. (2000). Children's serial recall errors: implications for theories of short-term memory development. Journal of Experimental Child Psychology, 76, 222-252.

[50] Henson, R.N.A., Burgess, N. & Frith, C.D. (2000). Recoding, storage, rehearsal and grouping in verbal short-term memory: an fMRI study. Neuropsychologia, 38, 426-440.

[51] Henson, R.N.A. (2001). Neural working memory: applications of the Working Memory model to neuropsychology and neuroimaging. In Andrade, J. (Ed.), Working Memory: a work in progress (pp. 151-173). London: Routledge.

[52] Henson, R., Hartley, T., Burgess, N., Hitch, G. & Flude, B. (2003). Selective interference with verbal short-term memory for serial order information: a new paradigm and tests of a timing signal hypothesis. Quarterly Journal of Experimental Psychology, 56A, 1307-1334.

[53] Henson, R.N.A., Norris, D.G., Page, M.P.A., & Baddeley, A.D. (1996). Unchained memory: error patterns rule out chaining models of immediate serial recall. Quarterly Journal of Experimental Psychology, 49A, 80-115.

[54] Henson, R.N.A. & Willshaw, D. J. (1995). Short-term associative memory. Proceedings of the INNS World Congress on Neural Networks, 1995, Washington DC.

[55] Henson, R.N.A. (1993). Short-term associative memories. Unpublished masters thesis, University of Edinburgh.

[56] Henson, R.N.A., Buechel, C., Josephs, O. & Friston, K. (1999). The slice-timing problem in event-related fMRI. Neuroimage, 9, 125.

[57] Henson, R.N.A., Rugg, M. D. & Friston, K. J. (2001). The choice of basis functions in event-related fMRI. HBM01 abstract, Neuroimage, 13, 149.

[58] Henson, R.N.A., Andersson, J. & Friston, K. J. (2000). Multivariate SPM: Application to basis function characterisations of event-related fMRI responses. Neuroimage, 11, 468.

[59] Henson, R.N.A., Price, C., Rugg, M.D., Turner, R. & Friston, K. (2002). Detecting latency differences in event-related BOLD responses: application to words versus nonwords, and initial versus repeated face presentations. Neuroimage, 15, 83-97.

[21] Henson, R.N.A. & Josephs, O. (2002). Spoken cued-recall during event-related fMRI. HBM02 abstract, Neuroimage, 250.

[60] Josephs, O. & Henson, R.N.A. (1999) Event-related functional magnetic resonance imaging: modelling, inference and optimization. Philosophical Transactions of the Royal Society, B, 354, 1215-1228.

[61] Friston, K.J., Zarahn, E., Josephs, O., Henson, R.N.A. & Dale, A. (1999). Stochastic designs in event-related fMRI. Neuroimage, 10, 607-619.

[62] Mechelli, A., Price, C.J., Henson, R.N.A & Friston, K.J. (2003). Estimating efficiency a priori: a comparison of blocked and randomised designs. Neuroimage, 18, 798-805.

[63] Henson, R.N.A. (2004). Analysis of fMRI timeseries: Linear Time-Invariant models, event-related fMRI and optimal experimental design. Human Brain Function, 2^nd Edition. Frackowiak, Friston, Frith, Dolan & Price (Eds.), pp. 793-822. Elsevier, London.

[64] Friston, K.J, Glaser, D.E, Henson, R.N.A, Kiebel, S, Phillips, C & Ashburner, J. (2002). Classical and Bayesian inference in neuroimaging: Applications. Neuroimage, 16, 484-512.

[65] Kilner et al (sub).