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K J Friston

Publications and source records attributed to K J Friston.

At least 19 recordsLinked to original sources
PubMed2006

Extra-classical receptive field effects measured in striate cortex with fMRI.

The aim of this study was to measure the contextual influence of globally coherent motion on visual cortical responses using functional magnetic resonance imaging. Our motivation was to test a prediction from representational theories of perception (i.e. predictive coding) that primary visual responses should be suppressed by top-down influences during coherent motion. We used a sparse stimulus array such that each element could not fall within the same classical receptive field of primary visual cortex neurons (i.e. precluding lateral interactions within V1). This enabled us to attribute differences, in striate cortex responses, to extra-classical receptive field effects mediated by backward connections. In accord with theoretical predictions we were able to demonstrate suppression of striate cortex activations to coherent relative to incoherent motion. These results suggest that suppression of primary visual cortex responses to coherent motion reflect extra-classical effects mediated by backward connections.

Adult↗
PubMed2006

A critique of functional localisers.

In this critique, we review the usefulness of functional localising scans in functional MRI studies. We consider their conceptual motivations and the implications for experimental design and inference. Functional localisers can often be viewed as acquiring data from cells that have been removed from an implicit factorial design. This perspective reveals their potentially restrictive nature. We deconstruct two examples from the recent literature to highlight the key issues. We conclude that localiser scans can be unnecessary and, in some instances, lead to a biased and inappropriately constrained characterisation of functional anatomy.

Brain Mapping↗
PubMed2006

Encoding uncertainty in the hippocampus.

The medial temporal lobe may play a critical role in binding successive events into memory while encoding contextual information in implicit and explicit memory tasks. Information theory provides a quantitative basis to model contextual information engendered by conditional dependence between, or conditional uncertainty about, consecutive events in a sequence. We show that information theoretic indices characterizing contextual dependence within a sequential reaction time task (SRTT) predict regional responses, measured by fMRI, in areas associated with sequence learning and navigation. Specifically, activity of a distributed paralimbic system, centered on the left hippocampus, correlated selectively with predictability as measured with mutual information. This is clear evidence that the brain is sensitive to the probabilistic context in which events are encountered. This is potentially important for theories about how the brain represents uncertainty and makes perceptual inferences, particularly those based on predictive coding and hierarchical Bayes.

Adult↗
PubMed2005

Modelling cardiac signal as a confound in EEG-fMRI and its application in focal epilepsy studies.

Cardiac noise has been shown to reduce the sensitivity of functional Magnetic Resonance Imaging (fMRI) to an experimental effect due to its confounding presence in the blood oxygenation level-dependent (BOLD) signal. Its effect is most severe in particular regions of the brain and a method is yet to take it into account in routine fMRI analysis. This paper reports the development of a general and robust technique to improve the reliability of EEG-fMRI studies to BOLD signal correlated with interictal epileptiform discharges (IEDs). In these studies, ECG is routinely recorded, enabling cardiac effects to be modelled, as effects of no interest. Our model is based on an over-complete basis set covering a linear relationship between cardiac-related MR signal and the phase of the cardiac cycle or time after pulse (TAP). This method showed that, on average, 24.6 +/- 10.9% of grey matter voxels contained significant cardiac effects and 22.3 +/- 24.1% of those voxels exhibiting significantly IED-correlated BOLD signal also contained significant cardiac effects. We quantified the improvement of the TAP model over the original model, without cardiac effects, by evaluating changes in efficiency, with respect to estimating the contrast of the effects of interest. Over voxels containing significant, cardiac-related signal, efficiency was improved by 18.5 +/- 4.8%. Over the remaining voxels, no improvement was demonstrated. This suggests that, while improving sensitivity in particular regions of the brain, there is no risk that the TAP model will reduce sensitivity elsewhere.

Electroencephalography↗
PubMed2005

Action selectivity in parietal and temporal cortex.

The sensory-action theory proposes that the neural substrates underlying action representations are related to a visuomotor action system encompassing the left ventral premotor cortex, the anterior intraparietal (AIP) and left posterior middle temporal gyrus (LPMT). Using fMRI, we demonstrate that semantic decisions on action, relative to non-action words, increased activation in the left AIP and LPMT irrespective of whether the words were presented in a written or spoken form. Left AIP and LPMT might thus play the role of amodal semantic regions that can be activated via auditory as well as visual input. Left AIP and LPMT did not distinguish between different types of actions such as hand actions and whole body movements, although a right STS region responded selectively to whole body movements.

Acoustic Stimulation↗
PubMed2005

Hemodynamic correlates of EEG: a heuristic.

In this note we describe a heuristic, starting with a dimensional analysis, which relates hemodynamic changes to the spectral profile of ongoing EEG activity. In brief, this analysis suggests that 'activation', as indexed by increases in hemodynamic signals, should be associated with a loss of power in lower EEG frequencies, relative to higher frequencies. The fact that activation is expressed in terms of frequency (i.e., per second) is consistent with a dimensional analysis in the sense that activations reflect the rate of energy dissipation (per second). In this heuristic, activation causes an acceleration of temporal dynamics leading to (i) increased energy dissipation; (ii) decreased effective membrane time constants; (iii) increased effective coupling among neuronal ensembles; and (iv) a shift in the EEG spectral profile to higher frequencies. These predictions are consistent with empirical observations of how changes in the EEG spectrum are expressed hemodynamically. Furthermore, the heuristic provides a simple measure of neuronal activation based on spectral analyses of EEG.

Algorithms↗
PubMed2005

Stochastic models of neuronal dynamics.

Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker-Planck equation. The solution of this equation is the temporal evolution of a probability density over state-space, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a neuron. Measurements can be modelled by taking expectations over this density, e.g. mean membrane potential, firing rate or energy consumption per neuron. The key motivation behind our approach is that ERPs represent an average response over many neurons. This means it is sufficient to model the probability density over neurons, because this implicitly models their average state. Although the dynamics of each neuron can be highly stochastic, the dynamics of the density is not. This means we can use Bayesian inference and estimation tools that have already been established for deterministic systems. The potential importance of modelling density dynamics (as opposed to more conventional neural mass models) is that they include interactions among the moments of neuronal states (e.g. the mean depolarization may depend on the variance of synaptic currents through nonlinear mechanisms).Here, we formulate a population model, based on biologically informed model-neurons with spike-rate adaptation and synaptic dynamics. Neuronal sub-populations are coupled to form an observation model, with the aim of estimating and making inferences about coupling among sub-populations using real data. We approximate the time-dependent solution of the system using a bi-orthogonal set and first-order perturbation expansion. For didactic purposes, the model is developed first in the context of deterministic input, and then extended to include stochastic effects. The approach is demonstrated using synthetic data, where model parameters are identified using a Bayesian estimation scheme we have described previously.

Bayes Theorem↗
PubMed2005

A PET study exploring the laterality of brainstem activation in migraine using glyceryl trinitrate.

Migraine is a common disabling condition likely to be associated with dysfunction of brain pathways involved in pain and other sensory modalities. A cardinal, indeed signature, feature of the disorder that led to its name is that the pain may be lateralized. H(2)15O-labelled PET was used to study 24 migraineurs and eight healthy controls. The migraineurs were divided into three groups according to the site of their headache: right, left or bilateral. In each group, a migraine was induced using a glyceryl trinitrate (GTN) infusion. The subjects were scanned at predefined points: pre-infusion, during GTN, during migraine and post-migraine. SPM99 software was used to analyse the data. Significant brainstem activation was seen in the dorsal lateral pons (P < 0.05 after small volume correction) during the migraine state versus the pain-free state when comparing migraineurs with controls. When each group was analysed separately, to investigate laterality, it was found that the dorsal pontine activation was ipsilateral in the right-sided and left-sided groups and bilateral in the bilateral headache group with a left-sided preponderance. Consistent with previous work, the activation persisted after pain was controlled by sumatriptan. These results suggest that lateralization of pain in migraine is due to lateralized brain dysfunction.

Adult↗
PubMed2005

Mixed-effects and fMRI studies.

This note concerns mixed-effect (MFX) analyses in multisession functional magnetic resonance imaging (fMRI) studies. It clarifies the relationship between mixed-effect analyses and the two-stage "summary statistics" procedure (Holmes, A.P., Friston, K.J., 1998. Generalisability, random effects and population inference. NeuroImage 7, S754) that has been adopted widely for analyses of fMRI data at the group level. We describe a simple procedure, based on restricted maximum likelihood (ReML) estimates of covariance components, that enables full mixed-effects analyses in the context of statistical parametric mapping. Using this procedure, we compare the results of a full mixed-effects analysis with those obtained from the simpler two-stage procedure and comment on the situations when the two approaches may give different results.

Brain↗
PubMed2005

Attention to pain localization and unpleasantness discriminates the functions of the medial and lateral pain systems.

Functional imaging studies have identified a matrix of structures in the brain that respond to noxious stimuli. Within this matrix, a division of function between sensory-discriminative and affective responses has so far been demonstrated by manipulating either pain intensity or unpleasantness under hypnosis in two different normal volunteer groups studied on separate occasions. Our study used positron emission tomography (PET) to demonstrate this division of function under more natural conditions in a healthy group of volunteers, using a CO(2) laser to provide nociceptive stimuli that selectively activate A-delta and C-fibres without contamination by touch sensations. We measured the differential cerebral responses to noxious and innocuous laser stimuli during conditions of selective attention to either the unpleasantness or location of the stimuli. Attention to location increased responses in the contralateral (right) primary somatosensory and inferior parietal cortices. This result implies that these components of the lateral pain system are concerned mainly with the localization of pain. In contrast, attention to unpleasantness increased responses in bilateral perigenual cingulate and orbitofrontal cortices, contralateral (right) amygdala, ipsilateral (left) hypothalamus, posterior insula, M1 and frontal pole. These areas comprise key components of the medial pain and neuroendocrine systems and the results suggest that they have a role in the affective response to pain. Our results indicate the importance of attentional effects on the pattern of nociceptive processing in the brain. They also provide the first clear demonstration, within a single experiment, of a major division of function within the neural pain matrix.

Adult↗
PubMed2005

The role of the medial temporal lobe in autistic spectrum disorders.

The neural basis of autistic spectrum disorders (ASDs) is poorly understood. Studies of mnemonic function in ASD suggest a profile of impaired episodic memory with relative preservation of semantic memory (at least in high-functioning individuals). Such a pattern is consistent with developmental hippocampal abnormality. However, imaging evidence for abnormality of the hippocampal formation in ASD is inconsistent. These inconsistencies led us to examine the memory profile of children with ASD and the relationship to structural abnormalities. A cohort of high-functioning individuals with ASD and matched controls completed a comprehensive neuropsychological memory battery and underwent magnetic resonance imaging for the purpose of voxel-based morphometric analyses. Correlations between cognitive/behavioural test scores and quantified results of brain scans were also carried out to further examine the role of the medial temporal lobe in ASD. A selective deficit in episodic memory with relative preservation of semantic memory was found. Voxel-based morphometry revealed bilateral abnormalities in several areas implicated in ASD including the hippocampal formation. A significant correlation was found between parental ratings reflecting autistic symptomatology and the measure of grey matter density in the junction area involving the amygdala, hippocampus and entorhinal cortex. The data reveal a pattern of impaired and relatively preserved mnemonic function that is consistent with a hippocampal abnormality of developmental origin. The structural imaging data highlight abnormalities in several brain regions previously implicated in ASD, including the medial temporal lobes.

Adolescent↗
PubMed2004

Comparing dynamic causal models.

This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are used to make inferences about effective connectivity from functional magnetic resonance imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both concerning the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

Attention↗
PubMed2004

Modelling functional integration: a comparison of structural equation and dynamic causal models.

The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper, we review two different approaches to modelling effective connectivity from fMRI data, structural equation models (SEMs) and dynamic causal models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM, a distinction is made between the 'neuronal level' and the 'hemodynamic level'. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics, which in turn cause changes in the observed hemodynamics. In SEM, changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.

Algorithms↗
PubMed2003

Developmental amnesia: effect of age at injury.

Hypoxic-ischemic events sustained within the first year of life can result in developmental amnesia, a disorder characterized by markedly impaired episodic memory and relatively preserved semantic memory, in association with medial temporal pathology that appears to be restricted to the hippocampus. Here we compared children who had hypoxic-ischemic events before 1 year of age (early group, n = 6) with others who showed memory problems after suffering hypoxic-ischemic events between the ages of 6 and 14 years (late group, n = 5). Morphometric analyses of the whole brain revealed that, compared with age-matched controls, both groups had bilateral abnormalities in the hippocampus, putamen, and posterior thalamus, as well as in the right retrosplenial cortex. The two groups also showed similar reductions (approximately 40%) in hippocampal volumes. Neuropsychologically, the only significant differences between the two were on a few tests of immediate memory, where the early group surpassed the late group. The latter measures provided the only clear indication that very early injury can lead to greater functional sparing than injury acquired later in childhood, due perhaps to the greater plasticity of the infant brain. On measures of long-term memory, by contrast, the two groups had highly similar profiles, both showing roughly equivalent preservation of semantic memory combined with marked impairment in episodic memory. It thus appears that, if this selective memory disorder is a special syndrome related to the early occurrence of hypoxia-induced damage, then the effective age at injury for this syndrome extends from birth to puberty.

Adolescent↗
PubMed2003

Investigating individual differences in brain abnormalities in autism.

Autism is a psychiatric syndrome characterized by impairments in three domains: social interaction, communication, and restricted and repetitive behaviours and interests. Recent findings implicate the amygdala in the neurobiology of autism. In this paper, we report the results of a series of novel experimental investigations focusing on the structure and function of the amygdala in a group of children with autism. The first section attempts to determine if abnormality of the amygdala can be identified in an individual using magnetic resonance imaging in vivo. Using single-case voxel-based morphometric analyses, abnormality in the amygdala was detected in half the children with autism. Abnormalities in other regions were also found. In the second section, emotional modulation of the startle response was investigated in the group of autistic children. Surprisingly, there were no significant differences between the patterns of emotional modulation of the startle response in the autistic group compared with the controls.

Adolescent↗
PubMed2003

Studying spontaneous EEG activity with fMRI.

The multifaceted technological challenge of acquiring simultaneous EEG-correlated fMRI data has now been met and the potential exists for mapping electrophysiological activity with unprecedented spatio-temporal resolution. Work has already begun on studying a host of spontaneous EEG phenomena ranging from alpha rhythm and sleep patterns to epileptiform discharges and seizures, with far reaching clinical implications. However, the transformation of EEG data into linear models suitable for voxel-based statistical hypothesis testing is central to the endeavour. This in turn is predicated upon a number of assumptions regarding the manner in which the generators of EEG phenomena may engender changes in the blood oxygen level dependent (BOLD) signal. Furthermore, important limitations are posed by a set of considerations quite unique to 'paradigmless fMRI'. Here, these issues are assembled and explored to provide an overview of progress made and unresolved questions, with an emphasis on applications in epilepsy.

Action Potentials↗
PubMed2003

Posterior probability maps and SPMs.

This technical note describes the construction of posterior probability maps that enable conditional or Bayesian inferences about regionally specific effects in neuroimaging. Posterior probability maps are images of the probability or confidence that an activation exceeds some specified threshold, given the data. Posterior probability maps (PPMs) represent a complementary alternative to statistical parametric maps (SPMs) that are used to make classical inferences. However, a key problem in Bayesian inference is the specification of appropriate priors. This problem can be finessed using empirical Bayes in which prior variances are estimated from the data, under some simple assumptions about their form. Empirical Bayes requires a hierarchical observation model, in which higher levels can be regarded as providing prior constraints on lower levels. In neuroimaging, observations of the same effect over voxels provide a natural, two-level hierarchy that enables an empirical Bayesian approach. In this note we present a brief motivation and the operational details of a simple empirical Bayesian method for computing posterior probability maps. We then compare Bayesian and classical inference through the equivalent PPMs and SPMs testing for the same effect in the same data.

Algorithms↗