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Karl Friston

Publications and source records attributed to Karl Friston.

18 recordsLinked to original sources
PubMed2006

Hemodynamic correlates of epileptiform discharges: an EEG-fMRI study of 63 patients with focal epilepsy.

Using continuous EEG-correlated fMRI, we investigated the Blood Oxygen Level Dependent (BOLD) signal correlates of interictal epileptic discharges (IEDs) in 63 consecutively recruited patients with focal epilepsy. Semi-automated spike detection and advanced modeling strategies are introduced to account for different EEG event types, and to minimize false activations from uncontrolled motion. We show that: (1) significant hemodynamic correlates were detectable in over 68% of patients in whom discharges were captured and were highly, but not entirely, concordant with site(s) of presumed seizure generation where known; (2) deactivations were less concordant and may non-specifically reflect the consequential or downstream effects of IEDs on brain activity; (3) a striking pattern of retrosplenial deactivation was observed in 7 cases mainly with focal discharges; (4) the basic hemodynamic response to IEDs is physiological; (5) incorporating information about different types of IEDs, their durations and saturation effects resulted in more powerful models for the detection of fMRI correlates; (6) focal activations were more likely when there was good electroclinical localization, frequent stereotyped spikes, less head motion and less background EEG abnormality, but were also seen in patients in whom the electroclinical focus localization was uncertain. These findings provide important new information on the optimal use and interpretation of EEG-fMRI in focal epilepsy and suggest a possible role for EEG-fMRI in providing new targets for invasive EEG monitoring.

Adult↗
PubMed2006

EEG-fMRI of idiopathic and secondarily generalized epilepsies.

We used simultaneous EEG and functional MRI (EEG-fMRI) to study generalized spike wave activity (GSW) in idiopathic and secondary generalized epilepsy (SGE). Recent studies have demonstrated thalamic and cortical fMRI signal changes in association with GSW in idiopathic generalized epilepsy (IGE). We report on a large cohort of patients that included both IGE and SGE, and give a functional interpretation of our findings. Forty-six patients with GSW were studied with EEG-fMRI; 30 with IGE and 16 with SGE. GSW-related BOLD signal changes were seen in 25 of 36 individual patients who had GSW during EEG-fMRI. This was seen in thalamus (60%) and symmetrically in frontal cortex (92%), parietal cortex (76%), and posterior cingulate cortex/precuneus (80%). Thalamic BOLD changes were predominantly positive and cortical changes predominantly negative. Group analysis showed a negative BOLD response in the cortex in the IGE group and to a lesser extent a positive response in thalamus. Thalamic activation was consistent with its known role in GSW, and its detection in individual cases with EEG-fMRI may in part be related to the number and duration of GSW epochs recorded. The spatial distribution of the cortical fMRI response to GSW in both IGE and SGE involved areas of association cortex that are most active during conscious rest. Reduction of activity in these regions during GSW is consistent with the clinical manifestation of absence seizures.

Adolescent↗
PubMed2006

Large-scale neural models and dynamic causal modelling.

Dynamic causal modelling (DCM) is a method for estimating and making inferences about the coupling among small numbers of brain areas, and the influence of experimental manipulations on that coupling [Friston, K.J., Harrison, L., Penny, W., 2003 Dynamic causal modelling. Neuroimage 19, 1273-1302]. Large-scale neural modelling aims to construct neurobiologically grounded computational models with emergent behaviours that inform our understanding of neuronal systems. One such model has been used to simulate region-specific BOLD time-series [Horwitz, B., Friston, K.J., Taylor, J.G., 2000. Neural modeling and functional brain imaging: an overview. Neural Netw. 13, 829-846]. DCM was used to make inferences about effective connectivity using data generated by a model implementing a visual delayed match-to-sample task [Tagamets, M.A., Horwitz, B., 1998. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb. Cortex 8, 310-320]. The aim was to explore the validity of inferences made using DCM about the connectivity structure and task-dependent modulatory effects, in a system with a known connectivity structure. We also examined the effects of misspecifying regions of interest. Models with hierarchical connectivity and reciprocal connections were examined using DCM and Bayesian Model Comparison [Penny, W.D., Stephan, K.E., Mechelli, A., Friston, K.J., 2004. Comparing dynamic causal models. Neuroimage 22, 1157-1172]. This approach revealed strong evidence for those models with correctly specified anatomical connectivity. Furthermore, Bayesian model comparison favoured those models when bilinear effects corresponded to their implementation in the neural model. These findings generalised to an extended model with two additional areas and reentrant circuits. The conditional uncertainty of coupling parameter estimates increased in proportion to the number of incorrectly specified regions. These results highlight the role of neural models in establishing the validity of estimation and inference schemes. Specifically, Bayesian model comparison confirms the validity of DCM in relation to a well-characterised and comprehensive neuronal model.

Bayes Theorem↗
PubMed2006

Bayesian estimation of evoked and induced responses.

We describe an extension of our empirical Bayes approach to magnetoencephalography/electroencephalography (MEG/EEG) source reconstruction that covers both evoked and induced responses. The estimation scheme is based on classical covariance component estimation using restricted maximum likelihood (ReML). We have focused previously on the estimation of spatial covariance components under simple assumptions about the temporal correlations. Here we extend the scheme, using temporal basis functions to place constraints on the temporal form of the responses. We show how the same scheme can estimate evoked responses that are phase-locked to the stimulus and induced responses that are not. For a single trial the model is exactly the same. In the context of multiple trials, however, the inherent distinction between evoked and induced responses calls for different treatments of the underlying hierarchical multitrial model. We derive the respective models and show how they can be estimated efficiently using ReML. This enables the Bayesian estimation of evoked and induced changes in power or, more generally, the energy of wavelet coefficients.

Algorithms↗
PubMed2005

Editor's note.

Explore the source record for details and available documents.

Editorial↗
PubMed2005

Opponent appetitive-aversive neural processes underlie predictive learning of pain relief.

Termination of a painful or unpleasant event can be rewarding. However, whether the brain treats relief in a similar way as it treats natural reward is unclear, and the neural processes that underlie its representation as a motivational goal remain poorly understood. We used fMRI (functional magnetic resonance imaging) to investigate how humans learn to generate expectations of pain relief. Using a pavlovian conditioning procedure, we show that subjects experiencing prolonged experimentally induced pain can be conditioned to predict pain relief. This proceeds in a manner consistent with contemporary reward-learning theory (average reward/loss reinforcement learning), reflected by neural activity in the amygdala and midbrain. Furthermore, these reward-like learning signals are mirrored by opposite aversion-like signals in lateral orbitofrontal cortex and anterior cingulate cortex. This dual coding has parallels to 'opponent process' theories in psychology and promotes a formal account of prediction and expectation during pain.

Avoidance Learning↗
PubMed2005

A theory of cortical responses.

This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts.It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain's attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.

Biophysical Phenomena↗
PubMed2004

The prefrontal cortex shows context-specific changes in effective connectivity to motor or visual cortex during the selection of action or colour.

The role of the prefrontal cortex remains controversial. Neuroimaging studies support modality-specific and process-specific functions related to working memory and attention. Its role may also be defined by changes in its influence over other brain regions including sensory and motor cortex. We used functional magnetic imaging (fMRI) to study the free selection of actions and colours. Control conditions used externally specified actions and colours. The prefrontal cortex was activated during free selection, regardless of modality, in contrast to modality-specific activations outside prefrontal cortex. Structural equation modelling (SEM) of fMRI data was used to test the hypothesis that although the same regions of prefrontal cortex may be active in tasks within different domains, there is task-dependent effective connectivity between prefrontal cortex and non-prefrontal cortex. The SEM included high-order interactions between modality, selection and regional activity. There was greater coupling between prefrontal cortex and motor cortex during free selection and action tasks, and between prefrontal cortex and visual cortex during free selection of colours. The results suggest that the functions of the prefrontal cortex may be defined not only by selection-specific rather than modality-specific processes, but also by changing patterns of effective connectivity from prefrontal cortex to motor and sensory cortices.

Adult↗
PubMed2004

Dissociable roles of ventral and dorsal striatum in instrumental conditioning.

Instrumental conditioning studies how animals and humans choose actions appropriate to the affective structure of an environment. According to recent reinforcement learning models, two distinct components are involved: a "critic," which learns to predict future reward, and an "actor," which maintains information about the rewarding outcomes of actions to enable better ones to be chosen more frequently. We scanned human participants with functional magnetic resonance imaging while they engaged in instrumental conditioning. Our results suggest partly dissociable contributions of the ventral and dorsal striatum, with the former corresponding to the critic and the latter corresponding to the actor.

Adult↗
PubMed2003

Temporal difference models and reward-related learning in the human brain.

Temporal difference learning has been proposed as a model for Pavlovian conditioning, in which an animal learns to predict delivery of reward following presentation of a conditioned stimulus (CS). A key component of this model is a prediction error signal, which, before learning, responds at the time of presentation of reward but, after learning, shifts its response to the time of onset of the CS. In order to test for regions manifesting this signal profile, subjects were scanned using event-related fMRI while undergoing appetitive conditioning with a pleasant taste reward. Regression analyses revealed that responses in ventral striatum and orbitofrontal cortex were significantly correlated with this error signal, suggesting that, during appetitive conditioning, computations described by temporal difference learning are expressed in the human brain.

Adolescent↗
PubMed2003

Learning and inference in the brain.

This article is about how the brain data mines its sensory inputs. There are several architectural principles of functional brain anatomy that have emerged from careful anatomic and physiologic studies over the past century. These principles are considered in the light of representational learning to see if they could have been predicted a priori on the basis of purely theoretical considerations. We first review the organisation of hierarchical sensory cortices, paying special attention to the distinction between forward and backward connections. We then review various approaches to representational learning as special cases of generative models, starting with supervised learning and ending with learning based upon empirical Bayes. The latter predicts many features, such as a hierarchical cortical system, prevalent top-down backward influences and functional asymmetries between forward and backward connections that are seen in the real brain. The key points made in article are: (i). hierarchical generative models enable the learning of empirical priors and eschew prior assumptions about the causes of sensory input that are inherent in non-hierarchical models. These assumptions are necessary for learning schemes based on information theory and efficient or sparse coding, but are not necessary in a hierarchical context. Critically, the anatomical infrastructure that may implement generative models in the brain is hierarchical. Furthermore, learning based on empirical Bayes can proceed in a biologically plausible way. (ii). The second point is that backward connections are essential if the processes generating inputs cannot be inverted, or the inversion cannot be parameterised. Because these processes involve many-to-one mappings, are non-linear and dynamic in nature, they are generally non-invertible. This enforces an explicit parameterisation of generative models (i.e. backward connections) to afford recognition and suggests that forward architectures, on their own, are not sufficient for perception. (iii). Finally, non-linearities in generative models, mediated by backward connections, require these connections to be modulatory, so that representations in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically.

Bayes Theorem↗
PubMed2003

Variational Bayesian inference for fMRI time series.

We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models with Autoregressive (AR) error processes. We make use of the Variational Bayesian (VB) framework which approximates the true posterior density with a factorised density. The fidelity of this approximation is verified via Gibbs sampling. The VB approach provides a natural extension to previous Bayesian analyses which have used Empirical Bayes. VB has the advantage of taking into account the variability of hyperparameter estimates with little additional computational effort. Further, VB allows for automatic selection of the order of the AR process. Results are shown on simulated data and on data from an event-related fMRI experiment.

Algorithms↗
PubMed2003

Mixtures of general linear models for functional neuroimaging.

We set out a new general framework for making inferences from neuroimaging data, which includes a standard approach to neuroimaging analysis, statistical parametric mapping (SPM), as a special case. The model offers numerous conceptual and statistical advantages that derive from analyzing data at the "cluster level" rather than the "voxel level" and from explicit modeling of the shape and position of clusters of activation. This provides a natural and principled way to pool data from nearby voxels for parameter and variance-component estimation. The model can also be viewed as performing a spatio-temporal cluster analysis. The parameters of the model are estimated using an expectation maximization (EM) algorithm.

Acoustic Stimulation↗
PubMed2002

Beyond phrenology: what can neuroimaging tell us about distributed circuitry?

Unsupervised models of how the brain identifies and categorizes the causes of its sensory input can be divided into two classes: those that minimize the mutual information (i.e., redundancy) among evoked responses and those that minimize the prediction error. Although these models have the same goal, the way that goal is attained, and the functional architectures required, are fundamentally different. This review describes the differences, in the functional anatomy of sensory cortical hierarchies, implied by the two models. We then consider how neuroimaging can be used to disambiguate between them. The key distinction reduces to whether backward connections are employed by the brain to generate a prediction of sensory inputs. To ascertain whether backward influences are evident empirically requires a characterization of functional integration among brain systems. This review summarizes the approaches to measuring functional integration in terms of effective connectivity and proceeds to address the question posed by the theoretical considerations. In short, it will be shown that the conjoint manipulation of bottom-up and top-down inputs to an area can be used to test for interactions between them, in elaborating cortical responses. The conclusion, from these sorts of neuroimaging studies, points to the prevalence of top-down influences and the plausibility of generative models of sensory brain function.

Animals↗
PubMed2002

Functional integration and inference in the brain.

Self-supervised models of how the brain represents and categorises the causes of its sensory input can be divided into two classes: those that minimise the mutual information (i.e. redundancy) among evoked responses and those that minimise the prediction error. Although these models have similar goals, the way they are attained, and the functional architectures employed, can be fundamentally different. This review describes the two classes of models and their implications for the functional anatomy of sensory cortical hierarchies in the brain. We then consider how empirical evidence can be used to disambiguate between architectures that are sufficient for perceptual learning and synthesis. Most models of representational learning require prior assumptions about the distribution of sensory causes. Using the notion of empirical Bayes, we show that these assumptions are not necessary and that priors can be learned in a hierarchical context. Furthermore, we try to show that learning can be implemented in a biologically plausible way. The main point made in this review is that backward connections, mediating internal or generative models of how sensory inputs are caused, are essential if the process generating inputs cannot be inverted. Because these processes are dynamical in nature, sensory inputs correspond to a non-invertible nonlinear convolution of causes. This enforces an explicit parameterisation of generative models (i.e. backward connections) to enable approximate recognition and suggests that feedforward architectures, on their own, are not sufficient. Moreover, nonlinearities in generative models, that induce a dependence on backward connections, require these connections to be modulatory; so that estimated causes in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically. To ascertain whether backward influences are expressed functionally requires measurements of functional integration among brain systems. This review summarises approaches to integration in terms of effective connectivity and proceeds to address the question posed by the theoretical considerations above. In short, it will be shown that functional neuroimaging can be used to test for interactions between bottom-up and top-down inputs to an area. The conclusion of these studies points toward the prevalence of top-down influences and the plausibility of generative models of sensory brain function.

Animals↗
PubMed2002

Attention to action in Parkinson's disease: impaired effective connectivity among frontal cortical regions.

The neural basis of attention to action was studied in 12 patients with Parkinson's disease (Hoehn and Yahr grades II and III) and 12 healthy age-matched controls. The subjects were studied by functional MRI (fMRI) during performance of a simple paced overlearned motor sequence task, with and without an additional attentional task. For the attentional task, subjects were instructed to attend either to their actions or to a visual distractor task. Statistical parametric mapping was used to implement a random effects analysis of the regional task-related activations in patient and control populations. Structural equation modelling of fMRI time series was used to measure effective connectivity among prefrontal and premotor areas. In both groups, the motor task was associated with activation of a distributed network including the premotor, motor and parietal cortex, striatum and cerebellum. In control subjects, but not patients, attention to action (relative to execution of an overlearned sequence) was associated with further activation of prefrontal, parietal and paracingulate cortex, and the supplementary motor area (SMA). Patients with Parkinson's disease showed greater than normal activation of the SMA during execution of the simple overlearned motor sequence, but less augmentation when attending to their actions. In control subjects, attention to action, but not attention to the visual distractor task, increased the effective connectivity between prefrontal cortex and both the lateral premotor cortex and the SMA. This represents a specific increase in effective connectivity. Attentional modulation of effective connectivity between the prefrontal, premotor cortex and SMA was not observed in patients. This deficit indicates a context-specific functional disconnection between the prefrontal cortex and the supplementary and premotor cortex in Parkinson's disease.

Aged↗
PubMed2002

Attention to action: specific modulation of corticocortical interactions in humans.

The prefrontal cortex may exert cognitive control by a general mechanism of attentional selection of neuronal representations. We used functional magnetic resonance imaging to test this hypothesis in the motor system. Normal volunteers were scanned during performance of a simple motor task, with their attention either directed towards their actions, or diverted towards a visual search task, or neither. Attention to action increased activity in prefrontal, premotor and parietal cortex, compared with unattended performance of the same movements. Analysis of cortical activity by structural equation modelling of regional fMRI time series was used to measure effective connectivity among prefrontal, premotor and parietal cortices. Attention to action enhanced effective connectivity between dorsal prefrontal cortex and premotor cortex, whereas non-motor attention diminished it. These effects were not attributable to common inputs from parietal cortex to the prefrontal and premotor cortex. The results suggest a supra-modal role for the dorsal prefrontal cortex in attentional selection, operating within the motor system as well as sensory and mnemonic domains.

Acoustic Stimulation↗