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K Friston

Publications and source records attributed to K Friston.

At least 19 recordsLinked to original sources
PubMed2003

Multivariate autoregressive modeling of fMRI time series.

We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods.

Algorithms↗
PubMed2001

Modeling geometric deformations in EPI time series.

Even after realignment there is residual movement-related variance present in fMRI time-series, causing loss of sensitivity and, potentially, also specificity. One cause is the differential deformation of the sampling matrix, by field inhomogeneities, at different object positions, i.e., a movement-by-inhomogeneity interaction. This has been addressed previously by using empirical field measurements. In the present paper we suggest a forward model of how data is affected by an inhomogeneous field at different object positions. From this model we derive a method to solve the inverse problem of estimating the field inhomogeneities and their derivatives with respect to object position, directly from the EPI data and estimated realignment parameters. The field is modeled as a linear combination of cosine basis fields, which facilitates a fast way of implementing the necessary matrix operations. Simulations suggest that the solution is tractable and that the fields are estimable given the deformed images and knowledge of the relative positions at which they have been acquired. An experiment on a subject performing voluntary movements in the scanner yielded plausible estimates of the deformation fields and their application to "unwarp" the time series significantly reduced movement-related variance.

Adult↗
PubMed2001

A global estimator unbiased by local changes.

The global activity is an important confound when analyzing PET data in that its inclusion in the statistical model can substantially reduce error variance and increase sensitivity. However, by defining global activity as the average over all voxels one introduces a bias that is collinear with experimental factors. This leads to an underestimation of true activations and the introduction of artefactual deactivations. We propose a novel estimator for the global activity based on the notion of finding a maximally nonlocal mode in a multivariate characterization of the data, while maximizing the locality of the remaining modes. The approach uses singular value decomposition (SVD) to find a provisional set of modes, which are subsequently rotated such that a metric based on the above heuristic is maximized. This metric is a version of the stochastic sign change (SSC) criterion that has been used previously for normalizing medical images with focal defects. The estimator was evaluated on simulated and real functional imaging (PET) data. The simulations show that the bias of the global mean, introduced by focal activations, is reduced by 80--90% with the new estimator. Comparison with a previous unbiased estimator, using the empirical data, yielded similar results. The advantage of the new estimator is that it is not informed of experimental design and relies only on general assumptions regarding the nature of the signal.

Brain↗
PubMed2001

Interactions among neuronal systems assessed with functional neuroimaging.

Cortical organisation is based on the principles of functional specialisation and functional integration. Those two concepts exist at multiple spatial scales. For example at a macroscopic level, functionally segregated areas V1 and V5 are integrated within the dorsal visual stream. At a microscopic level, it is possible to infer the functional integration of (segregated) ocular dominance columns within area V1. At the macroscopic level functional specialisation and integration can be tested with functional neuroimaging, using fMRI/PET or EEG/MEG. A framework that allows making inferences on functional integration between brain regions using fMRI will be presented. The common feature of all techniques within that framework is that they can incorporate second order terms and therefore explicitly allow for contextual modulations. Two examples demonstrate how attention and paired associates learning can modulate effective connectivity within the visual system. Furthermore these models can incorporate non-linear modulatory (feedback) connections that can account for the changes in effective connectivity observed.

Attention↗
PubMed2000

Nonlinear PCA: characterizing interactions between modes of brain activity.

This paper presents a nonlinear principal component analysis (PCA) that identifies underlying sources causing the expression of spatial modes or patterns of activity in neuroimaging time-series. The critical aspect of this technique is that, in relation to conventional PCA, the sources can interact to produce (second-order) spatial modes that represent the modulation of one (first-order) spatial mode by another. This nonlinear PCA uses a simple neural network architecture that embodies a specific form for the nonlinear mixing of sources that cause observed data. This form is motivated by a second-order approximation to any general nonlinear mixing and emphasizes interactions among pairs of sources. By introducing these nonlinearities principal components obtain with a unique rotation and scaling that does not depend on the biologically implausible constraints adopted by conventional PCA. The technique is illustrated by application to functional (positron emission tomography and functional magnetic resonance imaging) imaging data where the ensuing first- and second-order modes can be interpreted in terms of distributed brain systems. The interactions among sources render the expression of any one mode context-sensitive, where that context is established by the expression of other modes. The examples considered include interactions between cognitive states and time (i.e. adaptation or plasticity in PET data) and among functionally specialized brain systems (using a fMRI study of colour and motion processing).

Acoustic Stimulation↗
PubMed2000

A direct quantitative relationship between the functional properties of human and macaque V5.

The nature of the quantitative relationship between single-neuron recordings in monkeys and functional magnetic resonance imaging (fMRI) measurements in humans is crucial to understanding how experiments in these different species are related, yet it remains undetermined. We measured brain activity in humans attending to moving visual stimuli, using blood oxygenation level-dependent (BOLD) fMRI. Responses in V5 showed a strong and highly linear dependence on increasing strength of motion signal (coherence). These population responses in human V5 had a remarkably simple mathematical relationship to previously observed single-cell responses in macaque V5. We provided an explicit quantitative estimate for the interspecies comparison of single-neuron activity and BOLD population responses. Our data show previously unknown dissociations between the functional properties of human V5 and other human motion-sensitive areas, thus predicting similar dissociations for the properties of single neurons in homologous areas of macaque cortex.

Adult↗
PubMed1999

The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans.

Autism is a biological disorder which affects social cognition, and understanding brain abnormalities of the former will elucidate the brain basis of the latter. We report structural MRI data on 15 high-functioning individuals with autistic disorder. A voxel-based whole brain analysis identified grey matter differences in an amygdala centered system relative to 15 age- and IQ-matched controls. Decreases of grey matter were found in anterior parts of this system (right paracingulate sulcus, left inferior frontal gyrus). Increases were found in posterior parts (amygdala/peri-amygdaloid cortex, middle temporal gyrus, inferior temporal gyrus), and in regions of the cerebellum. These structures are implicated in social cognition by animal, imaging and histopathological studies. This study therefore provides converging evidence of the physiological basis of social cognition.

Adult↗
PubMed1999

Rapid assessment of regional cerebral metabolic abnormalities in single subjects with quantitative and nonquantitative [18F]FDG PET: A clinical validation of statistical parametric mapping.

The [18F]fluorodeoxyglucose ([18F]FDG) method for measuring brain metabolism has not the wide clinical application that one might expect, partly because of its high cost and the complexity of the quantification procedure, but also because of reporting techniques based on region of interest (ROI) analysis, which are time-consuming and not fully objective. In this paper we report a clinical validation of statistical parametric mapping (SPM) using rCMRglc (quantitative) and radioactivity distribution (nonquantitative) [18F]FDG PET data. We show that a 10-min noninteractive voxel-based SPM analysis on a standard workstation enables objective assessment, including localization in stereotactic space, of regional glucose consumption abnormalities, whose reliability can be assessed on statistical and clinical grounds. Clinical validity was established using a small series of patients with degenerative or developmental disorders, including probable Alzheimer's disease, progressive aphasia, multiple sclerosis, developmental specific language impairment, and epilepsy. Analysis of quantitative and nonquantitative data showed the same pattern of results, suggesting that, for clinical purposes, quantitation and invasive arterial cannulation can be avoided. This should facilitate a wider application of the technique and the extension of SPM clinical analysis to H215O PET or high resolution SPECT perfusion studies.

Adolescent↗
PubMed1999

Learning-related neuronal responses in prefrontal cortex studied with functional neuroimaging.

We assessed time-dependent neuronal activity accompanying learning using functional magnetic resonance imaging (fMRI). An artificial grammar learning paradigm enabled us to dissociate activations associated with individual item learning from those involved in learning the underlying grammar system. We show that a localized region of right prefrontal cortex (PFC) is preferentially sensitive to individual item learning during the early stages of the experiment, while the left PFC region is sensitive to grammar learning which occurred across the entire course of the experiment. In addition to dissociating these two types of learning, we were able to characterize the effect of rule acquisition on neuronal responses associated with explicit learning of individual items. This effect was expressed as modulation of the time-dependent right PFC activations such that the early increase in activation associated with item learning was attenuated as the experiment progressed. In a further analysis we used structural equation modelling to explore time-dependent changes in inter-regional connectivity as a function of both item and grammar rule learning. Although there were no significant effects of item learning on the measured path strengths, rule learning was associated with a decrease in right fronto-parietal connectivity and an increase in connectivity between left and right PFC. Further fronto-parietal path strengths were observed to change, with an increase in left fronto-parietal and a decrease in right fronto-parietal connectivity path strength from right PFC to left parietal cortex. We interpret our findings in terms of a left frontal system mediating the semantic analysis of study items and directly influencing a right fronto-parietal system associated with episodic memory retrieval.

Adult↗
PubMed1999

Revealing interactions among brain systems with nonlinear PCA.

In this work, we present a nonlinear principal component analysis (PCA) that identifies underlying sources causing the expression of spatial modes or patterns of activity in neuroimaging time series where these sources can interact to produce second-order modes. This nonlinear PCA uses a neural network architecture that embodies a specific form for the mixing of sources that is based on a second-order approximation to any general nonlinear mixing. The modes obtained have a unique rotation and scaling that does not depend on the biologically implausible constraints adopted by conventional PCA. Interactions among sources render the expression of any mode or brain system sensitive to the expression of others. The example considers interactions among functionally specialized brain systems (using a fMRI study of colour and motion processing).

Brain↗
PubMed1998

A multimodal language region in the ventral visual pathway.

Reading words and naming pictures involves the association of visual stimuli with phonological and semantic knowledge. Damage to a region of the brain in the left basal posterior temporal lobe (BA37), which is strategically situated between the visual cortex and the more anterior temporal cortex, leads to reading and naming deficits. Additional evidence implicating this region in linguistic processing comes from functional neuroimaging studies of reading in normal subjects and subjects with developmental dyslexia. Here we test whether the visual component of reading is essential for activation of BA37 by comparing cortical activations elicited by word processing in congenitally blind, late-blind and sighted subjects using functional neuroimaging. Despite the different modalities used (visual and tactile), all groups of subjects showed a common activation of BA37 by words relative to non-word letter-strings. These findings agree with the proposal that BA37 is an association area that integrates converging inputs from many regions. Our study confirms a prediction of theories of brain function that depend on convergence zones; the absence of one input (that is, visual) does not alter the response properties of such a convergence region.

Adult↗
PubMed1998

Functional magnetic resonance imaging of the human brain: data acquisition and analysis.

It is now feasible to create spatial maps of activity in the human brain completely non-invasively using magnetic resonance imaging. Magnetic resonance imaging (MRI) images in which the spin magnetization is refocussed by gradient switching are sensitive to local changes in magnetic susceptibility, which can occur when the oxygenation state of blood changes. Cortical neural activity causes increases in blood flow, which usually result in changes in blood oxygenation. Hence changes of image intensity can be observed, given rise to the so-called Blood Oxygenation Level Dependent (BOLD) contrast technique. Use of echo-planar imaging methods (EPI) allows the monitoring over the entire brain of such changes in real time. A temporal resolution of 1-3 s, and a spatial resolution of 2 mm in-plane, can thus be obtained. Generally in a brain mapping experiment hundred of brain image volumes are acquired at repeat times of 1-6 s, while brain tasks are performed. The data are transformed into statistical maps of image difference, using the technique known as statistical parametric mapping (SPM). This method, based on robust multilinear regression techniques, has become the method of reference for analysis of positron emission tomography (PET) image data. The special characteristics of functional MRI data require some modification of SPM algorithms and strategies, and the MRI data must be gaussianized in time and space to conform to the assumptions of the statistics of Gaussian random fields. The steps of analysis comprise: removal of head movement effects, spatial smoothing, and statistical interference, which includes temporal smoothing and removal by fitting of temporal variations slower than the experimental paradigm. By these means, activation maps can be generated with great flexibility and statistical power, giving probability estimates for activated brain regions based on intensity or spatial extent, or both combined. Recent studies have shown that patterns of activation obtained in human brain for a given stimulus are independent of the order and spatial orientation with which MRI images are acquired, and hence that inflow effects are not important for EPI data with a TR much longer than T1.

Artifacts↗
PubMed1998

Different activation patterns in the visual cortex of late and congenitally blind subjects.

A key issue in developmental neuroscience is the role of activity-dependent mechanisms in the epigenetic induction of functional organization in visual cortex. Ocular blindness and ensuing visual deprivation is one of the rare models available for the investigation of experience-dependent cortical reorganization in man. In a PET study we demonstrate that congenitally blind subjects show task-specific activation of extrastriate visual areas and parietal association areas during Braille reading, compared with auditory word processing. In contrast, blind subjects who lost their sight after puberty show additional activation in the primary visual cortex with the same tasks. Studies in blind-raised monkeys show that crossmodal responses in extrastriate areas can be elicited by somatosensory stimulation. This is consistent with the crossmodal extrastriate activations elicited by tactile processing in our congenitally blind subjects. Since primary visual cortex does not show crossmodal responses in primate studies, the differential activation in late and congenitally blind subjects highlights the possibility of reciprocal activation by visual imagery in subjects with early visual experience.

Adult↗
PubMed1998

Identifying global anatomical differences: deformation-based morphometry.

The aim of this paper is to illustrate a method for identifying macroscopic anatomical differences among the brains of different populations of subjects. The method involves spatially normalizing the structural MR images of a number of subjects so that they all conform to the same stereotactic space. Multivariate statistics are then applied to the parameters describing the estimated nonlinear deformations that ensue. To illustrate the method, we compared the gross morphometry of male and female subjects. We also assessed brain asymmetry, the effect of handedness, and interactions among these effects.

Adult↗
PubMed1997

Lateral geniculate activations can be detected using intersubject averaging and fMRI.

Applications of fMRI in functional brain imaging are mainly confined to single subject designs, prohibiting the assessment of subject or group by condition interactions (i.e., differential activations) or areas of conjoint activation. In this paper a framework for fMRI group designs, using statistical parametric mapping, is introduced. It is generally believed that intersubject averaging, which requires spatial normalization and smoothing, will decrease the effective spatial resolution of fMRI or its sensitivity. A subcortical activation of the lateral geniculate nucleus (LGN) was therefore chosen to demonstrate the feasibility and power of intersubject averaging in the context of fMRI. Seven volunteers were studied, while looking at a blank screen or radially moving dots. LGN activation was demonstrated in all single subject analyses and in the group analysis.

Adult↗
PubMed1997

Multimodal image coregistration and partitioning--a unified framework.

This paper presents a method for the coregistration and partitioning (i.e., tissue segmentation) of brain images that have been acquired in different modalities. The basic idea is that instead of matching two images directly, one performs intermediate within-modality registrations to two template images that are already in register. One can use a least-squares minimization to determine the affine transformations that map between the templates and the images. By incorporating suitable constraints, a rigid body transformation which directly maps between the images can be extracted from these more general affine transformations. A further refinement capitalizes on the implicit normalization of both images into a standard space. This facilitates segmentation or partitioning of both original images into homologous tissue classifications. Once partitioned, the partitions can be jointly matched, further increasing the accuracy of the coregistration. In short, these techniques reduce the between-modality problem to a series of simpler within-modality problems. These methods are relatively robust, address a number of problems in image transformations, and require no manual intervention.

Brain↗
PubMed1997

Incorporating prior knowledge into image registration.

The first step in the spatial normalization of brain images is usually to determine the affine transformation that best maps the image to a template image in a standard space. We have developed a rapid and automatic method for performing this registration, which uses a Bayesian scheme to incorporate prior knowledge of the variability in the shape and size of heads. We compared affine registrations with and without incorporating the prior knowledge. We found that the affine transformations derived using the Bayesian scheme are much more robust and that the rate of convergence is greater.

Adolescent↗