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J B Poline

Publications and source records attributed to J B Poline.

At least 19 recordsLinked to original sources
PubMed2000

Structural group analysis of functional activation maps.

We present here a new method for cerebral activation detection over a group of subjects. This method is performed using individual activation maps of any sort. It aims at processing a group analysis while preserving individual information and at overcoming as far as possible limitations of the spatial normalization used to compare different subjects. We designed it such that it provides the individual occurrence of the activations detected at a group level. The localization can then be performed on the individual anatomy of each subject. The analysis starts with a hierarchical multiscale object-based description of each individual map. These descriptions are then compared, rather than comparing the images directly. The analysis is thus performed at an object level instead of voxel by voxel. It is made using a comparison graph, on which a labeling process is performed. The label field on the graph is modeled by a Markov random field, which allows us to introduce high-level rules of interrogation of the data. The process has been evaluated on simulated data and real data from a PET protocol.

Brain↗
PubMed2000

Distinct cortical areas for names of numbers and body parts independent of language and input modality.

Some models of word comprehension postulate that the processing of words presented in different modalities and languages ultimately converges toward common cerebral systems associated with semantic-level processing and that the localization of these systems may vary with the category of semantic knowledge being accessed. We used functional magnetic resonance imaging to investigate this hypothesis with two categories of words, numerals, and body parts, for which the existence of distinct category-specific areas is debated in neuropsychology. Across two experiments, one with a blocked design and the other with an event-related design, a reproducible set of left-hemispheric parietal and prefrontal areas showed greater activation during the manipulation of topographical knowledge about body parts and a right-hemispheric parietal network during the manipulation of numerical quantities. These results complement the existing neuropsychological and brain-imaging literature by suggesting that within the extensive network of bilateral parietal regions active during both number and body-part processing, a subset shows category-specific responses independent of the language and modality of presentation.

Adult↗
PubMed2000

Episodic memory in left temporal lobe epilepsy: a functional MRI study.

Left medial temporal lobe epilepsy (MTLE) is associated with verbal memory impairment usually related to hippocampal damage. We used functional MRI (fMRI) to investigate the patterns of functional activity in healthy volunteers and MTLE patients engaged in verbal episodic memory tasks to look for evidence of a reallocation of verbal memory in epileptic patients. fMRI data were collected from seven MTLE patients with left-sided hippocampal sclerosis and 10 healthy right-handed control subjects on a 3T scanner. Subjects were instructed to learn a list of 17 words (encoding) and then to recall them (retrieval) on successive trials. Healthy volunteers and patients both exhibited bilateral activation (right higher than left) of the parahippocampal gyrus during the retrieval. This effect was more marked in the control subjects. In contrast to the control subjects, patients exhibited consistent and extensive left prefrontal activations in all the memory tasks. These findings show that verbal memory tasks did not involve the same functional patterns in patients and healthy volunteers. This may be interpreted as a dysfunctional response due to the epilepsy and left hippocampal sclerosis, and could reflect the early onset and progressive course of the disease.

Adolescent↗
PubMed2000

Visual perception of motion and 3-D structure from motion: an fMRI study.

Functional magnetic resonance imaging was used to study the cortical bases of 3-D structure perception from visual motion in human. Nine subjects underwent three experiments designed to locate the areas involved in (i) motion processing (random motion versus static dots), (ii) coherent motion processing (expansion/ contraction versus random motion) and (iii) 3-D shape from motion reconstruction (3-D surface oscillating in depth versus random motion). Two control experiments tested the specific influence of speed distribution and surface curvature on the activation results. All stimuli consisted of random dots so that motion parallax was the only cue available for 3-D shape perception. As expected, random motion compared with static dots induced strong activity in areas V1/V2, V5+ and the superior occipital gyrus (SOG; presumptive V3/V3A). V1/V2 and V5+ showed no activity increase when comparing coherent motion (expansion or 3-D surface) with random motion. Conversely, V3/V3A and the dorsal parieto-occipital junction were highlighted in both comparisons and showed gradually increased activity for random motion, coherent motion and a curved surface rotating in depth, which suggests their involvement in the coding of 3-D shape from motion. Also, the ventral aspect of the left occipito-temporal junction was found to be equally responsive to random and coherent motion stimuli, but showed a specific sensitivity to curved 3-D surfaces compared with plane surfaces. As this region is already known to be involved in the coding of static object shape, our results suggest that it might integrate various cues for the perception of 3-D shape.

Adult↗
PubMed2000

Working memory control in patients with schizophrenia: a PET study during a random number generation task.

OBJECTIVE: The authors' goal was to investigate brain regions involved in the deficiency of working memory control processes in patients with schizophrenia. METHOD: Regional cerebral blood flow was measured with positron emission tomography in eight men with stabilized schizophrenia and eight healthy men while they were performing a graded random number generation task. Twelve scans were made for each subject. Covariations between randomness of responses and regional activation were analyzed. RESULTS: The pattern of covariation between randomness of responses and activation in the anterior cingulate and superior parietal regions differed between patients and healthy subjects. CONCLUSIONS: These results suggest a cinguloparietal dysfunction underlying the impairment of working memory control processes during a random number generation task in patients with schizophrenia.

Adult↗
PubMed1999

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference.

Bayes Theorem↗
PubMed1999

Statistical limitations in functional neuroimaging. II. Signal detection and statistical inference.

The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low-pass filtering in relation to functional-anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non-inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.

Biometry↗
PubMed1999

Ambiguous results in functional neuroimaging data analysis due to covariate correlation.

In this note we draw attention to a source of potential ambiguity in functional neuroimaging results when data analysis is based on the resolution of a linear model. This ambiguity arises whenever there exists correlation between the model covariates. A single-subject PET activation experiment helps to illustrate to what extent correlation can affect statistical results interpretation, possibly leading to misinterpretation of part of the activation pattern. This note is intended to clarify this point and to suggest the use of a simple and well-known procedure to deal with these situations. In the Appendix, we suggest a convenient mathematical formulation for statistical tests particularly useful in such cases.

Brain↗
PubMed1999

Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model.

The assessment of significant activations in functional imaging using voxel-based methods often relies on results derived from the theory of Gaussian random fields. These results solve the multiple comparison problem and assume that the spatial correlation or smoothness of the data is known or can be estimated. End results (i. e., P values associated with local maxima, clusters, or sets of clusters) critically depend on this assessment, which should be as exact and as reliable as possible. In some earlier implementations of statistical parametric mapping (SPM) (SPM94, SPM95) the smoothness was assessed on Gaussianized t-fields (Gt-f) that are not generally free of physiological signal. This technique has two limitations. First, the estimation is not stable (the variance of the estimator being far from negligible) and, second, physiological signal in the Gt-f will bias the estimation. In this paper, we describe an estimation method that overcomes these drawbacks. The new approach involves estimating the smoothness of standardized residual fields which approximates the smoothness of the component fields of the associated t-field. Knowing the smoothness of these component fields is important because it allows one to compute corrected P values for statistical fields other than the t-field or the Gt-f (e.g., the F-map) and eschews bias due to deviation from the null hypothesis. We validate the method on simulated data and demonstrate it using data from a functional MRI study.

Brain↗
PubMed1997

Attention to one or two features in left or right visual field: a positron emission tomography study.

In human vision, two features of the same object can be identified concurrently without loss of accuracy. Performance declines, however, when the features belong to different objects in opposite visual fields. We hypothesized that different positron emission tomography activation patterns would reflect these behavioral results. We first delineated an attention network for single discriminations in left or right visual field and then compared this with the activation pattern when subjects divided attention over two features of a single object or over two objects in opposite hemifields. When subjects attended to a single feature, parietal, premotor, and anterior cingulate cortex were activated. These effects were strongest in the right hemisphere and were, remarkably, unaffected by the direction of attention. In contrast, direction of attention affected occipital and frontal activity: right occipital and left lateral frontal activity were higher with attention to the left, whereas right lateral frontal activity was higher with attention to the right. When subjects identified two features of the same object, parietal, premotor, and anterior cingulate activity was enhanced further, predominantly this time in the left hemisphere. Again, there was no direction sensitivity. Direction-sensitive activation of lateral frontal cortex also was increased. Finally, when subjects divided their attention over opposite hemifields, activity in the direction-sensitive occipital and frontal regions fell to a level midway between those seen during exclusively leftward or rightward attention. Thus, the behavioral efficiency with which we attend to multiple features of a single peripheral object is paralleled by enhanced activity in structures generally active during peripheral selective attention as well as in structures that depend on the specific direction of attention, most notably lateral frontal cortex. In addition, in the direction-sensitive regions, dividing attention over hemifields causes a compromise pattern between the extreme levels obtained during unilateral attention.

Adolescent↗
PubMed1997

Combining spatial extent and peak intensity to test for activations in functional imaging.

Within the framework of statistical mapping, there are up to now only two tests used to assess the regional significance in functional images. One is based on the magnitude of the foci and tends to detect high intensity signals, while the second is based on the spatial extent of regions defined by a simple thresholding of the statistical map, a test that is more sensitive to extended signals. The aim of this paper is to combine the two tests into a single test that is more sensitive to a wider range of signals. This combined test is based on an analytical approximation of the distribution of these two parameters (size and height) and is applied in the context of statistical maps. The risk of error in noise-only 2D or 3D volumes is assessed under a wide range of experimental conditions obtained by varying both the resolution of the map and the threshold at which clusters are defined. In addition, we have investigated this new test on simulated signals, and applied it to an experimental PET dataset. The experimental risk of error is close to the predicted one, and the overall sensitivity increases when analyzing a volume containing different types of signals.

Adult↗
PubMed1997

MRI and PET coregistration--a cross validation of statistical parametric mapping and automated image registration.

Coregistration of functional PET and T1-weighted MR images is a necessary step for combining functional information from PET images with anatomical information in MR images. Several coregistration algorithms have been published and are used in functional brain imaging studies. In this paper, we present a comparison and cross validation of the two most widely used coregistration routines (Friston et al., 1995, Hum. Brain Map. 2: 165-189; Woods et al., 1993, J. Comput. Assisted Tomogr: 17: 536-546). Several transformations were applied to high-resolution anatomical MR images to generate simulated PET images so that the exact (rigid body) transformations between each MR image and its associated simulated PET images were known. The estimation error of a coregistration in relation to the known transformation allows a comparison of the performance of different coregistration routines. Under the assumption that the simulated PET images embody the salient features of real PET images with respect to coregistration, this study shows that the routines examined reliably solve the MRI to PET coregistration problem.

Algorithms↗
PubMed1997

Characterizing the response of PET and fMRI data using multivariate linear models.

This paper presents a new method for characterizing brain responses in both PET and fMRI data. The aim is to capture the correlations between the scans of an experiment and a set of external predictor variables that are thought to affect the scans, such as type, intensity, or shape of stimulus response. Its main feature is a Canonical Variates Analysis (CVA) of the estimated effects of the predictors from a multivariate linear model (MLM). The advantage of this over current methods is that temporal correlations can be incorporated into the model, making the MLM method suitable for fMRI as well as PET data. Moreover, tests for the presence of any correlation, and inference about the number of canonical variates needed to capture that correlation, can be based on standard multivariate statistics, rather than simulations. When applied to an fMRI data set previously analyzed by another CVA method, the MLM method reveals a pattern of responses that is closer to that detected in an earlier non-CVA analysis.

Analysis of Variance↗
PubMed1997

Grey matter correlates of syndromes in schizophrenia. A semi-automated analysis of structural magnetic resonance images.

BACKGROUND: Symptoms in schizophrenia cluster into syndromes, each of which may be associated with a particular pattern of cerebral blood flow. We sought to investigate whether these syndromes are also related to neuroanatomical changes. METHOD: A semi-automated method was used to examine structural magnetic resonance images in 12 patients with schizophrenia. The relationship between the relative regional grey matter volume and ratings of the syndromes of psychomotor poverty, disorganisation and reality distortion was investigated. RESULTS: There was a significant negative correlation between psychomotor poverty score and the relative volume of the left ventro-medial prefrontal grey matter, and a significant positive correlation between disorganisation and the relative volumes of the hippocampus, and the parahippocampal/fusiform gyrus bilaterally. CONCLUSION: The correlation between psychomotor poverty and left prefrontal grey matter volume resembles that previously seen with prefrontal blood flow in the same patient, suggesting that this functional abnormality is related to an underlying anatomical change.

Adult↗
PubMed1996

Reproducibility of PET activation studies: lessons from a multi-center European experiment. EU concerted action on functional imaging.

PET activation studies are performed widely to study human brain function. The question of reproducibility, reliability, and comparability of the results of such experiments has never been addressed on a large scale. Recently, 12 European PET centers performed the same cognitive activation experiment in a European Union funded concerted action. The experiment involved a standardized and validated cross-lingual experimental and control task involving verbal fluency. Each center contributed at least 6 subjects. In total there were 77 subjects and 247 scans in each of the two conditions, giving 494 scans in total. We have analyzed each center's dataset and pooled datasets using statistical parametric mapping. We present results that address the consistency of these analyses, discuss the factors that influence their sensitivity, and comment on a number of related methodological issues. We used a MANOVA to test for center, condition, and centre by condition effects and found a strong condition and center effect and weaker interactions. The main effect determining reproducibility was the overall sensitivity of the experiment, to which the scanner and number of scans contribute in a major way, with a marked advantage for 3D scanners and a large field of view. An important conclusion is that data from different centers can be pooled to improve the reliability of results, which is of particular importance for studies in patients with rare conditions.

Adult↗
PubMed1996

Nonlinear regression in parametric activation studies.

Parametric study designs can reveal information about the relationship between a study parameter (e.g., word presentation rate) and regional cerebral blood flow (rCBF) in functional imaging. The brain's responses in relation to study parameters might be nonlinear, therefore the (linear) correlation coefficient as often used in the analysis of parametric studies might not be a proper characterization. We present a noninteractive method, which fits nonlinear functions of stimulus or task parameters to rCBF responses, using second-order polynomial expansions. This technique is implemented in the context of the general linear model and statistical parametric mapping. We also consider the usefulness of statistical inferences, based on F fields, about similarities and differences of these nonlinear responses in different groups. This approach is illustrated with a 12-run H215O PET activation study using an auditory paradigm of increasing word presentation rates. A patient who had recovered from severe aphasia and a normal control were studied. We demonstrate the ability of this new technique to identify brain regions where rCBF is closely related to increasing word presentation rate in both subjects without constraining the nature of this relationship and where these nonlinear responses differ.

Aphasia↗
PubMed1996

Detecting activations in PET and fMRI: levels of inference and power.

This paper is about detecting activations in statistical parametric maps and considers the relative sensitivity of a nested hierarchy of tests that we have framed in terms of the level of inference (voxel level, cluster level, and set level). These tests are based on the probability of obtaining c, or more, clusters with k, or more, voxels, above a threshold u. This probability has a reasonably simple form and is derived using distributional approximations from the theory of Gaussian fields. The most important contribution of this work is the notion of set-level inference. Set-level inference refers to the statistical inference that the number of clusters comprising an observed activation profile is highly unlikely to have occurred by chance. This inference pertains to the set of activations reaching criteria and represents a new way of assigning P values to distributed effects. Cluster-level inferences are a special case of set-level inferences, which obtain when the number of clusters c = 1. Similarly voxel-level inferences are special cases of cluster-level inferences that result when the cluster can be very small (i.e., k = 0). Using a theoretical power analysis of distributed activations, we observed that set-level inferences are generally more powerful than cluster-level inferences and that cluster-level inferences are generally more powerful than voxel-level inferences. The price paid for this increased sensitivity is reduced localizing power: Voxel-level tests permit individual voxels to be identified as significant, whereas cluster-and set-level inferences only allow clusters or sets of clusters to be so identified. For all levels of inference the spatial size of the underlying signal f (relative to resolution) determines the most powerful thresholds to adopt. For set-level inferences if f is large (e.g., fMRI) then the optimum extent threshold should be greater than the expected number of voxels for each cluster. If f is small (e.g., PET) the extent threshold should be small. We envisage that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.

Brain↗