J-b. Poline - Academia.edu (original) (raw)

Papers by J-b. Poline

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., 2006

Abstract Standard inference in neuroimaging consists in describing brain activations elicited and... more Abstract Standard inference in neuroimaging consists in describing brain activations elicited and modulated by different kinds of stimuli. Recently however, paradigms have been studied in which the converse operation is performed, thus inferring behavioral or mental states associated with activation images. Here, we use the well-known retinotopy of the visual cortex to infer the visual content of real scenes from the activation patterns that they elicit. We present an explicit decoding technique, based on the current knowledge of the retinotopic ...

2010 2nd International Conference on Image Processing Theory, Tools and Applications, 2010

ABSTRACT As fMRI data is high dimensional, applications like connectivity studies, normalization ... more ABSTRACT As fMRI data is high dimensional, applications like connectivity studies, normalization or multivariate analyses, need to reduce data dimension while minimizing the loss of functional information. In our study we use connectivity profiles as a new functional feature to aggregate voxels into clusters. This offers two major advantages in comparison with the current clustering methods. It allows the analyst to deal with the spatial correlation of noise problem, that can lead to bad mergings in the functional domain, and it is based on the whole data independently of a priori information like the General Linear Model (GLM) regressors. We validated that the resulting clusters form a partition of the data in homogeneous regions according to both spatial and functional criteria.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2005

This paper proposes a method to infer a high level model of the white matter organization from a ... more This paper proposes a method to infer a high level model of the white matter organization from a population of subjects using MR diffusion imaging. This method takes as input for each subject a set of trajectories stemming from any tracking algorithm. Then the inference results from two nested clustering stages. The first clustering converts each individual set of trajectories into a set of bundles supposed to represent large white matter pathways. The second clustering matches these bundles across subjects in order to provide a list of candidates for the bundle model. The method is applied on a population of eleven subjects and leads to the inference of 17 such candidates.

NeuroImage, 2000

This paper concerns temporal filtering in fMRI time-series analysis. Whitening serially correlate... more This paper concerns temporal filtering in fMRI time-series analysis. Whitening serially correlated data is the most efficient approach to parameter estimation. However, if there is a discrepancy between the assumed and the actual correlations, whitening can render the analysis exquisitely sensitive to bias when estimating the standard error of the ensuing parameter estimates. This bias, although not expressed in terms of the estimated responses, has profound effects on any statistic used for inference. The special constraints of fMRI analysis ensure that there will always be a misspecification of the assumed serial correlations. One resolution of this problem is to filter the data to minimize bias, while maintaining a reasonable degree of efficiency. In this paper we present expressions for efficiency (of parameter estimation) and bias (in estimating standard error) in terms of assumed and actual correlation structures in the context of the general linear model. We show that: (i) Wh...

Statistical Parametric Mapping, 2007

NeuroImage, 1996

PET activation studies are performed widely to study human brain function. The question of reprod... more 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.

IEEE Transactions on Signal Processing, 2000

Different approaches have been considered so far to cope with the temporal correlation of fMRI da... more Different approaches have been considered so far to cope with the temporal correlation of fMRI data for brain activity detection. However, it has been reported that modeling this serial correlation has little influence on the estimate of the hemodynamic response function (HRF). In this paper, we examine this issue when performing a joint detectionestimation of brain activity in a given homogeneous region of interest (ROI). Following [1], we adopt a space-varying AR(1) temporal noise model and assess its influence, on both the estimation of the HRF and the detection of brain activity, using synthetic and real fMRI data. We show that this model yields a significant gain in detection specificity (lower false positive rate).

Human Brain Mapping, 1995

This paper concerns the spatial and intensity transformations that map one image onto another. We... more This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions.

Journal of neurophysiology, 1998

The aim of this study was to investigate the functional anatomy of distributed cortical and subco... more The aim of this study was to investigate the functional anatomy of distributed cortical and subcortical motor areas in the human brain that participate in the central control of overlearned complex sequential unimanual finger movements. On the basis of previous research in nonhuman primates, a principal involvement of basal ganglia medial premotor loops [corrected] was predicted for central control of finger sequences performed automatically. In pertinent areas, a correlation of activation levels with the complexity of a motor sequence was hypothesized. H2 15O positron emission tomography (PET) was used in a group of seven healthy male volunteers [mean age 32.0 +/- 10.4 yr] to determine brain regions where levels of regional cerebral blood flow (rCBF) correlated with graded complexity levels of five different key-press sequences. All sequences were overlearned before PET and involved key-presses of fingers II-V of the right hand. Movements of individual fingers were kept constant th...