Jean-Baptiste Poline

Fast reproducible identification and large-scale databasing of individual functional cognitive networks.

BackgroundAlthough cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level.Results81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects.ConclusionThis collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.

A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study.

An empirical comparison of surface-based and volume-based group studies in neuroimaging

Being able to detect reliably functional activity in a population of subjects is crucial in human brain mapping, both for the understanding of cognitive functions in normal subjects and for the analysis of patient data. The usual approach proceeds by normalizing brain volumes to a common three-dimensional template. However, a large part of the data acquired in fMRI aims at localizing cortical activity, and methods working on the cortical surface may provide better inter-subject registration than the standard procedures that process the data in the volume. Nevertheless, few assessments of the performance of surface-based (2D) versus volume-based (3D) procedures have been shown so far, mostly because inter-subject cortical surface maps are not easily obtained. In this paper we present a systematic comparison of 2D versus 3D group-level inference procedures, by using cluster-level and voxel-level statistics assessed by permutation, in random effects (RFX) and mixed-effects analyses (MFX). We consider different schemes to perform meaningful comparisons between thresholded statistical maps in the volume and on the cortical surface. We find that surface-based multi-subject statistical analyses are generally more sensitive than their volume-based counterpart, in the sense that they detect slightly denser networks of regions when performing peak-level detection; this effect is less clear for cluster-level inference and is reduced by smoothing. Surface-based inference also increases the reliability of the activation maps.

Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators

Medical imaging datasets often contain deviant observations, the so-called outliers, due to acquisition or preprocessing artifacts or resulting from large intrinsic inter-subject variability. These can undermine the statistical procedures used in group studies as the latter assume that the cohorts are composed of homogeneous samples with anatomical or functional features clustered around a central mode. The effects of outlying subjects can be mitigated by detecting and removing them with explicit statistical control. With the emergence of large medical imaging databases, exhaustive data screening is no longer possible, and automated outlier detection methods are currently gaining interest. The datasets used in medical imaging are often high-dimensional and strongly correlated. The outlier detection procedure should therefore rely on high-dimensional statistical multivariate models. However, state-of-the-art procedures, based on the Minimum Covariance Determinant (MCD) estimator, are not well-suited for such high-dimensional settings. In this work, we introduce regularization in the MCD framework and investigate different regularization schemes. We carry out extensive simulations to provide backing for practical choices in absence of ground truth knowledge. We demonstrate on functional neuroimaging datasets that outlier detection can be performed with small sample sizes and improves group studies.

PyXNAT: XNAT in Python

As neuroimaging databases grow in size and complexity, the time researchers spend investigating and managing the data increases to the expense of data analysis. As a result, investigators rely more and more heavily on scripting using high-level languages to automate data management and processing tasks. For this, a structured and programmatic access to the data store is necessary. Web services are a first step toward this goal. They however lack in functionality and ease of use because they provide only low-level interfaces to databases. We introduce here PyXNAT, a Python module that interacts with The Extensible Neuroimaging Archive Toolkit (XNAT) through native Python calls across multiple operating systems. The choice of Python enables PyXNAT to expose the XNAT Web Services and unify their features with a higher level and more expressive language. PyXNAT provides XNAT users direct access to all the scientific packages in Python. Finally PyXNAT aims to be efficient and easy to use, both as a back-end library to build XNAT clients and as an alternative front-end from the command line.

Perceptual alternations between unbound moving contours and bound shape motion engage a ventral/ dorsal interplay

Visual shape and motion information, processed in distinct brain regions, should be combined to elicit a unitary coherent percept of an object in motion. In an fMRI study, we identified brain regions underlying the perceptual binding of motion and shape independently of the features-contrast, motion, and shape-used to design the moving displays. These displays alternately elicited a bound (moving diamond) or an unbound (disconnected moving segments) percept, and were either physically unchanging yet perceptually bistable or physically changing over time. The joint analysis of the blood-oxygen-level-dependent (BOLD) signals recorded during bound or unbound perception with these different stimuli revealed a network comprising the occipital lobe and ventral and dorsal visual regions. Bound percepts correlated with in-phase BOLD increases within the occipital lobe and a ventral area and decreased activity in a dorsal area, while unbound percepts elicited moderate BOLD modulations in these regions. This network was similarly activated by bistable unchanging displays and by displays periodically changing over time. The uncovered interplay between the two regions is proposed to reflect a generic binding process that dynamically weights the perceptual evidence supporting the different shape and motion interpretations according to the reliability of the neural activity in these regions.

High level group analysis of FMRI data based on dirichlet process mixture models

Inferring the position of functionally active regions from a multi-subject fMRI dataset involves the comparison of the individual data and the inference of a common activity model. While voxel-based analyzes, e.g. Random Effect statistics, are widely used, they do not model each individual activation pattern. Here, we develop a new procedure that extracts structures individually and compares them at the group level. For inference about spatial locations of interest, a Dirichlet Process Mixture Model is used. Finally, inter-subject correspondences are computed with Bayesian Network models. We show the power of the technique on both simulated and real datasets and compare it with standard inference techniques.

Frontiers in brain imaging methods grand challenge.

ibility and reliability of research results, and thus on methods that ensure optimal outcomes. We identify below several directions that we hope to push forward in the publication of brain imaging methods. In line with the principles of the Frontiers journals, we propose that openness (in code, data, and access) is of fundamental importance. Methods that M atter Methods for data acquisition and analysis are and will increasingly be central to scientific research using brain imaging. However, some methods matter more than others, either because they have a strong impact on the way we acquire, process, manage, interpret or reuse the data, or because they are more principled or mathematically grounded. Too often the impact of a new method is poorly assessed, and new methods are often developed that are unlikely to be applicable to actual studies. We hope to see a greater focus on the impact of improvements of acquisition and processing methods, and more work on how to make these methods accessible to the scientific community. o pen data and code

Processing 3D form and 3D motion: respective contributions of attention-based and stimulus-driven activity.

This study aims at segregating the neural substrate for the 3D-form and 3D-motion attributes in structure-from-motion perception, and at disentangling the stimulus-driven and endogenous-attention-driven processing of these attributes. Attention and stimulus were manipulated independently: participants had to detect the transitions of one attribute--form, 3D motion or colour--while the visual stimulus underwent successive transitions of all attributes. We compared the BOLD activity related to form and 3D motion in three conditions: stimulus-driven processing (unattended transitions), endogenous attentional selection (task) or both stimulus-driven processing and attentional selection (attended transitions). In all conditions, the form versus 3D-motion contrasts revealed a clear dorsal/ventral segregation. However, while the form-related activity is consistent with previously described shape-selective areas, the activity related to 3D motion does not encompass the usual visual motion areas, but rather corresponds to a high-level motion system, including IPL and STS areas. Second, we found a dissociation between the neural processing of unattended attributes and that involved in endogenous attentional selection. Areas selective for 3D-motion and form showed either increased activity at transitions of these respective attributes or decreased activity when subjects attention was directed to a competing attribute. We propose that both facilitatory and suppressive mechanisms of attribute selection are involved depending on the conditions driving this selection. Therefore, attentional selection is not limited to an increased activity in areas processing stimulus properties, and may unveil different functional localization from stimulus modulation.

Randomized Parcellation Based Inference

Neuroimaging group analyses are used to relate inter-subject signal differences observed in brain imaging with behavioral or genetic variables and to assess risks factors of brain diseases. The lack of stability and of sensitivity of current voxel-based analysis schemes may however lead to non-reproducible results. We introduce a new approach to overcome the limitations of standard methods, in which active voxels are detected according to a consensus on several random parcellations of the brain images, while a permutation test controls the false positive risk. Both on synthetic and real data, this approach shows higher sensitivity, better accuracy and higher reproducibility than state-of-the-art methods. In a neuroimaging-genetic application, we find that it succeeds in detecting a significant association between a genetic variant next to the COMT gene and the BOLD signal in the left thalamus for a functional Magnetic Resonance Imaging contrast associated with incorrect responses of the subjects from a Stop Signal Task protocol.