Structural analysis of fMRI data: a surface-based framework for multi-subject studies.

We present a method for fMRI data group analysis that makes the link between two distinct frameworks: surface-based techniques, which process data in the domain defined by the surface of the cortex, and structural techniques, which use object-based representations of the data as opposed to voxel-based ones. This work is a natural surface-based extension of the volume-based structural approach presented in a previous paper. A multi-scale surface-based representation of individual activation maps is first computed for each subject. Then the inter-subject matching and the activation detection decision are performed jointly by optimization of a Markovian model. Finally, a significance measure is computed in a non-parametric way for the results, in order to assess their relevance and control the risk of type I error. The method is applied on simulated and real data and the results are compared to those produced by standard analyses. The surface-based structural analysis is shown to be particularly robust to inter-subject spatial variability and to produce relevant results with good specificity and sensitivity. We also demonstrate the advantages of the surface-based approach by comparing with the results of a 3D structural analysis.

Automatic estimation of pelvic organ anatomical references.

Pelvic floor diseases cover pathologies of which physiopathology is not well understood. 2D sagittal MRI sequences used in the clinical assessment allow to visualize the dynamic behavior of the main organs involved (bladder, uterus-vagina and rectum). Clinicians use anatomical landmarks and measurements related to the pelvic organs in their pathology assessment. Usually, those tasks are performed manually which results in being both tedious and subject to operator dependency. A methodology is proposed to attempt a quantitative and objective characterization of the organ behaviors under abdominal strain condition. This approach automatically assesses the organ movements, through the estimation of characteristic angles (anorectal angle, uterovaginal angle, bladder inclination), and the tracking of anatomically significant points (anorectal angle vertex, uterovaginal angle vertex, bladder neck). From a multi-subject analysis, pathological organs have been distinguished from healthy ones, which shows the relevance of the computed features. In addition, a stability analysis has shown the soundness of the approach.

Surface-based structural group analysis of fMRI data.

As structural and surface-based analyses gain interest for activation detection, morphometry and intersubject matching purposes, this paper proposes a method to perform structural group analyses directly on the cortical surface. Scale-space blobs are extracted from surface-based functional maps and matched across subjects. The process aims at identifying activations within a population despite the various effects due to variability. Results of the method are presented with simulated activations and with data from a somatotopy protocol.

Anatomically informed convolution kernels for the projection of fMRI data on the cortical surface.

We present here a method that aims at producing representations of functional brain data on the cortical surface from functional MRI volumes. Such representations are required for subsequent cortical-based functional analysis. We propose a projection technique based on the definition, around each node of the grey/white matter interface mesh, of convolution kernels whose shape and distribution rely on the geometry of the local anatomy. For one anatomy, a set of convolution kernels is computed that can be used to project any functional data registered with this anatomy. The method is presented together with experiments on synthetic data and real statistical t-maps.