Y. Cointepas

Assessment of the early organization and maturation of infants' cerebral white matter fiber bundles: a feasibility study using quantitative diffusion tensor imaging and tractography.

The human infant is particularly immature at birth and brain maturation, with the myelination of white matter fibers, is protracted until adulthood. Diffusion tensor imaging offers the possibility to describe non invasively the fascicles spatial organization at an early stage and to follow the cerebral maturation with quantitative parameters that might be correlated with behavioral development. Here, we assessed the feasibility to study the organization and maturation of major white matter bundles in eighteen 1- to 4-month-old healthy infants, using a specific acquisition protocol customized to the immature brain (with 15 orientations of the diffusion gradients and a 700 s mm(-2)b factor). We were able to track most of the main fascicles described at later ages despite the low anisotropy of the infant white matter, using the FACT algorithm. This mapping allows us to propose a new method of quantification based on reconstructed tracts, split between specific regions, which should be more sensitive to specific changes in a bundle than the conventional approach, based on regions-of-interest. We observed variations in fractional anisotropy and mean diffusivity over the considered developmental period in most bundles (corpus callosum, cerebellar peduncles, cortico-spinal tract, spino-thalamic tract, capsules, radiations, longitudinal and uncinate fascicles, cingulum). The results are in good agreement with the known stages of white matter maturation and myelination, and the proposed approach might provide important insights on brain development.

In vivo evidence for the selective subcortical degeneration in Huntington's disease.

Although Huntingtons disease is largely considered to be a subcortical disease, there is no clear consensus on whether all deep grey matter loss is a direct downstream consequence of the massive degeneration of the medium-size spiny neurons in the striatum. Our aim was to characterise in vivo such preferential degeneration by analysing various distinct diffusion imaging measures including mean diffusivity, anisotropy, fibre orientation (using the information of the principal diffusion direction) and white matter tractography. All results converged to demonstrate the selective degeneration of connections in subcortical grey and white matter, degeneration which was likely to originate with the death of the striatal medium-size spiny neurons. Indeed, we found a significant increase of MD and FA in all the subcortical grey matter structures involved in the cortico-striato-thalamo-cortical loops. The atypical striatal and pallidal increase of FA was concurrent to a decrease of the dispersion of the fibre orientation, unambiguously characterising a preferential loss of connections along specific radiating directions from these structures while some others are comparatively spared. Analysis of striatal and pallidal white matter tracts revealed that striato-pallidal projections were the most affected. The ability of DTI to uncover the impact of such neurodegenerative disease on some specific neuronal/axonal populations is a further step towards the future definition of a surrogate marker of this disease. Beyond Huntingtons disease, we prove here that diffusion imaging technique, associated to adequate methodological analyses, can provide insight into any neurodegenerative disorder for which some neuronal populations or connections are selectively targeted over others.

Fiber tracking in q-ball fields using regularized particle trajectories

Most of the approaches dedicated to fiber tracking from diffusion-weighted MR data rely on a tensor model. However, the tensor model can only resolve a single fiber orientation within each imaging voxel. New emerging approaches have been proposed to obtain a better representation of the diffusion process occurring in fiber crossing. In this paper, we adapt a tracking algorithm to the q-ball representation, which results from a spherical Radon transform of high angular resolution data. This algorithm is based on a Monte-Carlo strategy, using regularized particle trajectories to sample the white matter geometry. The method is validated using a phantom of bundle crossing made up of haemodialysis fibers. The method is also applied to the detection of the auditory tract in three human subjects.

Uncinate fasciculus fiber tracking in mesial temporal lobe epilepsy. Initial findings

In temporal lobe epilepsy (TLE) due to hippocampal sclerosis (HS), ictal discharge spread to the frontal and insulo-perisylvian cortex is commonly observed. The implication of white matter pathways in this propagation has not been investigated. We compared diffusion tensor imaging (DTI) measurements along the uncinate fasciculus (UF), a major tract connecting the frontal and temporal lobes, in patients and controls. Ten right-handed patients referred for intractable TLE due to a right HS were investigated on a 1.5-T MR scanner including a DTI sequence. All patients had interictal fluorodeoxyglucose PET showing an ipsilateral temporal hypometabolism associated with insular and frontal or perisylvian hypometabolism. The controls consisted of ten right-handed healthy subjects. UF fiber tracking was performed, and its fractional anisotropy (FA) values were compared between patients and controls, separately for the right and left UF. The left-minus-right FA UF asymmetry index was computed to test for intergroup differences. Asymmetries were found in the control group with right-greater-than-left FA. This asymmetrical pattern was lost in the patient group. Right FA values were lower in patients with right HS versus controls. Although preliminary, these findings may be related to the preferential pathway of seizure spread from the mesial temporal lobe to frontal and insulo-perisylvian areas.

Human Subinsular Asymmetry Studied by Diffusion Tensor Imaging and Fiber Tracking

BACKGROUND AND PURPOSE: Our aim was to improve our understanding of the subinsular white matter microstructural asymmetries in healthy right-handed subjects. Structural brain asymmetries could be related to functional asymmetries such as hemisphere language dominance or handedness. Besides the known gray matter asymmetries, white matter asymmetries could also play a key role in the understanding of hemispheric specialization, notably that of language. MATERIALS AND METHODS: White matter asymmetries were studied by diffusion tensor imaging at 1.5T (41 diffusion-gradient directions; b-value set to 700 s/mm2; matrix, 1282; in-plane resolution, 1.875 × 1.875 mm; section thickness, 2.0 mm) and fiber tracking (BrainVISA software). The main white matter bundles passing through the subinsular area were segmented, and fractional anisotropy (FA) was measured along each of the segmented bundles. RESULTS: In line with published results, we found an asymmetry of the arcuate fasciculus and the subinsular white matter, namely left-greater-than-right FA in right-handed controls. Furthermore, by segmenting major tracts coursing through this region, we showed that the subinsular portions of the uncinate fasciculus (UF) and the inferior occipitofrontal fasciculus (IOF) contribute to this FA asymmetry. Those tracts have been reported to be likely implicated in the language network. CONCLUSION: Because the left hemisphere hosts language functions in most right-handers, the significant leftward asymmetry observed within the arcuate fasciculus, the subinsular part of the UF and IOF may be related to the hemispheric specialization for language.

Validation of a Standardized Normalization Template for Statistical Parametric Mapping Analysis of 123I-FP-CIT Images

123I-FP-CIT (123I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane) is a SPECT dopamine transporter (DAT) tracer that probes dopaminergic cell loss in Parkinsons disease (PD). Quantification of 123I-FP-CIT images is performed at equilibrium using a ratio (BR) of specific (striatal) to nonspecific (occipital) uptake with values obtained from regions of interest drawn manually over these structures. Statistical parametric mapping (SPM) is a fully automated voxel-based statistical approach that has great potential in the context of DAT imaging. However, the accuracy of the spatial normalization provided by SPM has not been validated for 123I-FP-CIT images. Our first aim was to create an 123I-FP-CIT template that does not require the acquisition of patient-specific MRI and to validate the spatial normalization procedure. Next, we hypothesized that this customized template could be used by different SPECT centers without affecting the outcomes of imaging analyses. Methods: The spatial normalization to the customized template created with SPM (template A1) was validated using 123I-FP-CIT images obtained from 6 subjects with essential tremor (ET) with normal DAT status and 6 PD patients. Variability in BR values due to the normalization was evaluated using striatal volume of interest (VOI). To determine whether different SPECT centers could use a unique 123I-FP-CIT template, we generated 3 other 123I-FP-CIT templates using different subjects and image-processing schemes. The interchangeability of these templates was assessed using (a) putamen BR values analyzed with the intraclass correlation coefficient (ICC) and the Bland–Altman graphical analysis, and (b) SPM analysis comparing the results of group comparisons—that is, ET versus PD, obtained after normalization to each of the 4 templates. Results: There was no significant difference between pre- and postnormalization striatal BR values in our study. The mean variability calculated with putamen VOI values after normalization to each template was <10%, with the lowest ICC of 98%. Intergroup analyses performed with VOI and SPM approaches provided similar results independently of the template used. Conclusion: SPM normalization was accurate even in subjects with low striatal 123I-FP-CIT uptake, making it a promising approach for automatic analysis of 123I-FP-CIT images using a single customized template at different centers.

Toward global tractography

Diffusion-based tractography is an ill-posed problem, because the step-by-step reconstruction of a fibre bundle trajectory cannot afford any serious mistake in the evaluation of the local fibre orientations. Such evaluation is difficult, however, because the myriad fibres passing through a single voxel follow different directions. Modelling tractography as a global inverse problem is a simple framework which addresses the ill-posed nature of the problem. The key idea is that the results of tractography in the neighbourhood of an ambiguous local diffusion profile can help to infer the local fibre directions. This paper provides an overview of past achievements of global tractography and proposes guidelines for a future research programme in the hope that the potential of the technique will increase the interest of the community.

Diffusion tensor imaging of the human optic nerve using a non-CPMG fast spin echo sequence

To investigate the diffusion tensor properties of the human optic nerve in vivo using a non‐Carr‐Purcell‐Meiboom‐Gill (CPMG) fast spin echo (FSE) sequence.

Coordinate-based versus structural approaches to brain image analysis

A basic issue in neurosciences is to look for possible relationships between brain architecture and cognitive models. The lack of architectural information in magnetic resonance images, however, has led the neuroimaging community to develop brain mapping strategies based on various coordinate systems without accurate architectural content. Therefore, the relationships between architectural and functional brain organizations are difficult to study when analyzing neuroimaging experiments. This paper advocates that the design of new brain image analysis methods inspired by the structural strategies often used in computer vision may provide better ways to address these relationships. The key point underlying this new framework is the conversion of the raw images into structural representations before analysis. These representations are made up of data-driven elementary features like activated clusters, cortical folds or fiber bundles. Two classes of methods are introduced. Inference of structural models via matching across a set of individuals is described first. This inference problem is illustrated by the group analysis of functional statistical parametric maps (SPMs). Then, the matching of new individual data with a priori known structural models is described, using the recognition of the cortical sulci as a prototypical example.

MR diffusion-based inference of a fiber bundle model from a population of subjects

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.