Similar white matter but opposite grey matter changes in schizophrenia and high-functioning autism.

OBJECTIVE: High-functioning autism (HFA) and schizophrenia (SZ) are two of the main neurodevelopmental disorders, sharing several clinical dimensions and risk factors. Their exact relationship is poorly understood, and few studies have directly compared both disorders. Our aim was thus to directly compare neuroanatomy of HFA and SZ using a multimodal MRI design. METHODS: We scanned 79 male adult subjects with 3T MRI (23 with HFA, 24 with SZ and 32 healthy controls, with similar non-verbal IQ). We compared them using both diffusion-based whole-brain tractography and T1 voxel-based morphometry. RESULTS: HFA and SZ groups exhibited similar white matter alterations in the left fronto-occipital inferior fasciculus with a decrease in generalized fractional anisotropy compared with controls. In grey matter, the HFA group demonstrated bilateral prefrontal and anterior cingulate increases in contrast with prefrontal and left temporal reductions in SZ. CONCLUSION: HFA and SZ may share common white matter deficits in long-range connections involved in social functions, but opposite grey matter abnormalities in frontal regions that subserve complex cognitive functions. Our results are consistent with the fronto-occipital underconnectivity theory of HFA and the altered connectivity hypothesis of SZ and suggest the existence of both associated and diametrical liabilities to these two conditions.

Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas.

This paper presents a method for automatic segmentation of white matter fiber bundles from massive dMRI tractography datasets. The method is based on a multi-subject bundle atlas derived from a two-level intra-subject and inter-subject clustering strategy. This atlas is a model of the brain white matter organization, computed for a group of subjects, made up of a set of generic fiber bundles that can be detected in most of the population. Each atlas bundle corresponds to several inter-subject clusters manually labeled to account for subdivisions of the underlying pathways often presenting large variability across subjects. An atlas bundle is represented by the multi-subject list of the centroids of all intra-subject clusters in order to get a good sampling of the shape and localization variability. The atlas, composed of 36 known deep white matter bundles and 47 superficial white matter bundles in each hemisphere, was inferred from a first database of 12 brains. It was successfully used to segment the deep white matter bundles in a second database of 20 brains and most of the superficial white matter bundles in 10 subjects of the same database.

Creation of a whole brain short association bundle atlas using a hybrid approach.

The Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we expanded a previously developed method for the automatic creation of a whole brain SWM bundle atlas. The method is based on a hybrid approach. First a cortical parcellation is used to extract fibers connecting two regions. Then an intra-and inter-subject hierarchical clustering are applied to find well-defined SWM bundles reproducible across subjects. In addition to the fronto-parietal and insula regions of the left hemisphere, the analysis was extended to the temporal and occipital lobes, including all their internal regions, for both hemispheres. Validation steps are performed in order to test the robustness of the method and the reproducibility of the obtained bundles. First the method was applied to two independent groups of subjects, in order to discard bundles without match across the two independent atlases. Then, the resulting intersection atlas was projected on a third independent group of subjects in order to filter out bundles without reproducible and reliable projection. The final multi-subject U-fiber atlas is composed of 100 bundles in total, 50 per hemisphere, from which 35 are common to both hemispheres. The atlas can be used in clinical studies for segmentation of the SWM bundles in new subjects, and measure DW values or complement functional data.

Short association bundle atlas based on inter-subject clustering from HARDI data.

This paper is focused on the study of short brain association fibers. We present an automatic method to identify short bundles of the superficial white matter based on inter-subject hierarchical clustering. Our method finds clusters of similar fibers, belonging to the different subjects, according to a distance measure between fibers. First, the algorithm obtains representative bundles and subsequently we perform an automatic labeling based on the anatomy, of the most stable connections. The analysis was applied to two independent groups of 37 subjects. Results between the two groups were compared, in order to keep reproducible connections for the atlas creation. The method was applied using linear and non-linear registration, where the non-linear registration showed significantly better results. A final atlas with 35 bundles in the left hemisphere and 27 in the right hemisphere from the whole brain was obtained. Finally results were validated using the atlas to segment 26 new subjects from another HARDI database.

Automatic segmentation of short association bundles using a new multi-subject atlas of the left hemisphere fronto-parietal brain connections.

Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of the pathogenesis associated to it. In this work we developed a method for the automatic creation of a SWM bundle multi-subject atlas. The atlas generation method is based on a cortical parcellation for the extraction of fibers connecting two different gyri. Then, an intra-subject fiber clustering is applied, in order to divide each bundle into sub-bundles with similar shape. After that, a two-step inter-subject fiber clustering is used in order to find the correspondence between the sub-bundles across the subjects, fuse similar clusters and discard the outliers. The method was applied to 40 subjects of a high quality HARDI database, focused on the left hemisphere fronto-parietal and insula brain regions. We obtained an atlas composed of 44 bundles connecting 22 pair of ROIs. Then the atlas was used to automatically segment 39 new subjects from the database.