Normalisierung von hochauflösenden DTI-Datensätzen zu einem Standardtemplate, Optimierung der Normalisierungsmethoden sowie quantitative und qualitative Analyse der Ergebnisse

Spatial normalization of individual data sets to a common template is a crucial step in many neuroimaging data analysis pipelines. Its accuracy has a profound impact on subsequent image analysis and is very likely to influence final results. Here, we investigate the spatial normalization of individual fractional anisotropy (FA) images from diffusion tensor imaging (DTI) to an FA template using three widely used image-processing software packages: FSL, SPM, and ANTs. We compared normalization results using each software’s default settings and after a step-wise adjustment of optional normalization parameters. 37 FA images from 19 healthy controls and 18 patients with non-lesional focal epilepsy were normalized to an FA template in Montreal Neurological Institute (MNI) space. Normalization results were evaluated qualitatively, using isoline display for visual inspection and quantitatively, calculating voxelwise cross-correlation and absolute difference values between each normalized individual FA and the template image. Average cross-correlation values after FSL normalization ranged from 0.903 with default settings to 0.939 with optimized settings with an average intensity difference of 4.7 to 3:7%, respectively. SPM achieved cross-correlation values from 0.788 to 0.877 and intensity differences from 7.0 to 5:5%. ANTs yielded the best quantitative normalization results with cross-correlation values ranging from 0.953 to 0.976 and intensity differences from 3.5 to 2:9%. Visual inspection showed that these results were achieved by ANTs using much stronger local deformations, at the expense of losing various individual anatomical features. These findings illustrate the significant differences between alternative normalization procedures and the effect of optimizing normalization parameters. It is important to adjust those settings to the specific data used and the specific questions asked to ensure a spatial normalization best suited for the intended subsequent image analyses

Structural and functional brain patterns predict formal thought disorder’s severity and its persistence in recent-onset psychosis:Results from the PRONIA Study

Background: Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level. Methods: 233 individuals with recent-onset psychosis were drawn from the multi-site European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multi-band fractional amplitude of low frequency fluctuations (fALFF), gray-matter volume (GMV) and white-matter volume (WMV) data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up. Results: Cross-sectionally, multivariate patterns of GMV within the salience, dorsal attention, visual and ventral attention networks separated the FThD severity subgroups (BAC=60.8%). Longitudinally, distributed activations/deactivations within all fALFF sub-bands (BACslow-5=73.2%, BACslow-4=72.9%, BACslow-3=68.0), GMV patterns overlapping with the cross-sectional ones (BAC=62.7%) and smaller frontal WMV (BAC=73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multi-modal balanced accuracy of BAC=77%. Conclusions: We report first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open the avenue for the development of neuroimaging-based diagnostic, prognostic and treatment options for the early recognition and management of FThD and associated poor outcomes