From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder.

Pattern classification and stratification approaches have increasingly been used in research on Autism Spectrum Disorder (ASD) over the last ten years with the goal of translation towards clinical applicability. Here, we present an extensive scoping literature review on those two approaches. We screened a total of 635 studies, of which 57 pattern classification and 19 stratification studies were included. We observed large variance across pattern classification studies in terms of predictive performance from about 60% to 98% accuracy, which is among other factors likely linked to sampling bias, different validation procedures across studies, the heterogeneity of ASD and differences in data quality. Stratification studies were less prevalent with only two studies reporting replications and just a few showing external validation. While some identified strata based on cognition and intelligence reappear across studies, biology as a stratification marker is clearly underexplored. In summary, mapping biological differences at the level of the individual with ASD is a major challenge for the field now. Conceptualizing those mappings and individual trajectories that lead to the diagnosis of ASD, will become a major challenge in the near future.

Pattern of Cerebellar Atrophy in Friedreich's Ataxia-Using the SUIT Template.

Whole-brain voxel-based morphometry (VBM) studies revealed patterns of patchy atrophy within the cerebellum of Friedreich's ataxia patients, missing clear clinico-anatomic correlations. Studies so far are lacking an appropriate registration to the infratentorial space. To circumvent these limitations, we applied a high-resolution atlas template of the human cerebellum and brainstem (SUIT template) to characterize regional cerebellar atrophy in Friedreich's ataxia (FRDA) on 3-T MRI data. We used a spatially unbiased voxel-based morphometry approach together with T2-based manual segmentation, T2 histogram analysis, and atlas generation of the dentate nuclei in a representative cohort of 18 FRDA patients and matched healthy controls. We demonstrate that the cerebellar volume in FRDA is generally not significantly different from healthy controls but mild lobular atrophy develops beyond normal aging. The medial parts of lobule VI, housing the somatotopic representation of tongue and lips, are the major site of this lobular atrophy, which possibly reflects speech impairment. Extended white matter affection correlates with disease severity across and beyond the cerebellar inflow and outflow tracts. The dentate nucleus, as a major site of cerebellar degeneration, shows a mean volume loss of about 30%. Remarkably, not the atrophy but the T2 signal decrease of the dentate nuclei highly correlates with disease duration and severity.

LISA improves statistical analysis for fMRI.

One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. Here, we propose a non-parametric and threshold-free framework called LISA to address this demand. It uses a non-linear filter for incorporating spatial context without sacrificing spatial precision. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. Compared to widely used other methods, it shows a boost in statistical power and allows to find small activation areas that have previously evaded detection. The spatial sensitivity of LISA makes it especially suitable for the analysis of high-resolution fMRI data acquired at ultrahigh field (≥7 Tesla).

Functional parcellation using time courses of instantaneous connectivity.

Functional neuroimaging studies have led to understanding the brain as a collection of spatially segregated functional networks. It is thought that each of these networks is in turn composed of a set of distinct sub-regions that together support each network's function. Considering the sub-regions to be an essential part of the brain's functional architecture, several strategies have been put forward that aim at identifying the functional sub-units of the brain by means of functional parcellations. Current parcellation strategies typically employ a bottom-up strategy, creating a parcellation by clustering smaller units. We propose a novel top-down parcellation strategy, using time courses of instantaneous connectivity to subdivide an initial region of interest into sub-regions. We use split-half reproducibility to choose the optimal number of sub-regions. We apply our Instantaneous Connectivity Parcellation (ICP) strategy on high-quality resting-state FMRI data, and demonstrate the ability to generate parcellations for thalamus, entorhinal cortex, motor cortex, and subcortex including brainstem and striatum. We evaluate the subdivisions against available cytoarchitecture maps to show that our parcellation strategy recovers biologically valid subdivisions that adhere to known cytoarchitectural features.