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The Sonic Hedgehog (SHH) pathway plays a central role in the developing mammalian CNS. In our study, we aimed to investigate the spatiotemporal SHH pathway expression pattern in human fetal brains. We analyzed 22 normal fetal brains for Shh, Patched, Smoothened, and Gli1-3 expression by immunohistochemistry. In the telencephalon, strongest expression of Shh, Smoothened, and Gli2 was found in the cortical plate (CP) and ventricular zone. Patched was strongly upregulated in the ventricular zone and Gli1 in the CP. In the cerebellum, SHH pathway members were strongly expressed in the external granular layer (EGL). SHH pathway members significantly decreased over time in the ventricular and subventricular zone and in the cerebellar EGL, while increasing levels were found in more superficial telencephalic layers. Our findings show that SHH pathway members are strongly expressed in areas important for proliferation and differentiation and indicate a temporal expression gradient in telencephalic and cerebellar layers probably due to decreased proliferation of progenitor cells and increased differentiation. Our data about the spatiotemporal expression of SHH pathway members in the developing human brain serves as a base for the understanding of both normal and pathological CNS development.
Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.