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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.
The multifunctional molecule netrin-1 is upregulated in various malignancies and has recently been presented as a major general player in tumorigenesis leading to tumor progression and maintenance in various animal models. However, there is still a lack of clinico-epidemiological data related to netrin-1 expression. Therefore, the aim of our study was to elucidate the association of netrin-1 expression and patient survival in brain metastases since those constitute one of the most limiting factors for patient prognosis. We investigated 104 brain metastases cases for netrin-1 expression using in-situ hybridization and immunohistochemistry with regard to clinical parameters such as patient survival and MRI data. Our data show that netrin-1 is strongly upregulated in most cancer subtypes. Univariate analyses revealed netrin-1 expression as a significant factor associated with poor patient survival in the total cohort of brain metastasis patients and in sub-entities such as non-small cell lung carcinomas. Interestingly, many cancer samples showed a strong nuclear netrin-1 signal which was recently linked to a truncated netrin-1 variant that enhances tumor growth. Nuclear netrin-1 expression was associated with poor patient survival in univariate as well as in multivariate analyses. Our data indicate both total and nuclear netrin-1 expression as prognostic factors in brain metastases patients in contrast to other prognostic markers in oncology such as patient age, number of brain metastases or Ki67 proliferation index. Therefore, nuclear netrin-1 expression constitutes one of the first reported molecular biomarkers for patient survival in brain metastases. Furthermore, netrin-1 may constitute a promising target for future anti-cancer treatment approaches in brain metastases.