Common Genetic Variant in VIT Is Associated with Human Brain Asymmetry.

Brain asymmetry varies across individuals. However, genetic factors contributing to this normal variation are largely unknown. Here we studied variation of cortical surface area asymmetry in a large sample of subjects. We performed principal component analysis (PCA) to capture correlated asymmetry variation across cortical regions. We found that caudal and rostral anterior cingulate together account for a substantial part of asymmetry variation among individuals. To find SNPs associated with this subset of brain asymmetry variation we performed a genome-wide association study followed by replication in an independent cohort. We identified one SNP (rs11691187) that had genome-wide significant association (P Combined = 2.40e-08). The rs11691187 is in the first intron of VIT. In a follow-up analysis, we found that VIT gene expression is associated with brain asymmetry in six donors of the Allen Human Brain Atlas. Based on these findings we suggest that VIT contributes to normal brain asymmetry variation. Our results can shed light on disorders associated with altered brain asymmetry.

Self-organization of synchronous activity propagation in neuronal networks driven by local excitation.

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence.

Topological pattern selection in recurrent networks.

The impact of adding correlation to a population of neurons on the information and the activity of the population is one of the fundamental questions in recent system neuroscience. In this paper, we would like to introduce topology-based correlation at the level of storing patterns in a recurrent network. We then study the effects of topological patterns on the activity and memory capacity of the network. The general aim of the present work is to show how the repertoire of possible stored patterns is determined by the underlying network topology. Two topological probability rules for pattern selection in recurrent network are introduced. The first one selects patterns according to a Gibbs-type distribution. We start with a Hopfield-type dynamics on a ring model and then a Langevin model on a general random graph is treated. The phenomenon of phase transition in pattern selection motivated us to introduce an alternative topological rule for pattern selection. In a network of N neurons on a random d-regular graph, two asymptotic cases, d/N→0 and d/N→1, have been discussed for the new rule, and it is shown that capacity of the network grows considerably as d/N→1. By introducing the notions of asymptotic eigenvector, we will be able to study the behaviour of the discrete model in the limit d/N→0. It will be proved that for degree d less than a critical value there is a positive role for noise, in which case increasing the number of patterns will improve the storage capacity.

Higher-level motion detection deficit in Parkinson's disease.

Previous research has suggested that Parkinson's disease (PD) impairs motion perception. First-order motion consists of moving luminance-defined attributes. Second-order motion, on the other hand, consists of moving patterns whose motion attributes are not luminance-defined. The detection of first and second-order motion is thought to be mediated by different mechanisms. Here, we compare the ability of Parkinson's disease patients (PDPs) to detect first-order/second-order motion with normal subjects. Subjects had to discriminate the drift direction of first-order motion (luminance-modulated noise) and a second-order motion pattern (named as noise base motion) over a range of stimulus speeds and strengths. Results show that the first-order motion detection deficits could only be seen with lower motion strengths suggesting a ceiling effect with higher motion strengths. However, second order motion detection deficits were seen across high and low motion strengths, suggesting that the second order motion detection may be more affected in PD than the first-order motion detection. Our results indicate that higher-level visual cortex plays an important role in PD patients' disabilities in motion perception.

Reduction of negative afterimage duration in Parkinson's disease patients: a possible role for dopaminergic deficiency in the retinal Interplexiform cell layer.

Dopaminergic deficiency may affect Parkinson's disease patients (PD) in the central as well as the peripheral tissues. In the retina, the neuromodulatory role of the dopaminergic Interplexiform cell layer (IP) plays a major role in the retinal light adaptation and may account for the duration of the negative afterimage. Here we present results showing a significant reduction of negative afterimage duration in PD patients. This supports the hypothesis that the retinal dopaminergic system may be the main cause for the long duration of negative afterimage. We suggest that the observed reduction of afterimage duration is due to possible dopaminergic deficiency in patients with Parkinson's disease.