Asymmetric stimulus representations bias visual perceptual learning.

The primate visual cortex contains various regions that exhibit specialization for different stimulus properties, such as motion, shape, and color. Within each region, there is often further specialization, such that particular stimulus features, such as horizontal and vertical orientations, are over-represented. These asymmetries are associated with well-known perceptual biases, but little is known about how they influence visual learning. Most theories would predict that learning is optimal, in the sense that it is unaffected by these asymmetries. However, other approaches to learning would result in specific patterns of perceptual biases. To distinguish between these possibilities, we trained human observers to discriminate between expanding and contracting motion patterns, which have a highly asymmetrical representation in the visual cortex. Observers exhibited biased percepts of these stimuli, and these biases were affected by training in ways that were often suboptimal. We simulated different neural network models and found that a learning rule that involved only adjustments to decision criteria, rather than connection weights, could account for our data. These results suggest that cortical asymmetries influence visual perception and that human observers often rely on suboptimal strategies for learning.

Training with optic flow stimuli promotes recovery in cortical blindness.

BACKGROUND: Cortical blindness is a form of severe vision loss that is caused by damage to the primary visual cortex (V1) or its afferents. This condition has devastating effects on quality of life and independence. While there are few treatments currently available, accumulating evidence shows that certain visual functions can be restored with appropriate perceptual training: Stimulus sensitivity can be increased within portions of the blind visual field. However, this increased sensitivity often remains highly specific to the trained stimulus, limiting the overall improvement in visual function. OBJECTIVE: Recent advances in the field of perceptual learning show that such specificity can be overcome with training paradigms that leverage the properties of higher-level visual cortical structures, which have greater capacity to generalize across stimulus positions and features. This targeting can be accomplished by using more complex training stimuli that elicit robust responses in these visual structures. METHODS: We trained cortically blind subjects with a complex optic flow motion stimulus that was presented in a location of their blind field. Participants were instructed to train with the stimulus at home for approximately 30 minutes per day. Once performance plateaued, the stimulus was moved deeper into the blind field. A battery of pre- and post-training measures, with careful eye tracking, was performed to quantify the improvements. RESULTS: We show that 1) optic flow motion discrimination can be relearned in cortically blind fields; 2) training with an optic flow stimulus can lead to improvements that transfer to different tasks and untrained locations; and 3) such training leads to a significant expansion of the visual field. The observed expansion of the visual field was present even when eye movements were carefully controlled. Finally, we show that regular training is critical for improved visual function, as sporadic training reduced the benefits of training, even when the total numbers of training sessions were equated. CONCLUSIONS: These findings are consistent with the hypothesis that complex training stimuli can improve outcomes in cortical blindness, provided that patients adhere to a regular training regimen. Nevertheless, such interventions remain limited in their ability to restore functional vision.

Energy efficiency as a normative account for predictive coding.

In this issue of Patterns, Ali et al. demonstrate that predictive coding emerges in an artificial neural network optimized to be energy efficient. The results offer an explanation for why brains may implement predictive coding.

Visual perceptual learning generalizes to untrained effectors.

Visual perceptual learning (VPL) is an improvement in visual function following training. Although the practical utility of VPL was once thought to be limited by its specificity to the precise stimuli used during training, more recent work has shown that such specificity can be overcome with appropriate training protocols. In contrast, relatively little is known about the extent to which VPL exhibits motor specificity. Previous studies have yielded mixed results. In this work, we have examined the effector specificity of VPL by training observers on a motion discrimination task that maintains the same visual stimulus (drifting grating) and task structure, but that requires different effectors to indicate the response (saccade vs. button press). We find that, in these conditions, VPL transfers fully between a manual and an oculomotor response. These results are consistent with the idea that VPL entails the learning of a decision rule that can generalize across effectors.

Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships.

Genetic variations such as single nucleotide polymorphisms (SNPs) can cause susceptibility to cancer. Although thousands of genetic variants have been identified to be associated with different cancers, the molecular mechanisms of cancer remain unknown. There is not a particular dataset of relationships between cancer and SNPs, as a bipartite network, for computational analysis and prediction. Link prediction as a computational graph analysis method can help us to gain new insight into the network. In this article, after creating a network between cancer and SNPs using SNPedia and Cancer Research UK databases, we evaluated the computational link prediction methods to foresee new SNP-Cancer relationships. Results show that among the popular scoring methods based on network topology, for relation prediction, the preferential attachment (PA) algorithm is the most robust method according to computational and experimental evidence, and some of its computational predictions are corroborated in recent publications. According to the PA predictions, rs1801394-Non-small cell lung cancer, rs4880-Non-small cell lung cancer, and rs1805794-Colorectal cancer are some of the best probable SNP-Cancer associations that have not yet been mentioned in any published article, and they are the most probable candidates for additional laboratory and validation studies. Also, it is feasible to improve the predicting algorithms to produce new predictions in the future.

The Role of the Subthalamic Nucleus in Inhibitory Control of Oculomotor Behavior in Parkinson's Disease.

Inhibiting inappropriate actions in a context is an important part of the human cognitive repertoire, and deficiencies in this ability are common in neurological and psychiatric disorders. An anti-saccade is a simple oculomotor task that tests this ability by requiring inhibition of saccades to peripheral targets (pro-saccade) and producing voluntary eye movements toward the mirror position (anti-saccades). Previous studies provide evidence for a possible contribution from the basal ganglia in anti-saccade behavior, but the precise role of different components is still unclear. Parkinson's disease patients with implanted deep brain stimulators (DBS) in subthalamic nucleus (STN) provide a unique opportunity to investigate the role of the STN in anti-saccade behavior. Previous attempts to show the effect of STN DBS on anti-saccades have produced conflicting observations. For example, the effect of STN DBS on anti-saccade error rate is not yet clear. Part of this inconsistency may be related to differences in dopaminergic states in different studies. Here, we tested Parkinson's disease patients on anti- and pro-saccade tasks ON and OFF STN DBS, in ON and OFF dopaminergic medication states. First, STN DBS increases anti-saccade error rate while patients are OFF dopamine replacement therapy. Second, dopamine replacement therapy and STN DBS interact: L-dopa reduces the effect of STN DBS on anti-saccade error rate. Third, STN DBS induces different effects on pro- and anti-saccades in different patients. These observations provide evidence for an important role for the STN in the circuitry underlying context-dependent modulation of visuomotor action selection.

Subspace-based Identification Algorithm for characterizing causal networks in resting brain.

The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity (FC). However the other main stream of brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity (EC), has been little explored. Inherent complexity of brain activities in resting-state, as observed in BOLD (Blood Oxygenation-Level Dependant) fluctuations, calls for exploratory methods for characterizing these causal networks. On the other hand, the inevitable effects that hemodynamic system imposes on causal inferences in fMRI data, lead us toward the methods in which causal inferences can take place in latent neuronal level, rather than observed BOLD time-series. To simultaneously satisfy these two concerns, in this paper, we introduce a novel state-space system identification approach for studying causal interactions among brain regions in the absence of explicit cognitive task. This algorithm is a geometrically inspired method for identification of stochastic systems, purely based on output observations. Using extensive simulations, three aspects of our proposed method are investigated: ability in discriminating existent interactions from non-existent ones, the effect of observation noise, and downsampling on algorithm performance. Our simulations demonstrate that Subspace-based Identification Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can reliably uncover the underlying causal interactions of resting-state fMRI. Furthermore, in contrast to previously established state-space approaches in Effective Connectivity studies, this method is able to characterize causal networks with large number of brain regions. In addition, we utilized the proposed algorithm for identification of causal relationships underlying anti-correlation of default-mode and Dorsal Attention Networks during the rest, using fMRI. We observed that Default-Mode Network places in a higher order in hierarchical structure of brain functional networks compared to Dorsal Attention Networks.