International Journal of Neural Systems

The depth cue is a fundamental piece of information for artificial and living beings who interact with the surrounding environment in order to handle objects and to avoid obstacles: in such situations, the disparity patterns, which arise when agents fixate objects, are vector fields. We propose a biologically-inspired computational model to estimate dense horizontal and vertical disparity maps by exploiting the cortical paradigms of the primate visual system: in particular, we aim to model the disparity sensitivity of the V1–MT visual pathway. The proposed model is based on a first processing stage composed of a bank of spatial band-pass filters and a static nonlinearity, mimicking complex binocular cells. Then, subsequent pooling stages and decoding strategies allow the model to estimate the vector disparity, after having represented it as a population of MT-like units. We assess the proposed model by using standard benchmarking stereo images, the Middlebury dataset, and specific stereo images that have horizontal and vertical disparities, which characterize the stimuli produced by active vision systems. Moreover, we systemically analyze how the different processing stages affect the model performance, and we discuss their implications for the neural modeling.

Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain–Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals. In Experiment II, 10 subjects were evaluated along five daily sessions, while BMI-controlling same visual animation, a buzzer, and a robotic hand exoskeleton. In both experiments, feedback received from the controlled device was proportional to SMR power (11–14Hz) detected by a real-time EEG-based system. Synchronization of slow EEG frequencies along the trials was evaluated using inter-trial-phase coherence (ITPC). Results: cortical oscillations of EEG in delta and theta frequencies synchronized at the onset and at the end of both active and task-free trials; ITPC was significantly modulated by feedback sensory modality received during the tasks; and ITPC synchronization progressively increased along the training. These findings suggest that phase-locking of slow rhythms and resetting by sensory afferences might be a functionally relevant mechanism in cortical control of motor function. We propose that analysis of phase synchronization of slow cortical rhythms might also improve identification of temporal edges in BMI tasks and might help to develop physiological markers for identification of context task switching and practice-related changes in brain function, with potentially important implications for design and monitoring of motor imagery-based BMI systems, an emerging tool in neurorehabilitation of stroke.

Neuroimaging studies have suggested the presence of abnormalities in the prefrontal–thalamic–cerebellar circuit in schizophrenia (SCH) and depression (DEP). However, the common and distinct structural and causal connectivity abnormalities in this circuit between the two disorders are still unclear. In the current study, structural and resting-state functional magnetic resonance imaging (fMRI) data were acquired from 20 patients with SCH, 20 depressive patients and 20 healthy controls (HC). Voxel-based morphometry analysis was first used to assess gray matter volume (GMV). Granger causality analysis, seeded at regions with altered GMVs, was subsequently conducted. To discover the differences between the groups, ANCOVA and post hoc tests were performed. Then, the relationships between the structural changes, causal connectivity and clinical variables were investigated. Finally, a leave-one-out resampling method was implemented to test the consistency. Statistical analyses showed the GMV and causal connectivity changes in the prefrontal–thalamic–cerebellar circuit. Compared with HC, both SCH and DEP exhibited decreased GMV in middle frontal gyrus (MFG), and a lower GMV in MFG and medial prefrontal cortex (MPFC) in SCH than DEP. Compared with HC, both patient groups showed increased causal flow from the right cerebellum to the MPFC (common causal connectivity abnormalities). And distinct causal connectivity abnormalities (increased causal connectivity from the left thalamus to the MPFC in SCH than HC and DEP, and increased causal connectivity from the right cerebellum to the left thalamus in DEP than HC and SCH). In addition, the structural deficits in the MPFC and its causal connectivity from the cerebellum were associated with the negative symptom severity in SCH. This study found common/distinct structural deficits and aberrant causal connectivity patterns in the prefrontal–thalamic–cerebellar circuit in SCH and DEP, which may provide a potential direction for understanding the convergent and divergent psychiatric pathological mechanisms between SCH and DEP. Furthermore, concomitant structural and causal connectivity deficits in the MPFC may jointly contribute to the negative symptoms of SCH.

The aim of the study was to assess causal coupling between neuronal activity, microvascular hemodynamics and blood supply oscillations in the Mayer wave frequency range. An electroencephalogram, cerebral blood oxygenation changes, an electrocardiogram and blood pressure were recorded during rest and during a movement task. Causal coupling between them was evaluated using directed transfer function, a measure based on the Granger causality principle. The multivariate autoregressive model was fitted to all the signals simultaneously, which made it possible to construct a complete scheme of interactions between the considered signals. The obtained pattern of interactions in the resting state estimated in the 0.05–0.15 Hz band revealed a predominant influence of blood pressure oscillations on all the other variables. Reciprocal connections between blood pressure and heart rate variability time series indicated the presence of feedback loops between these signals. During movement, the pattern of connections did not change dramatically. The number of connections decreased, but the couplings between blood pressure and heart rate variability signal were not significantly changed, and the strong influence of the decreased blood hemoglobin concentration on the oxygenated hemoglobin concentration persisted. For the first time our results provided a comprehensive scheme of interactions between electrical and hemodynamic brain signals, heart rate and blood pressure oscillations. Persistent reciprocal connections between blood pressure and heart rate variability time series suggest possible feedforward and feedback coupling of cardiovascular variables which may lead to the observed oscillations in Mayer wave range.

This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.

Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attention on account of its significant importance. Numerous methods have been proposed to fulfill this challenging task, though, the characterization of the fatigue mechanism still, to a large extent, remains to be investigated. To address this problem, we, in this work, develop a novel Multiplex Limited Penetrable Horizontal Visibility Graph (Multiplex LPHVG) method, which allows in not only detecting fatigue driving but also probing into the brain fatigue behavior. Importantly, we use the method to construct brain networks from EEG signals recorded from different subjects performing simulated driving tasks under alert and fatigue driving states. We then employ clustering coefficient, global efficiency and characteristic path length to characterize the topological structure of the networks generated from different brain states. In addition, we combine average edge overlap with the network measures to distinguish alert and mental fatigue states. The high-accurate classification results clearly demonstrate and validate the efficacy of our multiplex LPHVG method for the fatigue detection from EEG signals. Furthermore, our findings show a significant increase of the clustering coefficient as the brain evolves from alert state to mental fatigue state, which yields novel insights into the brain behavior associated with fatigue driving.