Signal Propagation in the Human Visual Pathways: An Effective Connectivity Analysis

Visual evoked potentials waveform analysis to measure intracortical damage in a preclinical model of multiple sclerosis

Introduction

Visual evoked potentials (VEPs) are a non-invasive technique routinely used in clinical and preclinical practice. Discussion about inclusion of VEPs in McDonald criteria, used for Multiple Sclerosis (MS) diagnosis, increased the importance of VEP in MS preclinical models. While the interpretation of the N1 peak is recognized, less is known about the first and second positive VEP peaks, P1 and P2, and the implicit time of the different segments. Our hypothesis is that P2 latency delay describes intracortical neurophysiological dysfunction from the visual cortex to the other cortical areas.

Methods

In this work, we analyzed VEP traces that were included in our two recently published papers on Experimental Autoimmune Encephalomyelitis (EAE) mouse model. Compared with these previous publications other VEP peaks, P1 and P2, and the implicit time of components P1-N1, N1-P2 and P1-P2, were analyzed in blind.

Results

Latencies of P2, P1-P2, P1-N1 and N1-P2 were increased in all EAE mice, including group without N1 latency change delay at early time points. In particular, at 7 dpi the P2 latency delay change was significantly higher compared with N1 latency change delay. Moreover, new analysis of these VEP components under the influence of neurostimulation revealed a decrease in P2 delay in stimulated animals.

Discussion

P2 latency delay, P1-P2, P1-N1, and N1-P2 latency changes which reflect intracortical dysfunction, were consistently detected across all EAE groups before N1 change. Results underline the importance of analyzing all VEP components for a complete overview of the neurophysiological visual pathway dysfunction and treatment efficacy.

Evaluating the Alterations Induced by Virtual Reality in Cerebral Small-World Networks Using Graph Theory Analysis with Electroencephalography

Virtual reality (VR), a rapidly evolving technology that simulates three-dimensional virtual environments for users, has been proven to activate brain functions. However, the continuous alteration pattern of the functional small-world network in response to comprehensive three-dimensional stimulation rather than realistic two-dimensional media stimuli requires further exploration. Here, we aimed to validate the effect of VR on the pathways and network parameters of a small-world organization and interpret its mechanism of action. Fourteen healthy volunteers were selected to complete missions in an immersive VR game. The changes in the functional network in six different frequency categories were analyzed using graph theory with electroencephalography data measured during the pre-, VR, and post-VR stages. The mutual information matrix revealed that interactions between the frontal and posterior areas and those within the frontal and occipital lobes were strengthened. Subsequently, the betweenness centrality (BC) analysis indicated more robust and extensive pathways among hubs. Furthermore, a specific lateralized channel (O1 or O2) increment in the BC was observed. Moreover, the network parameters improved simultaneously in local segregation, global segregation, and global integration. The overall topological improvements of small-world organizations were in high-frequency bands and exhibited some degree of sustainability.

Abnormal effective connectivity in visual cortices underlies stereopsis defects in amblyopia

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Abnormal effective connectivity inherent stereopsis defects in amblyopia was studied.

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A weakened connection from V2v to LO2 relates to stereopsis defects in amblyopia.

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Higher-order visual cortices may serve as key nodes to the stereopsis defects.

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An independent longitudinal dataset was used to validate the obtained results.

The neural basis underlying stereopsis defects in patients with amblyopia remains unclear, which hinders the development of clinical therapy. This study aimed to investigate visual network abnormalities in patients with amblyopia and their associations with stereopsis function. Spectral dynamic causal modeling methods were employed for resting-state functional magnetic resonance imaging data to investigate the effective connectivity (EC) among 14 predefined regions of interest in the dorsal and ventral visual pathways. We adopted two independent datasets, including a cross-sectional and a longitudinal dataset. In the cross-sectional dataset, we compared group differences in EC between 31 patients with amblyopia (mean age: 26.39 years old) and 31 healthy controls (mean age: 25.71 years old) and investigated the association between EC and stereoacuity. In addition, we explored EC changes after perceptual learning in a novel longitudinal dataset including 9 patients with amblyopia (mean age: 15.78 years old). We found consistent evidence from the two datasets indicating that the aberrant EC from V2v to LO2 is crucial for the stereoscopic deficits in the patients with amblyopia: it was weaker in the patients than in the controls, showed a positive linear relationship with the stereoscopic function, and increased after perceptual learning in the patients. In addition, higher-level dorsal (V3d, V3A, and V3B) and ventral areas (LO1 and LO2) were important nodes in the network of abnormal ECs associated with stereoscopic deficits in the patients with amblyopia. Our research provides insights into the neural mechanism underlying stereopsis deficits in patients with amblyopia and provides candidate targets for focused stimulus interventions to enhance the efficacy of clinical treatment for the improvement of stereopsis deficiency.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Bridging Neural and Computational Viewpoints on Perceptual Decision-Making

Sequential sampling models have provided a dominant theoretical framework guiding computational and neurophysiological investigations of perceptual decision-making. While these models share the basic principle that decisions are formed by accumulating sensory evidence to a bound, they come in many forms that can make similar predictions of choice behaviour despite invoking fundamentally different mechanisms. The identification of neural signals that reflect some of the core computations underpinning decision formation offers new avenues for empirically testing and refining key model assumptions. Here, we highlight recent efforts to explore these avenues and, in so doing, consider the conceptual and methodological challenges that arise when seeking to infer decision computations from complex neural data.

Toward a multiscale modeling framework for understanding serotonergic function

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin.

The effects of changes in object location on object identity detection: A simultaneous EEG-fMRI study

Object identity and location are bound together to form a unique integration that is maintained and processed in visual working memory (VWM). Changes in task-irrelevant object location have been shown to impair the retrieval of memorial representations and the detection of object identity changes. However, the neural correlates of this cognitive process remain largely unknown. In the present study, we aim to investigate the underlying brain activation during object color change detection and the modulatory effects of changes in object location and VWM load. To this end we used simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings, which can reveal the neural activity with both high temporal and high spatial resolution. Subjects responded faster and with greater accuracy in the repeated compared to the changed object location condition, when a higher VWM load was utilized. These results support the spatial congruency advantage theory and suggest that it is more pronounced with higher VWM load. Furthermore, the spatial congruency effect was associated with larger posterior N1 activity, greater activation of the right inferior frontal gyrus (IFG) and less suppression of the right supramarginal gyrus (SMG), when object location was repeated compared to when it was changed. The ERP-fMRI integrative analysis demonstrated that the object location discrimination-related N1 component is generated in the right SMG.

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools

The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website.