scholarly journals Visual Snow Syndrome as a Network Disorder: A Systematic Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Antonia Klein ◽  
Christoph J. Schankin
Keyword(s):  
Large Scale ◽  
Case Reports ◽  
Visual Disturbance ◽  
Google Scholar ◽  
New Information ◽  
Visual Areas ◽  
Visual Cortices ◽  
Large Scale Networks ◽  
Definition Of ◽  
The Brain

Aim: By reviewing the existing clinical studies about visual snow (VS) as a symptom or as part of visual snow syndrome (VSS), we aim at improving our understanding of VSS being a network disorder.Background: Patients with VSS suffer from a continuous visual disturbance resembling the view of a badly tuned analog television (i.e., VS) and other visual, as well as non-visual symptoms. These symptoms can persist over years and often strongly impact the quality of life. The exact prevalence is still unknown, but up to 2.2% of the population could be affected. Presently, there is no established treatment, and the underlying pathophysiology is unknown. In recent years, there have been several approaches to identify the brain areas involved and their interplay to explain the complex presentation.Methods: We collected the clinical and paraclinical evidence from the currently published original studies on VS and its syndrome by searching PubMed and Google Scholar for the term visual snow. We included original studies in English or German and excluded all reviews, case reports that did not add new information to the topic of this review, and articles that were not retrievable in PubMed or Google Scholar. We grouped the studies according to the methods that were used.Results: Fifty-three studies were found for this review. In VSS, the clinical spectrum includes additional visual disturbances such as excessive floaters, palinopsia, nyctalopia, photophobia, and entoptic phenomena. There is also an association with other perceptual and affective disorders as well as cognitive symptoms. The studies that have been included in this review demonstrate structural, functional, and metabolic alterations in the primary and/or secondary visual areas of the brain. Beyond that, results indicate a disruption in the pre-cortical visual pathways and large-scale networks including the default mode network and the salience network.Discussion: The combination of the clinical picture and widespread functional and structural alterations in visual and extra-visual areas indicates that the VSS is a network disorder. The involvement of pre-cortical visual structures and attentional networks might result in an impairment of “filtering” and prioritizing stimuli as top-down process with subsequent excessive activation of the visual cortices when exposed to irrelevant external and internal stimuli. Limitations of the existing literature are that not all authors used the ICHD-3 definition of the VSS. Some were referring to the symptom VS, and in many cases, the control groups were not matched for migraine or migraine aura.

scholarly journals Cerebral cartography and connectomics

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140173 ◽  
Author(s):  
Olaf Sporns
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Network Science ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Hierarchical Organization ◽  
Large Scale Networks ◽  
Definition Of ◽  
The Brain

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

scholarly journals Conceptual and Practical Issues in the Pharmacological Treatment of Brain Injury

1993 ◽  
Vol 4 (3) ◽  
pp. 227-237 ◽  
Author(s):  
Donald G. Stein ◽  
Marylou M. Glasier ◽  
Stuart W. Hoffman
Keyword(s):  
Central Nervous System ◽  
Brain Injury ◽  
Time Course ◽  
Chemical Activity ◽  
Behavioral Testing ◽  
Naturally Occurring ◽  
New Information ◽  
Definition Of ◽  
Injury And Repair ◽  
The Brain

It is only within the last ten years that research on treatment for central nervous system (CNS) recovery after injury has become more focused on the complexities involved in promoting recovery from brain injury when the CNS is viewed as an integrated and dynamic system. There have been major advances in research in recovery over the last decade, including new information on the mechanics and genetics of metabolism and chemical activity, the definition of excitotoxic effects and the discovery that the brain itself secretes complex proteins, peptides and hormones which are capable of directly stimulating the repair of damaged neurons or blocking some of the degenerative processes caused by the injury cascade. Many of these agents, plus other nontoxic naturally occurring substances, are being tested as treatment for brain injury. Further work is needed to determine appropriate combinations of treatments and optimum times of administration with respect to the time course of the CNS disorder. In order to understand the mechanisms that mediate traumatic brain injury and repair, there must be a merging of findings from neurochemical studies with data from intensive behavioral testing.

scholarly journals Brain network dynamics are hierarchically organized in time

2017 ◽  
Vol 114 (48) ◽  
pp. 12827-12832 ◽  
Author(s):  
Diego Vidaurre ◽  
Stephen M. Smith ◽  
Mark W. Woolrich
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Network ◽  
Specific Measure ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Subject Specific ◽  
Large Scale Networks ◽  
Network Patterns ◽  
The Brain

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

scholarly journals Attention Networks and the Intrinsic Network Structure of the Human Brain

2021 ◽  
Author(s):  
Sebastian Markett ◽  
David Nothdurfter ◽  
Antonia Focsa ◽  
Martin Reuter ◽  
Philippe Jawinski
Keyword(s):  
Network Structure ◽  
Network Theory ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Relevant Information ◽  
Attention Network ◽  
Attention Networks ◽  
Large Scale Networks ◽  
The Brain

Attention network theory states that attention is not a unified construct but consists of three independent systems that are supported by separable distributed networks: an alerting network to deploy attentional resources in anticipation of upcoming events, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. It is well established that the brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks with highest spatial precision at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

scholarly journals Trips and Neurotransmitters: Discovering Principled Patterns across 6,850 Hallucinogenic Experiences

2021 ◽  
Author(s):  
Galen Ballentine ◽  
Sam Freesun Friedman ◽  
Danilo Bzdok
Keyword(s):  
Large Scale ◽  
Free Form ◽  
Association Cortex ◽  
Receptor Subtypes ◽  
Drug Induced ◽  
Psychological Phenomena ◽  
Input Pole ◽  
Large Scale Networks ◽  
Induced Changes ◽  
The Brain

Psychedelics are thought to alter states of consciousness by disrupting how the higher association cortex governs bottom-up sensory signals. Individual hallucinogenic drugs are usually studied in participants in controlled laboratory settings. Here, we have explored word usage in 6,850 free-form testimonials with 27 drugs through the prism of 40 neurotransmitter receptor subtypes, which were then mapped to 3D coordinates in the brain via their gene transcription levels from invasive tissue probes. Despite the variable subjective nature of hallucinogenic experiences, our pattern-learning approach delineated how drug-induced changes of conscious awareness (e.g., dissolving self-world boundaries or fractal distortion of visual perception) are linked to cortex-wide anatomical distributions of receptor density proxies. The dominant explanatory factor related ego-dissolution-like phenomena to a constellation of 5-HT2A, D2, KOR, and NMDA receptors, anchored especially in the brain's deep hierarchy (epitomized by the associative higher-order cortex) and shallow hierarchy (epitomized by the visual cortex). Additional factors captured psychological phenomena in which emotions (5-HT2A and Imidazoline1) were in tension with auditory (SERT, 5-HT1A) or visual (5-HT2A) sensations. Each discovered receptor-experience factor spanned between a higher-level association pole and a sensory input pole, which may relate to the previously reported collapse of hierarchical order among large-scale networks. Simultaneously considering many psychoactive molecules and thousands of natural language descriptions of drug experiences our framework finds the underlying semantic structure and maps it directly to the brain. These advances could assist in unlocking their wide-ranging potential for medical treatment.

scholarly journals Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 297-311
Author(s):  
Jae-Sung Lim ◽  
Jae-Joong Lee ◽  
Choong-Wan Woo
Keyword(s):  
Cognitive Impairment ◽  
Large Scale ◽  
Imaging Techniques ◽  
Brain Network ◽  
Clinical Neurology ◽  
Post Stroke ◽  
Large Scale Networks ◽  
High Level ◽  
New Treatments ◽  
The Brain

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

scholarly journals Approximational Projection of a Network with an Arbitrary Structure

2018 ◽  
Vol 7 (3.2) ◽  
pp. 550
Author(s):  
Valerii Usenko ◽  
Olga Kodak ◽  
Iryna Usenko
Keyword(s):  
Network Structure ◽  
Large Scale ◽  
System Parameters ◽  
Arbitrary Structure ◽  
Large Scale Networks ◽  
Definition Of

Improving the efficiency of the functioning of the engineering networks of cities involves solving the issues of expanding and deepening the process of studying the interconnection of its components.An approximate definition of the property of the network structure's connectivity belongs to the conceptual class of diminishing the dimension of the multiplicity of system parameters. It studies the structures of networks with arbitrary reliability of its constituent parts. The reflection of the reliability values of the components of a redundant engineering network structure is appropriate for large-scale networks. The multiplicity of the network is projected onto the subspace of its parameters with variable values of the argument of the function of the integrity of the connectivity.  

scholarly journals Content-specific activity in frontoparietal and default-mode networks during prior-guided visual perception

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Carlos González-García ◽  
Matthew W Flounders ◽  
Raymond Chang ◽  
Alexis T Baria ◽  
Biyu J He
Keyword(s):  
Prior Knowledge ◽  
Large Scale ◽  
Specific Activity ◽  
Perceptual Processing ◽  
Visual Images ◽  
Default Mode ◽  
Neural Representations ◽  
Representational Space ◽  
Visual Areas ◽  
The Brain

How prior knowledge shapes perceptual processing across the human brain, particularly in the frontoparietal (FPN) and default-mode (DMN) networks, remains unknown. Using ultra-high-field (7T) functional magnetic resonance imaging (fMRI), we elucidated the effects that the acquisition of prior knowledge has on perceptual processing across the brain. We observed that prior knowledge significantly impacted neural representations in the FPN and DMN, rendering responses to individual visual images more distinct from each other, and more similar to the image-specific prior. In addition, neural representations were structured in a hierarchy that remained stable across perceptual conditions, with early visual areas and DMN anchored at the two extremes. Two large-scale cortical gradients occur along this hierarchy: first, dimensionality of the neural representational space increased along the hierarchy; second, prior’s impact on neural representations was greater in higher-order areas. These results reveal extensive and graded influences of prior knowledge on perceptual processing across the brain.

Challenges of Complex Systems in Cognitive and Complex Systems

2010 ◽  
pp. 367-384
Author(s):  
Klaus Mainzer
Keyword(s):  
Neural Networks ◽  
Complex Systems ◽  
Large Scale ◽  
Computational Models ◽  
Self Control ◽  
Self Organization ◽  
Embodied Mind ◽  
Future Technology ◽  
Large Scale Networks ◽  
The Brain

After an introduction (1) the article analyzes complex systems and the evolution of the embodied mind (2), complex systems and the innovation of embodied robotics (3), and finally discusses challenges of handling a world with increasing complexity: Large-scale networks have the same universal properties in evolution and technology (4). Considering the evolution of the embodied mind (2), we start with an introduction of complex systems and nonlinear dynamics (2.1), apply this approach to neural self-organization (2.2), distinguish degrees of complexity of the brain (2.3), explain the emergence of cognitive states by complex systems dynamics (2.4), and discuss criteria for modeling the brain as complex nonlinear system (2.5). The innovation of embodied robotics (3) is a challenge of complex systems and future technology. We start with the distinction of symbolic and embodied AI (3.1). Embodied robotics is inspired by the evolution of life. Modern systems biology integrates the molecular, organic, human, and ecological levels of life with computational models of complex systems (3.2). Embodied robots are explained as dynamical systems (3.3). Self-organization of complex systems needs self-control of technical systems (3.4). Cellular neural networks (CNN) are an example of self-organizing complex systems offering new avenues for neurobionics (3.5). In general, technical neural networks support different kinds of learning robots (3.6). Embodied robotics aims at the development of cognitive and conscious robots (3.7).

Sign in / Sign up

Export Citation Format

Share Document