White matter alterations in glaucoma and monocular blindness differ outside the visual system

AbstractThe degree to which glaucoma has effects in the brain beyond the eye and the visual pathways is unclear. To clarify this, we investigated white matter microstructure (WMM) in 37 tracts of patients with glaucoma, monocular blindness, and controls. We used brainlife.io for reproducibility. White matter tracts were subdivided into seven categories ranging from those primarily involved in vision (the visual white matter) to those primarily involved in cognition and motor control. In the vision tracts, WMM was decreased as measured by fractional anisotropy in both glaucoma and monocular blind subjects compared to controls, suggesting neurodegeneration due to reduced sensory inputs. A test–retest approach was used to validate these results. The pattern of results was different in monocular blind subjects, where WMM properties increased outside the visual white matter as compared to controls. This pattern of results suggests that whereas in the monocular blind loss of visual input might promote white matter reorganization outside of the early visual system, such reorganization might be reduced or absent in glaucoma. The results provide indirect evidence that in glaucoma unknown factors might limit the reorganization as seen in other patient groups following visual loss.

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White matter alterations in glaucoma and vision-deprived brains differ outside the visual system

10.1101/2020.11.30.404434 ◽

2020 ◽

Author(s):

Sandra Hanekamp ◽

Branislava Ćurčić-Blake ◽

Bradley Caron ◽

Brent McPherson ◽

Anneleen Timmer ◽

...

Keyword(s):

White Matter ◽

Visual System ◽

Brain Plasticity ◽

Indirect Evidence ◽

Sensory Inputs ◽

Computing Platform ◽

White Matter Microstructure ◽

Patient Groups ◽

Cloud Computing Platform ◽

The Brain

AbstractThe degree to which glaucoma has effects beyond the eye –in the brain– is unclear. We investigated white matter microstructure (WMM) alterations in 37 tracts of patients with glaucoma, monocular blindness and controls. We used reproducible methods and the advanced cloud computing platform brainlife.io. White matter tracts were subdivided into seven categories ranging from primarily involved in vision (the visual white matter) to primarily involved in cognition and motor control. WMM in both glaucoma and monocular blind subjects was lower than controls in the visual white matter, suggesting neurodegenerative mechanisms due to reduced sensory inputs. In glaucoma participants WMM differences from controls decreased outside the visual white matter. A test-retest validation approach was used to validate these results. The pattern of results was different in monocular blind participants, where WMM properties increased outside the visual white matter as compared to controls. The pattern of results suggests that whereas in the blind loss of visual input might promote white matter reorganization outside of the early visual system, such reorganization might be reduced or absent in glaucoma. The results provide indirect evidence that in glaucoma unknown factors might limit the brain plasticity effects that in other patient groups follow visual loss.

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Blindsight and Unconscious Vision: What They Teach Us about the Human Visual System

The Neuroscientist ◽

10.1177/1073858416673817 ◽

2016 ◽

Vol 23 (5) ◽

pp. 529-541 ◽

Cited By ~ 18

Author(s):

Sara Ajina ◽

Holly Bridge

Keyword(s):

Visual Cortex ◽

Visual System ◽

Primary Visual Cortex ◽

Neural Activity ◽

Human Visual System ◽

Visual Information ◽

Visual Loss ◽

Physiological Conditions ◽

The World ◽

The Brain

Damage to the primary visual cortex removes the major input from the eyes to the brain, causing significant visual loss as patients are unable to perceive the side of the world contralateral to the damage. Some patients, however, retain the ability to detect visual information within this blind region; this is known as blindsight. By studying the visual pathways that underlie this residual vision in patients, we can uncover additional aspects of the human visual system that likely contribute to normal visual function but cannot be revealed under physiological conditions. In this review, we discuss the residual abilities and neural activity that have been described in blindsight and the implications of these findings for understanding the intact system.

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Development of white matter microstructure and executive functions during childhood and adolescence: a review of diffusion MRI studies

10.31234/osf.io/kyxbq ◽

2020 ◽

Author(s):

Anne-Lise Goddings ◽

David Roalf ◽

Catherine Lebel ◽

Christian K. Tamnes

Keyword(s):

White Matter ◽

Executive Functions ◽

Structural Connectivity ◽

Child And Adolescent ◽

Childhood And Adolescence ◽

Cross Sectional ◽

Indirect Measures ◽

White Matter Microstructure ◽

P Diffusion ◽

The Brain

Diffusion magnetic resonance imaging (dMRI) provides indirect measures of white matter microstructure that can be used to make inferences about structural connectivity within the brain. Over the last decade, a growing literature of cross-sectional and longitudinal studies have documented relationships between dMRI indices and cognitive development. In this review, we provide a brief overview of dMRI methods and how they can be used to study white matter and connectivity, briefly discuss challenges with using dMRI in child and adolescent populations, and review the extant literature examining the links between dMRI indices and executive functions during development. We explore the links between white matter microstructure and specific executive functions: inhibition, working memory and cognitive shifting, as well as performance on complex executive function tasks. Where there is concordance in findings across studies, this is highlighted, and potential explanations for discrepancies between results are discussed. Finally, we explore future directions that are necessary to better understand the links between child and adolescent development of executive functions and structural connectivity of the brain.

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Bayesian Connective Field Modeling: a Markov Chain Monte Carlo approach

10.1101/2020.09.03.281162 ◽

2020 ◽

Cited By ~ 1

Author(s):

Azzurra Invernizzi ◽

Koen V. Haak ◽

Joana C. Carvalho ◽

Remco J. Renken ◽

Frans W. Cornelissen

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Visual System ◽

Effect Size ◽

Data Driven ◽

Cortical Circuitry ◽

Early Visual System ◽

The Brain ◽

Mcmc Approach

AbstractThe majority of neurons in the human brain process signals from neurons elsewhere in the brain. Connective Field (CF) modeling is a biologically-grounded method to describe this essential aspect of the brain’s circuitry. It allows characterizing the response of a population of neurons in terms of the activity in another part of the brain. CF modeling translates the concept of the receptive field (RF) into the domain of connectivity by assessing the spatial dependency between signals in distinct cortical visual field areas. Standard CF model estimation has some intrinsic limitations in that it cannot estimate the uncertainty associated with each of its parameters. Obtaining the uncertainty will allow identification of model biases, e.g. related to an over- or under-fitting or a co-dependence of parameters, thereby improving the CF prediction. To enable this, here we present a Bayesian framework for the CF model. Using a Markov Chain Monte Carlo (MCMC) approach, we estimate the underlying posterior distribution of the CF parameters and consequently, quantify the uncertainty associated with each estimate. We applied the method and its new Bayesian features to characterize the cortical circuitry of the early human visual cortex of 12 healthy participants that were assessed using 3T fMRI. In addition, we show how the MCMC approach enables the use of effect size (beta) as a data-driven parameter to retain relevant voxels for further analysis. Finally, we demonstrate how our new method can be used to compare different CF models. Our results show that single Gaussian models are favoured over differences of Gaussians (i.e. center-surround) models, suggesting that the cortico-cortical connections of the early visual system do not possess center-surround organisation. We conclude that our new Bayesian CF framework provides a comprehensive tool to improve our fundamental understanding of the human cortical circuitry in health and disease.Highlights□ We present and validate a Bayesian CF framework based on a MCMC approach.□ The MCMC CF approach quantifies the model uncertainty associated with each CF parameter.□ We show how to use effect size beta as a data-driven threshold to retain relevant voxels.□ The cortical connective fields of the human early visual system are best described by a single, circular symmetric, Gaussian.

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Establishing the functional relevancy of white matter connections in the visual system and beyond

Brain Structure and Function ◽

10.1007/s00429-021-02423-4 ◽

2021 ◽

Author(s):

Mareike Grotheer ◽

Emily Kubota ◽

Kalanit Grill-Spector

Keyword(s):

White Matter ◽

Visual System ◽

Large Scale ◽

Functional Organization ◽

Temporal Cortex ◽

Fine Grained ◽

Brain Functions ◽

Functional Relationships ◽

Functional Regions ◽

The Brain

AbstractFor over a century, researchers have examined the functional relevancy of white matter bundles. Consequently, many large-scale bundles spanning several centimeters have been associated in their entirety with specific brain functions, such as language or attention. However, these coarse structural–functional relationships are at odds with modern understanding of the fine-grained functional organization of human cortex, such as the mosaic of category-selective regions in ventral temporal cortex. Here, we review a multimodal approach that combines fMRI to define functional regions of interest within individual’s brains with dMRI tractography to identify the white matter bundles of the same individual. Combining these data allows to determine which subsets of streamlines within a white matter bundle connect to specific functional regions in each individual. That is, this approach identifies the functionally defined white matter sub-bundles of the brain. We argue that this approach not only enhances the accuracy of interpreting the functional relevancy of white matter bundles, but also enables segmentation of these large-scale bundles into meaningful functional units, which can then be linked to behavior with enhanced precision. Importantly, this approach has the potential for making new discoveries of the fine-grained functional relevancy of white matter connections in the visual system and the brain more broadly, akin to the flurry of research that has identified functional regions in cortex.

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