scholarly journals Cranial endocast of the stem lagomorph Megalagus and brain structure of basal Euarchontoglires

2020 ◽  
Vol 287 (1929) ◽  
pp. 20200665
Author(s):  
Sergi López-Torres ◽  
Ornella C. Bertrand ◽  
Madlen M. Lang ◽  
Mary T. Silcox ◽  
Łucja Fostowicz-Frelik
Keyword(s):  
Eye Movements ◽  
Visual Tracking ◽  
Brain Morphology ◽  
Olfactory Bulbs ◽  
Critical Gap ◽  
Early Oligocene ◽  
Encephalization Quotient ◽  
Early Specialization ◽  
Brule Formation ◽  
The Brain

Early lagomorphs are central to our understanding of how the brain evolved in Glires (rodents, lagomorphs and their kin) from basal members of Euarchontoglires (Glires + Euarchonta, the latter grouping primates, treeshrews, and colugos). Here, we report the first virtual endocast of the fossil lagomorph Megalagus turgidus , from the Orella Member of the Brule Formation, early Oligocene, Nebraska, USA. The specimen represents one of the oldest nearly complete lagomorph skulls known. Primitive aspects of the endocranial morphology in Megalagus include large olfactory bulbs, exposure of the midbrain, a small neocortex and a relatively low encephalization quotient. Overall, this suggests a brain morphology closer to that of other basal members of Euarchontoglires (e.g. plesiadapiforms and ischyromyid rodents) than to that of living lagomorphs. However, the well-developed petrosal lobules in Megalagus , comparable to the condition in modern lagomorphs, suggest early specialization in that order for the stabilization of eye movements necessary for accurate visual tracking. Our study sheds new light on the reconstructed morphology of the ancestral brain in Euarchontoglires and fills a critical gap in the understanding of palaeoneuroanatomy of this major group of placental mammals.

The Brain Stem Saccadic Burst Generator Encodes Gaze in Three-Dimensional Space

2008 ◽  
Vol 99 (5) ◽  
pp. 2602-2616 ◽  
Author(s):  
Marion R. Van Horn ◽  
Pierre A. Sylvestre ◽  
Kathleen E. Cullen
Keyword(s):  
Eye Movements ◽  
Brain Stem ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Saccadic Eye Movements ◽  
Three Dimensions ◽  
Related Information ◽  
Computer Based ◽  
Different Depths ◽  
The Brain

When we look between objects located at different depths the horizontal movement of each eye is different from that of the other, yet temporally synchronized. Traditionally, a vergence-specific neuronal subsystem, independent from other oculomotor subsystems, has been thought to generate all eye movements in depth. However, recent studies have challenged this view by unmasking interactions between vergence and saccadic eye movements during disconjugate saccades. Here, we combined experimental and modeling approaches to address whether the premotor command to generate disconjugate saccades originates exclusively in “vergence centers.” We found that the brain stem burst generator, which is commonly assumed to drive only the conjugate component of eye movements, carries substantial vergence-related information during disconjugate saccades. Notably, facilitated vergence velocities during disconjugate saccades were synchronized with the burst onset of excitatory and inhibitory brain stem saccadic burst neurons (SBNs). Furthermore, the time-varying discharge properties of the majority of SBNs (>70%) preferentially encoded the dynamics of an individual eye during disconjugate saccades. When these experimental results were implemented into a computer-based simulation, to further evaluate the contribution of the saccadic burst generator in generating disconjugate saccades, we found that it carries all the vergence drive that is necessary to shape the activity of the abducens motoneurons to which it projects. Taken together, our results provide evidence that the premotor commands from the brain stem saccadic circuitry, to the target motoneurons, are sufficient to ensure the accurate control shifts of gaze in three dimensions.

A Control Systems Model of Smooth Pursuit Eye Movements with Realistic Emergent Properties

1989 ◽  
Vol 1 (1) ◽  
pp. 116-122 ◽  
Author(s):  
R. J. Krauzlis ◽  
S. G. Lisberger
Keyword(s):  
Eye Movements ◽  
Visual Tracking ◽  
Smooth Pursuit ◽  
Oculomotor System ◽  
Difficult Problem ◽  
Visual Signals ◽  
Emergent Properties ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements ◽  
Systems Model

Visual tracking of objects in a noisy environment is a difficult problem that has been solved by the primate oculomotor system, but remains unsolved in robotics. In primates, smooth pursuit eye movements match eye motion to target motion to keep the eye pointed at smoothly moving targets. We have used computer models as a tool to investigate possible computational strategies underlying this behavior. Here, we present a model based upon behavioral data from monkeys. The model emphasizes the variety of visual signals available for pursuit and, in particular, includes a sensitivity to the acceleration of retinal images. The model was designed to replicate the initial eye velocity response observed during pursuit of different target motions. The strength of the model is that it also exhibits a number of emergent properties that are seen in the behavior of both humans and monkeys. This suggests that the elements in the model capture important aspects of the mechanism of visual tracking by the primate smooth pursuit system.

Microstimulation of a Neural-Network Model for Visually Guided Saccades

1989 ◽  
Vol 1 (4) ◽  
pp. 317-326 ◽  
Author(s):  
Sabrina J. Goodman ◽  
Richard A. Andersen
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
Network Model ◽  
Neural Network Model ◽  
Posterior Parietal Cortex ◽  
Single Cells ◽  
Middle Layer ◽  
Retinal Position ◽  
Area 7A ◽  
The Brain

Microstimulation of many saccadic centers in the brain produces eye movements that are not consistent with either a strictly retinal or strictly head-centered coordinate coding of eye movements. Rather, stimulation produces some features of both types of coordinate coding. Recently we demonstrated a neural network model that was trained to localize the position of visual stimuli in head-centered coordinates at the output using inputs of eye and retinal position similar to those converging on area 7a of the posterior parietal cortex of monkeys (Zipser & Andersen 1988; Andersen & Zipser 1988). Here we show that microstimulation of this trained network, achieved by fully activating single units in the middle layer, produces “saccades” that are very much like the saccades produced by stimulating the brain. The activity of the middle-layer units can be considered to code the desired location of the eyes in head-centered coordinates; however, stimulation of these units does not produce the saccades predicted by a classical head-centered coordinate coding because the location in space appears to be coded in a distributed fashion among a population of units rather than explicitly at the level of single cells.

Recasting the Smooth Pursuit Eye Movement System

2004 ◽  
Vol 91 (2) ◽  
pp. 591-603 ◽  
Author(s):  
Richard J. Krauzlis
Keyword(s):  
Eye Movements ◽  
Brain Stem ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Saccadic Eye Movements ◽  
Extrastriate Cortex ◽  
High Acuity ◽  
Voluntary Eye Movements ◽  
New Perspective ◽  
The Brain

Primates use a combination of smooth pursuit and saccadic eye movements to stabilize the retinal image of selected objects within the high-acuity region near the fovea. Pursuit has traditionally been viewed as a relatively automatic behavior, driven by visual motion signals and mediated by pathways that connect visual areas in the cerebral cortex to motor regions in the cerebellum. However, recent findings indicate that this view needs to be reconsidered. Rather than being controlled primarily by areas in extrastriate cortex specialized for processing visual motion, pursuit involves an extended network of cortical areas, and, of these, the pursuit-related region in the frontal eye fields appears to exert the most direct influence. The traditional pathways through the cerebellum are important, but there are also newly identified routes involving structures previously associated with the control of saccades, including the basal ganglia, the superior colliculus, and nuclei in the brain stem reticular formation. These recent findings suggest that the pursuit system has a functional architecture very similar to that of the saccadic system. This viewpoint provides a new perspective on the processing steps that occur as descending control signals interact with circuits in the brain stem and cerebellum responsible for gating and executing voluntary eye movements. Although the traditional view describes pursuit and saccades as two distinct neural systems, it may be more accurate to consider the two movements as different outcomes from a shared cascade of sensory–motor functions.

Information-based Analysis of the Coupling between Dynamic Visual Stimuli, Eye Movements, and Brain Signals

2021 ◽  
pp. 2150048
Author(s):  
Hamidreza Namazi ◽  
Avinash Menon ◽  
Ondrej Krejcar
Keyword(s):  
Eye Movements ◽  
Moving Objects ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Vision Science ◽  
Brain Signals ◽  
Information Contents ◽  
The Brain

Our eyes are always in search of exploring our surrounding environment. The brain controls our eyes’ activities through the nervous system. Hence, analyzing the correlation between the activities of the eyes and brain is an important area of research in vision science. This paper evaluates the coupling between the reactions of the eyes and the brain in response to different moving visual stimuli. Since both eye movements and EEG signals (as the indicator of brain activity) contain information, we employed Shannon entropy to decode the coupling between them. Ten subjects looked at four moving objects (dynamic visual stimuli) with different information contents while we recorded their EEG signals and eye movements. The results demonstrated that the changes in the information contents of eye movements and EEG signals are strongly correlated ([Formula: see text]), which indicates a strong correlation between brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

Mutations affecting neurogenesis and brain morphology in the zebrafish, Danio rerio

Development ◽  
1996 ◽  
Vol 123 (1) ◽  
pp. 205-216 ◽  
Author(s):  
Y.J. Jiang ◽  
M. Brand ◽  
C.P. Heisenberg ◽  
D. Beuchle ◽  
M. Furutani-Seiki ◽  
...  
Keyword(s):  
Cell Types ◽  
Brain Morphology ◽  
Abnormal Development ◽  
Radial Glial Cells ◽  
Complementation Groups ◽  
Early Neurogenesis ◽  
Radial Glial ◽  
General Organization ◽  
Neurogenic Mutants ◽  
The Brain

In a screen for embryonic mutants in the zebrafish a large number of mutants were isolated with abnormal brain morphology. We describe here 26 mutants in 13 complementation groups that show abnormal development of large regions of the brain. Early neurogenesis is affected in white tail (wit). During segmentation stages, homozygous wit embryos display an irregularly formed neural keel, particularly in the hindbrain. Using a variety of molecular markers, a severe increase in the number of various early differentiating neurons can be demonstrated. In contrast, late differentiating neurons, radial glial cells and some nonneural cell types, such as the neural crest-derived melanoblasts, are much reduced. Somitogenesis appears delayed. In addition, very reduced numbers of melanophores are present posterior to the mid-trunk. The wit phenotype is reminiscent of neurogenic mutants in Drosophila, such as Notch or Delta. In mutant parachute (pac) embryos the general organization of the hindbrain is disturbed and many rounded cells accumulate loosely in the hindbrain and midbrain ventricles. Mutants in a group of 6 genes, snakehead(snk), natter (nat), otter (ott), fullbrain (ful), viper (vip) and white snake (wis) develop collapsed brain ventricles, before showing signs of general degeneration. atlantis (atl), big head (bid), wicked brain (win), scabland (sbd) and eisspalte (ele) mutants have different malformation of the brain folds. Some of them have transient phenotypes, and mutant individuals may grow up to adults.

Introduction to the Six Eye Movement Systems and the Visual Fixation System

2008 ◽  
Author(s):  
Agnes Wong
Keyword(s):  
Visual Acuity ◽  
Eye Movements ◽  
High Resolution ◽  
Information Overload ◽  
Survival Rates ◽  
Large Field ◽  
Field Of Vision ◽  
Very High Spatial Resolution ◽  
Different Characteristics ◽  
The Brain

One main reason that we make eye movements is to solve a problem of information overload. A large field of vision allows an animal to survey the environment for food and to avoid predators, thus increasing its survival rate. Similarly, a high visual acuity also increases survival rates by allowing an animal to aim at a target more accurately, leading to higher killing rates and more food. However, there are simply not enough neurons in the brain to support a visual system that has high resolution over the entire field of vision. Faced with the competing evolutionary demands for high visual acuity and a large field of vision, an effective strategy is needed so that the brain will not be overwhelmed by a large amount of visual input. Some animals, such as rabbits, give up high resolution in favor of a larger field of vision (rabbits can see nearly 360°), whereas others, such as hawks, restrict their field of vision in return for a high visual acuity (hawks have vision as good as 20/2, about 10 times better than humans). In humans, rather than using one strategy over the other, the retina develops a very high spatial resolution in the center (i.e., the fovea), and a much lower resolution in the periphery. Although this “foveal compromise” strategy solves the problem of information overload, one result is that unless the image of an object of interest happens to fall on the fovea, the image is relegated to the low-resolution retinal periphery. The evolution of a mechanism to move the eyes is therefore necessary to complement this foveal compromise strategy by ensuring that an object of interest is maintained or brought to the fovea. To maintain the image of an object on the fovea, the vestibulo-ocular (VOR) and optokinetic systems generate eye movements to compensate for head motions. Likewise, the saccadic, smooth pursuit, and vergence systems generate eye movements to bring the image of an object of interest on the fovea. These different eye movements have different characteristics and involve different parts of the brain.

How the Brain Moves the Eyes

2014 ◽  
pp. 1-25
Author(s):  
Shirley H. Wray
Keyword(s):  
Cardiac Surgery ◽  
Eye Movements ◽  
Progressive Supranuclear Palsy ◽  
Disease Progression ◽  
Frontotemporal Dementia ◽  
Smooth Pursuit ◽  
Cortical Areas ◽  
Initial Symptoms ◽  
The Brain ◽  
Symptoms And Signs

discusses the brain’s visual architecture for directing and controlling of eye movements:the striate, frontal and parietal cortical areas; and the eye movements themselves—saccades, smooth pursuit, and vergence. The susceptibility to disorders of these systems is illustrated in four detailed cases that follow disease progression from initial symptoms and signs to diagnosis and treatment. The case studies and video displays include a patient with Pick’s disease (frontotemporal dementia), another with Alzheimer’s dementia, and two examples of rare saccadic syndromes, one a patient with the slow saccade syndrome due to progressive supranuclear palsy and one with selective saccadic palsy following cardiac surgery.

Artifacts

2017 ◽  
pp. 98-127
Author(s):  
Riitta Hari ◽  
Aina Puce
Keyword(s):  
Data Analysis ◽  
Eye Movements ◽  
Cardiac Activity ◽  
Signal Space Separation ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Power Line Noise ◽  
Post Hoc ◽  
Space Separation ◽  
The Brain

This chapter focuses on different types of biological and nonbiological artifacts in MEG and EEG recordings, and discusses methods for their recognition and removal. Examples are given of various physiological artifacts, including eye movements, eyeblinks, saccades, muscle, and cardiac activity. Nonbiological artifacts, such as power-line noise, are also demonstrated. Some examples are given to illustrate how these unwanted signals can be identified and removed from MEG and EEG signals with methods such as independent component analysis (as applied to EEG data) and temporal signal-space separation (applied to MEG data). However, prevention of artifacts is always preferable to removing or compensating for them post hoc during data analysis. The chapter concludes with a discussion of how to ensure that signals are emanating from the brain and not from other sources.

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