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Central Visual Processing, part 2

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Neurociência Médica

Universidade Duke

4.9 (1,340 classificações) | 130K alunos inscritos

Medical Neuroscience explores the functional organization and neurophysiology of the human central nervous system, while providing a neurobiological framework for understanding human behavior. In this course, you will discover the organization of the neural systems in the brain and spinal cord that mediate sensation, motivate bodily action, and integrate sensorimotor signals with memory, emotion and related faculties of cognition. The overall goal of this course is to provide the foundation for understanding the impairments of sensation, action and cognition that accompany injury, disease or dysfunction in the central nervous system. The course will build upon knowledge acquired through prior studies of cell and molecular biology, general physiology and human anatomy, as we focus primarily on the central nervous system. This online course is designed to include all of the core concepts in neurophysiology and clinical neuroanatomy that would be presented in most first-year neuroscience courses in schools of medicine. However, there are some topics (e.g., biological psychiatry) and several learning experiences (e.g., hands-on brain dissection) that we provide in the corresponding course offered in the Duke University School of Medicine on campus that we are not attempting to reproduce in Medical Neuroscience online. Nevertheless, our aim is to faithfully present in scope and rigor a medical school caliber course experience. This course comprises six units of content organized into 12 weeks, with an additional week for a comprehensive final exam: - Unit 1 Neuroanatomy (weeks 1-2). This unit covers the surface anatomy of the human brain, its internal structure, and the overall organization of sensory and motor systems in the brainstem and spinal cord. - Unit 2 Neural signaling (weeks 3-4). This unit addresses the fundamental mechanisms of neuronal excitability, signal generation and propagation, synaptic transmission, post synaptic mechanisms of signal integration, and neural plasticity. - Unit 3 Sensory systems (weeks 5-7). Here, you will learn the overall organization and function of the sensory systems that contribute to our sense of self relative to the world around us: somatic sensory systems, proprioception, vision, audition, and balance senses. - Unit 4 Motor systems (weeks 8-9). In this unit, we will examine the organization and function of the brain and spinal mechanisms that govern bodily movement. - Unit 5 Brain Development (week 10). Next, we turn our attention to the neurobiological mechanisms for building the nervous system in embryonic development and in early postnatal life; we will also consider how the brain changes across the lifespan. - Unit 6 Cognition (weeks 11-12). The course concludes with a survey of the association systems of the cerebral hemispheres, with an emphasis on cortical networks that integrate perception, memory and emotion in organizing behavior and planning for the future; we will also consider brain systems for maintaining homeostasis and regulating brain state.

Visualizar o programa do curso

Habilidades que você aprenderá

Brain, Neurological Disorders, Neurobiology, Neurology

Avaliações

4.9 (1,340 classificações)

5 stars
1,246 ratings
4 stars
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3 stars
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2 stars
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1 star
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RR

Nov 25, 2016

While I greatly respect Dr. White's obvious immense knowledge of the neural anatomy, I feel taking this course did very little beyond showing me that perhaps medicine and anatomy wasn't for me.

SJ

Jun 27, 2017

I've always wanted to attend a course like this which offers such a detailed description of the fundamentals of neuroscience. Glad I found it and sure as hell recommended it to all my friends.

Na lição

Sensory Systems: The Visual System

This module will provide lessons that are designed to help you understand the basic mechanisms by which light is transduced into electrical signals that are then used to construct visual perceptions in the brain. Your studies of the visual system will benefit you at this point in the course, but also in later studies when we use the visual system as a model for understanding general principles of developmental plasticity. Lastly, it is worth noting how much of the forebrain contains elements of the visual pathways. Thus, injuries and disease in widespread regions of the brain may have a clinically important impact on visual function. All the more reason to learn these lessons well as you progress in Medical Neuroscience.

Central Visual Processing, part 114:11

Central Visual Processing, part 215:04

Central Visual Processing, part 319:52

Central Visual Pathways, part 112:20

Central Visual Pathways, part 213:40

Visual Field Deficits15:29

Pupillary Light Reflex9:01

Ministrado por

Leonard E. White, Ph.D.
Associate Professor

Transcrição

Now, in the original experiments done by Hubel and Wiesel and then many

experiments done by many hundreds of workers since then.

we know that if such a micro electrode is inserted into the cortex vertically, that

is, in the radial axis of the cortical microcircuit.

What we find is a series of neurons that we might encounter at different depths

through the cerebral cortex that pretty much all report the same aspects of the

visual stimulus. That is if we plotted their spacial

receptive fields we all we find that all of these spatial receptive fields more or

less overlap, across the depth of this recording.

And when we look and test for the orientation selectivity of these cells,

we find that they are all pretty much sharing the same orientation preference.

In this example, again, all preferring angles very much near 90 degrees.

Now a different story would be observed if the angle of the electrode penetration

is made much steeper. And that's what's shown in this

experiment. So now the micro electrode is coming in

the cortex at a very sharp angle, such that we are passing across a set of

cortical micro columns progressing from one to the next.

And what we find when we move across the cortex in this way, is that receptive

field position slowly begins to drift from one location through adjacent

locations until finally we reach a separation of a full receptive field

after we advance a micro electrode about a millimeter or so in the cortical

tissue. If we consider what happens with the

advance of that micro electrode with respect to the orientation selectivity of

the neurons, we find a similar shift. In one position, we may find neurons that

prefer horizontal. And as we progress across the cortex,

moving, let's say, in hundred micron increments, what we might see is a

gradual progression of orientation preference, as we move from one sight to

the next. And finally, we may discover that after

movement of about half a millimeter or so, that we might have a tuning function

that is entirely non-overlapping with the first neuron recorded, in this series

across a penetration. So this suggests that there is a columnar

organization of circuitry where all the neurons in the column tend to respond to

the same stimulus, and that this columnar circuitry is organized in some systematic

fashion, such that neighboring locations across the cortical surface are

representing nearby aspects of the visual stimulus.

And there is a smooth progression of change that occurs.

Now, since the time that David Hubel and Torsten Wiesel did their experiments that

earned them the Nobel Prize additional methods have been developed.

Some of which allow neuroscientists to look at large scale patterns of activity.

Not at the single cell level, but at the level of large populations of neurons

over several millimeters of visual cortex.

One such method Is known as optical imaging of intrinsic signals and it's

based upon the fact as brain regions become active, they draw oxygen off of

the hemoglobin that's being supplied to local regions.

That creates a bit of oxygen debt, and as a consequence arterials dilate and blood

flow increases to those regions. And there is exquisite coupling between

brain activity and blood supply that makes these signals a robust indicator of

neural activity. Now the method in question is called

optical imagining, because we don't need radioactivity as we would with PET

imaging. We don't need a magnet as we would with

MRI imaging. rather, using experimental animals and in

patients undergoing neurosurgical exposure of the brain, it's possible to

directly illuminate the brain surface with light and record the subtle changes

in the absorbance of light that happens as this oxygen and blood flow dynamic

changes in brain tissue. So what you're looking at here is the

results of an experiment that I actually performed.

And in this study, we looked at the responses of the visual cortex in an

experimental animal where the cortex is exposed to light and visual stimuli are

presented on a screen in front of the anesthetized animal.

So this panel that we have here in the upper right hand side of the image is a

view of a the living brain and all the little lines that you see, these are all

blood vessels that are supplying the brain with the blood that it needs to be

active. Now if we take an image of the brain with

no stimulus on the screen and compare that to an image taken while a stimulus

is presented, we can subtract those images and look for the very small change

in the absorbance of light that is a reflection of brain activity.

When we do, we have the image that is shown here on the left.

So we're looking at roughly about 8 millimeters by 6 millimeters to the

visual cortex. And what we see are a series of dark

spots and small bands. And these dark spots and bands are

cortical columns that were activated by the stimulus.

Now it's interesting to look at the literature from the time of Hubel and

Wiesel's original writings back in the 60s and 70s and see how even though they

were probing the brain with micro-electrodes, they still had a sense

of the organization that this method makes now quite clear.

And that is that there are columns of neurons that are responding to the same

visual stimulus that are distributed across the visual cortex.

And columns shift their preferences gradually from 1 location to the next.

Such that, if we compared the responses, let's say, to a horizontal stimulus, with

those to, let's say, a vertical stimulus, what we would find is that the columns

that are present in these 2 images precisely interdigitate.

So the image we see here in the upper right is the result of the subtraction of

the images acquired while the animal viewed horizontal stimuli and vertical

stimuli. And it's a way of looking at how

selective is the response. That is how well can we differentiate the

responses to one visual angle horizontal, from another vertical and we can repeat

this method over and over again with different kinds of stimuli different

pairs of orthogonal stimuli and generate a family of images that reflect the

interdigitation of columns that are representing orthogonal orientations.

Now there's a different way to represent this data that I'd like to show you here

by looking now at a dynamic representation of a set of these images

where we will just loop around and see how the illusion of motion in these

images gives us some insight into the organization of the functional map for

orientation preference. What I'd like to show you are some data

from my lab and the lab of my colleagues. Where we've looked at different

experimental animals and have measured the distribution of orientation columns

in their brains. And we've created an animation that runs

through all the images that were required as the orientation of the visual stimulus

was changed. So if I start this animation, and it will

just run over and over. What you can see, I think, is this

beautiful dynamic array of functional representation in the brain.

Of course, it looks like something's moving in the brain, nothing's moving in

the brain. What is changing is the distribution of

functional activity over time as the visual stimulus is rotated.

And one point I would like for you to appreciate is really how similar these

patterns look in three very different species of animals.

the Treeshrew is a small animal that makes its home in Southeast Asia.

The Galago is a Prosimian primate from Central and South America.

And the third of course is a domesticated carnivore.

So we're looking at the visual cortical patterns from animals that diverged about

65 million years ago. And yet there are very similar patterns

and seemingly a highly conserved cortical network that's giving rise to these

patterns. And if we look in even more detail I find

it interesting that we can localize certain locations in these images where

it looks like these dark domains of activity are swirling around small

points, or singularities. And we call these points pinwheel

centers. And it appears as though the organization

of this representation of orientation preference is one of a large collection

of pinwheel structures that are arrayed across the visual cortex.

Well one way that we can represent this kind of a dynamic set of experimental

data in a static fashion is to perform a little bit of image math and then apply a

color code which allows us to show what we call the map of orientation

preference. So we're looking at the very same region

of the visual cortex in an experimental animal.

And now we're using color to represent the preferred orientation at each

location in the visual cortex. So for example, where we see the color

red is representation of horizontal visual stimuli.

So these are all cortical columns or bands of cortex that prefer horizontal

among all possible orientations. And where we see blue regions in the

brain, or rather brain regions that have been colorized blue, these are cortical

columns that are representing near vertical.

And between red and blue are all possible orientations that are represented in the

brain. Now, the dark bands that we see, these

are structures that we can't resolve the preference.

But with newer techniques that allow us to see the activities of individual

neurons in the living brain, we know that these zones are really not dark at all.

Rather, they have cells that represent orientation just as robustly with as

sharp tuning functions as any other place in the brain.

But rather these cells with different preferences come together in very tight

spaces. And with this method, we can't resolve

their differences but with a cellularly based method of resolution called two

photon imaging of calcium signals, it's possible to detect the preferences of

neighboring neurons. Well when we do so, what we discover is

that there are locations such as right here, right here, and there, for example,

where there are point singularities, around which a set of cortical columns

are arrayed in a radially symmetrical fashion.

And at that point singularity, nearby neurons have very different orientation

preferences. Such that we're looking at the center of

one of these pinwheel formations. Well, I can go on and on about this as my

colleagues and I have in our research. I won't belabor this point any further.

But rather, I wanted to give you a bit of an inside look at the circuitry and the

physiology of this wonderful part of the mammalian brain, the primary visual

cortex. And all of this really has just been to

illustrate one computational property, and that is the property of orientation

preference. Now we and others have done experiments

where we've plotted out the activity maps, not just of, let's say a horizontal

bar moving along a particular axis of motion.

But let's say, a horizontal bar that's either drifting in just one direction or

drifting in the opposite direction. And what we discover is that we may find

a domain in the brain that represents a horizontal stimulus but that domain may

be subdivided. And across half of its region, there may

be cells that prefer upward motion of that horizontal bar.

And in the other half of that domain, we may have a clustering of neurons that

prefer downward motion of that horizontal bar.

This is what we call direction selectivity.

And as Hubel and Wiesel discovered, there are numerous neurons throughout the

visual pathways that respond best to motion presented in just one direction

with much weaker responses, if at all, in the opposite direction.

So, neurons in the visual cortex can be characterized by their orientation and

directional selectivity.

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