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.
Central Visual Processing, part 2
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Do curso por Duke University
Medical Neuroscience
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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.
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Leonard E. White, Ph.D.Associate Professor
Department of Neurology, Department of Neurobiology, Duke University School of Medicine; Department of Psychology & Neuroscience, Trinity College of Arts & Sciences; Director of Education, Duke Institute for Brain Sciences; Duke University
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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