Module 3: fMRI Data Structure

Loading...

En provenance du cours de Johns Hopkins University

Principles of fMRI 1

348 notes

Johns Hopkins University

Principles of fMRI 1

348 notes

This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data. A book related to the class can be found here: https://leanpub.com/principlesoffmri

À partir de la leçon

Week 1

This week we will introduce fMRI, and talk about data acquisition and reconstruction.

Module 1: Introduction and Ground Rules9:20

Module 2: Goals of fMRI Analysis12:22

Module 3: fMRI Data Structure6:47

Module 4.1: Psychological Inference Part 111:44

Module 4.2: Psychological Inference Part 28:33

Rencontrer les enseignants

Martin Lindquist, PhD, MSc
Professor, Biostatistics
Bloomberg School of Public Health | Johns Hopkins University
Tor Wager
PhD
Department of Psychology and Neuroscience, The Institute of Cognitive Science | University of Colorado at Boulder

Hi, in this module we're going to be talking about fMRI data structures.

So this is going to be important, when we get into the data analysis and

the statistical analysis of fMRI data.

We have to try to understand how the fMRI data is structured and

know a little bit about the nomenclature.

So standard fMRI experiments gives rise to massive amounts of data.

We talked about that in the last module, and

most standard fMRI studies consists of both structural and functional data.

And here we'll discuss the structure of the data and

some general terminology that's associated with it.

We're also going to provide a brief overview of

some of the characteristics of the data.

So when deciding an fMRI experiment, you have to balance the need for

adequate spatial resolution with adequate temporal resolution.

So the temporal resolution determines our ability to separate brain events in time.

In fMRI, the temporal resolution is determined by how quickly each

individual image is acquired.

And this is determined by something called the TR,

we'll return to this as we move along.

The spatial resolution determines our ability to distinguish changes in an image

across different spatial locations.

So for example, we we have a structural image, these are often called T1 images,

and again, we'll return to this in a later module.

These have high spatial resolution, but low temporal resolution.

Actually, no temporal resolution because they're just a static image.

But since they have such high spatial resolution,

they can be used to distinguish between different tissue types, and so

we see this cartoon image here that you can separate between gray and

white matter, and you can make out anatomical boundaries and whatnot.

Functional images, which are also called T2* weighted images, and

we'll talk about that again as we move along, have lower spacial resolution, so

they're much blurrier than their structural counterparts.

However, we can measure many of them, and so they have higher temporal resolution,

and they can therefore be used to relate changes in signal

to an experimental manipulation.

So in this cartoon here, we have a bunch of images that are acquired while you're

performing Condition A, and a bunch that are performed by condition B.

So, in the last module we gave an example where you finger tapping, which

might be condition A, and then you're resting, which could be condition B.

So they have measurements under both conditions, and then we might want to look

at the differences in signal between the two different conditions, and

that's something that we can do with functional images.

Another piece of terminology that's important is different

types of slices of the brain.

So we measure three-dimensional brain volume but

we often study slices of the brain.

So we often talk about things like coronal slices,

which is a slice in this direction.

Sagittal slices, which is a slice in this direction and

axial slice which is a slice in this direction.

So these are examples of coronal, sagittal, and axial slices.

FMRI or MRI images are typically acquired in axial slices one at at time.

So we go and do different slices in the x, y plane like this.

They can be performed either sequentially, so one slice at a time, or interleaved,

where we skip and slice and then come back and do it again.

We'll talk about this later on.

Together these slices are kind of glued together and made, to make up a three

dimensional brain volume which is what we ultimately analyze.

There's a bunch of terminology that we need to describe the acquisition.

The first thing that's important to know is what's called the field of view.

The field of view is the extent of the brain that's inside of the image.

So for example, the field of view might be around 20 centimeters.

In this example, I have it at 192 millimeters, so it's 19.2 centimeters.

So that tells us what's the extent in each direction of the brain volume.

The brain volume, again, is made up of multiple slices and

each slice has a certain thickness.

So we also have to say, what's the slice thickness?

Here, in example, we might choose a three millimeter thick slice.

Each slice is then split up into voxels, and for example,

in this cartoon, the voxel, the actual slice is split up

into 4096 different voxels in a 64 by 64 grid.

So if we have 64 voxels and our field of view is 192 millimeter, then

each voxel is going to be three millimeter in both the xy and y directions.

So in this case, if we have a matrix size of 64, a field of view of 192,

and a slice thickness of 3 millimeters, our

eventual voxel size will be 3 millimeters by 3 millimeters by 3 millimeters.

And so that's going to be the size of the unit of measurement that we're going to be

interested in fMR.

We can change that, for example, we can make the slice thickness thinner or

thicker, or we can make the matrix size higher, and

thus make the voxels smaller or bigger in the xy directions.

So an experiment studies many different subjects.

So typically enough from where I would we have experiments where

we look at multiple subjects that perform the same type of task.

And in each of these subjects might be scanned during multiple sessions.

Each of these sessions consists of several runs,

we might repeat the task several times on each subject while they're in the scanner.

And each run is going to consist of a series of brain volumes.

Now each of these brain volumes, again, is made up of multiple axial slices and

each of these slices contains many, many voxels.

And, again, each voxel has an intensity associated with this and

this is the basic measurement in fMRI.

So this sort of suggests that there's a hierarchy going from the experiment,

the subjects nested within this experiment,

the sessions nested within each subject, there's run within each session,

and then there's brain volumes within each run.

Each brain volume are measured sequentially over time,

and each voxel has a corresponding time series associated with it.

This is basically the final

level where we analyse the date on this time series level.

But again, we also want to be able to look at these time series and say,

what can they say about the population of subjects that we studied and what not?

Okay.

So that's the end of this module.

Here I've just attempted to kind of talk about some characteristics of fMRI data.

Talk about some nomenclature that's going to be important terminology,

and that will help you read method sections and what not.

Okay. I'll see you in the next lecture.

Bye.

Explorer notre catalogue

Rejoignez-nous gratuitement et obtenez des recommendations, des mises à jour et des offres personnalisées.

Coursera propose un accès universel à la meilleure formation au monde, en partenariat avec des universités et des organisations du plus haut niveau, pour proposer des cours en ligne.

© 2018 Coursera Inc. Tous droits réservés.

Télécharger dans l'App Store

Disponible sur Google Play