1 neuroimaging: from image to inference chris rorden –fmri limitations: relative to other tools...

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1 Neuroimaging: from image to Inference Chris Rorden fMRI limitations: relative to other tools used to infer brain function. fMRI signal: tiny, slow, hidden in noise. fMRI processing: a sample experiment. fMRI anatomy: stereotaxic space. See also: http://www.biac.duke.edu/education/courses/fall05/fmri/

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Page 1: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Neuroimaging: from image to Inference

Chris Rorden– fMRI limitations: relative to other tools used to infer

brain function.– fMRI signal: tiny, slow, hidden in noise.– fMRI processing: a sample experiment.– fMRI anatomy: stereotaxic space.

– See also:– http://www.biac.duke.edu/education/courses/fall05/fmri/

Page 2: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Modern neuroscience

Different tools exist for inferring brain function.

No single tool dominates, as each has limitations.

This course focuses on fMRI.

Temporal resolution

good (millisecond) poor (months)

good(neuron)

poor(whole brain)

scr

erp

fmri

pettmsnirs

lesionseegiap

Sp

ati

al re

solu

tion

Page 3: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Single Cell Recording

Directly measure neural activity.Exquisite timing informationPrecise spatial informationOften, no statistics required!

•Each line is one trial.•Each stripe is neuron firing.•Note: firing increases whenever monkey reaches or watches reaching.

Page 4: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Single Cell Recording

With SCR, we are very close to the data.We can clearly see big effects without

processing.Unfortunately, there are limitations:

– Invasive (needle in brain)Typically constrained to animals, so difficult to directly

infer human brain function.

– Limited field of view: just a few neurons at a time.

Page 5: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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fMRI Processing

Unlike SCR, we must heavily process fMRI data to extract a signal.

The signal in the raw fMRI data is influenced by many factors other than brain activity.

We need to filter the data to remove these artifacts.

We will examine why each of these steps is used.

Processing Steps1. Motion Correct

1. Spatial

2. Intensity

2. Physiological Noise Removal

3. Temporal Filtering

4. Temporal Slice Time Correct

5. Spatial Smoothing

6. Normalize

7. Statistics

Page 6: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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fMRI signal sluggish

Unlike SCR, huge delay between activity and signal change.

Visual cortex shows peak response ~5s after visual stimuli.

Indirect measure of brain activity

0 6 12 18 24

2

1

0

Time (seconds)

Page 7: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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What is the fMRI signal

fMRI is ‘Blood Oxygenation Level Dependent’ measure (BOLD).

Brain regions become oxygen rich after activity. Very indirect measure.

Page 8: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Lets conduct a study

Anatomical Hypothesis: lesion studies suggest location for motor-hand areas.

Ask person to tap finger while in MRI scanner – predict contralateral activity in motor hand area..

M1: movement

S1: sensation

Page 9: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Task

Task has three conditions:1. Up arrows: do nothing

2. Left arrows: press left button each time arrow flashes.

3. Right arrows: press right button every time arrow flashes.

Block design: each condition repeats rapidly for 11.2 sec.

No sequential repeats: block of left arrows always followed by block of either up or right arrows.

Page 10: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Data Collection

Participant Lies in scanner watching computer screen.

Taps left/right finger after seeing left/right arrows.

Collect 120 3D volumes of data, one every 3s (total time = 6min).

Page 11: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Raw Data

The scanner reconstructs 120 3D volumes.– Each volume = 64x64x36

voxels– Each voxel is 3x3x3mm.

We need to process this raw data to detect task-related changes.

Page 12: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Motion Correction

Unfortunately, people move their heads a little during scanning. We need to process the data to create motion-stabilized

images. Otherwise, we will not be looking at the same brain area over

time.

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Spatial smoothing

Each voxel is noisy By blurring the image, we can get a more stable signal

(neighbors show similar effects, noise spikes attenuated).

Page 14: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Predicted fMRI signal

We need to generate a statistical model. We convolve expected brain activity with

hemodynamic response to get predicted signal.

Predicted fMRI signal

=

Neural Signal HRF

Page 15: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Predicted fMRI signal

We generate predictions for neural responses for the left and right arrows across our dataset.

Statistics will identify which areas show this pattern of activity. Several possible statistical contrasts (crucial to inference):

1. Activity correlated with left arrows: visual cortex, bilateral motor.2. More activity for left than right arrows: contralateral motor.

Page 16: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Voxelwise statistics

We compute the probability for every voxel in the brain.

We observe that right arrows precede activation in the left motor cortex and right cerebellum.

Page 17: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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fMRI signal change is tiny, noise is high

Right motor cortex becomes brighter following movement of left hand. Note signal increases from ~12950 to ~13100, only about 1.2% And this is after all of our complicated processing to reduce noise.

0 100 200 300

L_Tap right

12900

13000

13100

Page 18: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Coordinates - normalization

Different people’s brains look different ‘Normalizing’ adjusts overall size and orientation

Raw Images Normalized Images

Page 19: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Why normalize?

Stereotaxic coordinates analogous to longitude– Universal description for anatomical location– Allows other to replicate findings– Allows between-subject analysis: crucial for inference that

effects generalize across humanity.

Page 20: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Goals for this course

fMRI is notoriously difficult technique– Sluggish signal– Poor signal/noise– Must find meaningful statistical contrasts

This seminar reveals how to– Devise meaningful contrasts– Maximize signal, minimize noise– Control for statistical errors.

Page 21: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Safety

MRI uses very strong magnet and radiofrequencies– 3T= ~x60,000 field that aligns compass– Metal and electronic devices are not compatible.

MRI scanning makes loud sounds– Rapid gradient switching creates auditory noise.– Auditory protection crucial.

MRI scanning is confined– Claustrophobia is a concern.

Page 22: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Summary of Lectures

1. Introduction2. Physics I: Hardware and Acquisition3. Physics II: Contrasts and Protocols4. fMRI Paradigm Design5. fMRI Statistics and Thresholding6. fMRI Spatial Processing7. fMRI Temporal Processing8. VBM & DTI: subtle structural changes9. Lesion Mapping: overt structural changes10. Advanced and Alternative Techniques

Page 23: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Which tools

There are many tools available for analysis.

Different strengths.We predominantly focus

on SPM and FSL.These are both free,

popular and have good user support.

Tool SFN04

SPM 78.5%

AFNI 9.1%

FSL 7.4%

BrainVoyager 4.1%

https://cirl.berkeley.edu/view/Grants/BrainPyMotivation

Page 24: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Reporting findings

How do we describe anatomy to others?

We could use anatomical names, but often hard to identify.

We could use Brodmann’s Areas, but this requires histology – not suitable for invivo research.

Both show large between-subject variability.

Requires anatomical coordinate system.

Page 25: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Relative Coordinates

On the globe we talk about North, South, East and West.

Lets explore the coordinates for the brain.

Page 26: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Orientation - animals

Cranialhead

Rostralbeak Caudal

tail

Dorsalback

Ventralbelly

Rostral Caudal

Ventral

Dorsal

Page 27: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Coordinates – Dorsal Ventral

Human dorsal/ventral differ for brain and spine.– Head/Foot, Superior/Inferior, Anterior/Posterior not ambiguous.

DorsalVentral

Dorsal

Ventral

DorsalVentral

Page 28: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Coordinates – Human

CR CR

C

R

Human rostral/caudal differ for brain and spine.– Head/Foot, Superior/Inferior, Anterior/Posterior not ambiguous.

Page 29: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Orientation

Human anatomy described as if person is standing

If person is lying down, we would still say the head is superior to feet.

Page 30: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Anatomy – Relative Directions

Posterior <> Anterior

Pos

teri

or <

> A

nte

rior

Ven

tral

<>

Dor

sal

lateral < medial > lateral

Anterior/Posterioraka Rostral/Caudal

Ventral/Dorsalaka Inferior/Superioraka Foot/Head

Page 31: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Coordinates - Anatomy

3 Common Views of Brain:– Coronal (head on)– Axial (bird’s eye), aka

Transverse. – Sagittal (profile)

sagittalcoronal

axial

Page 32: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Coronal

Corona: a coronal plane is parallel to crown that passes from ear to ear

Page 33: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Transverse

Transverse/Axial: perpendicular to the long axis

Example: cucumber slices are transverse to long axis.

Page 34: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Sagittal

Sagittal – ‘arrow like’– Sagittal cut divides object into left

and right– sagittal suture looks like an arrow.

top view

Page 35: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Sagittal and Midsagittal

A Sagittal slice down the midline is called the ‘midsagittal’ view.

midsagittal sagittal

Page 36: 1 Neuroimaging: from image to Inference Chris Rorden –fMRI limitations: relative to other tools used to infer brain function. –fMRI signal: tiny, slow,

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Oblique Slices

Slices that are not cut parallel to an orthogonal plane are called ‘oblique’.

The oblique blue slice is neither Coronal nor Axial.

Ax

Cor

Oblique