Brain Imaging: Reality/Hype

Introduction to neuroimaging: neurons, images, inference

Geoffrey K Aguirre, MD, PhDcfn.upenn.edu/aguirre

Center for Neuroscience & Society

Monday, March 8, 2010

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neural activity functional MRI image


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raw data over time processed result


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processed result

lying

inference


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neural activity


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x-ray human head


Griff Wason

CT scanner


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axial CT scan of the brain


Griff Wason

Allan M. Cormack Godfrey Hounsfield

1979 Nobel Prize in Physiology or Medicine


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inject radio-labeled sugar or water


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co-incidence detection for positron emission tomography


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PET scan of the brain


B0

M0B0

H2O


B0

Y

X

Z90° radio-frequency

pulse

high energy state


T1 signal

B0

Y

X

Z

high energy state

Y

X

Z

low energy state

Y

X

Z

protons relax

RF energy released

time

RF

ener

gy T1 time constant of decay


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T1 weighted T2 weighted


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Griff Wason

Paul Lauterbur Sir Peter Mansfield

2003 Nobel Prize in Physiology or Medicine


http://en.wikipedia.org/wiki/Paul_Lauterbur

http://en.wikipedia.org/wiki/Paul_Lauterbur

http://en.wikipedia.org/wiki/Peter_Mansfield

http://en.wikipedia.org/wiki/Peter_Mansfield

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diffusion weighted (DWI) gradient echo

FLAIR MRA


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susceptibility artifact from iron oxide particlessuspended in beeswax in hair


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hemoglobin contains iron atoms


paramagnetic diamagnetic

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Linus Pauling


Deoxyhemoglobin creates magnetic field inhomo-geneities, causing spin dephasing

Y

X

Z

protons de-phase


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gradient echoplanar image


Neuro-vascular coupling

↑ blood flow

↑ blood volume

↓ [deoxy-Hgb]

physiologic delay


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neuro-imagingcoupling

T2*

time(secs)

imag

ing

sign

alMonday, March 8, 2010

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voxels

3x3x3 mm

~ 10 million neurons


time

1 2 3 4 5 6 7 8 9neur

al a

ctiv

ity

fMR

I sig

nal

time (seconds)


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A hemodynamic proxy of neural activity

neuro-imagingcoupling

T2*

time(secs)

imag

ing

sign

al


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A nearly linear system

T2*

neural activity BOLD fMRI signal

low-pass~ linearsystem


The BOLD fMRI system in spaceSpatial resolution of about 0.025 cm3 In a typical 3 x 3 x 3 mm voxel, there are ~ 10 million neurons


-0.5

0

0.5

1

0 2 4 6 8 10 12 14time(secs)

Spatial resolution of ~0.025 cm3 (3x3x3 mm, ~10 million neurons)

Temporal resolution of 3-4 s

imag

ing

sign

al


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Compare conditions over timeBO

LD fM

RI s

igna

l

+ + +


0.0 -7.0

Color-coded statistical result:

t-statistic0.0 7.0

BOLD

fMR

I sig

nal

t-value = 2.1


surfacereconstruction

hi-resanatomical

digitalsmoothing

thresholding

Neuroaesthetics


The BOLD fMRI signal has no direct, absolute interpretation.

Must be compared between states studied close together in time.

A relative signal


Neural activity?

A hemodynamic proxy of neural activity

• fMRI measures blood flow; differences in vascular response can confound differences in neural activity

time(secs)

younger

older

imag

ing

sign

aldrugs, hormonal states, age, gender


A generalizable result?

Studied population

healthy collegestudents

middle-agedlawyers

children

? ?


An integrated measure

The BOLD fMRI signal integrates neural activity over seconds and millimeters.

(spike rate vs. local field potential)

Detectable • a bulk change in neural activity

Undetectable • a change in population code


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processed result

love

inference


Three basic types of neuroimaging studies

The key question to ask for each


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cognition / consciousness

T2*

. .

4system #1 system #2

workingmemory

physics / physiology


Three basic types of neuroimaging studies

The key question to ask for each


The brain area for “Love”?

Forward inference

?

which brain areas correspond to an isolated behavior?

Love


Forward inference

isolate behavior by subtracting conditions


Spouse - Friend= Love


Spouse vs. Friend over time



Forward inference

what if the “subtraction” includes other mental states?

= Familiarity?= Obligation?= Sexuality?

= Love



Forward inference

relate variations in the behavior to variations in neural response

brai

n ac

tivity

amount of loveLOVE!Lovelove?



Forward inference

relate individual differences in behavior to brain differences

brai

n ac

tivity

amount of love


LOVE!Love

love?


Forward inferencehow was the behavior isolated?


Clinton induces “Conflict”?

Focal reverse inference

use local brain activity to identify mental states or emotions a situation evokes

conflict

Conflict


• Why we love• What your brain looks like on faith• What makes us moral• When worry hijacks the brain• How we get addicted• Marketing to your mind

• Inside the grieving brain• It feels good and everybody does it [scratching]• Mind reading is now possible• This is your brain on optimism• Hot flashes [fMRI of menopause]

Untrue statements make us feel disgust, just like seeing

rotten food

People with complicated grief experience paradoxical

pleasure during sadness

Scratching evokes a sense of pleasure because it

decreases memory of pain




use local brain activity to identify mental states or emotions a situation evokes

conflict

Conflict



Focal reverse inferenceConflict

what if more than one emotion can activate a brain region?

?hope

decision making

unpleasant memories


Implementation

relate variations in stimulus properties or information processing to response

brai

n ac

tivity

speed of motion

“Boring neuroscience”


Focal reverse inferencehow strong is the association between local brain activity and the assumed evoked behavior?


Are you lying?

Distributed reverse inference

measure distributed patterns of response to classify mental states

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I picked the ace of spades!


• Why we love• What your brain looks like on faith• What makes us moral• When worry hijacks the brain• How we get addicted• Marketing to your mind

• Inside the grieving brain• It feels good and everybody does it [scratching]• Mind reading is now possible• This is your brain on optimism• Hot flashes [fMRI of menopause]

fMRI data can determine what tool you are currently thinking

about (hammer or wrench)

Which of 10,000 different pictures you are viewing can

be read from your cortex

The scanner can determine if you are lying or telling the

truth


Are you lying?


1) train a computer to learn the pattern of activity seen with different mental states

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Iʼll tell the truth


Are you lying?


1) train a computer to learn the pattern of activity seen with different mental states

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Iʼll lie and say I have a jack


Are you lying?


2) classify an unknown brain pattern as belonging to a particular state

?

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I picked the three of clubs!


Are you lying?


How stimulus or context dependent is the effect? Particularly relevant for lie detection...

?

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I never killed a man in Reno just to watch

him die!


Multi-voxel classificationcan the classification be generalized beyond the training context?


Forward inferencedetermine which brain region is associated with an isolated behavior

Focal reverse inferenceuse localized brain activity to determine which mental states are evoked by a complex behavior

Multi-voxel classificationuse distributed patterns of brain activity to predict which mental state is being experienced


Forward inference


Multi-voxel classification

how was the behavior isolated?

how strong is the association between local brain activity and the assumed evoked behavior?

can the classification be generalized beyond the training context?


Brain Imaging: Reality/Hype

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