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Sonia PoltoratskiVanderbilt UniversityfMRICreated January 2013, adapted for web February 2013

* Images are either my own or cited/documented. If I missed anything, please let me know and I will remove the image immediately!

* Brain anatomy image: http://commons.wikimedia.org/wiki/File:Human_brain.png

1crippling depressionunbridledjoy

intro psych

what is BOLD?dataanalysis...is the wild wild westknowledgebrainpictureA personal history of learning about/working with fMRI its not all fun and games!

*Science Cowboy image is made by Jeremy Kalgreen at http://wearscience.com/ (go on, order a t-shirt!)2Outline:MR PhysicsBOLD signalBasics of AnalysisEvolutionGood & Bad Practices

MR PhysicsMR in humans = proton nuclear magnetic resonance, which detects the presence of hydrogen nuclei

Since the single proton of hydrogen in unbalanced, normal thermal energy causes it to spin about itself

electron +-proton4SpinsThe protons positive charge generates an electrical currentIn a magnetic field, this loop current induces torque, called the magnetic moment ()The protons odd-numbered atomic mass gives it an angular momentum (J)proton++++++++++ JAnalogy: just as a moving electrical charge in a looped wire generates current

The right hand rule tells you the direction of the moment and momentum.

Both mu and J are vectors pointing in the same direction along the spin axis; if a nucleus has both of these, it is said to posses the NMR property.5Net magnetization (M)Negligible under normal conditionsThe sum of all magnetic moments

6magnetic fieldB0Magnetically susceptible objects in a magnetic filed will orient along the field lines rather than across them (but its not quite as simple as thisstay tuned)

In MR imaging, the main magnetic field of the scanner is often indicated with the symbol B0

7Proton PrecessionSpinning objects respond to applied forces by moving their axes perpendicular to the applied force

Since the protons are spinning, they do not simply align to the magnetic field

Consider a spinning top. it doesnt spin perfectly upright; rather, its top traces a circle perpendicular to the earths gravitational field. At any point, the top is not vertical but it doesnt fall over.

* Precession image: http://commons.wikimedia.org/wiki/File:Gyroscope_precession.gif8Proton PrecessionSpinning objects respond to applied forces by moving their axes perpendicular to the applied forcemagnetic fieldprecessionaxisspinaxis9Proton Precessionmagnetic fieldparallel state (low energy level)anti-parallel state (high energy level)Protons will precess in two states. The parallel state is slightly more stable, so there will always be relatively more protons in that than in the anti-parallel state. 10Net Magnetization (M)magnetic fieldMlongitudinaltransverseNet magnetization is determined by the relative number of spins in the low- and high-energy states. The transverse component will cancel out, but longitudinal component will remain and be either parallel or antiparallel to the magnetic field.

When tipped away from this equilibrium, the net magnetization will precess, just like a single magnetic moment.11Net Magnetization (M)Increasing magnetic field increase in net magnetizationmagnetic field strengthenergyhigh energy statelow energy stateEThe Zeeman EffectAt room temperature, the difference between high and low energy states is .003%/Tesla, which is very small.

Zeeman effect energy difference between parallel and antiparallel states increases linearly with magnetic field strength.

So, the net magnetization will increase with added Ts, because fewer protons will have the energy to move to the anti-parallel state.

However, you cannot measure net magnetization under equilibrium conditions. An analogy: you cannot measure the weight of an object by looking at it, when its in equilibrium. By lifting it, you perturb it in the gravitational field, and can thus measure its weight.12Signal Generationmagnetic field B0photons: electromagnetic fields oscillating at the resonate (Larmor) frequency of hydrogenexcitation B1To measure magnetic field in the scanner: excitation B1 sends out photons.

Excitation happens at the Larmor frequency, which is constant and unique for every nucleus. It is also the same frequency along which the atom will process.

Equilibrium is disturbed!13Signal Generation: Net Mmagnetic field B0excitation B1Mflip angleif we apply this frequency for a longer period of time, it can have a large effect on the spin system (by the principle of resonance). We can change the overall net magentization from lateral to transverse by the flip angle14Signal Receptionmagnetic field B0decaying, time-varying signal that depends on the molecular environment of the spinsreceptionImmediately after the excitation pulse is turned off, protons return to the low-energy state and release a signal.

The decay of signal is called relaxation, and different substances (water, fat, bone) have different rates of relaxation.

15Signal ReceptionT1 recovery (longitudinal relaxation):Individual spins return to their low-energy state, and net M becomes again parallel to the main field

T2 decay (transverse relaxation):Immediately after excitation, spins precess in phaseThis coherence is gradually lost

Images depict the spatial distribution of these properties- BOLD MRI signal reception relies on its sensitivity to the different relaxation properties of tissues.

Sneak preview: BOLD will depend on T2 decay16T1 Relaxation Times

FatGrey MatterCSFWhite MatterWhat do we notice about these relaxation times? (Super slow! We wont be able to do functional imaging with T1). This is also why we dont often use a 90 degree flip angle, even though it yields the best images.17T2 Decay Times

FatWhite MatterGrey MatterCSF18Image FormationMagnetic gradient: spatially varying magnetic field

Adding a second gradient field causes spins at different locations to precess at different frequencies in a predictable manner

Paul C. Lauterbur and Sir Peter Mansfield at the 2003 Nobel Prize CeremonyIn the early years of NMR, researchers strove to make their samples as homogenous as possible so that no spatial variability could corrupt the data. However, applying a non-constant field is what allows us to

Nobel Ceremony image (c): Paul C Lauterbur Photo Galler. Nobelprize.org. 11 Feb 2013. http://www.nobelprize.org/nobel_prizes/medicine/laureates/2003/lauterbur-photo.html19Image Formationlongitudinal magnetizationtransverse magnetizationacquired MR signal in k-space2D MR imageslice excitation2D spatial encoding2D inverse Fourier transformThis is the hairiest part of the process. Well go over how to select a slice, and introduce 2D spatial encoding, but the details of that are beyond fMRI Basics20Slice Excitationslice directionresonant frequency vs. positionB0 field is uniform, and all spatial locations have the same Larmor resonant frequency

21Slice Excitationslice directionfrequency range of RF pulseexcited sliceresonant frequencyvs. positionwhen gradient is appliedIntroducing the gradient allows us to tune the Larmor frequencies of only the spins in the slice to match the excitation pulse, reducing our problem from 3D to 2D.

22 Spatial EncodingA gradient field that differs along two dimensions results in a unique frequency assigned to each location in the space, influencing the locations spin phase

Phase encoding gradient: turned on before data acquisition so that spins accumulate differential phase offset over space

Frequency encoding gradient: turned on during data acquisition so that the frequency of spin precession changes over space

Resulting data is in units of spatial frequency, which can be converted into units of distance via inverse Fourier transform

Echo Planar Imaging (EPI) allows us to collect an entire imagine in milliseconds, either following 1 excitation (single-shot) or several (multi-shot)

2DThese two gradients (phase & frequency) will allow us to tag every 2D location in the slice.

Spatial frequency: phase, frequency, and amplitude.23T1-Weighted ImageT2-Weighted Image

Pop Quiz!MRI data acquisitionThe experimental data were collected at the Vanderbilt University Institute for Imaging Science using a 3T Philips Intera Achieva MRI scanner with an eight-channel head coil. The functional data were acquired using standard gradient-echo echoplanar T2*-weighted imaging with 28 slices, aligned approximately perpendicular to the calcarine sulcus and covering the entire occipital lobe as well as the posterior parietal and posterior temporal cortex (TR, 2 s; TE, 35 ms; flip angle, 80; FOV, 192 x 192; slice thickness 3 mm with no gap; in-plane resolution, 3 x 3 mm). In addition to the functional images, we collected a T1-weighted anatomical image for every subject (1 mm isotropic voxels). A custom bite bar system was used to minimize the subjects head motion.Keitzmann, Swisher, Konig, & Tong (2012) What terms in this method section have we covered?25Pop Quiz!MRI data acquisitionThe experimental data were collected at the Vanderbilt University Institute for Imaging Science using a 3T Philips Intera Achieva MRI scanner with an eight-channel head coil. The functional data were acquired using standard gradient-echo echoplanar T2*-weighted imaging with 28 slices, aligned approximately perpendicular to the calcarine sulcus and covering the entire occipital lobe as well as the posterior parietal and posterior temporal cortex (TR, 2 s; TE, 35 ms; flip angle, 80; FOV, 192 x 192; slice thickness 3 mm with no gap; in-plane resolution, 3 x 3 mm). In addition to the functional images, we collected a T1-weighted anatomical image for every subject (1 mm isotropic voxels). A custom bite bar system was used to minimize the subjects head motion.Keitzmann, Swisher, Konig, & Tong (2012) TR = response timeTE = echo timeFOV = field of view26Outline:MR PhysicsBOLD signalBasics of AnalysisEvolutionGood & Bad Practices

BOLD signalBlood-Oxygen-Level-Dependent Contrast (Thulborn et al., 1982; Ogawa, 1990)OxygenatedHemoglobin

Diamagnetic (no unpaired electrons or magnetic moment)

DeoxygenatedHemoglobin

Paramagnetic (significant magnetic moment)

20% greater magnetic susceptibility, which impacts T2 decay

magnetic susceptibility causes spin dephasing, which is related to T2 decay28BOLD signalBlood-Oxygen-Level-Dependent Contrast (Thulborn et al., 1982; Ogawa, 1990)OxygenatedHemoglobin

Diamagnetic (no unpaired electrons or magnetic moment)

DeoxygenatedHemoglobin

Paramagnetic (significant magnetic moment)

20% greater magnetic susceptibility, which impacts T2 decay

The more deoxygenated blood is present, the shorter the T2

Difference emerges at ~ 1.5TThulborn demonstrated this with a test tube of blood, Ogawa proposed it as a measure of physiology29Ogawa (1990)Blood oxygen content in rodents reflected in T2-weighted imagesMetabolic demand for oxygen (confirmed by concurrent EEG) is necessary for BOLD contrast

During an MRI experiment with an anesthetized mouse, I saw most of the dark lines disappear when the breathing air was switched to pure O2 in order to rescue the mouse as it appeared to start choking. This observation rang a bell.Tested that blood oxygen level would change the visibility of blood vessels on t2 weighted images

Exact relationship between blood flow, oxygen supply, and BOLD is still debated.

Quote: from Neuroimage fMRI 20th anniversary issue, 2012Image: from Ogawas university website, http://nri.gachon.ac.kr/kr/ocw_kr.html

30fMRI vs. Other Methodslog sizelog timebrain

map

column

layerneuron

dendrite

synapsemillisecond second minute hour dayMEG & ERPOptical ImagingTMSfMRIPETInduced LesionsNatural LesionsMulti-unit recordingSingle UnitPatch ClampLight MicroscopyGiven how non-invasive fMRI is, its pretty good in terms of spatial and temporal resolution. 31Outline:MR PhysicsBOLD signalBasics of AnalysisEvolutionGood & Bad Practices

Voxels

1mm x 1mm x 1.5mm voxels7mm x 7mm x 10mm voxels(Smith, 2004)Voxels are approximately cubic units that are essentially the spatial resolution of our data.

Full image citation:

Smith SM. (2004). Overview of fMRI analysis. The British Journal of Radiology 77: S167-175.33Preprocessing StagesSlice-timing correction: correcting for differences in acquisition times within a TR

Motion correction: re-alignment of images across the session

Spatial smoothing: blurring of neighboring data points, akin to low-pass filtering. 34Preprocessing StagesMean intensity adjustment: normalization of signal to account for global drifts over time

Temporal high-pass filtering: removal of low-frequency drifts in time course

Reasons for large field/signal drifts include heating up of the actual magnet.

Image from the Brain Voyager User Guide: http://support.brainvoyager.com/functional-analysis-preparation/27-pre-processing/73-users-guide-temporal-high-pass-filtering.html35Hemodynamic Response Functionpercent MR signal changetime (s)stimuluspeakinitial dipundershoot-10 -5 0 5 10 15 20 25JJJModeling the WaveformHRFblock designfit this model to the time series of each voxelGeneral Linear ModelingY = X . + observed data at a single voxeldesign matrixestimated parameterserrortest if the slope of is different from zero Design matrix: predicted timecourse from previous example

Parameters:Define the contribution of each component of the design matrix to the value of YEstimated so as to minimize the error, , i.e. least sums of squares

Error:Difference between the observed data, Y, and that predicted by the model, X.

A beta value is estimated for each column in design matrix

38t stat at each voxel

anatomical scan image=my FFA!Outline:MR PhysicsBOLD signalBasics of AnalysisEvolutionGood & Bad Practices

Nature (2012)Smith (2012). Brain imaging: fMRI 2.0. Nature 484: 24-26.

41Voxel ResolutionKanwisher, McDermott, & Chun (1997):3.25 x 3.25 x 6 mmMcGugin et al. (2013):1.25 x 1.25 x 1.25 mm

TR Duration(Tong Lab data)7Tesla, TR = 200ms(not my) unpublished data removed for web useStay tuned for the paper! Imaging at 7T has allowed us to collect a full brain image (TR) in 200ms.43Outline:MR PhysicsBOLD signalBasics of AnalysisEvolutionGood & Bad Practices

The Seductive Allure of Neuroimaging

(Weisberg et al., J Cog Neuro 2008)Non-experts judge explanations with neuroscience information as more satisfying than explanations without neuroscience, especially bad explanations.Details of the paper:

We wrote descriptions of 18 psychological phenomena (e.g., mutual exclusivity, attentional blink) that were meant to be accessible to a reader untrained in psychology or neuroscience. For each of these items, we created two types of explanations, good and bad, neither of which contained neuroscience.

[The curse is actually the curse of knowledge, which makes us think that people know facts that we do.]

The added neuroscience information had three important features: (1) It always specified that the area of activation seen in the study was an area already known to be involved in tasks of this type, circumventing the interpretation that the neuroscience information added value to the expla- nation by localizing the phenomenon. (2) It was always identical or nearly identical in the good explanation and the bad explanation for a given phenomenon. Any general effect of neuroscience information on judgment should thus be seen equally for good explanations and bad explanations. Additionally, any differences that may occur between the good explanation and bad explana- tion conditions would be highly unlikely to be due to any details of the neuroscience information itself. (3) Most importantly, in no case did the neuroscience information alter the underlying logic of the explanation itself.45The Nader Effect

What does this actually tell us about the brain? About cognition?46

At the UCLA Ahmanson-Lovelace Brain Mapping Center, Marco Iacoboni and his group used functional magnetic resonance imaging (fMRI) to measure brain responses in a group of subject while they were watching the Super Bowl ads (2006).

http://www.edge.org/3rd_culture/iacoboni06/iacoboni06_index.html

Does fMRI activation of a certain brain area, like the amygdala, tell us what the subject is really thinking?47Pitfalls in fMRIStudy DesignWhat is your contrast?What conclusions can we draw from fMRI activation?

Statistical Analysisvs

If I do a study where I contrast this picture of a puppy and this grating, have I found the puppy area of the brain? No, there are many differences between the two images that have nothing to do with puppy-ness.

Puppy image: http://commons.wikimedia.org/wiki/File:BernerSennenhund_Welpe_9_Tibo_vom_Niederlausitzer_Heidepark.jpg

48Correcting for Multiple Comparisons

(Bennett et al. 2010)Standard statistical threshold p < 0.001

Showed this dead salmon visual stimuli, and did GLM analysis as weve described.

Citation: Bennett et al. (2010). Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon: An Argument for Proper Multiple Comparisons Correction. Journal of Serendipitous and Unexpected Results.

This won the Ignobel prize in neuroscience: http://blogs.scientificamerican.com/scicurious-brain/2012/09/25/ignobel-prize-in-neuroscience-the-dead-salmon-study/49Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, & Social CognitionVul et al. (2009)

Noticed R > 0.8 correlations, seemingly higher than possible under constraints of fMRI and variability of personality measures

Non-independence error: Selecting a small number of voxels based on some traitOnly reporting the correlation of the trait to those voxels

54% of surveyed papers, including those published in Science, Nature, and NeuronVoodoo Correlations in Social Neuroscience

From the paper: Nonindependence error. This approach amounts to selecting one or more voxels based on a functional analysis and then reporting the results of the same analysis and functional data from just the selected voxels. This analysis distorts the results by selecting noise that exhibits the effect being searched for, and any measures obtained from such a nonindependent analysis are biased and untrustworthy.

Ongoing controversy/debate in the field, can be traced further on Ed Vuls website.

Citation: Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009) Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition (the paper formerly known as Voodoo Correlations in Social Neuroscience). Perspectives on Psychological Science, 4, 274290.50

Pitfalls in fMRIStudy DesignWhat is your contrast?What conclusions can we draw from fMRI activation?

Statistical AnalysisCorrection for Multiple ComparisonsIndependently-selected ROIsSoftware & Human Error

Act carefully and critically at all stages of fMRI research!*Science Cowboy image is made by Jeremy Kalgreen at http://wearscience.com/ 51The Finer Things in fMRIEvent-RelatedDesignfMRI-A:AdaptationMulti-Voxel Pattern AnalysisA pun! Accepted/good practices that, in a way, increase the resolution of our fMRI data.52Event Related DesignAllows us to mix events of different types, avoiding effects related to blocking

Events can be categorized or defined post-hoc based on subjects responses

In slow ERD, the BOLD response is allowed to return to baseline between eventsblock designJJJevent-related designJJJRapid Event Related DesignJJJQQQIIIevents:individual HRFs:summed HRFs:(BAD)Consider equally spaced, ordered events. The true individual HRFs may look like the middle panel, but when theyre all summed, we cant disentangle the contribution of each event.54Rapid Event Related DesignJJQQIIevents:individual HRFs:summed HRFs:(GOOD)jittered order & ISINow, we can deconvolve the summed HRFs into the (very same) individual signals.55fMRI-A: AdaptationNeuronal population is adapted by repetition of a stimulusSome property of the stimulus is changedRecovery from adaptation is assessed:Signal remains adapted = neurons are invariantSignal recovers = neurons are sensitive to the changed property(Grill-Spector & Malach, 2001)

The resolution of fMRI makes it difficult to distinguish between homogenous and heterogenous populations:Example: Face Viewpoint InvarianceAdapt to identical viewChange the property of interest

(Grill-Spector & Malach, 2001)In both cases, signal is reduced

In (L) case, signal recoversCitation: . Grill-Spector K & Malach R. (2001). fMR-Adaptation: a tool for studying the functional properties of human cortical neurons. Acta Psychologia 107, 293-32 (Special issue on cognitive neuroscience).57Multi-Voxel Pattern Analysis

(re: Kamitani & Tong, 2005) They reported the results of a visual mind-reading experiment, showing that it is possible to decode whether an observer is covertly attending to one set of oriented lines or the other when viewing an ambiguous plaid display. Activity patterns in early visual areas (V1V4) allowed for reliable prediction of the observers attentional state (80% accuracy).58Multi-Voxel Pattern AnalysisAKA: fMRI decoding, MVPA, multivariate analysisIn univariate analysis described so far, we:Assume independence of each voxelTest whether each voxel responds more to one condition than the other

MVPA is designed to test whether 2+ conditions can be distinguished based on activity pattern in a set of voxels

Critically, MVPA can sometimes identify differences in conditions when average activity is equal(review: Pratte & Tong, 2012)Typically, anywhere from a few dozen to several thousand voxels might be used for fMRI pattern analysis, so an activity pattern with N voxels would be represented in an N-dimensional space, and clouds of dots representing the two classes would be separated by a linear hyperplane. (Multiclass classifi- cation analysis involves calculating multiple hyperplanes to carve up this multidimensional space among three or more conditions.)59Multi-Voxel Pattern Analysis(review: Norman et al., 2006)

Subjects view stimuli from two categories & feature selective voxels are selected

Data is divided into training and test runs; Training voxel patterns are decomposed and tagged by category

Training runs are input to a classifier function

The classifier defines a multi-dimensional decision boundary, and category membership for the test run is predictedFrom paper: Figure 1. Illustration of a hypothetical experiment and how it could be analyzed using MVPA. (a) Subjects view stimuli from two object categories (bottles and shoes). Afeature selection procedure is used to determine which voxels will be included in the classification analysis (see Box 1). (b) The fMRI time series is decomposed intodiscrete brain patterns that correspond to the pattern of activity across the selected voxels at a particular point in time. Each brain pattern is labeled according to thecorresponding experimental condition (bottle versus shoe). The patterns are divided into a training set and a testing set. (c) Patterns from the training set are used to train aclassifier function that maps between brain patterns and experimental conditions. (d) The trained classifier function defines a decision boundary (red dashed line, right) inthe high-dimensional space of voxel patterns (collapsed here to 2-D for illustrative purposes). Each dot corresponds to a pattern and the color of the dot indicates itscategory. The background color of the figure corresponds to the guess the classifier makes for patterns in that region. The trained classifier is used to predict categorymembership for patterns from the test set. The figure shows one example of the classifier correctly identifying a bottle pattern (green dot) as a bottle, and one example ofthe classifier misidentifying a shoe pattern (blue dot) as a bottle

Citation: Norman KA, Polyn SM, Detre GJ, Haxby JV. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10:42430

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(xkcd.com)Creative Commons: http://xkcd.com/1163/61