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Sonia Poltoratski Vanderbilt University fMRI

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crippling depression unbridled joy intro psych what is BOLD? data analysis...is the wild wild west knowledge brain pictur e

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Outline: MR Physics BOLD signal Basics of Analysis Evolution Good & Bad Practices

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MR Physics MR 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 + - proton

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Spins The protons positive charge generates an electrical current In 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 + + + + + + + + + + J

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Net magnetization (M) Negligible under normal conditions

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magnetic field B0B0

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Proton Precession Spinning objects respond to applied forces by moving their axes perpendicular to the applied force

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Proton Precession Spinning objects respond to applied forces by moving their axes perpendicular to the applied force magnetic field precession axis spin axis

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Proton Precession magnetic field parallel state (low energy level) anti-parallel state (high energy level)

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Net Magnetization (M) magnetic field M longitudinal transverse

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Net Magnetization (M) Increasing magnetic field increase in net magnetization magnetic field strength energy high energy state low energy state E The Zeeman Effect

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Signal Generation magnetic field B 0 photons: electromagnetic fields oscillating at the resonate (Larmor) frequency of hydrogen excitation B 1

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Signal Generation: Net M magnetic field B 0 excitation B 1 M flip angle

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Signal Reception magnetic field B 0 decaying, time-varying signal that depends on the molecular environment of the spins reception

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Signal Reception T 1 recovery (longitudinal relaxation): Individual spins return to their low-energy state, and net M becomes again parallel to the main field T 2 decay (transverse relaxation): Immediately after excitation, spins precess in phase This coherence is gradually lost Images depict the spatial distribution of these properties - BOLD

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T1 Relaxation Times Fat Grey Matter CSF White Matter

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T2 Decay Times Fat White Matter Grey Matter CSF

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Image Formation Magnetic 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 Ceremony

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Image Formation longitudinal magnetization transverse magnetization acquired MR signal in k- space 2D MR image slice excitation 2D spatial encoding 2D inverse Fourier transform

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Slice Excitation slice direction resonant frequency vs. position

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Slice Excitation slice direction frequency range of RF pulse excited slice resonant frequency vs. position when gradient is applied

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Spatial Encoding A 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) 2D

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T1-Weighted ImageT2-Weighted Image

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Pop Quiz! MRI data acquisition The 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)

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Pop Quiz! MRI data acquisition The 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)

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Outline: MR Physics BOLD signal Basics of Analysis Evolution Good & Bad Practices

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BOLD signal Blood-Oxygen-Level-Dependent Contrast (Thulborn et al., 1982; Ogawa, 1990) Oxygenated Hemoglobin Diamagnetic (no unpaired electrons or magnetic moment) Deoxygenated Hemoglobin Paramagnetic (significant magnetic moment) 20% greater magnetic susceptibility, which impacts T2 decay

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BOLD signal Blood-Oxygen-Level-Dependent Contrast (Thulborn et al., 1982; Ogawa, 1990) Oxygenated Hemoglobin Diamagnetic (no unpaired electrons or magnetic moment) Deoxygenated Hemoglobin 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 ~ 1.5T

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Ogawa (1990) Blood oxygen content in rodents reflected in T2-weighted images Metabolic 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 O 2 in order to rescue the mouse as it appeared to start choking. This observation rang a bell.

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fMRI vs. Other Methods log size log time brain map column layer neuron dendrite synapse millisecond second minute hour day MEG & ERP Optical Imaging TMS fMRI PET Induced Lesions Natural Lesions Multi-unit recording Single Unit Patch Clamp Light Microscopy

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Outline: MR Physics BOLD signal Basics of Analysis Evolution Good & Bad Practices

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Voxels 1mm x 1mm x 1.5mm voxels7mm x 7mm x 10mm voxels (Smith, 2004)

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Preprocessing Stages Slice-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.

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Preprocessing Stages Mean intensity adjustment: normalization of signal to account for global drifts over time Temporal high-pass filtering: removal of low-frequency drifts in time course

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Hemodynamic Response Function percent MR signal change time (s) stimulus peak initial dip undershoot -10 -5 0 5 10 15 20 25

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Modeling the Waveform HRF block design fit this model to the time series of each voxel

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General Linear Modeling Y = X. + observed data at a single voxel design matrix estimated parameters error test if the slope of is different from zero

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t stat at each voxel anatomical scan image = my FFA!

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Outline: MR Physics BOLD signal Basics of Analysis Evolution Good & Bad Practices

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Nature (2012)

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Voxel Resolution Kanwisher, McDermott, & Chun (1997): 3.25 x 3.25 x 6 mm McGugin et al. (2013): 1.25 x 1.25 x 1.25 mm

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TR Duration (Tong Lab data) 7Tesla, TR = 200ms (not my) unpublished data removed for web use

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Outline: MR Physics BOLD signal Basics of Analysis Evolution Good & Bad Practices

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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.

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The Nader Effect

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Pitfalls in fMRI Study Design What is your contrast? What conclusions can we draw from fMRI activation? Statistical Analysis vs

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Correcting for Multiple Comparisons (Bennett et al. 2010)

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Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, & Social Cognition Vul 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 trait Only reporting the correlation of the trait to those voxels 54% of surveyed papers, including those published in Science, Nature, and Neuron Voodoo Correlations in Social Neuroscience

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Pitfalls in fMRI Study Design What is your contrast? What conclusions can we draw from fMRI activation? Statistical Analysis Correction for Multiple Comparisons Independently-selected ROIs Software & Human Error Act carefully and critically at all stages of fMRI research!

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The Finer Things in fMRI Event-Related Design fMRI-A: Adaptation Multi-Voxel Pattern Analysis

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Event Related Design Allows 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 events block design event-related design

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Rapid Event Related Design events: individual HRFs: summed HRFs: (BAD)

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Rapid Event Related Design events: individual HRFs: summed HRFs: (GOOD) jittered order & ISI

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fMRI-A: Adaptation 1. Neuronal population is adapted by repetition of a stimulus 2. Some property of the stimulus is changed 3. Recovery from adaptation is assessed: Signal remains adapted = neurons are invariant Signal 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:

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Example: Face Viewpoint Invariance Adapt to identical viewChange the property of interest (Grill-Spector & Malach, 2001) In both cases, signal is reducedIn (L) case, signal recovers

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Multi-Voxel Pattern Analysis (re: Kamitani & Tong, 2005)

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Multi-Voxel Pattern Analysis AKA: fMRI decoding, MVPA, multivariate analysis In univariate analysis described so far, we: Assume independence of each voxel Test 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)

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Multi-Voxel Pattern Analysis (review: Norman et al., 2006) a.Subjects view stimuli from two categories & feature selective voxels are selected b.Data is divided into training and test runs; Training voxel patterns are decomposed and tagged by category c.Training runs are input to a classifier function d.The classifier defines a multi- dimensional decision boundary, and category membership for the test run is predicted

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(xkcd.com)