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1. Introduction to fMRI 2. Basic fMRI Physics 3. Data Analysis

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Presentation on theme: "1. Introduction to fMRI 2. Basic fMRI Physics 3. Data Analysis"— Presentation transcript:

1 1. Introduction to fMRI 2. Basic fMRI Physics 3. Data Analysis 4. Localisation 5. Cortical Anatomy

2 1. Introduction to fMRI

3 MRI vs. fMRI Functional MRI (fMRI) studies brain function.
MRI studies brain anatomy.

4 Brain Imaging: Anatomy
CAT Photography PET MRI Source: modified from Posner & Raichle, Images of Mind

5  neural activity   blood oxygen   fMRI signal
MRI vs. fMRI MRI fMRI high resolution (1 mm) low resolution (~3 mm but can be better) one image fMRI Blood Oxygenation Level Dependent (BOLD) signal indirect measure of neural activity: active neurons shed oxygen and become more magnetic increasing the fMRI signal many images (e.g., every 2 sec for 5 mins)  neural activity   blood oxygen   fMRI signal

6 fMRI Activation Flickering Checkerboard
OFF (60 s) - ON (60 s) -OFF (60 s) - ON (60 s) - OFF (60 s) Time  Brain Activity Source: Kwong et al., 1992

7 PET and fMRI Activation
Source: Posner & Raichle, Images of Mind

8 fMRI Setup

9 fMRI Experiment Stages: Prep
1) Prepare subject Consent form Safety screening Instructions 2) Shimming putting body in magnetic field makes it non-uniform adjust 3 orthogonal weak magnets to make magnetic field as homogenous as possible 3) Sagittals Take images along the midline to use to plan slices Note: That’s one g, two t’s

10 fMRI Experiment Stages: Anatomicals
4) Take anatomical (T1) images high-resolution images (e.g., 1x1x2.5 mm) 3D data: 3 spatial dimensions, sampled at one point in time 64 anatomical slices takes ~5 minutes

Slice Thickness e.g., 6 mm Number of Slices e.g., 10 SAGITTAL SLICE VOXEL (Volumetric Pixel) 3 mm 6 mm Matrix Size e.g., 64 x 64 In-plane resolution e.g., 192 mm / 64 = 3 mm IN-PLANE SLICE Field of View (FOV) e.g., 19.2 cm

12 fMRI Experiment Stages: Functionals
5) Take functional (T2*) images images are indirectly related to neural activity usually low resolution images (3x3x5 mm) all slices at one time = a volume (sometimes also called an image) sample many volumes (time points) (e.g., 1 volume every 2 seconds for 150 volumes = 300 sec = 5 minutes) 4D data: 3 spatial, 1 temporal first volume (2 sec to acquire)

13 Activation Statistics
Functional images Time fMRI Signal (% change) ROI Time Course Condition ~2s Region of interest (ROI) Condition 1 Statistical Map superimposed on anatomical MRI image Condition 2 Time ... ~ 5 min

14 Statistical Maps & Time Courses
Use stat maps to pick regions Then extract the time course

15 2D  3D

16 Design Jargon: Runs session: all of the scans collected from one subject in one day run (or scan): one continuous period of fMRI scanning (~5-7 min) experiment: a set of conditions you want to compare to each other condition: one set of stimuli or one task 4 stimulus conditions + 1 baseline condition (fixation) Note: Terminology can vary from one fMRI site to another (e.g., some places use “scan” to refer to what we’ve called a volume). A session consists of one or more experiments. Each experiment consists of several (e.g., 1-8) runs More runs/expt are needed when signal:noise is low or the effect is weak. Thus each session consists of numerous (e.g., 5-20) runs (e.g., 0.5 – 3 hours)

17 Design Jargon: Paradigm
paradigm (or protocol): the set of conditions and their order used in a particular run volume #1 (time = 0) volume #105 (time = 105 vol x 2 sec/vol = 210 sec = 3:30) run epoch: one instance of a condition first “objects right” epoch second “objects right” epoch epoch 8 vol x 2 sec/vol = 16 sec Time

18 2. Basic fMRI Physics

19 Recipe for MRI 1) Put subject in big magnetic field (leave him there)
2) Transmit radio waves into subject [about 3 ms] 3) Turn off radio wave transmitter 4) Receive radio waves re-transmitted by subject Manipulate re-transmission with magnetic fields during this readout interval [ ms: MRI is not a snapshot] 5) Store measured radio wave data vs. time Now go back to 2) to get some more data 6) Process raw data to reconstruct images 7) Allow subject to leave scanner (this is optional) Source: Robert Cox’s web slides

20 History of NMR NMR = nuclear magnetic resonance
Felix Block and Edward Purcell 1946: atomic nuclei absorb and re-emit radio frequency energy 1952: Nobel prize in physics nuclear: properties of nuclei of atoms magnetic: magnetic field required resonance: interaction between magnetic field and radio frequency Bloch Purcell NMR  MRI: Why the name change? less likely but more amusing explanation: subjects got nervous when fast-talking doctors suggested an NMR most likely explanation: nuclear has bad connotations

21 History of fMRI MRI -1971: MRI Tumor detection (Damadian)
-1973: Lauterbur suggests NMR could be used to form images -1977: clinical MRI scanner patented -1977: Mansfield proposes echo-planar imaging (EPI) to acquire images faster fMRI -1990: Ogawa observes BOLD effect with T2* blood vessels became more visible as blood oxygen decreased -1991: Belliveau observes first functional images using a contrast agent -1992: Ogawa et al. and Kwong et al. publish first functional images using BOLD signal Ogawa

22 Necessary Equipment Magnet Gradient Coil RF Coil 4T magnet RF Coil
(inside) Magnet Gradient Coil RF Coil Source: Joe Gati, photos

23 The Big Magnet B0 x 80,000 = Very strong 1 Tesla (T) = 10,000 Gauss
Source: 1 Tesla (T) = 10,000 Gauss Earth’s magnetic field = 0.5 Gauss x 80,000 = 4 Tesla = 4 x 10,000  0.5 = 80,000X Earth’s magnetic field Robarts Research Institute 4T Continuously on Main field = B0 B0

24 Source:
Magnet Safety The whopping strength of the magnet makes safety essential. Things fly – Even big things! Source: Screen subjects carefully Make sure you and all your students & staff are aware of hazzards Develop stratetgies for screening yourself every time you enter the magnet Source: flying_objects.html

25 Subject Safety Anyone going near the magnet – subjects, staff and visitors – must be thoroughly screened: Subjects must have no metal in their bodies: pacemaker aneurysm clips metal implants (e.g., cochlear implants) interuterine devices (IUDs) some dental work (fillings okay) Subjects must remove metal from their bodies jewellery, watch, piercings coins, etc. wallet any metal that may distort the field (e.g., underwire bra) Subjects must be given ear plugs (acoustic noise can reach 120 dB) This subject was wearing a hair band with a ~2 mm copper clamp. Left: with hair band. Right: without. Source: Jorge Jovicich

26 Protons align with field
Outside magnetic field Protons align with field randomly oriented Inside magnetic field spins tend to align parallel or anti-parallel to B0 net magnetization (M) along B0 spins precess with random phase no net magnetization in transverse plane only % of protons/T align with field M longitudinal axis Longitudinal magnetization transverse plane Source: Mark Cohen’s web slides M = 0 Source: Robert Cox’s web slides

27 fMRI Basics – The functional magnetic resonance imaging
technique measures the amount of oxygen in the blood in small regions of the brain. These regions are called voxels. Neural activity uses up oxygen and the vasculature responds by providing more highly oxygenated blood to local brain regions. Thus a change in amount of oxygen in the blood is measured, and this is taken as a proxy for the amount of local neural activity. The measured signal is often called the BOLD signal (Blood Oxygen Level Dependent). Because neural activity is not measured directly, one needs to think about what the indirect signal really tells us, and how it’s spatial and temporal resolution are limited. Certainly, however,the BOLD signal tells us something about localization of neural activity in the brain.

28 BOLD signal Blood Oxygen Level Dependent signal
neural activity   blood flow   oxyhemoglobin   T2*   MR signal Mxy Signal Mo sin T2* task T2* control Stask S Scontrol time TEoptimum Source: fMRIB Brief Introduction to fMRI Source: Jorge Jovicich

29 BOLD signal Source: Doug Noll’s primer


31 Hypotheses vs. Data Hypothesis-driven Data-driven
Examples: t-tests, correlations, general linear model (GLM) a priori model of activation is suggested data is checked to see how closely it matches components of the model most commonly used approach Data-driven Independent Component Analysis (ICA) no prior hypotheses are necessary multivariate techniques determine the patterns in the data that account for the most variance across all voxels can be used to validate a model (see if the math comes up with the components you would’ve predicted) can be inspected to see if there are things happening in your data that you didn’t predict can be used to identify confounds (e.g., head motion) need a way to organize the many possible components new and upcoming

32 Comparing the two approaches
Region of Interest (ROI) Analyses Gives you more statistical power because you do not have to correct for the number of comparisons Hypothesis-driven ROI is not smeared due to intersubject averaging Easy to analyze and interpret Neglects other areas which may play a fundamental role Popular in North America Whole Brain Analysis Requires no prior hypotheses about areas involved Includes entire brain Can lose spatial resolution with intersubject averaging Can produce meaningless “laundry lists of areas” that are difficult to interpret Depends highly on statistics and threshold selected Popular in Europe NOTE: Though different experimenters tend to prefer one method over the other, they are NOT mutually exclusive. You can check ROIs you predicted and then check the data for other areas. Source: Tootell et al., 1995

33 Why do we need statistics?
MR Signal intensities are arbitrary -vary from magnet to magnet, coil to coil, within a coil (especially surface coil), day to day, even run to run -may also vary from area to area (some areas may be more metabolically active) We must always have a comparison condition within the same run We need to know whether the “eyeball tests of significance” are real. Because we do so many comparisons, we need a way to compensate.

34 Two approaches: ROI A. ROI approach MT
Do (a) localizer run(s) to find a region (e.g., show moving rings to find MT) Extract time course information from that region in separate independent runs See if the trends in that region are statistically significant Because the runs that are used to generate the area are independent from those used to test the hypothesis, liberal statistics can be used Example study: Tootell et al, 1995, Motion Aftereffect Localize “motion area” MT in a run comparing moving vs. stationary rings Extract time courses from MT in subsequent runs while subjects see illusory motion (motion aftereffect) MT Source: Tootell et al., 1995


36 BRAIN LOCALIZATION AND ANATOMY with an emphasis on cortical areas
Why so corticocentric? cortex forms the bulk of the brain subcortical structures are hard to image (more vulnerable to motion artifacts) and resolve with fMRI cortex is relevant to many cognitive processes neuroanatomy texts typically devote very little information to cortex Caveats of corticocentrism: other structures like the cerebellum are undoubtedly very important (contrary to popular belief it not only helps you “walk and chew gum at the same time” but also has many cognitive functions) but unfortunately are poorly understood as yet need to remember there may be lots of subcortical regions we’re neglecting

37 How can we define regions?
Talairach coordinates Anatomical localization Functional localization Region of interest (ROI) analyses

38 Talairach Coordinate System
Individual brains are different shapes and sizes… How can we compare or average brains? Talairach & Tournoux, 1988 squish or stretch brain into “shoe box” extract 3D coordinate (x, y, z) for each activation focus Note: That’s TalAIRach, not TAILarach! Source: Brain Voyager course slides

39 Rotate brain into ACPC plane
Find anterior commisure (AC) Corpus Callosum Fornix Find posterior commisure (PC) ACPC line = horizontal axis Pineal Body “bent asparagus” Note: official Tal sez use top of AC and bottom of PC Source: Duvernoy, 1999

40 Deform brain into Talairach space
Mark 8 points in the brain: anterior commisure posterior commisure front back top bottom (of temporal lobe) left right Squish or stretch brain to fit in “shoebox” of Tal system ACPC=0 y>0 y<0 z y AC=0 y>0 y<0 x Extract 3 coordinates

41 Left is what?!!! L R R L Neurologic (i.e. sensible) convention
left is left, right is right L R x = 0 - + Note: Make sure you know what your magnet and software are doing before publishing left/right info! Radiologic (i.e. stupid) convention left is right, right is left R L Note: If you’re really unsure which side is which, tape a vitamin E capsule to the one side of the subject’s head. It will show up on the anatomical image.

42 How to Talairach For each subject: For the group:
Rotate the brain to the ACPC Plane (anatomical) Deform the brain into the shoebox (anatomical) Perform the same transformations on the functional data For the group: Either Average all of the functionals together and perform stats on that Perform the stats on all of the data (GLM) and superimpose the statmaps on an averaged anatomical (or for SPM, a reference brain) Averaged anatomical for 6 subjects Averaged functional for 7 subjects

43 Talairach Atlas

44 Brodmann’s Areas Brodmann (1905):
Based on cytoarchitectonics: study of differences in cortical layers between areas Most common delineation of cortical areas More recent schemes subdivide Brodmann’s areas into many smaller regions Monkey and human Brodmann’s areas not necessarily homologous

45 Talairach Pros and Cons
Advantages widespread system allows averaging of fMRI data between subjects allows researchers to compare activation foci easy to use Disadvantages based on the squished brain of an elderly alcoholic woman (how representative is that?!) not appropriate for all brains (e.g., Japanese brains don’t fit well) activation foci can vary considerably – other landmarks like sulci may be more reliable

46 Anatomical Localization Sulci and Gyri
gray matter (dendrites & synapses) white matter (axons) FUNDUS BANK GYRUS SULCUS pial surface gray/white border SULCUS FISSURE GYRUS Source: Ludwig & Klingler, 1956 in Tamraz & Comair, 2000

47 Variability of Sulci Variability of Sulci
Source: Szikla et al., 1977 in Tamraz & Comair, 2000

48 Variability of Functional Areas
Watson et al., 1995 -functional areas (e.g., MT) vary between subjects in their Talairach locations -the location relative to sulci is more consistent Source: Watson et al. 1995

49 Cortical Surfaces Advantages surfaces are topologically more accurate
segment gray-white matter boundary render cortical surface inflate cortical surface sulci = concave = dark gray gyri = convex = light gray Advantages surfaces are topologically more accurate alignment across sessions and experiments allows task comparisons Source: Jody Culham

50 Cortical Inflation Movie
Movie: unfoldorig.mpeg Source: Marty Sereno’s web page

51 Cortical Flattening 2) make cuts along the medial surface
(Note, one cut typically goes along the fundus of the calcarine sulcus though in this example the cut was placed below) 1) inflate the brain 3) unfold the medial surface so the cortical surface lies flat 4) correct for the distortions so that the true cortical distances are preserved Source: Brain Voyager Getting Started Guide

52 Spherical Averaging Future directions of fMRI: Use cortical surface mapping coordinates Inflate the brain into a sphere Use sulci and/or functional areas to match subject’s data to template Cite “latitude” & “longitude” of spherical coordinates Movie: brain2ellipse.mpeg Source: Marty Sereno’s web page Source: Fischl et al., 1999

53 Spherical Averaging Source: MIT HST583 online course notes


55 14 Major Sulci Main sulci are formed early in development
Fissures are really deep sulci Typically continuous sulci Interhemispheric fissure Sylvian fissure Parieto-occipital fissure Collateral sulcus Central sulcus Calcarine Sulcus Typically discontinuous sulci Superior frontal sulcus Inferior frontal sulcus Postcentral sulcus Intraparietal sulcus Superior temporal sulcus Inferior temporal sulcus Cingulate sulcus Precentral sulcus Other minor sulci are much less reliable Source: Ono, 1990

56 Interhemispheric Fissure
-hugely deep (down to corpus callosum) -divides brain into 2 hemispheres

57 Sylvian Fissure -hugely deep -mostly horizontal
-insula (purple) is buried within it -separates temporal lobe from parietal and frontal lobes Sylvian Fissure

58 Parieto-occipital Fissure and Calcarine Sulcus
Cuneus (pink) -visual areas on medial side above calcarine (lower visual field) Parieto-occipital fissure (red) -very deep -often Y-shaped from sagittal view, X-shaped in horizontal and coronal views Lingual gyrus (yellow) -visual areas on medial side below calcarine and above collateral sulcus (upper visual field) Calcarine sulcus (blue) -contains V1

59 Collateral Sulcus -divides lingual (yellow) and parahippocampal (green) gyri from fusiform gyrus (pink)

60 Cingulate Sulcus -divides cingulate gyrus (turquoise) from precuneus (purple) and paracentral lobule (gold)

61 Central, Postcentral and Precentral Sulci
Central Sulcus (red) -usually freestanding (no intersections) -just anterior to ascending cingulate Precentral Sulcus (red) -often in two parts (superior and inferior) -intersects with superior frontal sulcus (T-junction) -marks anterior end of precentral gyrus (motor strip, yellow) Postcentral Sulcus (red) -often in two parts (superior and inferior) -often intersects with intraparietal sulcus -marks posterior end of postcentral gyrus (somatosensory strip, purple) ascending band of the cingulate

62 Intraparietal Sulcus -anterior end usually intersects with inferior postcentral (some texts call inferior postcentral the ascending intraparietal sulcus) -posterior end usually forms a T-junction with the transverse occipital sulcus (just posterior to the parieto-occipital fissure) -IPS divides the superior parietal lobule from the inferior parietal lobule (angular gyrus, gold, and supramarginal gyrus, lime) POF

63 Slice Views inverted omega = hand area of motor cortex

64 Superior and Inferior Temporal Sulci
Superior Temporal Sulcus (red) -divides superior temporal gyrus (peach) from middle temporal gyrus (lime) Inferior Temporal Sulcus (blue) -not usually very continuous -divides middle temporal gyrus from inferior temporal gyrus (lavender)

65 Superior and Inferior Frontal Sulci
Superior Frontal Sulcus (red) -divides superior frontal gyrus (mocha) from middle frontal gyrus (pink) Inferior Frontal Sulcus (blue) -divides middle frontal gyrus from inferior frontal gyrus (gold) orbital gyrus (green) and frontal pole (gray) also shown Frontal Eye fields lie at this junction

66 Medial Frontal -superior frontal gyrus continues on medial side
-frontal pole (gray) and orbital gyrus (green) also shown

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