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

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Presentation on theme: "1 1. Introduction to fMRI 2. Basic fMRI Physics 3. Data Analysis 4. Localisation 5. Cortical Anatomy."— Presentation transcript:

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

2 2 1. Introduction to fMRI

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

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

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

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

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

8 8 fMRI Setup

9 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 10 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 fMRI Experiment Stages: Anatomicals

11 11 Slice Thickness e.g., 6 mm Number of Slices e.g., 10 SAGITTAL SLICE IN-PLANE SLICE Field of View (FOV) e.g., 19.2 cm VOXEL (Volumetric Pixel) 3 mm 6 mm Slice Terminology Matrix Size e.g., 64 x 64 In-plane resolution e.g., 192 mm / 64 = 3 mm

12 12 first volume (2 sec to acquire) 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 …

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

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

15 15 2D  3D

16 16 Design Jargon: Runs run (or scan): one continuous period of fMRI scanning (~5-7 min) session: all of the scans collected from one subject in one day 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) 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) 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).

17 17 Design Jargon: Paradigm paradigm (or protocol): the set of conditions and their order used in a particular run Time 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

18 18 2. Basic fMRI Physics

19 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 slidesRobert Cox’s web slides

20 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 BlochPurcell NMR  MRI: Why the name change? most likely explanation: nuclear has bad connotations less likely but more amusing explanation: subjects got nervous when fast-talking doctors suggested an NMR

21 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 22 Necessary Equipment MagnetGradient CoilRF Coil Source: Joe Gati, photos RF Coil 4T magnet gradient coil (inside)

23 23 x 80,000 = 4 Tesla = 4 x 10,000  0.5 = 80,000X Earth’s magnetic field Robarts Research Institute 4T The Big Magnet Very strong Continuously on Source: 1 Tesla (T) = 10,000 Gauss Earth’s magnetic field = 0.5 Gauss Main field = B 0 B0B0

24 24 Magnet Safety The whopping strength of the magnet makes safety essential. Things fly – Even big things! 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: Source: flying_objects.html

25 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 26 Protons align with field Outside magnetic field Inside magnetic field randomly oriented spins tend to align parallel or anti-parallel to B 0 net magnetization (M) along B 0 spins precess with random phase no net magnetization in transverse plane only % of protons/T align with field Source: Mark Cohen’s web slidesMark Cohen’s web slides M M = 0 Source: Robert Cox’s web slidesRobert Cox’s web slides longitudinal axis transverse plane Longitudinal magnetization

27 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 28 BOLD signal Source: fMRIB Brief Introduction to fMRIfMRIB Brief Introduction to fMRI  neural activity   blood flow   oxyhemoglobin   T2*   MR signal Blood Oxygen Level Dependent signal time M xy Signal M o sin  T 2 * task T 2 * control TE optimum S task S control SS Source: Jorge Jovicich

29 29 BOLD signal Source: Doug Noll’s primer


31 31 Hypotheses vs. Data Hypothesis-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 32 Comparing the two approaches Source: Tootell et al., 1995 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. 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

33 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 34 Two approaches: ROI Source: Tootell et al., 1995 A. ROI approach 1.Do (a) localizer run(s) to find a region (e.g., show moving rings to find MT) 2.Extract time course information from that region in separate independent runs 3.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 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) Example study: Tootell et al, 1995, Motion Aftereffect MT


36 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 37 How can we define regions? 1.Talairach coordinates 2.Anatomical localization 3.Functional localization Region of interest (ROI) analyses

38 38 Talairach Coordinate System Source: Brain Voyager course slides Note: That’s TalAIRach, not TAILarach! 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

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

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

41 41 Left is what?!!! Neurologic (i.e. sensible) convention left is left, right is right LR Radiologic (i.e. stupid) convention left is right, right is left RL Note: Make sure you know what your magnet and software are doing before publishing left/right info! x = 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 42 How to Talairach For each subject: 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 a)Average all of the functionals together and perform stats on that b)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 subjectsAveraged functional for 7 subjects

43 43 Talairach Atlas

44 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 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 46 Anatomical Localization Sulci and Gyri gray matter (dendrites & synapses) white matter (axons) FUNDUS BANK SULCUS GYRUS SULCUS gray/white border pial surface FISSURE Source: Ludwig & Klingler, 1956 in Tamraz & Comair, 2000

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

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

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

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

51 51 Cortical Flattening Source: Brain Voyager Getting Started Guide 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

52 52 Spherical Averaging Source: Fischl et al., 1999 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 pageMarty Sereno’s web page

53 53 Spherical Averaging Source: MIT HST583 online course notes


55 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 56 Interhemispheric Fissure - hugely deep (down to corpus callosum) -divides brain into 2 hemispheres

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

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

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

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

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

62 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 63 Slice Views inverted omega = hand area of motor cortex

64 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 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 66 Medial Frontal -superior frontal gyrus continues on medial side -frontal pole (gray) and orbital gyrus (green) also shown

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