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1 Advanced Methods Chris Rorden –Advanced fMRI designs Adaptation fMRI Sparse fMRI Resting State fMRI –Advanced fMRI analysis ICA Effective and Functional.

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Presentation on theme: "1 Advanced Methods Chris Rorden –Advanced fMRI designs Adaptation fMRI Sparse fMRI Resting State fMRI –Advanced fMRI analysis ICA Effective and Functional."— Presentation transcript:

1 1 Advanced Methods Chris Rorden –Advanced fMRI designs Adaptation fMRI Sparse fMRI Resting State fMRI –Advanced fMRI analysis ICA Effective and Functional Connectivity Analysis –Alternative measures of activation Perfusion msMRI –Comparing SPM to FSL Some slides from Peter Bandettini CABI talk 18 November 2009

2 2 Adaptation Designs (from Kanwisher) Show two stimuli in rapid succession. See if a brain region can discriminate if these stimuli are the same or different. Classically, regions show adaptation – less time to process same information twice in a row. a.ka. ‘repetition suppression’ paradigm.

3 3 Adaptation Designs FFA activates strongly to faces Does it discriminate – yes: we see adaptation response. Similar adaptation is not seen for chairs, so suggests special role in face processing.

4 4 Sparse fMRI Standard fMRI acquires data continuously. –Loud noises can make it difficult to examine auditory stimuli. Sparse imaging includes a delay between each fMRI volume, so stimuli can be presented while scanner is silent. Time (sec)010 Continuous Time (sec)010 Sparse

5 5 Sparse fMRI Typically, sparse design like a block design – each acquisition measures effect of single stimuli. Stimuli must be presented ~5sec prior to acquisition. Sparse designs have less power than continuous designs, and it is difficult to estimate latency of BOLD response. Due to T1 effects, Sparse designs can still have good power. Time (sec)010 BOLD

6 6 Resting State fMRI Resting state fMRI allows us to estimate natural connectivity between regions: which regions cycle together. Essentially, have individual lie in scanner resting while you collect a lot of fMRI data. Must covary out low frequency scanner drift as well as high frequency physiological noise.

7 7 Resting State Correlations Rest: seed voxel in motor cortex B. Biswal et al., MRM, 34:537 (1995) Activation: hand movement

8 8 Independent Component Analysis In conventional analysis, we see if a HRF predicts our behavioral design. FSL includes MELODIC for ICA, includes nice description: – In ICA, we decompose fMRI data into different spatial and temporal components. –estimate the BOLD response. –estimate artifacts in the data, then run conventional analysis on denoised data. –find areas of ‘activation’ which respond in a non-standard way. –analyse data for which no model of the BOLD response is available (e.g. resting state fMRI).

9 9 ICA vs Conventional Analysis Conventional analysis is confirmatory: does my model predict data. Results depend on model ICA is exploratory: Is there anything interesting in the data? Can give unexpected results. What is the potential of ICA? FSL includes melodic, so you can examine our data. Many use melodic to remove artifacts.

10 10 Connectivity Classic fMRI detects all regions involved with task –Motor task would elicit motor cortex, cerebellum and supplementary motor area. –It would be much more insightful if we could see the direction of connections Examples include Dynamic Causal Modelling

11 11 Psycho-physiological Interaction (from Henson) Parametric, factorial design, in which one factor is psychological (eg attention)...and other is physiological (viz. activity extracted from a brain region of interest) Attention V1 V5 SPM{Z} attention no attention V1 activity V5 activity time V1 activity Attentional modulation of V1 - V5 contribution

12 12 Effective vs Functional Connectivity (Henson) No connection between B and C, yet B and C correlated because of common input from A, eg: A = V1 fMRI time-series B = 0.5 * A + e1 C = 0.3 * A + e2 Correlations: A BC A 1 B C ABC  2 =0.5, ns. Functional connectivity Effective connectivity

13 13 SPM2 Dynamic Causal Modelling (Henson) V1IFGV5SPC Motion Photic Attention.82 (100%).42 (100%).37 (90%).69 (100%).47 (100%).65 (100%).52 (98%).56 (99%) Friston et al. (2003) Büchel & Friston (1997) Effects Photic – dots vs fixation Motion – moving vs static Attenton – detect changes Attention modulates the backward- connections IFG→SPC and SPC→V5Attention modulates the backward- connections IFG→SPC and SPC→V5 The intrinsic connection V1→V5 is insignificant in the absence of motionThe intrinsic connection V1→V5 is insignificant in the absence of motion

14 14 Functional Connectivity Observe which region’s activity correlates. Can be done while resting in scanner –Hampson et al., Hum. Brain. Map., 2002

15 15 Perfusion imaging Use Gd or blood as contrast agent. Allows us to measure perfusion –Static images can detect stenosis and aneurysms (MRA) –Dynamic images can measure perfusion (PWI) Measure latency – acute latency appears to be strong predictor of functional deficits. Measure volume Can also measure task-related changes in blood flow (ASL), similar to fMRI.

16 16 ASL MR signal is based proportion of atoms aligned with the magnet. Slightly lower energy state aligned, so atoms preferentially align. More alignment in higher fields However, 180° pulse will reduce this signal.  = 3T Net Magnetization = 3T NM after 180° pulse 

17 17 Arterial Spin Labeling 1.Tag inflowing arterial blood 2.Acquire Tagged image 3.Repeat scan without tag 4.Acquire Control image 5.Subtract Control image – Tagged image The difference in magnetization between tagged and control images is proportional to regional cerebral blood flow

18 18 Data from Trio We collect 16 slices 3.5x3.5x6mm TR 2.2sec (4.4sec for tag+control pair). TE=12ms (very little BOLD artifact). Not wise to collect ASL faster than 2sec (otherwise, not enough transit time between volumes. Wise to use slower TR for individuals with impaired perfusion (stroke). Control Tagged Difference Mean of 73 differences

19 19 TI and TR influence contrast time TI (Inversion Time) TR (Repeat Time) TI must be long enough for tagged blood to wash in to tagged slice TR must be long enough to allow tagged blood to wash out of control slice

20 20 TR Optimal TR depends on the individual’s blood transit time. –~2.4s, the ‘tagged’ image has more tagged blood than the control image. –~1.8s, very low contrast: tagged blood in both control and tagged image. –~1.2s reverse contrast: tagged blood does not reach slice until the control image (except fast arteries).

21 21 Blood Transit Time BTT varies in individuals If the TR is very short, the blood will not yet reach the capillary beds. Therefore, the control image can appear darker than the tagged image! In particular, very little signal when BTT matches TR. Transit time actually faster during active than rest. Either calculate BTT for each individual MRM, 57, or use a long TR (4s, e.g. 8 s for control+tag pair)

22 22 Theory: Signal in ASL Tagged image: Inflowing inverted spins within the blood reducing tissue magnetization: more flow = darker Control: Inflowing blood has increased magnetization than saturated tissue: more flow = brighter Mumford et al. (2006) Control Tagged Control Tagged Acquisition Perfusion Signal Observation

23 23 BOLD and Perfusion ASL scans are designed to measure perfusion However, because they are T2* scans, they also have a BOLD artifact. To minimize BOLD, keep TE to a minimum BOLD is present in BOTH tagged and control image Because the tagged and control images are acquired several seconds apart, simple subtraction of tagged and control image is not a good idea for event related designs.

24 24 Analysis Strategies Simple subtraction –Subtract tagged image from subsequent control image –Halves the amount of samples (e.g. with 3sec TR, one sample every 6sec). –Problem: leading edge and falling edge of HRF will have very different signal in control and tagged image: poor choice for event- related designs.

25 25 Analysis Strategies Inter-trial subtraction –Subtract tagged image from control image acquired at the same interval after task onset. –Halves the amount of samples (e.g. with 3sec TR, one sample every 6sec). –Problem: events must be ordered to coincide with TRs (e.g. period of on-off blocks is an odd number of TRs).

26 26 Analysis Strategies FSL interpolates controlled and tagged images to estimate signal for both control and tagged images. The number of volumes is not halved,– analysis proceeds similar to fMRI data. Samples not completely independent, so DF is adjusted. The FSL difference signal is actually added to a mean image for all samples, so that the relative signal-noise is similar to fMRI

27 27 Analysis Easy to analyze ASL data with FSL: –Select perfusion check box –FSL simply subtract tagged image from neighboring control FSL is not optimal –Control and tagged image are not acquired simultaneously –Therefore, they sample different points of HRF. –There are alternatives Sinc interpolate to estimate simultaneous signals (interp_asl) Intertrial subtraction: compare control image with tagged image that was collected at same delay after event (Yang et al, 2000). Add both tagged and control images in a single model (Mumford et al, 2006). –In general, FSL approach only good for block designs.

28 28 Measuring the initial dip Time (seconds) ‘Initial dip’ than signal increase seen 5 sec later. –No venous artefacts –Later overcompensation may not be specific (‘watering a garden for the sake of a thirsty flower’). Very small signal –Difficult to realize benefit if you can’t achieve good spatial resolution. –Remains controversial – best parameters unknown.

29 29 Higher spatial resolution Contrast to noise ratio dependent on volume of hydrogen: –Standard T2* 3x3x3mm = 27mm3 –1.5*1.5x2mm = 4.5mm3 = 17% of SNR However, for small structures or edges, higher resolution reduces partial volume effects. –Therefore, higher resolution can improve % signal change observed For ideas on optimal voxelsize, see

30 30 Arterial Spin Labelling Benefits: –Direct measure of blood flow –Less drift: Better for assessment of very slow (>1min) changes. –Data whiter (less dominated by low frequency noise) –Signal more from tissue than veins. –Less spatial distortion than BOLD (BOLD requires long TE without spin-echo) –Perhaps better statistical power for group analysis (calibrated measure has less variability). Disadvantages –Requires two images: tagged and subtraction, therefore TR is twice as long. –Less statistical power for individual (fewer samples) –Can not collect many slices: can only see portion of brain, normalization difficult (hurts group statistics)

31 31 Super high resolution Venous effects decrease with field strength (e.g. at 1.5T, capillary/venous ratio much smaller than at 7T). Higher SNR with 7T can allow very high resolution imaging: –Example ocular dominance columns for left and right eye projection to visual cortex. –0.5x0.5x3mm (0.75mm3) – Spin-echo sequences (HSE T2) can be used as well as traditional GE T2* at these field strengths to detect BOLD.

32 32 Neural current MRI (Bandettini) In theory, MRI phase maps should show the direct neural firing as detected by MEG. Intracellular Current Magnetic Field Surface Field Distribution Across Spatial Scales

33 33 magnetic source/neural current MRI fMRI BOLD is very indirect measure. Can we directly measure brain activity? Neural firing influences magnetic field (e.g. MEG). Is this effect big enough to measure? Very controversial. Most designs do not remove BOLD confound Recent work not encouraging Image Phasemap

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