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Cartography and Chronometry. fMRI Graduate Course October 9, 2002. Why do you need to know?. Spatial resolution Tradeoffs between coverage and spatial resolution Influences viability of preprocessing steps Temporal resolution Tradeoffs between number of slices and TR
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Cartography and Chronometry fMRI Graduate Course October 9, 2002
Why do you need to know? • Spatial resolution • Tradeoffs between coverage and spatial resolution • Influences viability of preprocessing steps • Temporal resolution • Tradeoffs between number of slices and TR • Needed resolution depends upon design • Non-linearity of the hemodynamic response • Limits experimental designs • Affects subsequent analyses • Reduces power
What spatial resolution do we want? • Hemispheric • Lateralization studies • Selective attention studies • Systems / lobic • Relation to lesion data • Centimeter • Identification of active regions • Millimeter • Topographic mapping (e.g., motor, vision) • Sub-millimeter • Ocular Dominance Columns • Cortical Layers
What determines Spatial Resolution? • Voxel Size • In-plane Resolution • Slice thickness • Spatial noise • Head motion • Artifacts • Spatial blurring • Smoothing (within subject) • Coregistration (within subject) • Normalization (within subject) • Averaging (across subjects)
K – Space Revisited . . . . . . . . . . . . . . . . . . . . B A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FOV: 10cm, Pixel Size: 2 cm FOV: 10 cm, Pixel Size: 1 cm To increase spatial resolution we need to sample at higher spatial frequencies.
How large are functional voxels? = ~.08cm3 5.0mm 3.75mm 3.75mm Within a typical brain (~1300cm3), there may be about 20,000 functional voxels.
How large are anatomical voxels? = ~.004cm3 5.0mm .9375mm .9375mm Within a typical brain (~1300cm3), there may be about 300,000+ anatomical voxels.
Costs of Increased Spatial Resolution • Acquisition Time • In-plane • Higher resolution takes more time to fill K-space (resolution ~ size of K-space) • #Slices/second • Sample rates for 64*64 images • Early Duke fMRI: 2-4 sl/s • GE EPI: 12 sl/s • Duke Spiral (1.5T): 14 sl/s • Duke Inverse Spiral (4.0T): 21 sl/s • Reduced signal per voxel • What is our dependent measure?
Example: Ocular Dominance Goodyear & Menon, 2001
4sec 10sec Goodyear & Menon, 2001
Example: Visual System 100ms 500ms 1500 ms
T2* Blurring • Signal decays over time needed for collection of an image • For standard resolution images, this is not a critical issue • However, for high-resolution (in-plane) images, the time to acquire an image may be a significant fraction of T2* • Under these conditions, multi-shot imaging may be necessary.
What temporal resolution do we want? • 10,000ms: Change in arousal or emotional state • 1000ms: Decisions, recall from memory • 500-1000ms: Response time • 250ms: Reaction time • 10-100ms: • Difference between response times • Initial visual processing • 10ms: Neuronal activity in one area
Basic Sampling Theory • Nyquist Sampling Theorem • To be able to identify changes at frequency X, one must sample the data at 2X. • For example, if your task causes brain changes at 1 Hz (every second), you must take two images per second.
Aliasing • Mismapping of high frequencies (above the Nyquist limit) to lower frequencies • Results from insufficient sampling • Potential problem for designs with long TRs and fast stimulus changes
Frequency Analyses t < -1.96 t < +1.96 McCarthy et al., 1996
Phase Analyses • Design • Left/right alternating flashes • 6.4s for each • Task frequency: • 1 / 12.8 = 0.078 McCarthy et al., 1996
Why do we want to measure differences in timing within a brain region? • Determine relative ordering of activity • Make inferences about connectivity • Anatomical • Functional • Relate activity timing to other measures • Stimulus presentation • Reaction time • Relative amplitude
Timing Differences across Regions Presented left hemifield before right hemifield (0-1000ms delays) fMRI vs RT (LH) Plot of LH signal as function of RH signal fMRI vs. Stimulus Menon et al., 1998
Activation maps Relative onset time differences Menon et al., 1998
V1 FFG Huettel et al., 2001
Secondary Visual Cortex (FFG) Primary Visual Cortex (V1) Subject 1 5.5s 4.0s Subject 2 Huettel et al., 2001
Linear Systems • Scaling • The ratio of inputs determines the ratio of outputs • Example: if Input1 is twice as large as Input2, Output1 will be twice as large as Output2 • Superposition • The response to a sum of inputs is equivalent to the sum of the response to individual inputs • Example: Output1+2+3 = Output1+Output2+Output3
Possible Sources of Nonlinearity • Stimulus time course neural activity • Activity not uniform across stimulus (for any stimulus) • Neural activity Vascular changes • Different activity durations may lead to different blood flow or oxygen extraction • Minimum bolus size? • Minimum activity necessary to trigger? • Vascular changes BOLD measurement • Saturation of BOLD response necessitates nonlinearity • Vascular measures combining to generate BOLD have different time courses From Buxton, 2001
Effects of Stimulus Duration • Short stimulus durations evoke BOLD responses • Amplitude of BOLD response often depends on duration • Stimuli < 100ms evoke measurable BOLD responses • Form of response changes with duration • Latency to peak increases with increasing duration • Onset of rise does not change with duration • Rate of rise increases with duration • Key issue: deconfounding duration of stimulus with duration of neuronal activity
Boynton et al., 1996 Linear model for HDR Varied contrast of checkerboard bars as well as their interval (B) and duration (C).
Differences in Nonlinearity across Brain Regions Birn, et al, 2001
SMA vs. M1 Birn, et al, 2001
fMRI Hemodynamic Response 1500ms 500ms 100ms Calcarine Sulci Fusiform Gyri
* Calcarine 1500ms 500ms 100ms Fusiform
Refractory Periods • Definition: a change in the responsiveness to an event based upon the presence or absence of a similar preceding event • Neuronal refractory period • Vascular refractory period
Dale & Buckner, 1997 • Responses to consecutive presentations of a stimulus add in a “roughly linear” fashion • Subtle departures from linearity are evident
Intra-Pair Interval (IPI) Inter-Trial Interval (16-20 seconds) 6 sec IPI 4 sec IPI 2 sec IPI 1 sec IPI Single-Stimulus 500 ms duration Huettel & McCarthy, 2000
Methods and Analysis • 16 male subjects (mean age: 27y) • GE 1.5T scanner • CAMRD • Gradient-echo EPI • TR : 1 sec • TE : 50 msec • Resolution: 3.125 * 3.125 * 7 mm • Analysis • Voxel-based analyses • Waveforms derived from active voxels within anatomical ROI Huettel & McCarthy, 2000
Hemodynamic Responses to Closely Spaced Stimuli Huettel & McCarthy, 2000
Refractory Effects in the fMRI Hemodynamic Response Signal Change over Baseline(%) Time since onset of second stimulus (sec) Huettel & McCarthy, 2000
Refractory Effects across Visual Regions HDRs to 1st and 2nd stimuli in a pair (calcarine cortex) Relative amplitude of 2nd stimulus in pair across regions
Intra-Pair Interval (IPI) Inter-Trial Interval (16-20 seconds) 6 sec IPI 1 sec IPI Single-Stimulus
Single 05 10 15 20 25 30 35 40 45 50 55 60 6s IPI 1s IPI Signal Change over baseline (%) Time since stimulus onset (sec) Figure 2 Mean HDRs L R
Refractory Effect Summary • Duration • HDR evoked by a long-duration stimulus is less than predicted by convolution of short-duration stimuli • Present for durations < ~6s • Interstimulus interval • HDR evoked by a stimulus is reduced by a preceding similar stimulus • Present for intervals < ~6s • Differences across brain regions • Some regions show considerable departures from linearity • May result from differences in processing • Source of non-linearity not well understood • Neuronal effects comprise at least part of the overall effect • Vascular differences may also contribute
Using refractory effects to study cognition: fMRI Adaptation Studies
Neuronal Adaptation Grill-Spector & Malach, 2001 Several neuronal populations vs. homogeneous population Adaptation If neurons are insensitive to the feature being varied, then their activity will adapt. Viewpoint Sensitive Viewpoint Invariant