fmri before any optimizations (fall of 1991)

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1 Optimizations (or at least improvements)! in fmri! Peter A. Bandettini, Ph.D.!! Section on Functional Imaging Methods! Laboratory of Brain and Cognition! &! Functional MRI Facility! In general! Technology Coil arrays High field strength High resolution Novel functional contrast Fluctuations Dynamics Spatial patterns Methodology Functional Connectivity Multi-modal integration Pattern-effect imaging Real time feedback Task design Healthy Brain Organization Clinical Pathology fmri before any optimizations (fall of 1991) Interpretation Applications

2 TR = 2 sec! TE = 50 ms! One slice! In plane 3.75 x 3.75! 2.5 cm!!

3 Technology MRI Diff. tensor 1.5T,3T, 4T Mg + 7T >8 channels EPI on Clin. Syst. Venography EPI Real time fmri Nav. pulses SENSE vaso Local Human Head Gradient Coils Quant. ASL Z-shim Baseline Susceptibility ASL Spiral EPI Dynamic IV volume BOLD Multi-shot fmri Simultaneous ASL and BOLD Current Imaging? Methodology Blood T2 Hemoglobin Baseline Volume IVIM Interpretation Applications Correlation Analysis CO 2 Calibration Motion Correction Latency and Width Mod Parametric Design Multi-Modal Mapping Surface Mapping ICA Free-behavior Designs Phase Mapping Mental Chronometry Linear Regression Multi-variate Mapping Event-related Deconvolution Fuzzy Clustering BOLD models PET correlation B IV vs EV o dep. ASL vs. BOLD Layer spec. latency Pre-undershoot PSF of BOLD TE dep Resolution Dep. Excite and Inhibit Extended Stim. Post-undershoot Linearity Metab. Correlation SE vs. GE CO 2 effect NIRS Correlation Fluctuations Optical Im. Correlation Veins Inflow Balloon Model Electrophys. correlation Complex motor Language Imagery Memory Emotion Motor learning Children Tumor vasc. Drug effects BOLD -V1, M1, A1 Presurgical Attention Ocular Dominance Mirror neurons Volume - Stroke V1, V2..mapping Priming/Learning Clinical Populations Volume-V1 Plasticity Face recognition Performance prediction 1991! Focus of this lecture! Technology High field strength Coil arrays High resolution Novel functional contrast Fluctuations / Correlations Dynamics Interpretation Methodology Paradigm Designs Processing Methods Healthy Brain Organization Applications Field Strength Echo Time Spin-echo vs Gradient Echo Velocity Nulling RF coil arrays High Spatial Resolution High Temporal Resolution Choice of Flip Angle Choice of Slice Thickness Paradigm Design Ultimate Sensitivity? Separating good and bad signal in Resting State fmri. Understanding dynamic nonlinarities Understanding and Using fmri Patterns

4 Focus of this lecture! Technology High field strength Coil arrays High resolution Novel functional contrast Methodology Paradigm Designs Processing Methods Characteristics of the BOLD signal: T2* effect.!! Fluctuations / Correlations Dynamics Healthy Brain Organization Interpretation Applications Contrast depends on:! activation-induced changes in T2* and resting T2*! Functional Contrast at Optimal TE Contrast Contrast at 1.5T (dr2* = -.8 1/s) TE (ms) T2*! Contrast Contrast at 3T (dr2* = /s) TE (ms) T2*! Contrast T2* (ms) 1.5T 3T

5 Transverse Relaxation! transverse! magnetization! T2! Spin-Echo vs. Gradient-Echo fmri T2*! 90! 180! 180! 30ms! 100ms! Spin echo vs. Gradient echo! R2* & R2 R2* R2 compartment! radius <3 µm 3 to 15 µm > 15 µm!

6 Bolus Injection of Gadolinium Fast Intermediate Slow contrast R2* & R2 R2* GE R2 SE 2.5 to 3 µm 3 to 15 µm 15 to µm compartment size GE TE = 30 ms 5 µm SE TE = 110 ms 5 µm to 8 µm 8 µm to 380 µm Spin-Echo TE = 105 ms TR = Gradient-Echo TE = 50 ms Gradient-Echo functional TE = 50 ms Spin-Echo functional TE = 105 ms 3T! Intravascular Contribution Field strength dependence of intravascular signal! Spin-echo, %HbO 2 = 60!! TE (ms) Intravascular Contribution Source of most contrast in venograms..! Gradient-echo, %HbO 2 = 60! TE (ms)

7 BOLD effect to highlight veins: 3 Tesla! Bove-Bettis, et al (2004), SMRT MRM 30: (1993)! Pros and Cons of Spin-Echo Increased specificity (esp at high fields where IV signal is low) Less sensitive to rapidly flowing blood Less signal dropout. Less slices per TR Lower fcnr by x 2 to 4. Acquisition window still T2* Very large IV signal still present at most field strengths. I would only use at 7T if also imaging at high ressolution and interested in something like columns or layers.

8 no diffusion weighting! diffusion weighting! so let s remove the intravascular signal... Velocity Nulled (or diffusion weighted) fmri. 5 µm 5 µm 5 µm to 8 µm 8 µm to 380 µm 5 µm to 8 µm 8 µm to 380 µm b = 0! b = 10! b = 50! b = 160! J. L. Boxerman, P. A. Bandettini, K. K. Kwong, J. R. Baker, T. L. Davis, B. R. Rosen, R. M. Weisskoff, The intravascular contribution to fmri signal change: monte carlo modeling and diffusion - weighted studies in vivo. Magn. Reson. Med. 34, 4-10 (1995).

9 Arterial inflow! (BOLD TR < 500 ms)! Hemodynamic Specificity! Time (sec) Perfusion BOLD Venous inflow! (Perf. No VN)! No Velocity Nulling Velocity Nulling TI ASL (IV) (IV) SE GE High Field Tradeoffs Increased SNR Increased functional contrast Ability to reduce voxel volume Reduced intravascular signal Increased SAR Decreased B0 and B1 homogeneity (still somewhat prohibitive) Increased costs and effort Coil Arrays At standard resolution, enhanced At standard resolution, enhanced sensitivity to fluctuations sensitivity to fluctuations

10 8 channel parallel receiver coil! 16 channel parallel receiver coil! Sensitivity vs. Time needed to scan GE birdcage! GE 8 channel coil! Nova 8 channel coil! J. Bodurka, et al, Magnetic Resonance in Medicine 51 (2004) Temporal Signal to Noise Ratio (TSNR) vs. Signal to Noise Ratio (SNR) suggested voxel volume 3T, birdcage: 2.5 mm 3 3T, 16 channel: 1.8 mm 3 7T, 16 channel: 1.4 mm 3 K. Murphy, J. Bodurka, P. A. Bandettini, How long to scan? The relationship between fmri temporal signal to noise and the necessary scan duration. NeuroImage, 34, (2007) J. Bodurka, F. Ye, N Petridou, K. Murphy, P. A. Bandettini, NeuroImage, 34, (2007)

11 MRI vs. fmri! Going to High Spatial Resolution high resolution! (1 mm)! MRI! fmri! one image!! many images! (e.g., every 2 sec for 5 mins)! Single Shot Echo Planar Imaging (EPI)! Multishot Imaging T2* decay! T2* decay! EPI Window 1! T2* decay! EPI Readout Window! 20 to 40 ms! EPI Window 2!

Multi Shot EPI! Excitations 1 2 4 8 Matrix Size 64 x 64 128 x 128 256 x 128 256 x 256 30 ms! 10 sec! to! 1 min! Partial k-space imaging T2* decay!

12 Multi Shot EPI! Excitations Matrix Size 64 x x x x ms! 10 sec! to! 1 min! Partial k-space imaging T2* decay! EPI Window! A. Jesmanowicz, P. A. Bandettini, J. S. Hyde, Single shot half k-space high resolution EPI for fmri at 3T. Magn. Reson. Med. 40, (1998).

SENSE Imaging A. Jesmanowicz, P. A. Bandettini, J. S. Hyde, Single shot half k-space high resolution EPI for fmri at 3T. Magn. Reson. Med. 40, 754-762 (1998).

13 SENSE Imaging A. Jesmanowicz, P. A. Bandettini, J. S. Hyde, Single shot half k-space high resolution EPI for fmri at 3T. Magn. Reson. Med. 40, (1998). 5 to 30 ms Pruessmann, et al. (and Sodickson et al) Technology 3T single-shot SENSE EPI using 16 channels: 1.25x1.25x2mm Cheng, et al. (2001) Neuron,32:

Orientation Columns in Human V1 as Revealed by fmri at 7T Phase Map Going to High Temporal Resolution Yacoub et al. PNAS 2008 0 180 Phase Scalebar = 0.

K. Kwong, T. L. Davis, R. B. H. Tootell, E. C. Wong, P. T. Fox, J. W. Belliveau, R. M. Weisskoff, B. R. Rosen, (1997).

14 Orientation Columns in Human V1 as Revealed by fmri at 7T Phase Map Going to High Temporal Resolution Yacoub et al. PNAS Phase Scalebar = 0.5 mm BOLD Signal T2* - Weighted How rapidly can one switch on and off? Time (sec.) T1 - Weighted Flow Signal Time (sec.) P. A. Bandettini, K. K. Kwong, T. L. Davis, R. B. H. Tootell, E. C. Wong, P. T. Fox, J. W. Belliveau, R. M. Weisskoff, B. R. Rosen, (1997). Characterization of cerebral blood oxygenation and flow changes during prolonged brain activation. Human Brain Mapping 5, ! P. A. Bandettini,, Functional MRI using the BOLD approach: dynamic characteristics and data analysis methods, in "Diffusion and Perfusion: Magnetic Resonance Imaging" (D. L. Bihan, Ed.), p , Raven Press, New York, 1995.

15 Blamire et al.! Motor Cortex Duration 2 (sec) Signal sec 1 sec 2 sec 3 sec 5 sec Time (sec) Blamire, A. M., et al. (1992). Dynamic mapping of the human visual cortex by highspeed magnetic resonance imaging. Proc. Natl. Acad. Sci. USA 89: Bandettini, et al., The functional dynamics of blood oxygenation level contrast in the motor cortex, 12'th Proc. Soc. Magn. Reson. Med., New York, p (1993). How brief of a stimulus can one give?! 1000 msec! 100 msec! 34 msec! ms ms m ms ms m ms ms 0.5 5! 10! 15! 20! Time (sec)! R. L. Savoy, et al., Pushing the temporal resolution of fmri: studies of very brief visual stimuli, onset variability and asynchrony, and stimulus-correlated changes in noise, 3'rd Proc. Soc. Magn. Reson., Nice, p (1995).! Time (sec)

16 Latency Variation Timing Hemi-Field Experiment + 2 sec Latency - 2 sec Right Hemisphere Left Hemisphere Magnitude Venogram Time (sec) Delay (sec) P. A. Bandettini, (1999) "Functional MRI" Hemi-field with 500 msec asynchrony" Average of 6 runs Standard Deviations Shown! 3.2" 2.4" 1.6" Percent! MR! 0.8" Signal! Strength! 0" -0.8" s! 0 s! s! 500 ms! -! 500 ms! Right Hemifield! =! Left Hemifield! -1.6" -2.4" 0" 10" 20" 30" Time (seconds)!

17 Hemodynamic Response Modulation 250 ms! 250 ms! Right Hemifield! Left Hemifield! s! 0 s! -! =! Bottleneck In Processing (upstream) s! Delayed Processing (downstream) Even if no hemodynamic variability exists 1% Noise 4% BOLD 256 time pts /run 1 second TR 1 run: t Number delay = 107ms delay estimate (ms) 500 Smallest latency Variation Detectable (ms) (p < 0.001) sec on/off 8 sec on/off Number of runs

18 PNAS September 26, 2000 vol. 97 no. 20 P. A. Bandettini, (1999) "Functional MRI" ! Contents lists available at ScienceDirect NeuroImage journal homepage: Choosing a Flip Angle Physiological noise effects on the flip angle selection in BOLD fmri J. Gonzalez-Castillo a,, V. Roopchansingh b, P.A. Bandettini a,b, J. Bodurka c a Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, 10 Center Dr, Bethesda, MD 20892, USA b Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, 10 Center Dr, Bethesda, MD 20892, USA c Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK 74136, USA How do we typically select flip angle? Ernst Angle Θ = Cos -1 (e -TR/T1 )

20 Fig. 12. (A) Experimental and Theoretical curves describing dependence of tissue contrast with ip angle for three tissue contrast of interest: GM vs. WM (ΔS ), GM vs. CSF Effect of Slice Thickness on TSNR

21 Focus of this lecture! Technology High field strength Coil arrays High resolution Novel functional contrast Methodology Paradigm Designs Processing Methods Fluctuations / Correlations Dynamics Healthy Brain Organization P. S. F. Bellgowan, P. A. Bandettini, P. van Gelderen, A. Martin, J. Bodurka, Improved BOLD detection in the medial temporal region using parallel imaging and voxel volume reduction. NeuroImage, 29, (2006) Interpretation Applications Neuronal Activation Input Strategies 1. Block Design 2. Frequency Encoding 3. Phase Encoding 4. Event-Related 5. Orthogonal Block Design 6. Free Behavior Design. Neuronal Activation Input Strategies 1. Block Design 2. Frequency Encoding 3. Phase Encoding 4. Event-Related 5. Orthogonal Block Design 6. Free Behavior Design.

22 Neuronal Activation Input Strategies 1. Block Design 2. Frequency Encoding 3. Phase Encoding 4. Event-Related 5. Orthogonal Block Design 6. Free Behavior Design. P. A. Bandettini, A. Jesmanowicz, E. C. Wong, J. S. Hyde, Processing strategies for time-course data sets in functional MRI of the human brain. Magn. Reson. Med. 30, (1993). Tapping left and right fingers at two different on/off frequencies Neuronal Activation Input Strategies 1. Block Design 2. Frequency Encoding 3. Phase Encoding 4. Event-Related 5. Orthogonal Block Design 6. Free Behavior Design. P. A. Bandettini, A. Jesmanowicz, E. C. Wong, J. S. Hyde, Processing strategies for time-course data sets in functional MRI of the human brain. Magn. Reson. Med. 30, (1993).

23 E. A. DeYoe, P. A. Bandettini, J. Nietz, D. Miller, P. Winas, Functional magnetic resonance imaging (FMRI) of the human brain. J. Neuroscience Methods 54, (1994). E. A. DeYoe, G. Carman, P. Bandettini, G. S., W. J., R. Cox, D. Miller, J. Neitz, Mapping striate and extrastriate visual areas in human cerebral cortex. Proc. Nat'l. Acad. Sci. 93, (1996). Neuronal Activation Input Strategies 1. Block Design 2. Frequency Encoding 3. Phase Encoding 4. Event-Related 5. Orthogonal Block Design 6. Free Behavior Design. E. A. DeYoe, G. Carman, P. Bandettini, G. S., W. J., R. Cox, D. Miller, J. Neitz, Mapping striate and extrastriate visual areas in human cerebral cortex. Proc. Nat'l. Acad. Sci. 93, (1996).

R. L. Buckner, P. A. Bandettini, K. M. O'Craven, R. L. Savoy, S. E. Peterson, M. E. Raichle, T. L. Brady, B. R. Rosen, fmri detection and time course of distributed cortical activations during single trials of a cognitive task.

24 R. L. Buckner, P. A. Bandettini, K. M. O'Craven, R. L. Savoy, S. E. Peterson, M. E. Raichle, T. L. Brady, B. R. Rosen, fmri detection and time course of distributed cortical activations during single trials of a cognitive task. Proc. Nat'l. Acad. Sci. USA 93, (1996). P. A. Bandettini, R. W. Cox. Functional contrast in constant interstimulus interval event - related fmri: theory and experiment. Magn. Reson. Med. 43: (2000).! ( ISI, SD )! Contrast to Noise Images! Visual Activation Paradigm: 1, 2, & 3 Trials!! 20, 20! 12, 2! 10, 2! 8, 2! 6, 2! 4, 2! 2, 2! S1! 0 sec! 20 sec! S2! 0 sec!2 sec! 20 sec! P. A. Bandettini, R. W. Cox. Functional contrast in constant interstimulus interval event - related fmri: theory and experiment. Magn. Reson. Med. 43: (2000).! 0 sec!2 sec! 4 sec! 20 sec!

25 Detectability vs. Average ISI 5" RAW DATA! 5" ESTIMATED RESPONSES! 4" T!H!R!E!E!-!T!R! I!A!L! 4" SD = 4000 s. 3" 2" 1" T!W!O! -! T!R!I!A!L! O!N!E!-!T!R! I!A!L! 3" 2" 1" Detectability SD = 1000 ms. 0" -!1! 0! 1! 2! 3! 4! 5! 6! 7! 8! 9! 1!0!1!1!1!2!1!3!1!4!1!5!1!6!1!7!1!8!1!9! T! I!M!E!(!S!E!C! )! 0" -!1! 0! 1! 2! 3! 4! 5! 6! 7! 8! 9! 1!0!1!1!1!2!1!3!1!4!1!5!1!6!1!7!1!8!1!9! T! I!M!E!(!S!E!C! )! SD = 250 ms average ISI (s) R. M. Birn, R. W. Cox, P. A. Bandettini, Detection versus estimation in Event-Related fmri: choosing the optimal stimulus timing. NeuroImage 15: , (2002). Estimation accuracy vs. average ISI 20 fmri during tasks that involve brief motion! Blocked Design! Estimation Accuracy SD = 250 ms. SD = 1000 ms. task! motion! BOLD response! t! 5 SD = 4000 ms. Event-Related Design! motion! BOLD response! average ISI (sec) R. M. Birn, R. W. Cox, P. A. Bandettini, Detection versus estimation in Event-Related fmri: choosing the optimal stimulus timing. NeuroImage 15: , (2002). task! R. M. Birn, P. A. Bandettini, R. W. Cox, R. Shaker, Event - related fmri of tasks involving brief motion. Human Brain Mapping 7: (1999).!

26 Speaking - Blocked Trial! Speaking - ER-fMRI! motion! avg! t! Expected! Response! Expected! Response! t! BOLD! response! avg! R. M. Birn, P. A. Bandettini, R. W. Cox, R. Shaker, Event - related fmri of tasks involving brief motion. Human Brain Mapping 7: (1999).! R. M. Birn, P. A. Bandettini, R. W. Cox, R. Shaker, Event - related fmri of tasks involving brief motion. Human Brain Mapping 7: (1999).! More Motion Artifacts! Detection of Motion (t-stat)! 20! 10! 0! 10! SD=2s! Overt Responses - Simulations! SD = stimulus duration! Event-Related! constant ISI! (SD=1s, ISI=15s)! min SD=1s! SD=4s! Blocked! min SD=3s! SD=8s! min SD=7s! min SD=5s! min SD=9s! Event-Related! varying ISI! SD=6s! 20! 0! 10! 20! 30! 40! 50! 60! Detection of BOLD (t-stat)! 70! 80! 90! 100! Better BOLD Detection! Blocked design (30s/ 30s)! Overt Responses! Blocked design (10s/10s)! Event-related! Varying ISI (1s min. SD)! Event-related! Varying ISI (5s min. SD)! Event-related! constant ISI (1s. SD, 15sISI)! t-stat.! 15! 0! -15! R.M. Birn, R. W. Cox, P. A. Bandettini, NeuroImage, 23, (2004)!

27 Neuronal Activation Input Strategies 1. Block Design 2. Frequency Encoding 3. Phase Encoding 4. Event-Related 5. Orthogonal Block Design 6. Free Behavior Design. Example of a Set of Orthogonal Contrasts for Multiple Regression! CTL DELAY I T I FACE WM DELAY I T I Courtney et al. HOUSE WM DELAY I T I Nonselective Visual Stimulation Faces & Houses vs Ctl. Stimuli Face Stimuli vs House Stimuli Memory delays vs. ctl. delays Face WM delays vs House WM delays Anticipatory delays vs ITIs Encoding vs. Recognition Ctl. Stim. vs. Ctl. Response Neuronal Activation Input Strategies 1. Block Design 2. Frequency Encoding 3. Phase Encoding 4. Event-Related 5. Orthogonal Block Design 6. Free Behavior Design. What is the Ultimate Sensivitity of fmri?

28 The#univariate#approach# EXPERIMENTAL#PARADIGM## SUBJECT# Is#the#whole#brain#ac0vated#by#even#simple#tasks?# # 9"hours"of"averaging" Unconstrained"response"model" Clustering"(K9means"or"Hierarchical)## " DATA#PREO# PROCESSING# J. Gonzalez-Castillo, Z. Saad, D. A. Handwerker, P. A. Bandettini, Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. Proceedings of the National Academy of Sciences 109, 14: pp (2012) RESPONSE# MODEL# X# HRF# STATISTICAL# ANALYSIS# =# PREDICTED#HEMODYNAMIC# RESPONSE# STATISTICAL#MAP#OF# ACTIVATION# Data#averaging# Mul0ple#Response#Models# EXPERIMENTAL#PARADIGM## SUBJECT# EXPERIMENTAL#PARADIGM## SUBJECT# " " " " " " DATA#PREO# PROCESSING# PREOPROCESSING# STATISTICAL# ANALYSIS# RESPONSE# #MODELS# STATISTICAL#ANALYSIS# RESPONSE# MODEL# X# HRF# =# PREDICTED#HEMODYNAMIC# RESPONSE# STATISTICAL#MAP#OF# ACTIVATION# X# HRF# =# PREDICTED#HEM.#RESPONSES# STATISTICAL#MAP#OF# ACTIVATION#

# SUSTAINED#RESPONSE#MODEL# ONSET/OFFSET#RESPONSE#MODEL# +" DIFFERENT#RESPONSE#SHAPES#ARE#PRESENT#ACROSS#DIFFERENT# REGIONS#OF#THE#BRAIN#FOR#A#SINGLE#STIMULUS#TYPE# Uludag"et"al."Magn"Reson"Imaging.

29 Predic0ve#Response#Model#effect#on#fMRI#Results#(III)# BLOCK#DESIGN#&#HEMIFIELD#VISUAL#STIMULATION# IS#THE#SPARSENESS#OF#FMRI#ACTIVATIONS#REAL?# OR# IS#IT#THE#RESULT#OF#INSUFFICIENT#TSNR#+#OVERLY#STRICT#RESPONSE#MODELS?# SUSTAINED#RESPONSE#MODEL# ONSET/OFFSET#RESPONSE#MODEL# +" DIFFERENT#RESPONSE#SHAPES#ARE#PRESENT#ACROSS#DIFFERENT# REGIONS#OF#THE#BRAIN#FOR#A#SINGLE#STIMULUS#TYPE# N 25"""30"""35"""40"""45"""50"""55"" " avg" Saad"et"al" Experimental#Methods#(I)# Experimental#Methods#(II)# 3"Healthy"Volunteers:"1M/2F;"Age"="27"±"2.5" 3T"GE"Signa"HDx" Anatomical"Scan:"MPRAGE" ".9x.9x1.2"mm 3 " "192"Slices" FuncZonal"Scans:"GRE@EPI" TR/TE"="2s/30ms" In@Plane"Res"="64x64"" #Slices"="32"Oblique" "FOV"="240mm" "Slice"Thickness"="3.8"mm"" "Flip"Angle"="75 " 3x" VISIT" ANATOMICAL" FUNCTIONAL"SCANS" 1" 2" 3" 4" 5" 6" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" 100"FUNCTIONAL"RUNS/SUBJECT" 500"TRIALS/SUBJECT" 7" 8" 9" 10" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" :"""""(1x""""""""""")" +""(10x"""""""""""""""""""""""""""""")" 9"HOURS"OF"DATA/SUBJECT" X"100" "(QA"Axial"EPIs)"

30 Data#Analysis# Data#Analysis# DATA#PREOPROCESSING# DATA#PREOPROCESSING# Remove"Physiological"Noise" DATA#AVERAGING# Remove"Physiological"Noise" DATA#AVERAGING# Slice"Timing"CorrecZon" Head"MoZon"CorrecZon" N avg avg "="100" 10"Random"PermutaZons" per"n avg "Level" Slice"Timing"CorrecZon" Head"MoZon"CorrecZon" N avg avg "="100" STATISTICAL#ANALYSIS# 10"Random"PermutaZons" per"navg"level" SUSTAINED#RESPONSE# ONLY#(SUS)# Discard"IniZal"5"Volumes" Discard"IniZal"5"Volumes" Remove"MoZon"&"1 st "Der." Intensity"NormalizaZon" Remove"MoZon"&"1 st "Der." Intensity"NormalizaZon" ONSET#+#SUSTAINED#+# OFFSET#RESPONSE#(SUS)# Example"N avg" ="5" UNCONSTRAINED#MODEL# (UNC)# Example"N avg" ="5" CLUSTERING#ANALYSIS# Results:#TSNR#vs.###Averaged#Scans# Results:#TimeOseries#in#Primary#Visual#Cortex# TSNR" LEFT" RIGHT" RIGHT" LEFT" Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" 0s" 30s" 50s" 90s" 110s" 150s" 170s" 210s" 230s" 270s" 290s" 340s" Number"of"Averaged"Scans"(N avg )" INDIVIDUAL#RUNS# 104" AVERAGING# 1" TSNR WM "="339"" " TSNR WM #INCREASED#BY#APPROX.# A#FACTOR#OF#6#FROM## N avg# =#1#TO#N avg# =#100# 100" TSNR WM "="2218"" " """30"""50"""""""""90"""110"""""150"170""""""210"230""""""270"290"""""""" Time"(s)" 102" 100" 98" """"""30"50"""""""""90""110""""""150"170""""""210"230"""""270"290"""""""" Time"(s)" Gonzalez@CasZllo"J,"Saad"ZS,Handwerker"DA,"InaZ""SJ,"Brenowitz"N,"Bandepni"PA,"PNAS"(in"press)""

Results:#TimeOseries#in#Anterior#Insular#Cortex# Results:#TimeOseries#in#Primary#Auditory#Cortex# LEFT" RIGHT" RIGHT" LEFT" Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" 0s" 30s"

31 Results:#TimeOseries#in#Anterior#Insular#Cortex# Results:#TimeOseries#in#Primary#Auditory#Cortex# LEFT" RIGHT" RIGHT" LEFT" Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" 0s" 30s" 50s" 90s" 110s" 150s" 170s" 210s" 230s" 270s" 290s" 340s" LEFT" RIGHT" RIGHT" LEFT" Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" 0s" 30s" 50s" 90s" 110s" 150s" 170s" 210s" 230s" 270s" 290s" 340s" INDIVIDUAL#RUNS# AVERAGING# INDIVIDUAL#RUNS# AVERAGING# 101" 100.5" 100" 100" """30"""50"""""""""90"""110"""""""150"170"""""""210"230"""""""270"290"""""""" Time"(s)" 99" """30"""50"""""""""90"""110"""""""150"170"""""""210"230"""""""270"290"""""""" Time"(s)" """30"""50"""""""""90"""110"""""""150"170"""""""210"230"""""""270"290"""""""" Time"(s)" 99.5" """30"""50"""""""""90"""110"""""""150"170"""""""210"230"""""""270"290"""""""" Time"(s)" Results:#TimeOseries#in#ParietoOOccipital#Junc0on# Results:#BOLD#responses#are#present#all#over#the#brain# LEFT" RIGHT" RIGHT" LEFT" Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" TASK# Rest" 0s" 30s" 50s" 90s" 110s" 150s" 170s" 210s" 230s" 270s" 290s" 340s" INDIVIDUAL#RUNS# AVERAGING# 102" 100" """30"""50"""""""""90"""110"""""""150"170"""""""210"230"""""""270"290"""""""" Time"(s)" 98" """30"""50"""""""""90"""110"""""""150"170"""""""210"230"""""""270"290"""""""" Time"(s)" Responses#0meOlocked#with#the#task#were#observed#in#over#90%#of#the# voxels#for#all#three#subjects#

32 Area#of#Ac0va0on#vs.##Scans# 100" T" p Bonf <0.05" Subject"1" Gonzalez@CasZllo"J,"Saad"ZS,Handwerker"DA,"InaZ""SJ,"Brenowitz"N,"Bandepni"PA,"PNAS"(in"press)"" ARE#RESPONSE#SHAPES#RANDOMLY#DISTRIBUTED#ACROSS#THE#BRAIN?# # OR# # DO#THEY#CLUSTER#IN#A#FUNCTIONALLY/ANTOMICALLY#MEANINGFUL# MANNER?# Clustering#Analysis:#KOmeans#applied#to#fMRI#data# INPUT# Time"series""of"length"30"for"each"of"N"voxels"(e.g.,"all"GM"voxels)" Pearson"CorrelaZon"Distance""D"="1" "r"" D"="0"(r"="1)"if"Zme"series"from"2"voxels"are"perfectly"correlated" D"="2"(r"="@1)"if"Zme"series"from"2"voxels"are"perfectly"anZ@correlated" K"="Set"a"priori"by"the"experimenter" KOMEANS# CLUSTERING# K=2" WITHINOSUBJECT#AVERAGED# RESPONSES#ACROSS#ALL# RUNS#AND#TRIALS# OUTPUT# The"output"consists"on"K"clusters,"each"defined"by:" Set"of"Voxels"(not"necessarily"conZguous)" Centroid"Time"Series"="Average"of"Zme"series"across"all"voxels"in"the"cluster" NO#SPATIAL#INFORMATION#ENTERS#THE#CLUSTERING#ALGORITHM#

33 Clustering#Analysis:#Whole#Brain#GM#Results# SUBJECT#03# #K=20# Clustering#Analysis:#Whole#Brain#GM#Results# SUBJECT#03# #K=20# NOT#RANDOMLY#DISTRIBUTED#IN#SPACE# SYMETRICAL#ACROSS#HEMISPHERES# FUNCTIONALLY#&#ANATOMICALLY#MEANINGFUL# REPRODUCIBLE#PARCELLATIONS#ACROSS#SUBJECTS# NOT#RANDOMLY#DISTRIBUTED#IN#SPACE# SYMETRICAL#ACROSS#HEMISPHERES# FUNCTIONALLY#&#ANATOMICALLY#MEANINGFUL# REPRODUCIBLE#PARCELLATIONS#ACROSS#SUBJECTS# Clustering#Analysis:#Whole#Brain#GM#Results# SUBJECT#03# #K=20# Clustering#Analysis:#Whole#Brain#GM#Results# SUBJECT#03# #K=20# NOT#RANDOMLY#DISTRIBUTED#IN#SPACE# SYMETRICAL#ACROSS#HEMISPHERES# FUNCTIONALLY#&#ANATOMICALLY#MEANINGFUL# REPRODUCIBLE#PARCELLATIONS#ACROSS#SUBJECTS# NOT#RANDOMLY#DISTRIBUTED#IN#SPACE# SYMETRICAL#ACROSS#HEMISPHERES# FUNCTIONALLY#&#ANATOMICALLY#MEANINGFUL# REPRODUCIBLE#PARCELLATIONS#ACROSS#SUBJECTS#

34 Clustering#Analysis:#Whole#Brain#GM#Results# SUBJECT#03# #K=20# NOT#RANDOMLY#DISTRIBUTED#IN#SPACE# SYMETRICAL#ACROSS#HEMISPHERES# FUNCTIONALLY#&#ANATOMICALLY#MEANINGFUL# REPRODUCIBLE#PARCELLATIONS#ACROSS#SUBJECTS# Clusters#as#a#func0on#of#K# Clusters#as#a#func0on#of#Clustering#Algorithm#(I)# KOMEANS#(d=Correla0on)# #K#=#05# K=10" K=15" K=30" K=70" K=05" K=25" K=02" K=20" HIERARCHICAL#CLUSTERING# #(link=ward,#d=euclidean)# #K#=#05# CPCC#=#0.84# SUBJECT"01"

Clusters#as#a#func0on#of#Clustering#Algorithm#(II)# KOMEANS#(d=Correla0on)# #K#=#20# Clustering#Analysis:#Subcor0cal#GM#Results# FREESURFER#ANATOMICAL#SEGMENTATION# HIERARCHICAL#CLUSTERING#

35 Clusters#as#a#func0on#of#Clustering#Algorithm#(II)# KOMEANS#(d=Correla0on)# #K#=#20# Clustering#Analysis:#Subcor0cal#GM#Results# FREESURFER#ANATOMICAL#SEGMENTATION# HIERARCHICAL#CLUSTERING# #(link=ward,#d=euclidean)# #K#=#20# CPCC#=#0.84# Thalamus" "Putamen" ""Caudate" Pallidus" N."Accumbens" KOMEANS#CLUSTERING#(K=5)# (C)" Future#Direc0ons#(I)# (1)#Evaluate#the#Stability#of#the#Clusters# K#=#02# Future#Direc0ons#(II)# (2)#Evaluate#how#these#clusters#relate#to#Res0ng#State#Networks# K#=#02# SET#01# KOMEANS#(Correla0on)# #K#=#15# TASKOBASED# NETWORKS# KOMEANS#(Correla0on)# #K#=#15# SET#02# KOMEANS#(Correla0on)# #K#=#15# RESTING#STATE# NETWORKS#

36 Separating good and bad signal! in Resting State fmri.! Res0ng#state#clustering#in#mul0ple#and#single#subjects.# Mul=9echo"denoising" Hierarchical"clustering# # P. Kundu, S. J. Inati, J. W. Evans, W.-M. Luh, P. A. Bandettini, Differentiating BOLD and non-bold signals in fmri time series using multi-echo EPI. NeuroImage 60, pp (2012) Resting State Correlations Endogenous Oscillations Visual ("resting state" OR fluctuations OR "spontaneous oscillations" OR "endogenous oscillations") AND fmri Visuospatial Executive Sensory Auditory Dorsal Pathway Activation: correlation with reference function Rest: seed voxel in motor cortex Ventral Pathway De Luca, Neuroimage 29(4) 2006 B. Biswal et al., MRM, 34:537 (1995)

37 Noise and Fluctuations Sources of time series fluctuations Blood, brain and CSF pulsation Vasomotion Breathing cycle (B 0 shifts with lung expansion) Bulk motion Scanner instabilities Changes in blood CO 2 (changes in breathing) Spontaneous neuronal activity Fig. 4. Pie charts showing the fmri data variance explained (VE, %, upper bold) by nonthermal noise sources 1 4, thermal noise and spontaneous activity. We also show fmri signal change (SC, %, lower italic) attributed to the same noise sources. Average (S.E.) values across subjects are shown. The contribution of thermal noise at the ROI level was negligible. Bianciardi et al. Magnetic Resonance Imaging 27: , 2009 Toes Finger s Thumb Eyebro w Tongu e

38 Sources of time series fluctuations: MR Signal 5 % Breath-holding Group Maps (N = 7) Blood, brain and CSF pulsation Vasomotion Breathing cycle (B 0 shifts with lung expansion) Bulk motion Scanner instabilities Changes in blood CO 2 (changes in breathing) Spontaneous neuronal activity Respiration Cue time (s) Anatomy Breath-hold response (average Z-score) 5 Z R.M. Birn, J. A. Diamond, M. A. Smith, P. A. Bandettini, NeuroImage, 31, Spontaneous changes in respiration and end-tidal CO2 Respiration T max Respiration induced signal changes Breath-holding Rest time (s) min time (s) BOLD Respiration Cue BOLD Respiration RVT Respiration Volume / Time (RVT) RVT = max - min T Time (sec) Time (sec) time (s) 1 Z Z CO CC RVT Shift (s) RVT precedes end tidal CO 2 by 5 sec. 150 (N=7) 0 R.M. Birn, J. A. Diamond, M. A. Smith, P. A. Bandettini, NeuroImage, 31, (2006) 151-4

RVT Correlation Maps & Functional Connectivity Maps Effect of Respiration Rate Consistency on Resting Correlation Maps Resting state correlation with signal from posterior cingulate 10 Resting state

39 RVT Correlation Maps & Functional Connectivity Maps Effect of Respiration Rate Consistency on Resting Correlation Maps Resting state correlation with signal from posterior cingulate 10 Resting state correlation with RVT signal 6 Spontaneously Varying Respiration Rate 10 Constant Respiration Rate 10 Z Z Z Z Group (n=10) Group (n=10) R.M. Birn, J. A. Diamond, M. A. Smith, P. A. Bandettini, NeuroImage, 31, (2006) R.M. Birn, J. A. Diamond, M. A. Smith, P. A. Bandettini, NeuroImage, 31, (2006) Respiration Changes vs. BOLD fmri response to a single Deep Breath How are the BOLD changes related to respiration variations? Respiration 40s Signal (%) fmri Signal RVT time (s) Deep time (s) Breath fmri Signal? RRF(t) = 0.6 t 2.1 e t t 3.54 e t 4.25 deconv. Respiration Response Function (RRF) time (s) time (s) 154 R.M. Birn, M. A. Smith, T. B. Jones, P. A. Bandettini, NeuroImage, 40, (2008) 155

40 Respiration response function predicts BOLD signal associated with breathing changes better than activation response function. Breath-holding Signal (%) s 40-60s time (s) Rate Changes 20s 40s Signal (%) time (s) Depth Changes 20s 40s Signal (%) time (s) R.M. Birn, M. A. Smith, T. B. Jones, P. A. Bandettini, NeuroImage, 40, (2008) 156 BOLD magnitude calibration Before Calibration After Calibration Breath Hold Respiration-induced S TE#Dependence*of*BOLD* BOLD*T2**signal*has*echo#time*(TE)*dependence,*such* that*the*percent*signal*change*of*a*bold*signal*time* course*scales*linearly*with*te* 1 * % S (BOLD) BOLD calib = % S (Resp) Rest Depth Change Rate Change S i S i = R 2TE i TE-Dependence model Acquiring*multi#echo*(ME)*fMRI*enables*analysis*of*TE# dependence*for*any*signal,*task#correlated*or* spontaneous* 2 * TE#dependence*can*be*quantified,*per#voxel,*as*an*F# statistic*for*the*te#dependence*model* * 1 Menon,"R.,"Ogawa,"S.,"Tank,"D.,"Ugurbil,"K.,"1993."Tesla"gradient"recalled"echo"characterisZcs"of"phoZc"sZmulaZon@induced"signal"changes"in"the"human"primary"visual" cortex."magnezc"resonance"in"medicine"30,"380" * 2 PelZer,"S.J.,"Noll,"D.C.,"2000."Analysis"of"fMRI"signal"and"noise"component"TE"dependence."Neuroimage"11,"S623." *

41 High κ κ Low κ κ Rank

Effect"of"de@noising"on"funcional"connecZvity"measures" Denoising" Detailed whole brain functional organization from resting state fluctuations?

42 Denoising" Detailed whole brain functional organization from resting state fluctuations? Rest%1% Motor" Angular" Gyrus" Rest%2% (color%matched%to%rest%1)% Motor" Angular" Gyrus" Kundu, Guillod, Inati, Luh, Bandettini

Test@retest"of"group"clustering"at"350"clusters" Rest%1% Rest%2% (color%matched%to%rest%1)% The issue of global signal regression Two other issues with imaging resting state fluctuations:

43 Rest%1% Rest%2% (color%matched%to%rest%1)% The issue of global signal regression Two other issues with imaging resting state fluctuations: 1.Global signal correction or not? 2.Short range correlations may be scanner-related. K. Murphy, R. M. Birn, D. A. Handwerker, T. B. Jones, P. A. Bandettini, NeuroImage, 44, (2009)

44 The issue of correlation across voxels due scanner instabilities Focus of this lecture! Technology High field strength Coil arrays High resolution Novel functional contrast Methodology Paradigm Designs Processing Methods Fluctuations / Correlations Dynamics Healthy Brain Organization Interpretation Applications N. Kriegeskorte, J. Bodurka, P. Bandettini, International Journal of Imaging Systems and Technology, 18 (5-6), (2008) Interpretation Neuronal Activation Measured Signal???? Hemodynamics Noise Understanding Dynamic Nonlinearities! In fmri!

45 Nonlinearity of BOLD response! measured! ideal (linear)! visual! stimulation! Logothetis et al. Nature, 412, ! 250 ms! 500 ms! 1000 ms! 2000 ms! 500 ms! 1000 ms! 2000 ms! 4000 ms! motor! task! Bandettini and Ungerleider, Nature Neuroscience, 4, ! Duty Cycle Effects! Understanding and Using! Activation Patterns! In fmri! R.M. Birn, P. A. Bandettini, The effect of stimulus duty cycle and "off" duration on BOLD response linearity. NeuroImage, 27, (2005)

46 Ventral temporal category representations Object categories are associated with distributed representations in ventral temporal cortex Dissimilarity Matrix Creation dissimilarity matrix compute dissimilarity (1-correlation across space) response patterns... ROI in Brain Haxby et al. Nature 2001 stimuli N. Kriegeskorte, et al, Neuron 60, 1-16 (2008) Visual Stimuli Human IT (1000 visually most responsive voxels) Human Early Visual Cortex (1057 visually most responsive voxels)

47 Monkey-Human Comparison Procedure Human fmri in four subjects (repeated sessions, >12 runs per subject) "quick" event-related design (stimulus duration: 300ms, stimulus onset asynchrony: 4s) fixation task (with discrimination of fixation-point color changes) occipitotemporal measurement slab (5-cm thick) small voxels (1.95!1.95!2mm 3 ) 3T magnet, 16-channel coil (SENSE, acc. fac. 2) Monkey (Kiani et al. 2007) single-cell recordings in two monkeys rapid serial presentation (stimulus duration: 105ms) fixation task electrodes in anterior IT (left in monkey 1, right in monkey 2) 674 cells total windowed spike count (140-ms window starting 71ms after stimulus onset) average of 4 subjects fixation-color task 316 voxels average of 2 monkeys fixation task >600 cells Lower spatial frequency clumping Boynton (2005), News & Views on Kamitani & Tong (2005) and Haynes & Rees (2005) Kamitani & Tong (2005)

48 Focus of this lecture! Technology High field strength Coil arrays High resolution Novel functional contrast Fluctuations / Correlations Dynamics Interpretation Methodology Paradigm Designs Processing Methods Healthy Brain Organization Applications Field Strength Echo Time Spin-echo vs Gradient Echo Velocity Nulling RF coil arrays High Spatial Resolution High Temporal Resolution Choice of Flip Angle Choice of Slice Thickness Paradigm Design Ultimate Sensitivity? Separating good and bad signal in Resting State fmri. Understanding dynamic nonlinarities Understanding and Using fmri Patterns Technology MRI Diff. tensor 1.5T,3T, 4T Mg + 7T >8 channels EPI on Clin. Syst. Venography EPI Real time fmri Nav. pulses SENSE vaso Local Human Head Gradient Coils Quant. ASL Z-shim Baseline Susceptibility ASL Spiral EPI Dynamic IV volume BOLD Multi-shot fmri Simultaneous ASL and BOLD Current Imaging? Methodology Blood T2 Hemoglobin Baseline Volume IVIM Interpretation Applications Correlation Analysis CO 2 Calibration Motion Correction Latency and Width Mod Parametric Design Multi-Modal Mapping Surface Mapping ICA Free-behavior Designs Phase Mapping Mental Chronometry Linear Regression Multi-variate Mapping Event-related Deconvolution Fuzzy Clustering BOLD models PET correlation B IV vs EV o dep. ASL vs. BOLD Layer spec. latency Pre-undershoot PSF of BOLD TE dep Resolution Dep. Excite and Inhibit Extended Stim. Post-undershoot Linearity Metab. Correlation SE vs. GE CO 2 effect NIRS Correlation Fluctuations Optical Im. Correlation Veins Inflow Balloon Model Electrophys. correlation Complex motor Language Imagery Memory Emotion Motor learning Children Tumor vasc. Drug effects BOLD -V1, M1, A1 Presurgical Attention Ocular Dominance Mirror neurons Volume - Stroke V1, V2..mapping Priming/Learning Clinical Populations Volume-V1 Plasticity Face recognition Performance prediction

Daniel Bulte. Centre for Functional Magnetic Resonance Imaging of the Brain. University of Oxford

Daniel Bulte. Centre for Functional Magnetic Resonance Imaging of the Brain. University of Oxford Daniel Bulte Centre for Functional Magnetic Resonance Imaging of the Brain University of Oxford Overview Signal Sources BOLD Contrast Mechanism of MR signal change FMRI Modelling Scan design details Factors