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Simultaneous EEG- fMRI : from acquisition to application.

Simultaneous EEG- fMRI : from acquisition to application. Karen Mullinger. Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy University of Nottingham. Introduction Aspects of getting good quality data Optimising experimental set-up General pointers

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Simultaneous EEG- fMRI : from acquisition to application.

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  1. Simultaneous EEG-fMRI: from acquisition to application. Karen Mullinger Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy University of Nottingham

  2. Introduction Aspects of getting good quality data Optimising experimental set-up General pointers Facilitating good: gradient artefact correction pulse artefact correction Summary Application Neurovascular coupling. Latest results (food for thought) Overview

  3. Why Simultaneous EEG –fMRI? • Very powerful spatiotemporal tool • Same experimental environment • Same attention and awareness • Same brain activity • Necessary when brain activity can’t be predicted fMRI EEG

  4. EEG Artefact Sources • Gradient Artefact (GA): Switching of the gradient fields, causes large changes in magnetic flux inducing electrical signals within the EEG. Average slice Artefact

  5. EEG Artefact Sources • Pulse Artefact (PA):Precise source unclear but linked to the cardiac cycle. 1) Pulsatile blood flow effects (Hall effect). 2) Small head nod 3) Scalp expansion

  6. The Result! 200µV

  7. Good quality EEG data Two aspects to EEG-fMRI: • Experimental set-up and data collection • Best post-processing methods

  8. Good quality EEG data Experimental set-up and data collection

  9. General advice • Low impedances of EEG channels • Less noisy EEG signals • Subject comfort and padding • Minimise movement → reduced artefacts

  10. General advice: Motion Aim: • To investigate effect of motion artefacts on EEG-BOLD correlates Method: • 4 subjects • Standard 32 channel EEG recording. • EEG data were recorded during Dual Echo EPI: • 40 slices, 84×84 matrix, 3×3×4 mm3voxels • TR=3s TE1/TE2 =20/48ms • Episodic memory task: required to move a cursor with a roller-ball to respond. Jansen, M. et al, NeuroImage 59, 261-270 (2012)

  11. General advice: Motion Analysis: • EEG • Gradient (AAS) and Pulse (OBS) artefact correction • ICA to remove residual artefacts • Noisy channels removed • Filtered 4-8Hz (Theta band) • fMRI • Motion and physiological correction • Echoes combined • Regressors: • Continuous theta regressor • Head motion (from motion parameters) • Artefacts remaining after correction (from visual inspection) Jansen, M. et al, NeuroImage 59, 261-270 (2012)

  12. General advice: Motion Not convolved with HRF Convolved with HRF Jansen, M. et al, NeuroImage 59, 261-270 (2012)

  13. General advice: Motion Task: Foot motion Not convolved with HRF Convolved with HRF CAREFUL how you interpret results! Jansen, M. et al, NeuroImage 59, 261-270 (2012)

  14. General advice • Low impedances of EEG channels • Less noisy EEG signals • Subject comfort and padding • Minimise movement → reduced artefacts • Isolate amplifiers/cables from scanner bed • Minimise vibration of equipment

  15. General advice 7T, no scanning Amplifier on the scanner bore Amplifier suspended. Mullinger, K.J. et al, MRI 26(7), 968-977 (2008)

  16. General advice • Low impedances of EEG channels • Less noisy EEG signals • Subject comfort and padding • Minimise movement → reduced artefacts • Isolate amplifiers/cables from scanner bed • Minimise vibration of equipment • Turn cyrocooler compression pumps off • Minimise noise sources

  17. General advice 7T, no scanning Everything on Cryopumps off.. ...and room lights, gradient and patient airflow Mullinger, K.J. et al, MRI 26(7), 968-977 (2008)

  18. Gradient artefact

  19. Average Artefact Subtraction (AAS) Allen, P.J. et al. NeuroImage 12, 230-239 (2000)

  20. Artefact Correction requirements AAS Requires: • Artefact to be highly repeatable across cycles • Precisely recording the artefact waveform and the beginning of each volume. • These requirements must be closely adhered to as the unfiltered GA is at least 10,000 times larger than an evoked response • Residual artefacts are problematic

  21. Precise sampling • Acquire EEG data at 5kHz • Ensure your slice TR is a multiple of the scanner clock period (i.e. 200μs) WARNING: • TR entered into console is not always the TR outputted due to rounding issues!! • Philips System for equidistant EPI: TR Calculator* *Need clinical science agreement for this

  22. Precise sampling • Synchronise the MR Scanner and EEG clocks using the output from the MR scanner. • Philips system: use the 10MHz output from the MR scanner clock to drive the EEG clock Mandelkow, H. et al, NeuroImage 32(3)1120-1126 (2006) Mullinger, K.J. et al, JMRI 27(3): p. 607-616 (2008)

  23. Standard Deviation associated with average slice artifact TR = 2s, synchronised TR = 2s, not synchronised TR = 2.0001s, synchronised Experimental Results Average slice artifact 180 dynamics, 20 slices, 3 subjects Results from electrode F7 for a single subject Mullinger, K.J. et al, JMRI 27(3): 607-616 (2008)

  24. Minimising GA amplitude • Why? • Prevent channel saturation • Allow higher EEG recording bandwidth • Improve artefact correction • How? • Position subjects 4cm in foot direction (naision at isocentre = 0cm). Approximately at Fp1&2. Yan, W.X., et al. NeuroImage46(2):459-471. (2009) Mullinger, K.J. et al, NeuroImage, 54(3):1942-1950 (2011)

  25. Optimal Position: standard fMRI Aim: • Compare GA produced by a multi-slice EPI sequence at standard and optimal subject positions. Method: • 6 subjects • Experiments were carried out with the nasion at: • iso-centre • optimal (+4 cm) z-offset • Standard 32 channel EEG recording, 250 Hz low pass filter. • EEG data were recorded during standard EPI: • 32 slices, 84×84 matrix, 3×3×4 mm3voxels • TR=2.5s TE =40ms; slice repetition frequency = 12.8 Hz • Cued foot movement: 5s every 30s (total: 8 minutes): cumulative head movements of <1 mm.

  26. Optimal position: Results • RMS of average artefact before correction • 40% average reduction in RMS over all channels • STD across slices after correction • 36% reduction in RMS at slice harmonics after correction Isocentre Optimal position

  27. Pulse artefact

  28. Pulse Artefact Correction • Many methods of PA correction • Average artefact subtraction (AAS)1 • Optimal basis sets (OBS)2 • Independent component analysis (ICA)3 • Varying levels of success reported • Most require correctly identifying the QRS complex within the ECG trace. ECG [1] Allen, P.J. et al, NeuroImage8(3), 229-239 (1998) [2] Srivastava, G. et al, NeuroImage24, 50-60 (2005) [3] Niazy, R.K. et al, NeuroImage28, 720-737 (2005)

  29. Pulse Artifact Problems: • ECG is affected by gradients as well. • Sometimes hard to get a good ECG trace. • Trace is sometimes saturated.

  30. Solution on a Philips system*: • Use vector cardiogram (VCG) from MR Scanner which is unaffected by gradients1. • R peak markers are also placed automatically in the physlogfile2 which can be used for pulse artefact correction directly. [1] Chia et al. JMRI, 12:678-688 (2000) [2] Fischer et al. MRM, 42:361-370 (1999) *Need research login to access physlog file

  31. Mean Standard Deviation Results • Data gradient-corrected and low-pass filtered at 70 Hz • EEG trace from Tp10 averaged over all cardiac cycles in 2 minute period. • 0 time=R peak marker from VCG No correction Using ECG markers Using VCG markers

  32. Pulse Artefact • Variation between cardiac cycles makes correction of difficult • Problems increase with field strength • Need a greater understanding of pulse artefact • Precise source unclear but linked to the cardiac cycle. Average pulse artefact 1) Pulsatile blood flow effects R-peak T7 2) Small head nod 3) Scalp expansion Debener, S. et al, Int. J. Psychophys, 2008, 67(3), p.189-199

  33. Measuring the PA constituents • 6 subjects • Recorded EEG data in 3T MR scanner • 4 conditions: • Relaxed • Bite Bar and vacuum cushion (stop head nod) • Swimming cap (stop Hall effect) • 2&3 (left with scalp expansion). Yan, W.X., et al., HBM, 2010. 31(4): p. 604-620. Mullinger, K.J. et al, #667 WTh HBM 2011. Quebec.

  34. A Amplitude (µV) Relax B C D PA Experimental Results EEG Restrained Restrained & insulated Insulated Average RMS Subject RMS

  35. Summary • SNR of EEG data inside the MR scanner still lower than outside. • Higher MR fields → increasing EEG artefact problems. • Experimental set-up is important.

  36. Data Acquisition Summary • To improve gradient artefact correction: • Chose TR and number of slices wisely • Synchronise scanner clocks • Optimally position the subject • To improve pulse artefact correction: • Use VCG to monitor cardiac trace

  37. Application

  38. Investigating origin of Negative BOLD • Negative BOLD Response (NBR): Regions where there is a stimulus related decrease in BOLD signal. • Reported in visual1, motor2 and somatosensory3 cortices. From: [2] Stefanovic et al. Neuroimage 22;2004. [1] Shmuelet al.Neuron36(6);2002. [3] Kastrup et al. Neuroimage 41(4);2008.

  39. Negative BOLD • NBR origin unclear: • Neuronal basis • Haemodynamic artefact (blood steal) • Invasive recordings in monkeys show a decrease in local field potentials (LFP) and spiking activity in regions of NBR, and suggest at least 60% of NBR is neuronal in origin1. • Clarification in humans is needed. [1]Shmuel et al. Nat Neurosci. 9(4);2006.

  40. Aim To use simultaneous measurements of BOLD, ASL and EEG to investigate the relationship between natural fluctuations in the NBR and somatosensory evoked potentials (SEPs) during median nerve stimulation (MNS)1 [1] Mullinger et al Proc. ISMRM #109; 2011

  41. Method Simultaneous EEG-fMRI: • Philips Achieva3T MR scanner; 8 channel SENSE head coil. • 64 channel Brain Products EEG system. Localiser:GE-EPI BOLD sequence used for planning. Experiment: • FAIR Double Acquisition Background Suppression1 sequence used for simultaneous BOLD and background suppressed ASL data acquisition (TR=2.6s, TE=13/33ms (ASL/BOLD), label delay=1400ms, 3x3x5mm3voxels, 212mm FOV, SENSE factor 2; background suppression TI1/TI2=340ms/560ms). • Cardiac and respiration monitored. • MR and EEG scanner clocks synchronised. • EEG electrode positions digitised (Polhemus system, Isotrack). [1] Wesolowski et al. Proc. ISMRM, #6132;2009.

  42. Paradigm • 13 right handed subjects (8 males, 26±3 yrs) • Stimulate median nerve of right wrist • Amplitude: just above motor threshold to cause thumb distension • 2 Hz stimulation, 0.5ms pulses (Digitimer DS7A) 20 pulses per block 10s 20s 10s 40 blocks

  43. Analysis EEG pre-processing • Gradient and pulse artefact correction using average artefact subtraction(Brain Vision Analyzer2) • Data inspection: • 3 subjects excluded due to gross (>3mm) or stimulus-locked movement. • Noisy channels and/or blocks rejected • Down-sampled: 600Hz • Re-referenced: Average of non-noisy channels • Filtered: 2-40 Hz

  44. Analysis EEG Beamformer1 Fitted2 basis set to SEP for each block to find peak-to-peak P100-N140 amplitude T-stat map: active window: 0.01-0.16s passive window: 0.3-0.45s VE timecourse for single block Averaged over 20 responses in a block [1] Brookes et al.NeuroImage 40(3);2008 [2] Mayhew et al.Clin. Neurophysiol. 117(6);2006

  45. Analysis fMRI pre-processing • Motion corrected (FLIRT, FSL) • BOLD data physiologically corrected (RETROICOR) • Interpolated to effective TR=2.6s • ASL: perfusion weighted image: Tag-Control • BOLD image pairs averaged • Normalised to MNI template • Smoothed: 5mm FWHM kernel

  46. Analysis fMRI General Linear Models Boxcar:  SEP amplitude modulator: • 2nd level fixed effects analysis on BOLD and ASL data Timecourse for each region & subject obtained; averaged over subjects & blocks Group ROI defined for positive and negative correlation. BOLD: P<0.05 FWE ASL: P<0.001 uncorr

  47. Results BOLD ASL Positively correlated with Boxcar Negatively correlated with Boxcar Negatively correlated with SEP amplitude MNI peak co-ordinates (-42,-20,50) Positive (34,-16,46) Negative (36,-18,50) SEP • No positive correlation amplitude of SEP and fMRI in S1.

  48. Results Solid line = BOLD, Dashed line= ASL

  49. Results Constants: M = 7.2%1, α = 0.38, β = 1.2 Isocontours of CMRO2 (Davis Model2) R = 0.9704, P<0.1*10-4 Gradient = 0.42 2 Coupling ratio agrees with Stefanovic3 [1] Kastrup et al.,Neuroimage 41(4);2008. [2] Davis et al., PNAS, 95;1998 [3] Stefanovicet al., NeuroImage22;2004 [1] Kastrup et al.,Neuroimage 41(4);2008. [2] Davis et al., PNAS, 95;1998

  50. Discussion • No positive correlation of fMRI and evoked potentials in S11. • Ipsilateral NBR cannot be explained by blood steal2as bilateral S1 regions are fed from different vascular territories. • CMRO2 shown in NBR region - suggests a neuronal origin of the response. [1]Klingneret al.Neuroimage53(1); 2010 [2] Wade et al.Neuron 36(6);2002

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