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Effect of field inhomogeneity due to head motion on BOLD fMRI signal Anahita Talebi Amiri1,2, F. IΕik KarahanoΔlu3, Paul Wighton2, Dara Manoach4, Dimitri Van De Ville1 André van der Kouwe2 1Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2Massachusetts General Hospital, A.A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA, 3Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA, 4Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Introduction Recent studies have focused on the effect of motion on the BOLD signal in terms of geometry, neglecting how BOLD is influenced by field inhomogeneity (FI) [1-2]. Even after real-time motion tracking or image registration, changes in field due to motion cause the field to change abruptly. We hypothesize a relationship between the amount of change in the field, reflected in the phase map, and the proportion of variation in the BOLD signal, through the influence β of FI on π»π . Results Effect of field inhomogeneity (FI) in the MR images Mean signal intensity β’ Problem 1: Geometric distortion β’ Problem 2: Decrements in the baseline of signal intensity . 8 β’ Problem 3: BOLD fMRI signal Variation in Changes in 6 BOLD fMRI amplitude 4 signal before and after 2 movement are 0 not coherent. There is not any -2 solution for this Stimulus issue, yet. -4 0 10 20 30 Volume 40 50 60 Data acquisition and methods Acquiring phase and magnitude images: β’ under water-excitation condition to suppress shifted fat Brain activation during visual stimulation based on estimated π»πβ . Accurately modeling the relationship between field changes and changes in T2* may provide a method to correct for the secondary effect of motion on the BOLD signal in brain activation studies. Histogram of estimated FI using Eq. 2 and 3 Distribution of FI using Eq. 2 and 3 (water phantom), in a well-shimmed state. Little variation of FI is expected. ππ°π»πβ shows far less variation than ππ°πππππβπππ . This suggests that these methods are not equivalent. Average signal intensity inside VOI A11 = -241.8 [uT/m] 942 938 934 A11 = -191.8 [uT/m] A11 = -216.8 [uT/m] 930 100 90 80 70 60 50 40 30 20 10 0 -80 (1) π° = π°π β’ a 3D EPI pulse seq. to eliminate the spin-history effect d) c) Whole brain is excited multiple times and after each excitation, a slice of k-space is acquired. Volumes depict kspace. To prove that FI decreases signal intensity, the data sets are acquired: β’ from a water phantom with 4 different ππΈs (FLASH pulse sequence) β’ for each measurement, FI was manipulated by applying a field gradient along the X-axis To test whether equations 2 and 3 are equivalent [1-3]: β’ 12 data sets with ππΈs between 2 and 24 ms (step size 2 ms) were collected on the water phantom. β’ For each measurement, we varied the shim parameter, A11, between 10 and 80 µT/m (step size 10 µT/m). πΎ β β² π 2 = π 2 + π 2 = π 2 + βπ½ [π»π§] (2) 2π ππ°π»πβ = πΉπβ β πΉπ [π―π] ππ°πππππβπππ = πΈβπ· = [π―π], πΈ = πΈ ππ -40 -10 -20 -30 0 10 β’ The motion-induced signal drop cannot be fully eliminated by regressing out the motion parameters [4-6] , i.e., the residual drop can be explained by changes in FI. This variation may be used to compensate for the residual signal difference. β’ Accurately predicting the R2' contribution to the BOLD signal requires more careful modeling of the underlying field variation and cannot be deduced simply from Eq. 3. β’ It is shown that πΉπΌπ2β and πΉπΌπβππ πβπππ are not equivalent. Increasing FI, by manipulating the shim, leads to a shift and broadening of the T2* distribution and more variation of ππ°πππππβπππ . ππ°π»πβ , πππ = βπππ. ππ ΞΌπ» [ ] π πππ = βπππ. ππ 80 80 60 60 40 40 20 20 0 0 3 3.5 π―π ππ°πππππβπππ , πππ = βπππ. ππ ΞΌπ» [ ] π (3) 100 80 20 0 3.5 π―π πππ = βπππ. ππ 3.5 π―π 3 4 ΞΌπ» [ ] π 4 πππ = βπππ. ππ ΞΌπ» [ ] π 50 60 60 40 40 30 20 20 20 0 -100 πππ = βπππ. ππ ΞΌπ» [ ] π 60 40 3 4 ΞΌπ» [ ] π 40 βπ ππ βπ»π¬ -50 Conclusion βπ»π¬ β π» π π b) -60 Average signal intensity decreases as the shim parameter increases in the negative direction along the Xaxis. This suggests that in brain studies, underlying field changes due to changes in position cause variation of the BOLD signal. We observed concomitant variation in the estimated T2*. A11 = -291.8 [uT/m] Suppress shifted fat To generate the brain activation map, π2β is estimated (equation 1) using β’ three data sets with interleaved ππΈs a) -70 FI [Hz] 0 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 presence of shifted fat ππ°π»πβ ππ°πππππβπππ 10 -50 0 π―π 50 100 0 -100 -50 0 50 π―π 100 0 -100 -50 0 50 100 π―π Distribution of the estimated ο¬eld inhomogeneity (Eq. 2 and 3) for three different data sets. Shim values were manipulated manually. References [1] B. Dymerska, B. A. Poser, M. Barth, S. Trattnig, and S. D. Robinson, βDynamic correction of geometric distortion in single-echo EPI for large head motion at 7T.β Ultra High Feld MRI Workshop, pp. 0β10, 2016. [2] J. Cohen-Adad, βWhat can we learn from t2* maps of the cortex?β NeuroImage, vol. 93, Part 2, pp. 189 β 200, 2014, invivo Brodmann Mapping of the Human Brain. [3] L. Valkovic and C. Windischberger, βMethod for geometric distortion correction in fMRI based on three echo planar phase images,β Measurement Science Review, vol. 10, no. 4, pp. 116β119, 2010. [4] K. R. Van Dijk, M. R. Sabuncu, and R. L. Buckner, βThe inο¬uence of head motion on intrinsic functional connectivity MRI,β NeuroImage, vol. 59, no. 1, pp. 431β438, 2012. [5] T. D. Satterthwaite, D. H. Wolf, J. Loughead, K. Ruparel, M. A. Elliott, H. Hakonarson, R. C. Gur, and R. E. Gur, βImpact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth,β NeuroImage, vol. 60, no. 1, pp. 623β632, 2012. [6] M. G. Bright and K. Murphy, βIs fMRI βnoiseβ really noise? resting state nuisance regressors remove variance with network structure,β NeuroImage, vol. 114, pp. 158β169, 2015.