{"paper_id":"b34424bd-2c09-4097-8ca2-b719324e3f09","body_text":"1 \n \nOptimising 7T-fMRI for imaging regions of magnetic susceptibility 1 \n 2 \nSaskia L. Frisby1, Marta M. Correia1, Minghao Zhang2, Christopher T. Rodgers2, Timothy T. 3 \nRogers3, Matthew A. Lambon Ralph1, and Ajay D. Halai1 4 \n 5 \n1MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF  6 \n2Wolfson Brain Imaging Centre, Cambridge Biomedical Campus, Cambridge CB2 0QQ 7 \n3Department of Psychology, University of Wisconsin-Madison, 1202 W Johnson Street, 8 \nMadison 53706 9 \n 10 \nCorrespondence to: 11 \nDr. Saskia L. Frisby 12 \nsaskia.frisby@mrc-cbu.cam.ac.uk 13 \n+44 (0)1223 769703 14 \nand 15 \nDr. Ajay D. Halai 16 \najay.halai@mrc-cbu.cam.ac.uk 17 \n+44 (0)1223 767655 18 \nMRC Cognition & Brain Sciences Unit 19 \nUniversity of Cambridge 20 \n15 Chaucer Road 21 \nCambridge 22 \nCB2 7EF 23 \n 24 \nFor the purpose of open access, the authors have applied a Creative Commons Attribution 25 \n(CC BY) licence to any Author Accepted Manuscript arising from this work. 26 \n 27 \n 28 \n 29 \n 30 \nSingle PDF Article File\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n2 \n \nAbstract 31 \nThe temporal signal-to-noise ratio (tSNR) of functional magnetic resonance imaging (fMRI) is 32 \nparticularly poor in ventral anterior temporal and orbitofrontal regions because of magnetic 33 \nfield inhomogeneity, a problem that is exacerbated at higher field strengths. In this 7T-fMRI 34 \nstudy we compared three methods of improving sensitivity in these areas: parallel transmit, 35 \nwhich uses multiple transmit elements, controlled independently, to homogenise the flip 36 \nangle experienced by the tissue; multi-echo, which entails collection of multiple volumes at 37 \ndifferent echo times following a single radiofrequency pulse; and multiband, in which 38 \nmultiple slices are acquired simultaneously. We found that parallel transmit and multi-echo 39 \nincreased the magnitude of the BOLD signal change, but only multi-echo increased BOLD 40 \nmagnitude in areas prone to susceptibility artefacts. Multiband and denoising of multi-echo 41 \ndata with independent components analysis (ICA) both improved precision of GLM fit. 42 \nExploratory results suggested that multi-echo and ICA denoising can both benefit 43 \nmultivariate analyses. In conclusion, a multi-echo, multiband sequence improved fMRI 44 \nquality in areas prone to susceptibility artefacts while maintaining sensitivity across the 45 \nwhole brain. We recommend this approach for studies investigating the functional roles of 46 \nventral temporal and orbitofrontal regions with 7T fMRI. 47 \n 48 \nKeywords: 7T-fMRI, parallel transmit, multi-echo, multiband, temporal lobe, orbitofrontal 49 \ncortex 50 \n 51 \n 52 \n  53 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n3 \n \n1. Introduction 54 \nThe temporal signal-to-noise ratio (tSNR) of functional magnetic resonance imaging 55 \n(fMRI) varies across the brain. The ventral anterior temporal cortex and orbitofrontal cortex, 56 \nfor example, are located next to air-filled sinuses and so are affected by magnetic field 57 \ninhomogeneity that causes signal dropout and geometric distortions (Devlin et al., 2000; 58 \nHalai et al., 2014, 2015, 2024). This makes it challenging to use fMRI to investigate the roles 59 \nthat these regions may play in a myriad of cognitive processes – including vision (Devereux 60 \net al., 2018), language (Borghesani et al., 2016), multimodal semantic cognition (Lambon 61 \nRalph et al., 2017), emotion (Fernandez et al., 2017), social cognition (Binney et al., 2016; 62 \nZahn et al., 2007), theory of mind (DuPre et al., 2016), and executive function (Duncan, 63 \n2010). Signal dropout and geometric distortions are more severe at higher field strengths, 64 \nimplying that it may be especially difficult to measure task-related activity in susceptible 65 \nregions with ultra-high-field fMRI (e.g., 7T-fMRI). However, 7T-fMRI also has many 66 \nadvantages in regions unaffected by magnetic susceptibility artefacts: 7T-fMRI offers 67 \nimproved tSNR relative to 3T-fMRI (Morris et al., 2019), which can be used to reduce voxel 68 \nsize and enable applications such as laminar fMRI (Koopmans et al., 2011) or to reduce 69 \nacquisition times and enable shorter scan times for special populations such as patients with 70 \nneurodegenerative diseases (Cope et al., 2023). 7T-fMRI also benefits from improved spatial 71 \nspecificity relative to 3T-fMRI because the signal from cortical microvasculature is enhanced 72 \nwhile the signal from large veins is reduced (Marques & Norris, 2018). Improving signal 73 \nhomogeneity would allow researchers studying the whole brain (or focusing on regions 74 \nprone to susceptibility artefacts) to take full advantage of 7T-fMRI; therefore, in this study 75 \nwe compared three methods of doing so. 76 \nOne possible method for counteracting signal dropout is parallel transmit (pTx), 77 \nwhich uses multiple transmit elements, controlled independently, to homogenise the flip 78 \nangle pattern experienced by the tissue (Deniz et al., 2019). A recent study used pTx to 79 \ncounteract signal dropout in ventral anterior temporal regions for echo-planar imaging (EPI) 80 \n7T fMRI (Ding et al., 2022). pTx improved tSNR across the brain compared to a standard 81 \nsequence, particularly in the temporal lobes. However, there was no improvement in 82 \nfunctional contrast during a semantic association task that is known to recruit the anterior 83 \ntemporal lobes in 3T-fMRI studies (Jung et al., 2017).  84 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n4 \n \nA second method of recovering signal in these regions is multi-echo (ME) imaging 85 \n(Kundu et al., 2017; Poser et al., 2006; Posse, 2012). T2* is known to vary across the brain 86 \n(Hagberg et al., 2002); in areas prone to magnetic susceptibility artefacts, T2* is particularly 87 \nshort due to increased intravoxel dephasing. A single echo provides sensitivity to a narrow 88 \nrange of T2* values; the echo time (TE) is therefore selected to provide the best compromise 89 \nof sensitivity to T2* across the whole brain. Combining data from multiple echoes increases 90 \nthe range of T2* that can be imaged with high fidelity. ME has been shown to improve 91 \nfunctional contrast (Poser & Norris, 2009) and spatial specificity (Boyacioğlu et al., 2015) at 92 \n7T and 3T (Fernandez et al., 2017; Halai et al., 2024; Kirilina et al., 2016; Lynch et al., 2020). 93 \nHaving multiple echoes also facilitates the separation of signal and noise because signals 94 \ndecay in a well-characterised way across echoes, whereas noise does not. This principle 95 \nunderpins multi-echo independent components analysis (ME-ICA), via which ICA 96 \ncomponents that are TE-independent, and thus are likely to be noise rather than blood-97 \noxygen-level-dependent (BOLD) signal, can be removed (Dipasquale et al., 2017; Kundu et 98 \nal., 2011, 2013, 2015, 2017). This method may enhance signal detection in areas prone to 99 \nsusceptibility artefacts on top of the advantage offered by ME alone (e.g. Lombardo et al., 100 \n2016). ME sequences have some potential disadvantages. For example, multi-echo can 101 \nlengthen repetition time (TR). In-plane acceleration is frequently needed to achieve a 102 \nsufficiently short first TE, which reduces tSNR (Yun & Shah, 2017). In turn, a short first TE, 103 \ncombined with hardware constraints, often limits the minimum voxel size (Koopmans et al., 104 \n2011). Critically, in many previous studies examining the benefits of ME sequences 105 \ncompared to single-echo (SE), the “single-echo” data were extracted from the ME dataset 106 \n(Amemiya et al., 2019; Bhavsar et al., 2014; Caballero-Gaudes et al., 2019; Cohen et al., 107 \n2017, 2017, 2018; Dipasquale et al., 2017; Evans et al., 2015; Fernandez et al., 2017; Gilmore 108 \net al., 2022; Kovářová et al., 2022). This means that the “single-echo” data will inherit 109 \nsuboptimal parameters that are ME-specific, making the comparison unfair.  110 \n A third strategy for improving acquisition is multiband (MB) imaging, also known as 111 \nsimultaneous multi-slice, in which multiple slices are acquired simultaneously (Barth et al., 112 \n2016; Moeller et al., 2010; Setsompop et al., 2012). Typically MB has been applied to 113 \nincrease temporal resolution – multiple studies have shown that MB can reduce noise 114 \naliasing, increase statistical power and counteract the increase in TR associated with ME 115 \n(Feinberg et al., 2010; Griffanti et al., 2014; Halai et al., 2024; Puckett et al., 2018; Smith et 116 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n5 \n \nal., 2013). Benefits specific to task fMRI have been less clear (Demetriou et al., 2018; Todd 117 \net al., 2016) since some noise features are unlikely to be correlated with task regressors and 118 \ncan be removed with high-pass filtering. Other possible disadvantages of MB include a 119 \nreduction in tSNR due to increases in g-factor effects (Demetriou et al., 2018; Risk et al., 120 \n2021; Setsompop et al., 2012) and leakage of signal into the simultaneously-excited slices 121 \n(Todd et al., 2016).  122 \nThere is therefore a need to evaluate 7T-fMRI sequences to determine which 123 \nparameters are important for counteracting signal dropout and improving sensitivity in 124 \nregions prone to susceptibility artefacts without compromising signal quality elsewhere. We 125 \ntested five possible sequences. These consisted of pTx, plus a 2 x 2 factorial design varying 126 \nnumber of echoes and multiband factor: single-echo single band (SESB), single-echo 127 \nmultiband (SEMB), multi-echo single band (MESB), and multi-echo multiband (MEMB). We 128 \nused a semantic judgment task that is known to evoke activity across the semantic network, 129 \nincluding areas severely affected and those relatively unaffected by susceptibility artefacts 130 \n(Jung et al., 2017).  131 \nWe had two univariate effects of interest – activation magnitude and activation 132 \nprecision (Halai et al., 2024). Activation magnitude is the magnitude of the BOLD signal 133 \nchange, operationalised as the 1st-level beta values extracted from each voxel. We 134 \nhypothesised that both pTx and ME would recover signal and hence increase activation 135 \nmagnitude relative to the SESB sequence (which we used as a baseline). Activation precision 136 \nis the reliability of the BOLD signal change, analogous to the functional contrast-to-noise 137 \nratio (fCNR) and operationalised as the 1st-level t-values extracted from each voxel. We 138 \nhypothesised that MB sequences, with greater effective degrees of freedom, would increase 139 \nactivation precision relative to single band (SB) sequences. We also had two secondary 140 \nhypotheses: first, since ME-ICA denoising removes TE-independent noise, greater activation 141 \nprecision would be observed in multi-echo denoised data (MEdn) relative to ME data 142 \nwithout denoising; second, that any MB advantage would be due to the increase in the 143 \nnumber of volumes.  144 \nOur study focused primarily on univariate effects. However, multivariate analysis 145 \ntechniques, which exploit variance and covariance between voxels and can accommodate 146 \nparticipant-specific differences in activation patterns (Coutanche, 2013; Davis et al., 2014; 147 \nDavis & Poldrack, 2013) are rapidly gaining popularity. These methods frequently rely on the 148 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n6 \n \nassumption of good-quality signal across the whole brain (Frisby et al., 2023) and so we 149 \nconducted an exploratory analysis, following the method of Haxby et al. (2001), to decode 150 \ntask condition from the data acquired with each sequence. Finally, we tested for the 151 \npresence of slice leakage artefacts in our MB data. 152 \n To summarise, our study aimed to compare pTx, ME and MB as methods for 153 \nimproving sensitivity in ventral temporal and orbitofrontal regions while maintaining image 154 \nquality across the rest of the brain. 155 \n 156 \n2. Methods 157 \n2.1. Participants 158 \n20 healthy native speakers of British English (age range 18-50, mean age 33.45 years, 12 159 \nfemale, 8 male) participated in the study. All were right-handed, had normal or corrected-160 \nto-normal vision, and had no neurological or sensory disorders. All participants gave written 161 \ninformed consent. The research was approved by a local National Health Service (NHS) 162 \nethics committee (04/Q105/66). 163 \n 164 \n2.2. Stimuli and task 165 \nAll participants performed a semantic association task and a visual pattern matching task 166 \n(hereafter called the “control task”) adapted from a previous study (Jung et al., 2017). Each 167 \nstimulus consisted of three pictures presented simultaneously (Figure 1). Some pictures 168 \nwere line drawings taken from the Pyramids and Palm Trees Test (Howard & Patterson, 169 \n1992) and some were colour cartoons or photographs taken from the Camel and Cactus Test 170 \n(Bozeat et al., 2000). In the semantic task, participants were instructed to indicate which of 171 \nthe two pictures at the bottom of the screen had the closest semantic relationship to the 172 \npicture at the top (hereafter the “probe picture”). In the control task, participants were 173 \ninstructed to indicate which of two scrambled pictures (generated from the pictures used in 174 \nthe semantic task) at the bottom of the screen was identical to a scrambled probe. There 175 \nwere 248 unique picture triplets, so some stimuli were repeated between runs, but no 176 \nstimulus was repeated within a run. E-Prime software (Psychology Software Tools Inc., 177 \nPittsburgh, USA) was used to display stimuli and record responses. Stimuli were rear-178 \nprojected onto a screen at the back of the MRI scanner, and observers viewed stimuli 179 \nthrough a mirror mounted to the head coil directly above the eyes. 180 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n7 \n \n 181 \nFigure 1: One block of the semantic association task (left of the arrow) and one block of the 182 \ncontrol task (right of the arrow). Each trial consisted of a fixation cross (500 ms) followed by 183 \nthree pictures presented simultaneously. Participants had to select which of the two 184 \npictures at the bottom was more semantically similar to (in the semantic task) or visually 185 \nidentical to (in the control task) the picture at the top (the probe picture). Each block lasted 186 \n16 s in total. Reprinted with permission from (Halai et al., 2024).  187 \n  188 \nThe study had a block design with three types of block: semantic, control and rest. 189 \nEach task block consisted of four trials. Each trial consisted of a fixation cross presented for 190 \n500 ms followed by a stimulus presented for 3500 ms. Each rest block consisted of a fixation 191 \ncross presented for 16 s. Each run began 16 s after the start of the MR sequence and then 192 \nconsisted of 30 blocks presented in the order: semantic, control, semantic, control, rest. 193 \nThere were five runs per participant, collected in a single session. Accuracy and reaction 194 \ntime were measured for each trial. Since reaction time is not normally distributed, both 195 \nmetrics were compared across tasks using Wilcoxon’s signed-rank tests and across 196 \nsequences using Friedman’s nonparametric ANOVA. 197 \n 198 \n2.3. Image acquisition 199 \nAll images were acquired on a whole-body MAGNETOM Terra 7T MRI (Siemens 200 \nHealthcare, Germany). An 8Tx32Rx head coil (Nova Medical, USA) was used to run all 201 \nstructural and functional imaging sequences.  202 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n8 \n \nAn MP2RAGE anatomical scan was acquired with the following parameters: 224 203 \nsagittal slices (interleaved acquisition), FOV 240 x 225.12 x 240 mm3, voxel size 0.75 mm 204 \nisotropic, TR 4300 ms, inversion times 840 and 2370 ms, TE 1.99 ms, nominal flip angles 5° 205 \nand 6°, GRAPPA acceleration factor 3, and duration 8 minutes 50 seconds.  206 \nNext, manual B0-shimming was performed over the volume to be used for EPI. The 207 \naim was to reduce the water linewidth, defined as full width of the spectrum at half height, 208 \nto below 40 Hz. However, for some participants the adjustments proved time-consuming 209 \nand so adjustment time was capped at 30 minutes from the start of scanning after which 210 \nthe best shim parameters were adopted. The actual average water linewidth was 42 Hz 211 \n(standard deviation = 9 Hz; missing data for 4 participants). Then a dummy pTx-EPI scan of 212 \none volume was acquired to trigger the acquisition of subject-specific B0 and per-channel B1+ 213 \nfield maps. The brain was divided into 5 slabs along the slice direction and slab-specific 2-214 \nspoke pTx excitation pulses were designed offline. Variable-rate selective excitation (VERSE; 215 \nHargreaves et al., 2004) was applied to reduce specific absorption rate (SAR) for the pTx 216 \nsequence only.  217 \nThere were five functional runs of EPI, one run of each sequence. Key parameters 218 \nare given in Table 1. The order of sequences was counterbalanced across participants. The 219 \nfollowing parameters were held constant across sequences: 48 axial slices (interleaved 220 \nacquisition), FOV 210 x 210 x 210 mm3 to cover the whole brain in most participants (visual 221 \ninspection ensured that the ventral anterior temporal lobe was included in the FOV for all 222 \nparticipants and the FOV was tilted up at the nose to avoid ghosting of the eyes into the 223 \ntemporal lobe), voxel size 2.5 mm isotropic (no gap), and A-P phase encoding direction. 224 \nAfter each run, 5 further volumes were acquired with the phase encoding direction changed 225 \nto P-A to facilitate distortion correction during preprocessing. 226 \n 227 \n SESB SEMB MESB MEMB pTx \nNo. echoes 1 1 3 3 1 \nMultiband factor 1 2 1 2 1 \nTR (ms) 3020 1510 3020 1510 3000 \nTE1 (ms) 25.00 25.00 11.80 11.80 25.00 \nTE2 (ms) - - 27.05 27.05 - \nTE3 (ms) - - 42.30 42.30 - \niPAT type Off Off GRAPPA GRAPPA GRAPPA \niPAT factor Off Off 3 3 2 \nPhase partial \nFourier \n7/8 7/8 7/8 7/8 Off \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n9 \n \nNominal flip \nangle (°) \n50 50 78 63 40 \nBandwidth \n(Hz/Px) \n2204 2204 2204 2204 1804 \nNumber of \nvolumes acquired \n171 340 171 340 172 \nNumber of pulses 1 1 1 1 5 (VERSE 2-\nspoke pTx \npulses) \n 228 \nTable 1: parameters of each sequence. 229 \n 230 \n2.4. Data analysis 231 \nAll analysis code is available at https://github.com/slfrisby/7TOptimisation/. 232 \n 233 \n2.4.1. Preprocessing 234 \n For ease of data sharing we converted all DICOMs to BIDS format (Gorgolewski et al., 235 \n2016) using heudiconv v1.0.0 (Halchenko et al., 2024). 236 \nStandard reproducible preprocessing pipelines designed for 3T-fMRI, such as 237 \nfMRIprep (Esteban et al., 2019; Gorgolewski et al., 2011; Markiewicz et al., 2024) perform 238 \npoorly on 7T-fMRI EPI data. Therefore, the analysis pipeline was split into stages using 239 \ndifferent software packages.  240 \nSince it is notoriously difficult to perform a good-quality brain extraction on 241 \nMP2RAGE data (because of salt-and-pepper noise in the background and cavities), two 242 \npipelines were used. The MP2RAGE T1w (combined) image first had its background noise 243 \nremoved using O’Brien regularisation (O’Brien et al., 2014) and was then submitted to the 244 \nCAT12 pipeline for segmentation (Gaser et al., 2023; in SPM12; 245 \nhttps://www.fil.ion.ucl.ac.uk/spm/). The bias- and global-intensity corrected T1w image 246 \nproduced was provided to as input to the anatomy pipeline in fMRIPrep 21.0.1. TheT1w 247 \nimage was skull-stripped with a Nipype implementation of the antsBrainExtraction.sh 248 \nworkflow (ANTs 2.3.3; Avants et al., 2009, 2011; https://github.com/ANTsX/ANTs/). Volume-249 \nbased spatial normalisation to standard space (MNI152NLin2009cAsym) was performed 250 \nthrough nonlinear registration with antsRegistration.sh (ANTs). 251 \n Functional preprocessing was performed using in-house code, composed of 252 \nfunctions from AFNI (v.18.3.03; Cox, 1996; Cox & Hyde, 1997; https://afni.nimh.nih.gov/), 253 \nFSL (v.5.0; Andersson et al., 2003; Jenkinson et al., 2012; Smith, 2002; Smith et al., 2004; 254 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n10 \n \nhttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), tedana (v.23.0.1; DuPre et al., 2021; Kundu et al., 255 \n2011, 2013; The tedana Community et al., 2023; 256 \nhttps://tedana.readthedocs.io/en/stable/index.html) and ANTS (v. 2.2.0; Avants et al., 2009, 257 \n2011; https://github.com/ANTsX/ANTs/). EPIs were despiked using 3dDespike (AFNI), slice 258 \ntiming was corrected to the middle slice using 3dTshift (AFNI), motion was corrected with 259 \n3dvolreg and 3dAllineate (AFNI) using the first volume of each run as a reference (for ME 260 \ndatasets, the TE1 image was aligned and the resulting transform was applied to the TE2 and 261 \nTE3 images), and skull-stripped using BET (FSL) to create a participant-specific brain mask. 262 \nFor ME datasets only, tedana was used to create two timeseries – one in which the 263 \nechoes were optimally combined based on T2* weighting (Posse, 2012), and one in which 264 \nthe T2* optimally-combined data were denoised using ICA. tedana conducts denoising by 265 \ndecomposing data using PCA and ICA, classifying components according to whether the 266 \nsignal scales linearly with TE (as the BOLD signal does), and reconstructing the data using 267 \nonly BOLD-like components. The brain mask created with BET was used as the mask for this 268 \nstage. 269 \nNext, for all datasets, unwarping was conducted using topup and applytopup (FSL). 270 \nField displacement maps were calculated using ten volumes (five with A-P phase encoding 271 \ndirection, extracted from the start of each functional run, and five with P-A phase encoding 272 \ndirection, collected separately after each run) and the resulting correction was applied to all 273 \nimages. Finally, the mean EPI for each run was coregistered to the skull-stripped native 274 \nstructural image using a rigid-body registration with AntsRegistrationSyN.sh (ANTS). EPIs 275 \nwere then transformed into standard space (MNI152NLin2009cAsym) by combining the 276 \ntransforms from native EPI to native T1 and the transforms from native T1 to standard space 277 \nand applying those transforms to the EPIs using antsApplyTransforms (ANTS). Images were 278 \nsmoothed with a 6 mm FWHM Gaussian filter in SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) 279 \nfor GLM analysis. 280 \nA separate functional preprocessing pipeline was used to create images for slice 281 \nleakage analysis. This pipeline differed from the main pipeline in the following ways. 282 \nDespiking was omitted to avoid the removal of noise. For ME datasets only, rather than run 283 \nthe full tedana workflow, we conducted optimal combination of data from multiple echoes 284 \n(but no denoising) using the t2smap command using all voxels in the volume (i.e. no brain 285 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n11 \n \nmask). All coregistration steps were omitted (the images remained in native EPI space) and 286 \nno smoothing was applied. 287 \n 288 \n2.4.2. 1st-level (within-participant) GLM 289 \nData were analysed using the general linear model (GLM) approach implemented in 290 \nSPM12 in MATLAB r2019a. We had 5 timeseries of primary interest – standard single-echo 291 \nsingle band (SESB), parallel transmit (pTx), single-echo multiband (SEMB), multi-echo single 292 \nband (MESB) and multi-echo multiband (MEMB). For the latter two sequences, data from all 293 \nechoes were optimally combined but were not ICA-denoised. We also generated two 294 \ntimeseries with ME-ICA denoising - multi-echo single band with ICA denoising (MESBdn) and 295 \nmulti-echo multiband with ICA denoising (MEMBdn) - and two downsampled MB timeseries 296 \ncreated by extracting odd-numbered volumes to match the number of volumes in the single 297 \nband timeseries - odd-numbered volumes of single-echo multiband (SEMBodd) and odd-298 \nvolumes of multi-echo multiband (MEMBodd). 299 \nAt the individual subject level, each block of the semantic and control task was 300 \nmodelled as a boxcar function (resting blocks were modelled implicitly) and these boxcar 301 \nfunctions were subsequently convolved with SPM’s difference of gammas haemodynamic 302 \nresponse function. The six motion parameters extracted during preprocessing were used as 303 \nregressors of no interest. The micro-time resolution was set as the number of slices (n = 48), 304 \nthe micro-time onset was set as the reference slice for slice-time correction (n = 24), and the 305 \nhigh-pass filter cutoff was 128 seconds. The same MNI template used during preprocessing 306 \nwas used as a mask for the analysis. The parameter estimation method was restricted 307 \nmaximum likelihood estimation (ReML) and serial correlations were accounted for using an 308 \nautoregressive AR(1) model during estimation. For univariate analyses the contrast of 309 \ninterest was greater activation for the semantic task than the control task (S > C). For 310 \nexploratory multivariate pattern analysis (MVPA) each block (12 semantic and 12 control) 311 \nwas modelled individually in order to obtain one beta image per block.  312 \nFinally, for the slice leakage analysis, the modelling was rerun on the minimally-313 \npreprocessed timeseries without any brain mask. We obtained both univariate contrasts of 314 \ninterest (S > C) and beta images per block (for MVPA). 315 \n 316 \n2.4.3. 2nd-level (across-participant) GLM 317 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n12 \n \n2.4.3.1. Region-of-interest (ROI) analysis 318 \nRegions of interest (ROIs) were defined based on a large-scale distortion-corrected 319 \n3T-fMRI study of the semantic network (Humphreys et al., 2015). For each comparison, ROIs 320 \nwere analysed only if they overlapped by at least one voxel with the whole-brain contrast of 321 \ninterest (S > C) summed over all sequences in the comparison. 322 \n 323 \n2.4.3.1.1. Univariate analysis 324 \nActivation magnitude (1st-level beta values) and activation precision (1st-level t-325 \nvalues) were extracted from each ROI using a publicly-available script, roi_extract.m 326 \n(https://github.com/MRC-CBU/riksneurotools/blob/master/Util/roi_extract.m). There were 327 \ntwo planned t-tests for activation magnitude (pTx > SESB, ME > SE) and four planned t-tests 328 \nfor activation precision (MB > SB, MEdn > ME, MBodd > SB, SB > MBodd). For each planned 329 \nt-test, results were Bonferroni-corrected for the number of ROIs included.  330 \n 331 \n2.4.3.1.2. Exploratory multivariate pattern analysis (MVPA) 332 \nThe input to this analysis was the activation magnitude values extracted from each 333 \nblock individually (12 semantic and 12 control). For each block and each ROI, a vector of 334 \nbeta values within that ROI was created. The cosine dissimilarity between every possible 335 \npair of blocks was calculated. MVPA performance was operationalised as the mean 336 \nbetween-task dissimilarity minus the mean within-task dissimilarity and paired t-tests were 337 \nused to compare the metric across sequences (all planned t-tests described in the univariate 338 \nROI analysis were conducted; Haxby et al., 2001).  339 \n 340 \n2.4.3.2. Whole-brain analysis 341 \nAll the above contrasts were assessed at the whole-brain level using t-tests (for 342 \ncomparing pTx and SESB) or using random-effects ANOVAs with one-sample t-tests on the 343 \nsummary statistic (for the three factorial designs: varying echo and band (SESB, SEMB, MESB 344 \nand MEMB); varying denoising and band (MESBdn, MEMBdn, MESB and MEMB); and 345 \nvarying echo and downsampled band (SEMBodd, MEMBodd, SEMB and MEMB)). The 346 \nANOVAs were conducted using a publicly-available script, batch_spm_anova.m 347 \n(https://github.com/MRC-CBU/riksneurotools/blob/master/SPM/batch_spm_anova.m). The 348 \ngroup t-maps were assessed for significance by using a voxel-height threshold of p < 0.001 349 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n13 \n \nto define clusters and then a cluster-defining family-wise-error corrected threshold of p < 350 \n0.05 for statistical inference.  351 \n 352 \n2.4.3.3. Slice leakage analysis 353 \nThe group-level, whole-brain contrast of interest (S > C) in standard space was 354 \ninspected and the coordinates of the peak t-values of the top 5 clusters were identified. 355 \nThese coordinates were back-projected to obtain 5 sets of corresponding coordinates in 356 \neach participant’s native EPI space (hereafter “seeds”, labelled A). Next, voxels to which 357 \nsignal might be warped were identified as artefact locations based on phase shift (FOV/2, 358 \nlabelled B) and, in the MEMB data, GRAPPA (labelled Ag) and phase shift plus GRAPPA 359 \n(labelled Bg). A spherical ROI, 4 voxels in radius, was defined around each seed location and 360 \npossible artefact location using a modified version of the scripts developed for McNabb et 361 \nal. (2020; https://github.com/DrMichaelLindner/MAP4SL/; our version available at 362 \nhttps://github.com/slfrisby/7TOptimisation/).  363 \n Both univariate (McNabb et al., 2020) and multivariate (Halai et al., 2024) slice 364 \nleakage tests were conducted. Activation magnitude was extracted from minimally-365 \npreprocessed data, and, for the multivariate analysis, the difference between mean within-366 \ntask similarity and mean between-task similarity was calculated as in the ROI analysis. 367 \nPaired t-tests were conducted for each seed and artefact location between each sequence 368 \nof interest (SEMB and MEMB) and a control sequence. For artefact locations based on phase 369 \nshift (B), the control sequence was the corresponding SB sequence (SESB for SEMB and 370 \nMESB for MEMB). For artefact locations based on GRAPPA, the control sequence was the 371 \ncorresponding SE sequence, because SE sequences were collected without GRAPPA (SEMB 372 \nfor MEMB). Results were Bonferroni-corrected for the number of peaks (n = 5) and the 373 \nnumber of possible artefact regions (n = 1 for SEMB and n = 3 for MEMB). 374 \n   375 \n3. Results 376 \n3.1. Excluded participants 377 \nTwo participants were excluded because of excessive head motion (this was defined 378 \nby calculating, for each participant, the percentage of volumes per run with absolute 379 \ntranslation values of over 2 mm or absolute rotation values over 1°, averaging these 380 \npercentages over runs, and excluding any participant whose mean percentage was greater 381 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n14 \n \nthan 2 standard deviations above the mean percentage across participants). One participant 382 \nwas excluded because of technical problems during data acquisition which meant that the 383 \npTx run failed. All subsequent analyses were conducted on the remaining 17 participants. 384 \n 385 \n3.2. Behavioural results 386 \nThe 17 participants had good performance on both tasks and, importantly, there 387 \nwere no significant differences between the 5 sequences in terms of accuracy (Friedman’s χ2 388 \n= 5.26, p = 0.26) or reaction time (Friedman’s χ2 = 7.20, p = 0.16). The semantic and control 389 \ntasks did not differ reliably in accuracy (Wilcoxon’s z = 0; p = 0.06) or reaction time 390 \n(Wilcoxon’s z = 7; p = 1.00). 391 \n 392 \n3.3. Region-of-interest analysis 393 \n ROIs that overlapped with the whole-brain contrast of interest (S > C) for at least 394 \none of the five sequences are shown in Figure 2. 395 \n 396 \nFigure 2: Regions of interest, taken from a meta-analysis of semantic tasks by (Humphreys et 397 \nal., 2015). All spheres are 8 mm in radius. Regions of interest are overlaid on the 398 \nMNI152NLin2009cAsym template. LTP = left temporal pole; LvATL = left ventral anterior 399 \ntemporal lobe; RITG = right inferior temporal gyrus; LFP = left frontal pole; LmMTG = left 400 \nmedial middle temporal gyrus; LpMTG = left posterior middle temporal gyrus; LIFGpt = left 401 \ninferior temporal gyrus pars triangularis. 402 \n 403 \n 404  \nLTP LvATL RITG LFP LmMTG LpMTG LIFGpt \nActivation magnitude \npTx > SESB - 0.2488 0.0240* - - 0.0042** 0.4114 \nME > SE 0.0502 0.0001** 0.0333* 0.2951 0.0092* 0.0003* 0.4155 \nActivation precision \nMB > SB 0.4569 0.0007** 0.0019** 0.0989 0.2187 0.0266* 0.0016** \nMEdn > ME 0.3117 <0.0001** <0.0001** 0.0014** 0.1363 <0.0001** <0.0001** \nMBodd > SB 0.4225 0.1057 0.0481* - 0.5697 0.1879 0.3300 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n15 \n \nMVPA \npTx > SESB - 0.8569 0.3280 - - 0.1529 0.9179 \nME > SE 0.0096* 0.0063** 0.1612 0.0655 0.0465* 0.0024** 0.2508 \nMB > SB 0.2455 0.1417 0.0667 0.3021 0.3949 0.2693 0.5207 \nMEdn > ME 0.0027** <0.0001** <0.0001** 0.0023** 0.0001** 0.0012** 0.0007** \nSB > MB 0.7416 0.4570 0.7222 - 0.3803 0.6716 0.3242 \nMBodd > SB 0.2584 0.5430 0.2778 - 0.6197 0.3284 0.6758 \n 405 \nTable 2: p-values for all t-tests within regions of interest (ROIs) based on the semantic 406 \nnetwork. * = p < 0.05, ** = p < 0.05 (Bonferroni-corrected for the number of ROIs), - = ROI 407 \ndoes not overlap by at least one voxel with the whole-brain contrast of interest (S > C) 408 \nsummed over all sequences in the comparison, LTP = left temporal pole, LvATL = left ventral 409 \nanterior temporal lobe, RITG = right inferior temporal gyrus, LFP = left frontal pole, LmMTG 410 \n= left medial middle temporal gyrus, LpMTG = left posterior middle temporal gyrus, LIFGpt = 411 \nleft inferior temporal gyrus pars triangularis.  412 \n 413 \n3.3.1. Univariate analysis 414 \nTable 2 shows the p-values for all planned t-contrasts. pTx provided significantly 415 \nbetter activation magnitude than SESB in the left posterior middle temporal gyrus (LpMTG). 416 \nME sequences provided significantly better activation magnitude than SE sequences in the 417 \nleft ventral anterior temporal lobe (LvATL). 418 \nMB sequences offered significantly better activation precision than SB sequences in 419 \nthe LvATL, right inferior temporal gyrus (RITG) and left inferior frontal gyrus pars triangularis 420 \n(LIFGpt). MEdn sequences offered significantly better activation precision than ME in the 421 \nLvATL, RITG, LpMTG, LIFGpt and left frontal pole (LFP). There was no significant difference in 422 \neither direction between MBodd sequences and SB sequences (see Supplementary Table 1 423 \nfor detailed results, plus reverse contrasts). We also found no significant interaction 424 \nbetween multi-echo and multi-band. 425 \n 426 \n3.3.2. Exploratory MVPA 427 \nTable 2 also shows the p-values for t-tests comparing our MVPA dissimilarity metric 428 \nbetween sequences. The ME sequences produced significantly better performance than SE 429 \nsequences in the LvATL and LpMTG. MEdn sequences produced better performance than 430 \nME sequences in every ROI. All other comparisons failed to reach significance. 431 \n 432 \n3.4. Whole-brain analysis 433 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n16 \n \nFigure 3 shows the results for activation magnitude. Figure 3A shows a single cluster, 434 \nwithin the right fusiform gyrus and lateral inferior occipital cortex, that showed greater 435 \nactivation magnitude with pTx than with SESB (pTx > SESB). Figure 3B shows the main effect 436 \nof ME over SE, featuring clusters in the fusiform and inferior temporal gyri bilaterally plus 437 \nthe left orbitofrontal cortex (cluster and peak information is provided in Supplementary 438 \nTable 2).  439 \n 440 \n 441 \nFigure 3: Effects on activation magnitude: (A) effect of parallel transmit (pTx > SESB); (B) 442 \neffect of echo (ME > SE). Results are cluster-corrected at p < 0.05 based on an uncorrected 443 \nvoxel threshold of p < 0.001 and are overlaid on the MNI152NLin2009cAsym template.   444 \n 445 \nFigure 4 shows the results for activation precision. Both the main effect of MB over 446 \nSB and the main effect of ME-ICA denoising over ME without ICA denoising extended down 447 \nthe temporal lobe and included frontal regions (Supplementary Table 2). There was no 448 \nsignificant difference between downsampled MB sequences and SB sequences in either 449 \ndirection. 450 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n17 \n \n 451 \nFigure 4: Effects on activation precision: (A) effect of multiband (MB > SB); (B) effect of ME-452 \nICA denoising (MEdn > ME). Results are cluster-corrected at p < 0.05 based on an 453 \nuncorrected voxel threshold of p < 0.001 and are overlaid on the MNI152NLin2009cAsym 454 \ntemplate.   455 \n 456 \nTo summarise, these results aligned with results from our ROI analyses – ME 457 \nincreased activation magnitude and both MB and ME-ICA denoising increased activation 458 \nprecision (see Supplementary Figures 1-6 for detailed results, plus reverse contrasts). 459 \n 460 \n 461 \n3.5. Slice leakage analysis 462 \nSeed and possible artefact locations for an example participant are shown in Figure 463 \n5A. Figure 5B shows activation magnitude for an example seed and its corresponding 464 \npossible artefact locations for all participants in each MB sequence and its corresponding 465 \nsequences. Figure 5C shows MVPA performance. Violin plots for all other peaks are shown 466 \nin Supplementary Figure 7. t-tests were conducted at each location, but no comparison 467 \nreached statistical significance (p-values are shown in Supplementary Table 3). We therefore 468 \nconcluded that there was no evidence for slice leakage in either of our MB datasets. 469 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n18 \n \n 470 \nFigure 5: Slice leakage analysis. (A) Seed and possible artefact locations for a single 471 \nparticipant. The ROIs (radius 4 voxels) indicate the seed location (blue), possible artefact 472 \nlocation based on phase shift (green), possible artefact location based on GRAPPA (red), and 473 \npossible artefact location based on phase shift and GRAPPA (yellow) in native EPI space. 474 \nInformed consent was obtained from the participant for this image to be published. (B) 475 \nMean activation magnitude within each sphere for each MB sequence and the 476 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n19 \n \ncorresponding control sequences for the first seed location (plots for other seeds are shown 477 \nin Supplementary Figure 7; statistics for all seeds are shown in Supplementary Table 3). (C) 478 \nMean MVPA dissimilarity within each sphere for each MB sequence and the corresponding 479 \ncontrol sequences. 480 \n 481 \n 482 \n4. Discussion 483 \nThe tSNR of fMRI varies across the brain. This is especially evident in ventral anterior 484 \ntemporal and orbitofrontal regions, which are located next to the air-filled sinuses and are 485 \ntherefore affected by signal dropout and geometric distortions (Devlin et al., 2000; Halai et 486 \nal., 2014, 2015, 2024). This study is the first 7T-fMRI study systematically comparing pTx, ME 487 \nand MB as methods for improving sensitivity in these regions while maintaining sensitivity 488 \nacross the brain. We found that pTx improved activation magnitude in posterior temporal 489 \nand occipital regions. ME, however, resulted in improved activation magnitude extending 490 \ndown the temporal lobe and including inferior frontal regions. Both MB and ME-ICA 491 \ndenoising resulted in improved activation precision in the same areas. In an exploratory 492 \nanalysis we found that ME and ME-ICA improved MVPA performance but MB did not. No 493 \nslice leakage artefacts were associated with our multiband sequences.  494 \n Although parallel transmit produced better activation magnitude in posterior 495 \ntemporal and occipital regions than the baseline (SESB) sequence without compromising 496 \nactivation magnitude elsewhere, activation magnitude in anterior temporal areas was 497 \ncomparable for both sequences. These results replicated those of Ding et al. (2022) who 498 \nfailed to find improved task contrast in anterior temporal regions with pTx in spite of 499 \nimproved tSNR in the resting state. Note that, despite visible signal dropout on the EPIs, 500 \neven the baseline sequence was able to identify semantic activity extending into ventral 501 \nanterior temporal regions (Figure 6). It is possible that this finding reflects the increased 502 \nsensitivity of 7T-fMRI compared to 3T-fMRI, where semantic activity is rarely observed 503 \nwithout using a method such as multi-echo (Halai et al., 2014, 2015, 2024) or spin-echo 504 \n(Binney et al., 2010; Embleton et al., 2010). That a standard sequence can detect signal in 505 \nthese regions might come as a surprise to many neuroimaging researchers as 7T-fMRI is not 506 \nonly associated with increased sensitivity, but also with exacerbated magnetic susceptibility 507 \nartefacts. This finding should therefore embolden researchers to use 7T fMRI for 508 \nexperimental designs that are difficult to conduct at a lower field strength - for example, 509 \nstudies of special populations, who may benefit from shorter scan times (Cope et al., 2023), 510 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n20 \n \nor studies requiring high spatial specificity (Marques & Norris, 2018). We do acknowledge 511 \nthat the way we have utilised 7T fMRI in this study will not be suitable for, for example, 512 \nstudies that require ultra-high resolution (in our case, scanner hardware constrained voxel 513 \nsize when combined with a short first echo time). 514 \n 515 \n 516 \n 517 \nFigure 6: Contrast despite signal dropout and geometric distortions. (A) Mean EPI across all 518 \nsubjects for the baseline sequence (SESB) registered to MNI152NLin2009cAsym space. (B) 519 \nSelected regions of interest overlaid on the mean EPI image. (C) Main effect of activation 520 \nmagnitude for the contrast of interest (S > C) for the baseline sequence. (D) Main effect of 521 \nactivation magnitude for the contrast of interest (S > C) for the MEMB sequence. Results are 522 \ncluster-corrected at p < 0.05 based on an uncorrected voxel threshold of p < 0.001. 523 \n 524 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n21 \n \n The factorial design enabled us to disentangle the effects of ME and MB. As 525 \nhypothesised, ME improved activation magnitude and these effects were localised to 526 \ninferior temporal and orbitofrontal areas. Adverse effects on activation magnitude were 527 \nnegligible - although ME reduced activation precision in two very small clusters 528 \n(Supplementary Figure 4), these were not within areas that the semantic task is known to 529 \nrecruit (Jung et al., 2017). These results demonstrated that having one very short echo is a 530 \nsuccessful method of increasing activation magnitude in areas where T2* is particularly short 531 \n(Halai et al., 2014, 2015, 2024; Jung et al., 2017). ME also opens up the opportunity for 532 \nsophisticated denoising such as tedana (DuPre et al., 2021; Kundu et al., 2011, 2013; The 533 \ntedana Community et al., 2023), which improved activation precision in agreement with 534 \nprevious findings (Amemiya et al., 2019; Gonzalez-Castillo et al., 2016). Small clusters in 535 \nwhich ME-ICA increased activation magnitude (Supplementary Figure 5) or decreased 536 \nactivation precision (Supplementary Figure 6) were not within regions recruited by the task 537 \n(Jung et al., 2017). 538 \n As hypothesised, multiband improved activation precision in areas of inhomogeneity 539 \nand in agreement with other work (Puckett et al., 2018). Supplementary Figure 4 shows two 540 \nvery small clusters in which multiband reduced activation precision, but which lie outside 541 \nregions recruited by the task (Jung et al., 2017). After downsampling the MB timeseries to 542 \nmatch the number of volumes in the SB timeseries, there was no longer any difference 543 \nbetween the SB and MB sequences; this suggests that it is the increase in the number of 544 \nvolumes that accounts for the benefits of multiband. Additionally, there was no evidence for 545 \nsignal leakage into the simultaneously-acquired slice location (McNabb et al., 2020; Todd et 546 \nal., 2016), at least for our modest multiband factor of 2. We note that MB and ME seem to 547 \nhave independent effects as the interaction was not significant within any ROI. However, 548 \nME seems to offer larger activation magnitude (Figure 6) and MB offers better activation 549 \nprecision. MB can help reduce the longer TR associated with ME and may decrease noise 550 \naliasing in studies using short TRs (~1s).  551 \n Although this study focused primarily on univariate effects, we also identified 552 \nimprovements to multivariate metrics that will be of interest to researchers using 553 \nincreasingly-popular MVPA tools (Frisby et al., 2023). ME was found to improve MVPA 554 \nperformance compared to SE sequences and, in turn, MEdn sequences performed 555 \nsignificantly better than ME sequences without denoising in every region of interest (with 556 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n22 \n \nno detrimental effects). Future studies employing multivariate methods should strongly 557 \nconsider using both ME acquisitions and ME-ICA preprocessing.   558 \n This study provides evidence supporting use of ME and/or MB sequences to detect 559 \nsignal in regions prone to susceptibility artefacts. However, it is important to note that 560 \nindividual 7T MRI scanners have their own hardware and software limitations; this means 561 \nthat each site must optimise and test feasible parameters locally. For example, our system 562 \ndid not allow us to take advantage of the higher spatial resolution offered by 7T-fMRI while 563 \nretaining an adequately short first echo for our multi-echo sequences. Puckett et al. (2018) 564 \nused comparable voxel sizes but had a shorter first TE than us (9.9 ms vs our 11.8 ms); 565 \nMiletić et al. (2020) had higher resolution (1.6 mm vs our 2.5 mm isotropic) and a shorter 566 \nfirst echo of 9.66 ms. Additionally, our 7T Terra pTx system imposes very conservative SAR 567 \nlimits (1W per channel, 8W total) for pTx scans but permits 20W total (i.e. 2.5W per 568 \nchannel) for circularly polarised mode (CP-mode or “TrueForm”) scans (equivalent to single 569 \ntransmit). We therefore had to use VERSE-modified pTx pulses which are known to be more 570 \nsensitive to B0 inhomogeneity. We also acknowledge that pTx can be combined with multi-571 \necho or multiband (Ding et al., 2023; Wu et al., 2016). Although we used the pTx head coil 572 \nfor all scans, the combination of sequences was not implemented on our scanner at the 573 \ntime of running this study and therefore needs to be tested empirically in future. Thus, 574 \nrather than identifying the limits of what is possible, we offer an accessible framework that 575 \nwill enable researchers to leverage 7T-fMRI for investigating regions affected by 576 \nsusceptibility artefacts despite the hardware and software constraints of individual 577 \nscanners. 578 \n 579 \n5. Conclusion 580 \nIn this study we compared pTx, ME and MB as methods of improving sensitivity in 581 \ntemporal and frontal areas prone to magnetic susceptibility artefacts. Both pTx and ME 582 \nimproved activation magnitude, but only ME showed improvements in artefact-prone 583 \nregions. MB and ME-ICA improved activation precision in these areas. Exploratory results 584 \nsuggested that both ME and ME-ICA may also benefit MVPA. We demonstrated that a multi-585 \necho, multiband sequence running on the 8Tx32Rx pTx head coil (in CP mode) can detect 586 \nsignal in susceptible regions while maintaining sensitivity across the whole brain and is 587 \ntherefore a versatile choice for future studies using high field strength and investigating the 588 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n23 \n \nfunctional roles of ventral temporal and/or orbitofrontal cortex (Binney et al., 2016; 589 \nBorghesani et al., 2016; Devereux et al., 2018; Duncan, 2010; DuPre et al., 2016; Fernandez 590 \net al., 2017; Lambon Ralph et al., 2017; Zahn et al., 2007). 591 \n 592 \nData and code availability: Data will be made publicly available upon peer review and 593 \nacceptance. Code is publicly available at: https://github.com/slfrisby/7TOptimisation/. 594 \n 595 \nCRediT statement: Saskia L. Frisby: Conceptualisation, Methodology, Investigation, Formal 596 \nAnalysis, Writing – Original Draft, Writing – Review & Editing, Visualisation; Marta M. 597 \nCorreia: Conceptualisation, Methodology, Writing – Review & Editing, Funding Acquisition; 598 \nMinghao Zhang: Methodology, Writing – Review & Editing; Christopher T. Rodgers: 599 \nMethodology, Writing – Review & Editing, Supervision; Timothy T. Rogers: Writing – Review 600 \n& Editing, Supervision; Matthew A. Lambon Ralph: Writing – Review & Editing, Supervision, 601 \nFunding Acquisition; Ajay D. Halai: Conceptualisation, Methodology, Formal Analysis, 602 \nWriting – Review & Editing, Supervision, Funding Acquisition. 603 \n 604 \nFunding: This work was supported by an MRC Unit grant (SUAG/019 G116768) to M.M.C., 605 \nan MRC PhD studentship (MR N013433-1) to M.Z., an MRC programme grant 606 \n(MR/R023883/1) and intramural funding (MC_UU_00005/18) to M.A.L.R, and an MRC 607 \nCareer Development Award (MR/V031481/1) to A.D.H. This work was also supported by the 608 \nNIHR Cambridge Biomedical Research Centre (NIHR203312) and an MRC Clinical Research 609 \nInfrastructure Award for 7T (MR/M008983/1). The views expressed are those of the authors 610 \nand not necessarily those of the NIHR or the Department of Health and Social Care.  611 \n 612 \nCompeting interests: C.T.R. receives research support from Siemens. 613 \n 614 \nAcknowledgements: We thank the participants and the Wolfson Brain Imaging Centre 615 \nradiographers for their assistance. 616 \n 617 \n 618 \nReferences 619 \n 620 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint \n\n \n24 \n \nAmemiya, S., Yamashita, H., Takao, H., & Abe, O. (2019). Integrated multi-echo denoising 621 \nstrategy improves identification of inherent language laterality. 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Proceedings of the 930 \nNational Academy of Sciences, 104(15), 6430–6435. 931 \nhttps://doi.org/10.1073/pnas.0607061104 932 \n 933 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 18, 2025. ; https://doi.org/10.1101/2025.03.17.643748doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}