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Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session anddoi:10.1016/j.neuroimage.2013.07.065 pmid:23911673 pmcid:PMC3898940 fatcat:uvsm3lxqc5d35fplhpf773bqw4
more »... ween session head movements. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1 mm. Estimates of relative co-registration error were b1.5 mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6 month period. We found that the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5 mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG.
Typically MEG source reconstruction is used to estimate the distribution of current flow on a single anatomically derived cortical surface model. In this study we use two such models representing superficial and deep cortical laminae. We establish how well we can discriminate between these two different cortical layer models based on the same MEG data in the presence of different levels of co-registration noise, Signal-to-Noise Ratio (SNR) and cortical patch size. We demonstrate that it isdoi:10.1016/j.neuroimage.2014.07.015 pmid:25038441 pmcid:PMC4229503 fatcat:fbmiqxul2ra7lgks4vy2h2dbwq
more »... ble to make a distinction between superficial and deep cortical laminae for levels of co-registration noise of less than 2mm translation and 2° rotation at SNR > 11 dB. We also show that an incorrect estimate of cortical patch size will tend to bias layer estimates. We then use a 3D printed head-cast (Troebinger et al., 2014) to achieve comparable levels of co-registration noise, in an auditory evoked response paradigm, and show that it is possible to discriminate between these cortical layer models in real data.
., Addis, Knapp, Roberts, & Schacter, 2012; Addis, Pan, Vu, Laiser, & Schacter 2009; Addis, Wong, & Schacter, 2007; Bonnici, Chadwick, Lutti, et al., 2012; . ... The dummy volumes necessary to reach steady state and the GRAPPA reconstruction kernel were acquired prior to the acquisition of the image data as described in Lutti et al. (2013) . ...doi:10.1016/j.cortex.2015.09.002 pmid:26478961 pmcid:PMC4686003 fatcat:4hty5rrlwzejxcw57j7orbrc3m
Population receptive field (pRF) mapping represents an invaluable non-invasive tool for the study of sensory organization and plasticity within the human brain. Despite the very appealing result that fMRI derived pRF measures agree well with measurements made from other fields of neuroscience, current techniques often require very computationally expensive non-linear optimization procedures to fit the models to the data which are also vulnerable to bias due local minima issues. In this work wedoi:10.1101/172189 fatcat:qslincvxarcfrht4yopxadwbrq
more »... resent a general framework for pRF model estimation termed Convex Optimized Population Receptive Field (CO-pRF) mapping and show how the pRF fitting problem can be linearized in order to be solved by extremely fast and efficient algorithms. The framework is general and can be readily applied to a variety of pRF models and measurement schemes. We provide an example of the CO-pRF methodology as applied to a computational neuroimaging approach used to map sensory processes in human visual cortex - the CSS-pRF model. Via simulation and in-vivo fMRI results we demonstrate that the CO-pRF approach achieves robust model fitting even in the presence of noise or reduced data, providing parameter estimates closer to the global optimum across 93% of in-vivo responses as compared to a typical nonlinear optimization procedure. Furthermore the example CO-pRF application substantially reduced model fitting times by a factor of 50. We hope that the availability of such highly accelerated and reliable pRF estimation algorithms will facilitate the spread of pRF techniques to larger imaging cohorts and the future study of neurological disorders and plasticity within the human brain.
., 2009; Lutti et al., 2014) . ... (Lutti et al., , 2012 , and an established analytical frameworkvoxel-based quantification (VBQ) (Draganski et al., 2011) . ...doi:10.3389/fnhum.2014.00380 pmid:25018716 pmcid:PMC4072968 fatcat:zutvf57sxjavfibsae6c6tjpbe
Learning to predict threat is important for survival. Such learning may be driven by differences between expected and encountered outcomes, termed prediction errors (PEs). While PEs are crucial for reward learning, the role of putative PE signals in aversive learning is less clear. Here, we used functional magnetic resonance imaging in humans to investigate neural PE signals. Four cues, each with a different probability of being followed by an aversive outcome, were presented multiple times. Wedoi:10.1101/2020.07.10.197665 fatcat:kdr67vwrmrh6vjsli2junrpz3a
more »... found that neural activity only at omission - but not at occurrence - of predicted threat related to PEs in the medial prefrontal cortex. More expected omission was associated with higher neural activity. In no brain region did neural activity fulfill necessary computational criteria for full signed PE representation. Our result suggests that, different from reward learning, aversive learning may not be primarily driven by PE signals in one single brain region.
In addition, B1-field maps (4 mm isotropic resolution) were acquired using a 3D EPI SE/STE method (Lutti et al., 2010 and used to correct the R1 maps for RF transmit field inhomogeneity effects. ...doi:10.1016/j.nicl.2013.04.017 pmid:24179820 pmcid:PMC3777756 fatcat:rzsacxiimbfzfm5qpngspqrkpu
Human Brain Mapping
B0-field mapping data was acquired using a 2-D double-echo FLASH sequence to correct for geometric distortions in the 3-D EPI data as described in Lutti et al. (2010 Lutti et al. ( , 2012 ucl.ac.uk/spm ... , & Weiskopf, 2010; Lutti et al., 2012) and to correct for effects of RF transmit inhomogeneities on the quantitative maps Helms and Dechent, 2009; Weiskopf et al., 2013) . ...doi:10.1002/hbm.23929 pmid:29271053 pmcid:PMC5873432 fatcat:h4oj3rede5fd5eoxymgnzx4eyi
In particular, we employed high-quality mapping of the B1+ transmit field (Lutti et al. 2010 (Lutti et al. , 2012 and highly sensitive multi-echo 3D FLASH R 1 mapping to achieve maximal resolution, accuracy ... et al. 2010 (Lutti et al. , 2012 FOV = 256 × 192 × 192 mm 3 , matrix = 64 × 48 × 48, TE SE /TE STE = 39.38/ 72.62 ms, TR = 500 ms, acquisition time 3 min 48 s), which was corrected for off-resonance ...doi:10.1093/cercor/bhs213 pmid:22826609 pmcid:PMC3729202 fatcat:hlhfbscd3nfxrmy4wcust4edc4
To maximize the accuracy of the R 1 maps, inhomogeneity in the flip angle was corrected by mapping the B 1 þ transmit field according to the procedure detailed in the study by Lutti et al. ...doi:10.1002/mrm.25210 pmid:24700606 pmcid:PMC4359013 fatcat:ylwi5sc7o5hwtdvilyatidmcyi
et al., 2010 Lutti et al., , 2012 . ... ., 2005; Yarnykh, 2007) and some of them systematically compared (Lutti et al., 2010) . ...doi:10.1016/j.neuroimage.2013.06.005 pmid:23756203 fatcat:eqcrzeu6bzfnzecolxfsogz7iy
Head movements are a major source of MRI artefacts that hamper radiological assessment and computer-based morphological and functional measures of the human brain. Prospective motion correction techniques continuously update the MRI scanner based on head position information provided by an external tracking system. While prospective motion correction significantly improves data quality, strong motion artefacts may remain with large head motions or when motion takes place at sensitive times ofdoi:10.1101/230490 fatcat:6ffbosenuvewbcknx3yac2peiq
more »... e acquisition. Here we present a framework that allows the suspension of data acquisition when head motion is predicted to have a strong negative impact on data quality. The predictor, calculated in real-time during the acquisition, accounts for the amplitude of the signal acquired at the time of the motion, thereby offering a re-acquisition strategy more efficient than relying on head speed alone. The suspension of data acquisition is governed by the trade-off between image degradation due to motion and prolonging the scan time. This trade-off can be tuned by the user according to the desired level of image quality and the tolerability of participants. We test the framework using two motion experiments and two head coils. Significant improvements in data quality are obtained with stringent threshold values for the suspension of acquisition. Substantial reductions in motion artefact levels are also achieved with minimal prolongation of scan time. However, high levels of motion artefacts occasionally remain despite stringent thresholds with the 64-channel head coil, an effect that might be attributed to head movement in the sharp sensitivity profile of this coil.
., 2011; Lutti et al., 2014) . ...doi:10.1101/547620 fatcat:epcslcay2ncvfiaokmlshpurvu
Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterize disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data-driven solution that assigns weights to eachdoi:10.1101/2021.06.15.448467 fatcat:vt5gjldiyrfrtk6ktcqjwxj4ga
more »... ge, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of brain MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.
For this the local flip angle needs to be measured by dedicated B1 + mapping MR sequences (Lutti et al., 2010a) . ... Careful post-processing steps must also be taken in order to achieve good results (Lutti et al., 2010a; Preibisch and Deichmann, 2009) . ...doi:10.1016/j.neuroimage.2010.10.023 pmid:20965260 pmcid:PMC3018573 fatcat:jovnx5s5srhofndbbm3kbeyt64
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