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Journal articles on the topic 'Artefakty fMRI'

Author: Grafiati

Published: 28 June 2021

Last updated: 17 February 2022

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1

Chowdhury, Muhammad E. H., Karen J. Mullinger, Paul Glover, and Richard Bowtell. "Reference layer artefact subtraction (RLAS): A novel method of minimizing EEG artefacts during simultaneous fMRI." NeuroImage 84 (January 2014): 307–19. http://dx.doi.org/10.1016/j.neuroimage.2013.08.039.

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Spencer, Glyn S., James A. Smith, Muhammad E. H. Chowdhury, Richard Bowtell, and Karen J. Mullinger. "Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: Implications for motion artefact correction." NeuroImage 173 (June 2018): 188–98. http://dx.doi.org/10.1016/j.neuroimage.2018.02.034.

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3

Leclercq, Yves, Jessica Schrouff, Quentin Noirhomme, Pierre Maquet, and Christophe Phillips. "fMRI Artefact Rejection and Sleep Scoring Toolbox." Computational Intelligence and Neuroscience 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/598206.

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We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FAST”, to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional magnetic resonance imaging data acquired while a subject is asleep. FAST tackles three crucial issues typical of this kind of data: (1) data manipulation (viewing, comparing, chunking, etc.) of long continuous M/EEG recordings, (2) rejection of the fMRI-induced artefact in the EEG signal, and (3) manual sleep-scoring of the M/EEG recording. Currently, the toolbox can efficiently deal with these issues via a GUI, SPM8 batching system or hand-written script. The tools developed are, of course, also useful for other EEG applications, for example, involving simultaneous EEG-fMRI acquisition, continuous EEG eye-balling, and manipulation. Even though the toolbox was originally devised for EEG data, it will also gracefully handle MEG data without any problem. “FAST” is developed in Matlab as an add-on toolbox for SPM8 and, therefore, internally uses its SPM8-meeg data format. “FAST” is available for free, under the GNU-GPL.

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Ferreira, José L., Yan Wu, René M. H. Besseling, Rolf Lamerichs, and Ronald M. Aarts. "Gradient Artefact Correction and Evaluation of the EEG Recorded Simultaneously with fMRI Data Using Optimised Moving-Average." Journal of Medical Engineering 2016 (June 28, 2016): 1–17. http://dx.doi.org/10.1155/2016/9614323.

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Over the past years, coregistered EEG-fMRI has emerged as a powerful tool for neurocognitive research and correlated studies, mainly because of the possibility of integrating the high temporal resolution of the EEG with the high spatial resolution of fMRI. However, additional work remains to be done in order to improve the quality of the EEG signal recorded simultaneously with fMRI data, in particular regarding the occurrence of the gradient artefact. We devised and presented in this paper a novel approach for gradient artefact correction based upon optimised moving-average filtering (OMA). OMA makes use of the iterative application of a moving-average filter, which allows estimation and cancellation of the gradient artefact by integration. Additionally, OMA is capable of performing the attenuation of the periodic artefact activity without accurate information about MRI triggers. By using our proposed approach, it is possible to achieve a better balance than the slice-average subtraction as performed by the established AAS method, regarding EEG signal preservation together with effective suppression of the gradient artefact. Since the stochastic nature of the EEG signal complicates the assessment of EEG preservation after application of the gradient artefact correction, we also propose a simple and effective method to account for it.

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Yan, Winston X., Karen J. Mullinger, Matt J. Brookes, and Richard Bowtell. "Understanding gradient artefacts in simultaneous EEG/fMRI." NeuroImage 46, no. 2 (June 2009): 459–71. http://dx.doi.org/10.1016/j.neuroimage.2009.01.029.

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6

Beckmann, C. F., J. A. Noble, and S. M. Smith. "Artefact detection in FMRI data using independent component analysis." NeuroImage 11, no. 5 (May 2000): S614. http://dx.doi.org/10.1016/s1053-8119(00)91544-1.

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7

Kasper, L., S. Marti, SJ Vannesjö, C. Hutton, R. Dolan, N. Weiskopf, KP Prüssmann, and KE Stephan. "Cardiac artefact correction for human brainstem fMRI at 7T." NeuroImage 47 (July 2009): S100. http://dx.doi.org/10.1016/s1053-8119(09)70854-7.

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8

Chowdhury, Muhammad E. H., Karen J. Mullinger, Paul Glover, and Richard Bowtell. "Corrigendum to “Reference layer artefact subtraction (RLAS): A novel method of minimizing EEG artefacts during simultaneous fMRI” [Neuroimage 84 (2014) 307–319]." NeuroImage 98 (September 2014): 547. http://dx.doi.org/10.1016/j.neuroimage.2014.04.034.

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9

Bungert, Andreas, Christopher D. Chambers, Mark Phillips, and C. John Evans. "Reducing image artefacts in concurrent TMS/fMRI by passive shimming." NeuroImage 59, no. 3 (February 2012): 2167–74. http://dx.doi.org/10.1016/j.neuroimage.2011.10.013.

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10

Paasonen, Jaakko, Hanne Laakso, Tiina Pirttimäki, Petteri Stenroos, Raimo A. Salo, Ekaterina Zhurakovskaya, Lauri J. Lehto, et al. "Multi-band SWIFT enables quiet and artefact-free EEG-fMRI and awake fMRI studies in rat." NeuroImage 206 (February 2020): 116338. http://dx.doi.org/10.1016/j.neuroimage.2019.116338.

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11

Moosmann, Matthias, Vinzenz H. Schönfelder, Karsten Specht, René Scheeringa, Helge Nordby, and Kenneth Hugdahl. "Realignment parameter-informed artefact correction for simultaneous EEG–fMRI recordings." NeuroImage 45, no. 4 (May 2009): 1144–50. http://dx.doi.org/10.1016/j.neuroimage.2009.01.024.

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12

Chowdhury, Muhammad, Amith Khandakar, Belayat Hossain, and Khawla Alzoubi. "Effects of the Phantom Shape on the Gradient Artefact of Electroencephalography (EEG) Data in Simultaneous EEG–fMRI." Applied Sciences 8, no. 10 (October 18, 2018): 1969. http://dx.doi.org/10.3390/app8101969.

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Electroencephalography (EEG) signals greatly suffer from gradient artefacts (GAs) due to the time-varying field gradients in the magnetic resonance (MR) scanner during the simultaneous acquisition of EEG and functional magnetic resonance imaging (fMRI) data. The GAs are the principal contributors of artefacts while recording EEG inside an MR scanner, and most of them come from the interaction of the EEG cap and the subject’s head. Many researchers have been using a spherical phantom to characterize the GA in EEG data in combined EEG–fMRI studies. In this study, we investigated how the phantom shape could affect the characterization of the GA. EEG data were recorded with a spherical phantom, a head-shaped phantom, and six human subjects, individually, during the execution of customized and standard echo-planar imaging (EPI) sequences. The spatial potential maps of the root-mean-square (RMS) voltage of the GA over EEG channels for the trials with a head-shaped phantom closely mimicked those related to the human head rather than those obtained for the spherical phantom. This was confirmed by measuring the average similarity index (0.85/0.68). Moreover, a paired t-test showed that the head-shaped phantom’s and the spherical phantom’s data were significantly different (p < 0.005) from the subjects’ data, whereas the difference between the head-shaped phantom’s and the spherical phantom’s data was not significant (p = 0.07). The results of this study strongly suggest that a head-shaped phantom should be used for GA characterization studies in concurrent EEG–fMRI.

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13

Seghier, Mohamed L. "Clustering of fMRI data: the elusive optimal number of clusters." PeerJ 6 (October 3, 2018): e5416. http://dx.doi.org/10.7717/peerj.5416.

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Model-free methods are widely used for the processing of brain fMRI data collected under natural stimulations, sleep, or rest. Among them is the popular fuzzy c-mean algorithm, commonly combined with cluster validity (CV) indices to identify the ‘true’ number of clusters (components), in an unsupervised way. CV indices may however reveal different optimal c-partitions for the same fMRI data, and their effectiveness can be hindered by the high data dimensionality, the limited signal-to-noise ratio, the small proportion of relevant voxels, and the presence of artefacts or outliers. Here, the author investigated the behaviour of seven robust CV indices. A new CV index that incorporates both compactness and separation measures is also introduced. Using both artificial and real fMRI data, the findings highlight the importance of looking at the behavior of different compactness and separation measures, defined here as building blocks of CV indices, to depict a full description of the data structure, in particular when no agreement is found between CV indices. Overall, for fMRI, it makes sense to relax the assumption that only one unique c-partition exists, and appreciate that different c-partitions (with different optimal numbers of clusters) can be useful explanations of the data, given the hierarchical organization of many brain networks.

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Buur, Pieter F., Benedikt A. Poser, and David G. Norris. "A dual echo approach to removing motion artefacts in fMRI time series." NMR in Biomedicine 22, no. 5 (June 2009): 551–60. http://dx.doi.org/10.1002/nbm.1371.

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15

Marquis, Renaud, Maya Jastrzębowska, and Bogdan Draganski. "Novel imaging techniques to study the functional organization of the human brain." Clinical and Translational Neuroscience 1, no. 1 (June 1, 2017): 2514183X1771410. http://dx.doi.org/10.1177/2514183x17714104.

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Despite more than a century of investigation into the cortical organization of motor function, the existence of motor somatotopy is still debated. We review functional magnetic resonance imaging (fMRI) studies examining motor somatotopy in the cerebral cortex. In spite of a substantial overlap of representations corresponding to different body parts, especially in non-primary motor cortices, geographic approaches are capable of revealing somatotopic ordering. From the iconic homunculus in the contralateral primary cortex to the subtleties of ipsilateral somatotopy and its relations with lateralization, we outline potential reasons for the lack of segregation between motor representations. Among these are the difficulties in distinguishing activity that arises from multiple muscular effectors, the need for flexible motor control and coordination of complex movements through functional integration and artefacts in fMRI. Methodological advances with regard to the optimization of experimental design and fMRI acquisition protocols as well as improvements in spatial registration of images and indices aiming at the quantification of the degree of segregation between different functional representations are inspected. Additionally, we give some hints as to how the functional organization of motor function might be related to various anatomical landmarks in brain morphometry.

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16

Grootoonk, S., A. Howseman, S.-J. Blakemore, C. Hutton, J. Ashburner, and R. Turner. "Assessment of Motion Artefacts in fMRI Time-Series Affected by Task-Correlated Subject Motion." NeuroImage 7, no. 4 (May 1998): S600. http://dx.doi.org/10.1016/s1053-8119(18)31433-2.

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17

Masterton, Richard A. J., David F. Abbott, Steven W. Fleming, and Graeme D. Jackson. "Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings." NeuroImage 37, no. 1 (August 2007): 202–11. http://dx.doi.org/10.1016/j.neuroimage.2007.02.060.

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18

Jansen, Marije, Thomas P. White, Karen J. Mullinger, Elizabeth B. Liddle, Penny A. Gowland, Susan T. Francis, Richard Bowtell, and Peter F. Liddle. "Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data." NeuroImage 59, no. 1 (January 2012): 261–70. http://dx.doi.org/10.1016/j.neuroimage.2011.06.094.

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19

Muresan, L., R. Renken, J. B. T. M. Roerdink, and H. Duifhuis. "Automated Correction of Spin-History Related Motion Artefacts in fMRI: Simulated and Phantom Data." IEEE Transactions on Biomedical Engineering 52, no. 8 (August 2005): 1450–60. http://dx.doi.org/10.1109/tbme.2005.851484.

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20

Javed, Ehtasham, Ibrahima Faye, Aamir Saeed Malik, and Jafri Malin Abdullah. "Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA." Journal of Neuroscience Methods 291 (November 2017): 150–65. http://dx.doi.org/10.1016/j.jneumeth.2017.08.020.

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21

Griffanti, Ludovica, Gholamreza Salimi-Khorshidi, Christian F. Beckmann, Edward J. Auerbach, Gwenaëlle Douaud, Claire E. Sexton, Enikő Zsoldos, et al. "ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging." NeuroImage 95 (July 2014): 232–47. http://dx.doi.org/10.1016/j.neuroimage.2014.03.034.

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22

Mullinger, Karen J., Winston X. Yan, and Richard Bowtell. "Reducing the gradient artefact in simultaneous EEG-fMRI by adjusting the subject's axial position." NeuroImage 54, no. 3 (February 2011): 1942–50. http://dx.doi.org/10.1016/j.neuroimage.2010.09.079.

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23

Chowdhury, M. E. H., Karen J. Mullinger, and Richard Bowtell. "Simultaneous EEG–fMRI: evaluating the effect of the cabling configuration on the gradient artefact." Physics in Medicine and Biology 60, no. 12 (June 4, 2015): N241—N250. http://dx.doi.org/10.1088/0031-9155/60/12/n241.

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24

Maziero, Danilo, Tonicarlo R. Velasco, Nigel Hunt, Edwin Payne, Louis Lemieux, Carlos E. G. Salmon, and David W. Carmichael. "Towards motion insensitive EEG-fMRI: Correcting motion-induced voltages and gradient artefact instability in EEG using an fMRI prospective motion correction (PMC) system." NeuroImage 138 (September 2016): 13–27. http://dx.doi.org/10.1016/j.neuroimage.2016.05.003.

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25

de Munck, Jan C., Petra J. van Houdt, Sónia I. Gonçalves, Erwin van Wegen, and Pauly P. W. Ossenblok. "Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction." NeuroImage 64 (January 2013): 407–15. http://dx.doi.org/10.1016/j.neuroimage.2012.09.022.

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26

Wilke, Marko, and Torsten Baldeweg. "A multidimensional artefact-reduction approach to increase robustness of first-level fMRI analyses: Censoring vs. interpolating." Journal of Neuroscience Methods 318 (April 2019): 56–68. http://dx.doi.org/10.1016/j.jneumeth.2019.02.008.

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27

Daniel, Alexander J., James A. Smith, Glyn S. Spencer, João Jorge, Richard Bowtell, and Karen J. Mullinger. "Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI." Human Brain Mapping 40, no. 2 (October 19, 2018): 578–96. http://dx.doi.org/10.1002/hbm.24396.

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28

Hyder, Fahmeed, Peter Herman, Christopher J. Bailey, Arne Møller, Ronen Globinsky, Robert K. Fulbright, Douglas L. Rothman, and Albert Gjedde. "Uniform distributions of glucose oxidation and oxygen extraction in gray matter of normal human brain: No evidence of regional differences of aerobic glycolysis." Journal of Cerebral Blood Flow & Metabolism 36, no. 5 (January 11, 2016): 903–16. http://dx.doi.org/10.1177/0271678x15625349.

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Regionally variable rates of aerobic glycolysis in brain networks identified by resting-state functional magnetic resonance imaging (R-fMRI) imply regionally variable adenosine triphosphate (ATP) regeneration. When regional glucose utilization is not matched to oxygen delivery, affected regions have correspondingly variable rates of ATP and lactate production. We tested the extent to which aerobic glycolysis and oxidative phosphorylation power R-fMRI networks by measuring quantitative differences between the oxygen to glucose index (OGI) and the oxygen extraction fraction (OEF) as measured by positron emission tomography (PET) in normal human brain (resting awake, eyes closed). Regionally uniform and correlated OEF and OGI estimates prevailed, with network values that matched the gray matter means, regardless of size, location, and origin. The spatial agreement between oxygen delivery (OEF≈0.4) and glucose oxidation (OGI ≈ 5.3) suggests that no specific regions have preferentially high aerobic glycolysis and low oxidative phosphorylation rates, with globally optimal maximum ATP turnover rates (VATP ≈ 9.4 µmol/g/min), in good agreement with 31P and 13C magnetic resonance spectroscopy measurements. These results imply that the intrinsic network activity in healthy human brain powers the entire gray matter with ubiquitously high rates of glucose oxidation. Reports of departures from normal brain-wide homogeny of oxygen extraction fraction and oxygen to glucose index may be due to normalization artefacts from relative PET measurements.

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Schwarzbauer, Christian, and David A. Porter. "Single shot partial dual echo (SPADE) EPI—an efficient acquisition scheme for reducing susceptibility artefacts in fMRI." NeuroImage 49, no. 3 (February 2010): 2234–37. http://dx.doi.org/10.1016/j.neuroimage.2009.10.059.

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Ferreira, José L., Yan Wu, and Ronald M. Aarts. "Enhancement of the Comb Filtering Selectivity Using Iterative Moving Average for Periodic Waveform and Harmonic Elimination." Journal of Healthcare Engineering 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/7901502.

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A recurring problem regarding the use of conventional comb filter approaches for elimination of periodic waveforms is the degree of selectivity achieved by the filtering process. Some applications, such as the gradient artefact correction in EEG recordings during coregistered EEG-fMRI, require a highly selective comb filtering that provides effective attenuation in the stopbands and gain close to unity in the pass-bands. In this paper, we present a novel comb filtering implementation whereby the iterative filtering application of FIR moving average-based approaches is exploited in order to enhance the comb filtering selectivity. Our results indicate that the proposed approach can be used to effectively approximate the FIR moving average filter characteristics to those of an ideal filter. A cascaded implementation using the proposed approach shows to further increase the attenuation in the filter stopbands. Moreover, broadening of the bandwidth of the comb filtering stopbands around −3 dB according to the fundamental frequency of the stopband can be achieved by the novel method, which constitutes an important characteristic to account for broadening of the harmonic gradient artefact spectral lines. In parallel, the proposed filtering implementation can also be used to design a novel notch filtering approach with enhanced selectivity as well.

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31

Weiss, Franziska, Vera Zamoscik, Stephanie N. L. Schmidt, Patrick Halli, Peter Kirsch, and Martin Fungisai Gerchen. "Just a very expensive breathing training? Risk of respiratory artefacts in functional connectivity-based real-time fMRI neurofeedback." NeuroImage 210 (April 2020): 116580. http://dx.doi.org/10.1016/j.neuroimage.2020.116580.

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32

Debener, Stefan, Alexander Strobel, Bettina Sorger, Judith Peters, Cornelia Kranczioch, Andreas K. Engel, and Rainer Goebel. "Improved quality of auditory event-related potentials recorded simultaneously with 3-T fMRI: Removal of the ballistocardiogram artefact." NeuroImage 34, no. 2 (January 2007): 587–97. http://dx.doi.org/10.1016/j.neuroimage.2006.09.031.

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33

Kolesar, Tiffany A., Elena Bilevicius, and Jennifer Kornelsen. "Salience, central executive, and sensorimotor network functional connectivity alterations in failed back surgery syndrome." Scandinavian Journal of Pain 16, no. 1 (July 1, 2017): 10–14. http://dx.doi.org/10.1016/j.sjpain.2017.01.008.

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AbstractObjectiveThis study examined the altered patterns of functional connectivity in task-positive resting state networks in failed back surgery syndrome (FBSS) patients compared to healthy controls using functional magnetic resonance imaging (fMRI). This work stems from a previous study in which alterations in the task-negative default mode network were investigated.DesignParticipants underwent a 7-minute resting state fMRI scan in which they lay still, with eyes closed, in the absence of a task.SettingScanning took place at the National Research Council’s 3 Tesla MRI magnet in Winnipeg, Canada.SubjectsFourteen patients with FBSS and age- and gender-matched controls participated in this study. Three patients were removed from the analyses due to image artefact (n = 1) and effective pain treatment (n = 2). Eleven patients (5 female, mean age 52.7 years) and their matched controls were included in the final analyses.MethodsResting state fMRI data were analyzed using an independent component analysis, yielding three resting state networks of interest: the salience network (SN), involved in detection of external stimuli, central executive network (CEN), involved in cognitions, and sensorimotor network (SeN), involved in sensory and motor integration. Analysis of Variance contrasts were performed for each network, comparing functional connectivity differences between FBSS patients and healthy controls.ResultsAlterations were observed in all three resting state networks, primarily relating to pain and its processing in the FBSS group. Specifically, compared to healthy controls, FBSS patients demonstrated increased functional connectivity in the anterior cingulate cortex within the SN, medial frontal gyrus in the CEN, and precentral gyrus within the SeN. FBSS patients also demonstrated decreased functional connectivity in the medial frontal gyrus in the SeN compared to healthy controls. Interestingly, we also observed internetwork functional connectivity in the SN and SeN.ConclusionsFBSS is associated with altered patterns of functional connectivity in the SN, CEN, and SeN. Taken together with our previous work, this reveals that a chronic pain condition can have a dramatic effect on the connectivity of multiple resting state networks.ImplicationsThese data suggest that a chronic pain condition—FBSS—is associated with disruptions to networks of functional connectivity in brain areas that are involved in numerous functions, including pain processing, sensation, and movement. It is possible that the alterations in these networks may contribute to other common chronic pain comorbidities, such as disrupted cognitions or anxiety. Previous research shows that during experimentally-induced pain, these networks can return to initial levels of functioning, indicating that these functional alterations are likely not permanent.

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Krammer, Martin, Clemens Schiffer, and Martin Benedikt. "ProMECoS: A Process Model for Efficient Standard-Driven Distributed Co-Simulation." Electronics 10, no. 5 (March 9, 2021): 633. http://dx.doi.org/10.3390/electronics10050633.

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Co-simulation techniques have evolved significantly over the last 10 years. System simulation and hardware-in-the-loop testing are used to develop innovative products in many industrial sectors. Despite the success of these simulation techniques, their efficient application requires a systematic approach. In practice the integration and coupling of heterogeneous systems still require enormous efforts. At this point in time no unified process for integration and simulation of DCP-based co-simulation scenarios is available. In this article we present ProMECoS, a process model for efficient, standard-driven distributed co-simulation. It defines the necessary tasks required to prepare, instantiate and execute distributed co-simulations according to the DCP standard. Furthermore, it enables the exploitation of front-loading benefits, thus reducing the overall system development effort. ProMECoS is based on the IEEE 1730 standard for Distributed Simulation Engineering and Execution Process. It adopts the artefacts of the DCP specification, and defines additional process artefacts. The DCP specification and its associated default integration methodology were developed by a balanced consortium in context of the ITEA 3 project ACOSAR. The DCP is compatible to the well-adopted FMI standard. Therefore both standards can be used together for seamless development using models, software, and real components. ProMECoS provides the necessary guidance for efficient product development and testing.

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Leclercq, Yves, Evelyne Balteau, Thanh Dang-Vu, Manuel Schabus, André Luxen, Pierre Maquet, and Christophe Phillips. "Rejection of pulse related artefact (PRA) from continuous electroencephalographic (EEG) time series recorded during functional magnetic resonance imaging (fMRI) using constraint independent component analysis (cICA)." NeuroImage 44, no. 3 (February 2009): 679–91. http://dx.doi.org/10.1016/j.neuroimage.2008.10.017.

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Kolasa, Grzegorz, and Filip Rybakowski. "Application of functional near-infrared spectroscopy in psychiatry and physical activity studies." Pharmacotherapy in Psychiatry and Neurology 35, no. 2 (2019): 131–45. http://dx.doi.org/10.33450/fpn.2019.10.001.

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Objectives. Functional near-infrared spectroscopy (fNIRS) is one of the fastest developing neuroimaging modalities. Features, such as non-invasiveness, simplicity of application and resistance to motion artefacts, allow to take measurements and to create scientific experiments imitating real life conditions. In this review, we want to focus on the potential of fNIRS in the fields of psychiatry, neurorehabilitation and physical exercise. Additionally, we present the advantages of fNIRS over other neuroimaging techniques like fMRI, PET and EEG/EMG. We also consider potential directions of development and challenges which emerge in front of the fNIRS society. Literature review. The main application in the discipline of neurorehabilitation is to monitor and to observe the repair mechanism of neurons after brain traumas. The non-invasiveness of infra-red light permits to investigate patients of both adult and child psychiatry. The utility of fNIRS as a diagnostic tool and a predictor is proven. Researchers are looking for functional abnormalities within the prefrontal cortex. fNIRS creates new possibilities in terms of exploration of the physical exercise. Recent articles consider which type of effort has the best effect on the hemodynamic response in the cortex. It seems that investigating the impact of the physical activity in group of psychiatric patients is an interesting direction. Conclusions. Currently, we are at the breakthrough in the fNIRS technology. The number of new studies, more precise methods of data analysis, and availability of good quality systems help us to better understand how to design scientific experiments properly and reliably measure the activity of the cerebral cortex.

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Srinivasu, P. Naga, T. Srinivasa Rao, G. Srinivas, and P. V. G. D. Prasad Reddy. "A Computationally Efficient Skull Scraping Approach for Brain MR Image." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 833–44. http://dx.doi.org/10.2174/2213275912666190809111928.

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Background: In the process of volumetric evaluation of the damaged region in the human brain from a MR image it is very crucial to remove the non-brain tissue from the acquainted image. At times there is a chance during the process of assessing the damaged region through automated approaches might misinterpret the non-brain tissues like skull as damaged region due to their similar intensity features. So in order to address such issues all such artefacts. Objective: In order to mechanize an efficient approach that can effectively address the issue of removing the non-brain tissues with minimal computation effort and precise accuracy. It is very essential to keep the computational time to be as minimal as possible because the processes of skull removal is used in conjunction with segmentation algorithm, and if the skull scrapping approach has consumed a considerable amount of time, they it would impact the over segmentation and volume assessment time which is not advisable. Methods: In this paper a completely novel approach named Structural Augmentation has been proposed, that could efficiently remove the skull region from the MR image. The proposed approach has several phases that include applying of Hybridized Contra harmonic and Otsu AWBF filtering for noise removal and threshold approximation through Otsu based approach and constructing the bit map based on the approximated threshold. Morphological close operation followed by morphological open operation with reference to a structural element through the generated bitmap image. Results: The experiment are carry forwarded on a real time MR images of the patient at KGH hospital, Visakhapatnam and the images from open sources repositories like fmri. The experiment is conducted on the images of varied noise variance that are tabulated in the results and implementation section of the article. The accuracy of the proposed method has been evaluated through metrics like Accuracy, Sensitivity, Specificity through true positive, true negative, False Positive and False negative evaluations. And it is observed that the performance of the proposed algorithm seems to be reasonable good. Conclusion: The skull scrapping through structural Augmentation is computationally efficient when compared with other conventional approaches concerning both computational complexity and the accuracy that could be observed on experimentation. The Adaptive Weighted Bilateral Filter that acquire the weight value from the approximated contra harmonic mean will assist in efficient removal of poison noised by preserving the edge information and Otsu algorithm is used to determine the appropriate threshold value for constructing the bitmap image of the original MRI image which is efficient over the earlier mean based approach for estimating the threshold. Moreover, the efficiency of the proposed approach could be further improved by using customized structural elements and incorporating the fuzzy based assignments among the pixels that belong to brain tissue and skull effectively.

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B, Hossain, and Iftekhar MA. "Comparative Study of Different Pulse Artefact Correction Techniques during Concurrent EEG-FMRI Using FMRIB." Journal of Electrical & Electronic Systems 5, no. 2 (2016). http://dx.doi.org/10.4172/2332-0796.1000178.

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Maziero, Danilo, Victor A. Stenger, and David W. Carmichael. "Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion." Brain Topography, September 23, 2021. http://dx.doi.org/10.1007/s10548-021-00870-0.

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Gorodisky, Lior, Ethan Livne, Tali Weiss, Aharon Weissbrod, Reut Weissgross, Eva Mishor, Edna Furman-Haran, and Noam Sobel. "Odor Canopy: A Method for Comfortable Odorant Delivery in MRI." Chemical Senses, January 3, 2021. http://dx.doi.org/10.1093/chemse/bjaa085.

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Abstract:

Abstract Functional magnetic resonance imaging (fMRI) has become the leading method for measuring the human brain response to sensory stimuli. However, olfaction fMRI lags behind vision and audition fMRI for two primary reasons: First, the olfactory brain areas are particularly susceptible to imaging artefacts, and second, the olfactory stimulus is particularly difficult to control in the fMRI environment. A component of the latter is related to the odorant-delivery human-machine interface, namely the point where odorants exit the dispensing apparatus to reach at the nose. Previous approaches relied on either nasal cannulas or nasal masks, each associated with particular drawbacks and discomforts. Here we provide detailed descriptions and instructions for transforming the MRI head-coil into an olfactory microenvironment, or odor canopy, where odorants can be switched on and off in less than 150 milliseconds without cannula or mask. In a proof-of-concept experiment we demonstrate that odor canopy provides for clearly dissociable odorant presence and absence, with no non-olfactory cues. Moreover, we find that odor canopy is rated more comfortable than nasal-mask, and we demonstrate that using odor canopy in the fMRI generates a typical olfactory brain-response. We conclude in recommending this approach for minimized discomfort in fMRI of olfaction.

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Yan, Winston X., Karen J. Mullinger, Gerda B. Geirsdottir, and Richard Bowtell. "Physical modeling of pulse artefact sources in simultaneous EEG/fMRI." Human Brain Mapping, 2009, NA. http://dx.doi.org/10.1002/hbm.20891.

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