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Library.bib
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@article{kundu_differentiating_2012,
title = {Differentiating {BOLD} and non-{BOLD} signals in {fMRI} time series using multi-echo {EPI}},
volume = {60},
issn = {1095-9572},
doi = {10.1016/j.neuroimage.2011.12.028},
abstract = {A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R(2)* and initial signal intensity (S(0)) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R(2)* and S(0) change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like "functional network" components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.},
language = {eng},
number = {3},
journal = {NeuroImage},
author = {Kundu, Prantik and Inati, Souheil J. and Evans, Jennifer W. and Luh, Wen-Ming and Bandettini, Peter A.},
month = apr,
year = {2012},
pmid = {22209809},
pmcid = {PMC3350785},
keywords = {Humans, Brain, Female, Algorithms, Artifacts, Reproducibility of Results, Functional Neuroimaging, Sensitivity and Specificity, Echo-Planar Imaging, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Young Adult},
pages = {1759--1770},
file = {Kundu et al_2012_Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo.pdf:/home/mflores/snap/zotero-snap/common/Zotero/storage/9FM6CKRX/Kundu et al_2012_Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo.pdf:application/pdf},
}
@article{moia_ica-based_2021,
title = {{ICA}-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo {BOLD} {fMRI}},
volume = {233},
issn = {1053-8119},
url = {https://www.sciencedirect.com/science/article/pii/S1053811921001919},
doi = {10.1016/j.neuroimage.2021.117914},
abstract = {Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.},
language = {en},
urldate = {2023-01-19},
journal = {NeuroImage},
author = {Moia, Stefano and Termenon, Maite and Uruñuela, Eneko and Chen, Gang and Stickland, Rachael C. and Bright, Molly G. and Caballero-Gaudes, César},
month = jun,
year = {2021},
keywords = {Independent component analysis, Multi-echo fMRI, Breath-hold, Cerebrovascular reactivity, Denoising, Precision functional mapping},
pages = {117914},
file = {Moia et al_2021_ICA-based denoising strategies in breath-hold induced cerebrovascular.pdf:/home/mflores/snap/zotero-snap/common/Zotero/storage/P9V98FZK/Moia et al_2021_ICA-based denoising strategies in breath-hold induced cerebrovascular.pdf:application/pdf;ScienceDirect Snapshot:/home/mflores/snap/zotero-snap/common/Zotero/storage/H9GTZURP/S1053811921001919.html:text/html},
}
@article{perrachione_optimized_2013,
title = {Optimized {Design} and {Analysis} of {Sparse}-{Sampling} {fMRI} {Experiments}},
volume = {7},
issn = {1662-453X},
url = {https://www.frontiersin.org/articles/10.3389/fnins.2013.00055},
abstract = {Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase the number of samples and improve statistical power.},
urldate = {2022-12-16},
journal = {Frontiers in Neuroscience},
author = {Perrachione, Tyler and Ghosh, Satrajit},
year = {2013},
file = {Full Text PDF:/home/mflores/snap/zotero-snap/common/Zotero/storage/6XS24H45/Perrachione and Ghosh - 2013 - Optimized Design and Analysis of Sparse-Sampling f.pdf:application/pdf},
}
@article{merrett_sparse_2021,
title = {Sparse and continuous sampling approaches to {fMRI} of overt vocalization tasks},
volume = {1},
issn = {2666-9560},
url = {https://www.sciencedirect.com/science/article/pii/S2666956021000489},
doi = {10.1016/j.ynirp.2021.100050},
abstract = {Sparse temporal sampling has become the dominant paradigm for functional magnetic resonance imaging studies of auditory stimuli or verbal responses, as it allows the presentation or production of stimuli during the relatively quiet periods when there is no gradient switching and ensures that task-related movements are not occurring during scan acquisitions. To date, however, there has been no direct comparison between sparse and continuous acquisition protocols for overt auditory-verbal studies (i.e., speaking or singing). The aim of this study was to determine whether sparse temporal sampling would reduce movement artefacts and show better network activation for overt singing compared to continuous imaging. Fifteen healthy adults performed the same overt singing task under both sparse and continuous scanning conditions. We noted significant variations in signal intensity between adjacent slices in our sparse acquisition, with (odd-numbered) slices acquired in the second half of each volume acquisition being of lower intensity and showing less reliable task-related activation, and thus requiring the removal of these slices prior to preprocessing. Edge artefacts, presumably due to movement, were observed in both acquisition types at a subthreshold level, although ventricular space artefacts were more apparent in the continuous data. However, statistical comparison revealed no significant differences in functional activation nor in motion correction parameters. Our results show that sparse imaging has the potential to introduce significant image artefacts affecting downstream analyses. While sparse sampling provides benefits that may be essential for certain studies (e.g., periods free from scanner noise), the technical requirements of such sequences should not be overlooked and inspection of raw data is essential. Our data also show that continuous imaging can be used for overt response auditory-verbal studies and may be of wider utility than previously appreciated.},
language = {en},
number = {4},
urldate = {2022-12-16},
journal = {Neuroimage: Reports},
author = {Merrett, Dawn L. and Tailby, Chris and Abbott, David F. and Jackson, Graeme D. and Wilson, Sarah J.},
month = dec,
year = {2021},
keywords = {fMRI, Auditory imaging, Overt response, Singing, Sparse temporal sampling},
pages = {100050},
file = {ScienceDirect Full Text PDF:/home/mflores/snap/zotero-snap/common/Zotero/storage/JKQ8K2XF/Merrett et al. - 2021 - Sparse and continuous sampling approaches to fMRI .pdf:application/pdf;ScienceDirect Snapshot:/home/mflores/snap/zotero-snap/common/Zotero/storage/UAP8BAXH/S2666956021000489.html:text/html},
}
@article{birn_experimental_2004,
title = {Experimental designs and processing strategies for {fMRI} studies involving overt verbal responses},
volume = {23},
issn = {1053-8119},
url = {https://www.sciencedirect.com/science/article/pii/S1053811904004112},
doi = {10.1016/j.neuroimage.2004.07.039},
abstract = {Event-related paradigms have been used increasingly in the past few years for the localization of function in tasks involving overt speech. These designs exploit the differences in the temporal characteristics between the rapid motion-induced and the slower hemodynamic signal changes. The optimization of these designs and the best way to analyze the acquired data has not yet been fully explored. The purpose of this study is to investigate various design and analysis strategies for maximizing the detection of function while minimizing task-induced motion artifacts. Both event-related and blocked paradigms can be specifically designed to meet these goals. Various event-related and blocked designs were compared both in simulation and in experiments involving overt word reading in their ability to detect function and to avoid speech-induced motion artifact. A blocked design with task and control durations of 10 s and an event-related design with a minimum stimulus duration (SD) of 5 s and an average interstimulus interval (ISI) of 10 s were found to optimally detect blood oxygenation level-dependent signal changes without significant motion artifact. Ignoring images acquired during the speech can help recover function in areas particularly affected by motion but substantially reduces the detection power in other regions. Using the stimulus timing as an additional regressor to model the motion offers little benefit in practice due to the variability of the motion-induced signal change.},
language = {en},
number = {3},
urldate = {2023-01-15},
journal = {NeuroImage},
author = {Birn, Rasmus M. and Cox, Robert W. and Bandettini, Peter A.},
month = nov,
year = {2004},
keywords = {Motion, Event-related paradigms, Overt speech},
pages = {1046--1058},
file = {ScienceDirect Full Text PDF:/home/mflores/snap/zotero-snap/common/Zotero/storage/5P2R2ER4/Birn et al. - 2004 - Experimental designs and processing strategies for.pdf:application/pdf},
}
@article{xu_denoising_2014,
title = {Denoising the {Speaking} {Brain}: {Toward} a {Robust} {Technique} for {Correcting} {Artifact}-{Contaminated} {fMRI} {Data} under {Severe} {Motion}},
volume = {103},
issn = {1053-8119},
shorttitle = {Denoising the {Speaking} {Brain}},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312243/},
doi = {10.1016/j.neuroimage.2014.09.013},
abstract = {A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity.},
urldate = {2022-12-19},
journal = {NeuroImage},
author = {Xu, Yisheng and Tong, Yunxia and Liu, Siyuan and Chow, Ho Ming and AbdulSabur, Nuria Y. and Mattay, Govind S. and Braun, Allen R.},
month = dec,
year = {2014},
pmid = {25225001},
pmcid = {PMC4312243},
pages = {33--47},
file = {PubMed Central Full Text PDF:/home/mflores/snap/zotero-snap/common/Zotero/storage/C4BNZDMP/Xu et al. - 2014 - Denoising the Speaking Brain Toward a Robust Tech.pdf:application/pdf},
}
@article{dupre_te-dependent_2021,
title = {{TE}-dependent analysis of multi-echo {fMRI} with tedana},
volume = {6},
issn = {2475-9066},
url = {https://joss.theoj.org/papers/10.21105/joss.03669},
doi = {10.21105/joss.03669},
abstract = {Functional magnetic resonance imaging (fMRI) is a popular method for in vivo neuroimaging. Modern fMRI sequences are often weighted towards the blood oxygen level dependent (BOLD) signal, which is closely linked to neuronal activity (Logothetis, 2002). This weighting is achieved by tuning several parameters to increase the BOLD-weighted signal contrast. One such parameter is “TE,” or echo time. TE is the amount of time elapsed between when protons are excited (the MRI signal source) and measured. Although the total measured signal magnitude decays with echo time, BOLD sensitivity increases (Silvennoinen et al., 2003). The optimal TE maximizes the BOLD signal weighting based on a number of factors, including several MRI scanner parameters (e.g., field strength), imaged tissue composition (e.g., grey vs. white matter), and proximity to air-tissue boundaries.},
language = {en},
number = {66},
urldate = {2023-05-24},
journal = {Journal of Open Source Software},
author = {DuPre, Elizabeth and Salo, Taylor and Ahmed, Zaki and Bandettini, Peter and Bottenhorn, Katherine and Caballero-Gaudes, César and Dowdle, Logan and Gonzalez-Castillo, Javier and Heunis, Stephan and Kundu, Prantik and Laird, Angela and Markello, Ross and Markiewicz, Christopher and Moia, Stefano and Staden, Isla and Teves, Joshua and Uruñuela, Eneko and Vaziri-Pashkam, Maryam and Whitaker, Kirstie and Handwerker, Daniel},
month = oct,
year = {2021},
pages = {3669},
file = {DuPre et al. - 2021 - TE-dependent analysis of multi-echo fMRI with teda.pdf:/home/mflores/snap/zotero-snap/common/Zotero/storage/JWKU2DGS/DuPre et al. - 2021 - TE-dependent analysis of multi-echo fMRI with teda.pdf:application/pdf},
}
@article{kao_multi-objective_2009,
title = {Multi-objective optimal experimental designs for event-related {fMRI} studies},
volume = {44},
issn = {1053-8119},
url = {https://www.sciencedirect.com/science/article/pii/S1053811908010367},
doi = {10.1016/j.neuroimage.2008.09.025},
abstract = {In this article, we propose an efficient approach to find optimal experimental designs for event-related functional magnetic resonance imaging (ER-fMRI). We consider multiple objectives, including estimating the hemodynamic response function (HRF), detecting activation, circumventing psychological confounds and fulfilling customized requirements. Taking into account these goals, we formulate a family of multi-objective design criteria and develop a genetic-algorithm-based technique to search for optimal designs. Our proposed technique incorporates existing knowledge about the performance of fMRI designs, and its usefulness is shown through simulations. Although our approach also works for other linear combinations of parameters, we primarily focus on the case when the interest lies either in the individual stimulus effects or in pairwise contrasts between stimulus types. Under either of these popular cases, our algorithm outperforms the previous approaches. We also find designs yielding higher estimation efficiencies than m-sequences. When the underlying model is with white noise and a constant nuisance parameter, the stimulus frequencies of the designs we obtained are in good agreement with the optimal stimulus frequencies derived by Liu and Frank, 2004, NeuroImage 21: 387-400. In addition, our approach is built upon a rigorous model formulation.},
language = {en},
number = {3},
urldate = {2023-05-24},
journal = {NeuroImage},
author = {Kao, Ming-Hung and Mandal, Abhyuday and Lazar, Nicole and Stufken, John},
month = feb,
year = {2009},
keywords = {Compound design criterion, Design efficiency, Genetic algorithms},
pages = {849--856},
file = {Kao et al_2009_Multi-objective optimal experimental designs for event-related fMRI studies.pdf:/home/mflores/snap/zotero-snap/common/Zotero/storage/UCT8GPIF/Kao et al_2009_Multi-objective optimal experimental designs for event-related fMRI studies.pdf:application/pdf;ScienceDirect Snapshot:/home/mflores/snap/zotero-snap/common/Zotero/storage/JVFIUUZJ/S1053811908010367.html:text/html},
}
@article{Barsalou2003,
title = {Grounding conceptual knowledge in modality-specific systems},
volume = {7},
issn = {13646613},
doi = {10.1016/S1364-6613(02)00029-3},
abstract = {The human conceptual system contains knowledge that supports all cognitive activities, including perception, memory, language and thought. According to most current theories, states in modality-specific systems for perception, action and emotion do not represent knowledge - rather, redescriptions of these states in amodal representational languages do. Increasingly, however, researchers report that re-enactments of states in modality-specific systems underlie conceptual processing. In behavioral experiments, perceptual and motor variables consistently produce effects in conceptual tasks. In brain imaging experiments, conceptual processing consistently activates modality-specific brain areas. Theoretical research shows how modality-specific re-enactments could produce basic conceptual functions, such as the type-token distinction, categorical inference, productivity, propositions and abstract concepts. Together these empirical results and theoretical analyses implicate modality-specific systems in the representation and use of conceptual knowledge.},
number = {2},
journal = {Trends in Cognitive Sciences},
author = {Barsalou, Lawrence W. and Simmons, W. Kyle and Barbey, Aron K. and Wilson, Christine D.},
year = {2003},
pmid = {12584027},
pages = {84--91},
file = {Barsalou et al. - 2003 - Grounding conceptual knowledge in modality-specifi.pdf:/home/mflores/snap/zotero-snap/common/Zotero/storage/WEJ5BDBN/Barsalou et al. - 2003 - Grounding conceptual knowledge in modality-specifi.pdf:application/pdf},
}
@article{Pulvermuller2013,
title = {How neurons make meaning: {Brain} mechanisms for embodied and abstract-symbolic semantics},
volume = {17},
issn = {13646613},
doi = {10.1016/j.tics.2013.06.004},
abstract = {How brain structures and neuronal circuits mechanistically underpin symbolic meaning has recently been elucidated by neuroimaging, neuropsychological, and neurocomputational research. Modality-specific 'embodied' mechanisms anchored in sensorimotor systems appear to be relevant, as are 'disembodied' mechanisms in multimodal areas. In this paper, four semantic mechanisms are proposed and spelt out at the level of neuronal circuits: referential semantics, which establishes links between symbols and the objects and actions they are used to speak about; combinatorial semantics, which enables the learning of symbolic meaning from context; emotional-affective semantics, which establishes links between signs and internal states of the body; and abstraction mechanisms for generalizing over a range of instances of semantic meaning. Referential, combinatorial, emotional-affective, and abstract semantics are complementary mechanisms, each necessary for processing meaning in mind and brain. © 2013 Elsevier Ltd.},
number = {9},
journal = {Trends in Cognitive Sciences},
author = {Pulvermüller, Friedemann},
year = {2013},
pmid = {23932069},
pages = {458--470},
file = {Full Text:/home/mflores/snap/zotero-snap/common/Zotero/storage/GZIQD96K/Pulvermüller - 2013 - How neurons make meaning Brain mechanisms for emb.pdf:application/pdf},
}
@article{liu_noise_2016,
title = {Noise contributions to the {fMRI} signal: {An} overview},
volume = {143},
issn = {1053-8119},
shorttitle = {Noise contributions to the {fMRI} signal},
url = {https://www.sciencedirect.com/science/article/pii/S1053811916304694},
doi = {10.1016/j.neuroimage.2016.09.008},
abstract = {The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.},
language = {en},
urldate = {2023-01-04},
journal = {NeuroImage},
author = {Liu, Thomas T.},
month = dec,
year = {2016},
keywords = {FMRI, Physiological noise, Motion, General linear model, Noise sources},
pages = {141--151},
file = {ScienceDirect Snapshot:/home/mflores/snap/zotero-snap/common/Zotero/storage/YBB9BJAX/S1053811916304694.html:text/html},
}
@article{merrett_sparse_2021,
title = {Sparse and continuous sampling approaches to {fMRI} of overt vocalization tasks},
volume = {1},
issn = {2666-9560},
url = {https://www.sciencedirect.com/science/article/pii/S2666956021000489},
doi = {10.1016/j.ynirp.2021.100050},
abstract = {Sparse temporal sampling has become the dominant paradigm for functional magnetic resonance imaging studies of auditory stimuli or verbal responses, as it allows the presentation or production of stimuli during the relatively quiet periods when there is no gradient switching and ensures that task-related movements are not occurring during scan acquisitions. To date, however, there has been no direct comparison between sparse and continuous acquisition protocols for overt auditory-verbal studies (i.e., speaking or singing). The aim of this study was to determine whether sparse temporal sampling would reduce movement artefacts and show better network activation for overt singing compared to continuous imaging. Fifteen healthy adults performed the same overt singing task under both sparse and continuous scanning conditions. We noted significant variations in signal intensity between adjacent slices in our sparse acquisition, with (odd-numbered) slices acquired in the second half of each volume acquisition being of lower intensity and showing less reliable task-related activation, and thus requiring the removal of these slices prior to preprocessing. Edge artefacts, presumably due to movement, were observed in both acquisition types at a subthreshold level, although ventricular space artefacts were more apparent in the continuous data. However, statistical comparison revealed no significant differences in functional activation nor in motion correction parameters. Our results show that sparse imaging has the potential to introduce significant image artefacts affecting downstream analyses. While sparse sampling provides benefits that may be essential for certain studies (e.g., periods free from scanner noise), the technical requirements of such sequences should not be overlooked and inspection of raw data is essential. Our data also show that continuous imaging can be used for overt response auditory-verbal studies and may be of wider utility than previously appreciated.},
language = {en},
number = {4},
urldate = {2022-12-16},
journal = {Neuroimage: Reports},
author = {Merrett, Dawn L. and Tailby, Chris and Abbott, David F. and Jackson, Graeme D. and Wilson, Sarah J.},
month = dec,
year = {2021},
keywords = {fMRI, Auditory imaging, Overt response, Singing, Sparse temporal sampling},
pages = {100050},
file = {ScienceDirect Full Text PDF:/home/mflores/snap/zotero-snap/common/Zotero/storage/JKQ8K2XF/Merrett et al. - 2021 - Sparse and continuous sampling approaches to fMRI .pdf:application/pdf;ScienceDirect Snapshot:/home/mflores/snap/zotero-snap/common/Zotero/storage/UAP8BAXH/S2666956021000489.html:text/html},
}
@article{posse_enhancement_1999,
title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
volume = {42},
issn = {1522-2594},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291522-2594%28199907%2942%3A1%3C87%3A%3AAID-MRM13%3E3.0.CO%3B2-O},
doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
abstract = {Improved data acquisition and processing strategies for blood oxygenation level-dependent (BOLD)-contrast functional magnetic resonance imaging (fMRI), which enhance the functional contrast-to-noise ratio (CNR) by sampling multiple echo times in a single shot, are described. The dependence of the CNR on T2*, the image encoding time, and the number of sampled echo times are investigated for exponential fitting, echo summation, weighted echo summation, and averaging of correlation maps obtained at different echo times. The method is validated in vivo using visual stimulation and turbo proton echoplanar spectroscopic imaging (turbo-PEPSI), a new single-shot multi-slice MR spectroscopic imaging technique, which acquires up to 12 consecutive echoplanar images with echo times ranging from 12 to 213 msec. Quantitative T2*-mapping significantly increases the measured extent of activation and the mean correlation coefficient compared with conventional echoplanar imaging. The sensitivity gain with echo summation, which is computationally efficient provides similar sensitivity as fitting. For all data processing methods sensitivity is optimum when echo times up to 3.2 T2* are sampled. This methodology has implications for comparing functional sensitivity at different magnetic field strengths and between brain regions with different magnetic field inhomogeneities. Magn Reson Med 42:87–97, 1999. © 1999 Wiley-Liss, Inc.},
language = {en},
number = {1},
urldate = {2023-05-24},
journal = {Magnetic Resonance in Medicine},
author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
year = {1999},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/\%28SICI\%291522-2594\%28199907\%2942\%3A1\%3C87\%3A\%3AAID-MRM13\%3E3.0.CO\%3B2-O},
keywords = {fMRI, BOLD contrast, MR, multi-echo, sensitivity, spectroscopic imaging},
pages = {87--97},
file = {Posse et al_1999_Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional.pdf:/home/mflores/snap/zotero-snap/common/Zotero/storage/4W757QW5/Posse et al_1999_Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional.pdf:application/pdf;Snapshot:/home/mflores/snap/zotero-snap/common/Zotero/storage/6333K42Z/(SICI)1522-2594(199907)42187AID-MRM133.0.html:text/html},
}