`_.
***************
Multi-echo data
diff --git a/docs/index.rst b/docs/index.rst
index daa2bc39e..fdc48348a 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -4,11 +4,6 @@
tedana: TE Dependent ANAlysis
#############################
-The ``tedana`` package is part of the ME-ICA pipeline, performing TE-dependent
-analysis of multi-echo functional magnetic resonance imaging (fMRI) data.
-``TE``-``de``\pendent ``ana``\lysis (``tedana``) is a Python module for denoising
-multi-echo functional magnetic resonance imaging (fMRI) data.
-
.. image:: https://img.shields.io/pypi/v/tedana.svg
:target: https://pypi.python.org/pypi/tedana/
:alt: Latest Version
@@ -17,9 +12,13 @@ multi-echo functional magnetic resonance imaging (fMRI) data.
:target: https://pypi.python.org/pypi/tedana/
:alt: PyPI - Python Version
+.. image:: https://joss.theoj.org/papers/10.21105/joss.03669/status.svg
+ :target: https://doi.org/10.21105/joss.03669
+ :alt: JOSS DOI
+
.. image:: https://zenodo.org/badge/110845855.svg
:target: https://zenodo.org/badge/latestdoi/110845855
- :alt: DOI
+ :alt: Zenodo DOI
.. image:: https://circleci.com/gh/ME-ICA/tedana.svg?style=shield
:target: https://circleci.com/gh/ME-ICA/tedana
@@ -62,14 +61,15 @@ multi-echo functional magnetic resonance imaging (fMRI) data.
About
*****
+``TE``-``de``pendent ``ana``lysis (``tedana``) is a Python library for denoising multi-echo functional magnetic resonance imaging (fMRI) data.
+``tedana`` originally came about as a part of the `ME-ICA`_ pipeline, although it has since diverged.
+An important distinction is that while the ME-ICA pipeline originally performed both pre-processing and TE-dependent analysis of multi-echo fMRI data,
+``tedana`` now assumes that you're working with data which has been previously preprocessed.
+
+
.. image:: https://user-images.githubusercontent.com/7406227/40031156-57b7cbb8-57bc-11e8-8c51-5b29f2e86a48.png
:target: http://tedana.readthedocs.io/
-``tedana`` originally came about as a part of the `ME-ICA`_ pipeline.
-The ME-ICA pipeline originally performed both pre-processing and TE-dependent
-analysis of multi-echo fMRI data; however, ``tedana`` now assumes that you're
-working with data which has been previously preprocessed.
-
For a summary of multi-echo fMRI, which is the imaging technique ``tedana`` builds on,
visit `Multi-echo fMRI`_.
@@ -114,14 +114,27 @@ When using tedana, please include the following citations:
tedana Available from: https://doi.org/10.5281/zenodo.1250561
![]()
+
- 2. Kundu, P., Inati, S. J., Evans, J. W., Luh, W. M. & Bandettini, P. A. (2011).
+ 2. DuPre, E. M., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L.,
+ Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S.,
+ Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S.,
+ Staden, I., Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M.,
+ Whitaker, K., & Handwerker, D. A. (2021).
+ TE-dependent analysis of multi-echo fMRI with tedana.
+ Journal of Open Source Software, 6(66), 3669.
+ doi:10.21105/joss.03669.
+
+
+
+ 3. Kundu, P., Inati, S. J., Evans, J. W., Luh, W. M., & Bandettini, P. A. (2011).
Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI.
NeuroImage, 60, 1759-1770.
+
- 3. Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., Vértes, P. E., Inati, S. J.,
+ 4. Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., Vértes, P. E., Inati, S. J.,
Saad, Z. S., Bandettini, P. A., & Bullmore, E. T. (2013).
Integrated strategy for improving functional connectivity mapping using multiecho fMRI.
Proceedings of the National Academy of Sciences, 110, 16187-16192.
diff --git a/docs/installation.rst b/docs/installation.rst
index 39998e399..c65c654f3 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -13,7 +13,10 @@ packages will need to be installed:
- scipy
- mapca
-You can then install ``tedana`` with:
+You can also install several optional dependencies, notably ``duecredit``.
+Please see the :doc:`FAQ ` for more information on how tedana uses ``duecredit``.
+
+After installing relevant dependencies, you can then install ``tedana`` with:
.. code-block:: bash
@@ -21,3 +24,16 @@ You can then install ``tedana`` with:
In addition to the Python package, installing ``tedana`` will add the ``tedana``
and ``t2smap`` workflow CLIs to your path.
+You can confirm that ``tedana`` has successfully installed by launching a Python instance and running:
+
+.. code-block:: python
+
+ import tedana
+
+You can check that it is available through the command line interface (CLI) with:
+
+.. code-block:: bash
+
+ tedana --help
+
+If no error occurs, ``tedana`` has correctly installed in your environment!
diff --git a/examples/plot_approach_figures.ipynb b/docs/notebooks/plot_approach_figures.ipynb
similarity index 100%
rename from examples/plot_approach_figures.ipynb
rename to docs/notebooks/plot_approach_figures.ipynb
diff --git a/examples/plot_metric_simulations.ipynb b/docs/notebooks/plot_metric_simulations.ipynb
similarity index 100%
rename from examples/plot_metric_simulations.ipynb
rename to docs/notebooks/plot_metric_simulations.ipynb
diff --git a/paper/figure_01.png b/paper/figure_01.png
new file mode 100644
index 000000000..5a8e3e7e1
Binary files /dev/null and b/paper/figure_01.png differ
diff --git a/paper/paper.bib b/paper/paper.bib
new file mode 100644
index 000000000..f6d89298a
--- /dev/null
+++ b/paper/paper.bib
@@ -0,0 +1,859 @@
+@ARTICLE{Caballero-Gaudes2019-lv,
+ title = "A deconvolution algorithm for multi-echo functional {MRI}:
+ Multi-echo Sparse Paradigm Free Mapping",
+ author = "Caballero-Gaudes, C{\'e}sar and Moia, Stefano and Panwar, Puja
+ and Bandettini, Peter A and Gonzalez-Castillo, Javier",
+ abstract = "This work introduces a novel algorithm for deconvolution of the
+ BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm
+ Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD
+ percent signal change on the echo time (TE) and using
+ sparsity-promoting regularized least squares estimation, ME-SPFM
+ yields voxelwise time-varying estimates of the changes in the
+ apparent transverse relaxation ($\Delta$R) without prior
+ knowledge of the timings of individual BOLD events. Our results
+ in multi-echo fMRI data collected during a multi-task
+ event-related paradigm at 3 Tesla demonstrate that the maps of R
+ changes obtained with ME-SPFM at the times of the stimulus trials
+ show high spatial and temporal concordance with the activation
+ maps and BOLD signals obtained with standard model-based
+ analysis. This method yields estimates of $\Delta$R having
+ physiologically plausible values. Owing to its ability to blindly
+ detect events, ME-SPFM also enables us to map $\Delta$R
+ associated with spontaneous, transient BOLD responses occurring
+ between trials. This framework is a step towards deciphering the
+ dynamic nature of brain activity in naturalistic paradigms,
+ resting-state or experimental paradigms with unknown timing of
+ the BOLD events.",
+ journal = "Neuroimage",
+ volume = 202,
+ pages = "116081",
+ month = nov,
+ year = 2019,
+ keywords = "BOLD fMRI; Deconvolution; Multi-echo; Single-trial",
+ language = "en",
+ doi = {10.1016/j.neuroimage.2019.116081}
+}
+
+@ARTICLE{Kundu2013-xm,
+ title = "Integrated strategy for improving functional connectivity mapping
+ using multiecho {fMRI}",
+ author = "Kundu, Prantik and Brenowitz, Noah D and Voon, Valerie and Worbe,
+ Yulia and V{\'e}rtes, Petra E and Inati, Souheil J and Saad, Ziad
+ S and Bandettini, Peter A and Bullmore, Edward T",
+ abstract = "Functional connectivity analysis of resting state blood oxygen
+ level-dependent (BOLD) functional MRI is widely used for
+ noninvasively studying brain functional networks. Recent findings
+ have indicated, however, that even small ($\leq$1 mm) amounts of
+ head movement during scanning can disproportionately bias
+ connectivity estimates, despite various preprocessing efforts.
+ Further complications for interregional connectivity estimation
+ from time domain signals include the unaccounted reduction in
+ BOLD degrees of freedom related to sensitivity losses from high
+ subject motion. To address these issues, we describe an
+ integrated strategy for data acquisition, denoising, and
+ connectivity estimation. This strategy builds on our previously
+ published technique combining data acquisition with multiecho
+ (ME) echo planar imaging and analysis with spatial independent
+ component analysis (ICA), called ME-ICA, which distinguishes BOLD
+ (neuronal) and non-BOLD (artifactual) components based on linear
+ echo-time dependence of signals-a characteristic property of BOLD
+ T*2 signal changes. Here we show for 32 control subjects that
+ this method provides a physically principled and nearly
+ operator-independent way of removing complex artifacts such as
+ motion from resting state data. We then describe a robust
+ estimator of functional connectivity based on interregional
+ correlation of BOLD-independent component coefficients. This
+ estimator, called independent components regression, considerably
+ simplifies statistical inference for functional connectivity
+ because degrees of freedom equals the number of independent
+ coefficients. Compared with traditional connectivity estimation
+ methods, the proposed strategy results in fourfold improvements
+ in signal-to-noise ratio, functional connectivity analysis with
+ improved specificity, and valid statistical inference with
+ nominal control of type 1 error in contrasts of connectivity
+ between groups with different levels of subject motion.",
+ journal = "Proceedings of the National Academy of Sciences of the United States of America",
+ volume = 110,
+ number = 40,
+ pages = "16187--16192",
+ month = oct,
+ year = 2013,
+ keywords = "human neuroimaging; resting state fMRI; time series",
+ language = "en",
+ doi = {10.1073/pnas.1301725110}
+}
+
+@ARTICLE{Kundu2012-bq,
+ title = "Differentiating {BOLD} and {non-BOLD} signals in {fMRI} time
+ series using multi-echo {EPI}",
+ author = "Kundu, Prantik and Inati, Souheil J and Evans, Jennifer W and
+ Luh, Wen-Ming and Bandettini, Peter A",
+ 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.",
+ journal = "Neuroimage",
+ volume = 60,
+ number = 3,
+ pages = "1759--1770",
+ month = apr,
+ year = 2012,
+ language = "en",
+ doi = {10.1016/j.neuroimage.2011.12.028}
+}
+
+% The entry below contains non-ASCII chars that could not be converted
+% to a LaTeX equivalent.
+@MISC{Heunis2020-bd,
+ title = "The effects of multi-echo {fMRI} combination and rapid
+ T2*-mapping on offline and real-time {BOLD} sensitivity",
+ author = "Heunis, S and Breeuwer, M and Caballero-Gaudes, C and Hellrung, Lydia and Huijbers, Willem and Jansen, Jacobus FA and Lamerichs, Rolf and Zinger, Svitlana and Aldenkamp, Albert P",
+ abstract = "A variety of strategies are used to combine multi-echo
+ functional magnetic resonance imaging (fMRI) data, yet recent
+ literature lacks a systematic comparison of the available
+ options. Here we compare six different approaches derived from
+ multi-echo data and …",
+ journal = "bioRxiv",
+ url = {https://doi.org/10.1101/2020.12.08.416768},
+ doi = {10.1101/2020.12.08.416768},
+ year = 2020
+}
+
+@BOOK{Penny2011-vi,
+ title = "Statistical Parametric Mapping: The Analysis of Functional Brain
+ Images",
+ author = "Penny, William D and Friston, Karl J and Ashburner, John T and
+ Kiebel, Stefan J and Nichols, Thomas E",
+ abstract = "In an age where the amount of data collected from brain imaging
+ is increasing constantly, it is of critical importance to
+ analyse those data within an accepted framework to ensure proper
+ integration and comparison of the information collected. This
+ book describes the ideas and procedures that underlie the
+ analysis of signals produced by the brain. The aim is to
+ understand how the brain works, in terms of its functional
+ architecture and dynamics. This book provides the background and
+ methodology for the analysis of all types of brain imaging data,
+ from functional magnetic resonance imaging to
+ magnetoencephalography. Critically, Statistical Parametric
+ Mapping provides a widely accepted conceptual framework which
+ allows treatment of all these different modalities. This rests
+ on an understanding of the brain's functional anatomy and the
+ way that measured signals are caused experimentally. The book
+ takes the reader from the basic concepts underlying the analysis
+ of neuroimaging data to cutting edge approaches that would be
+ difficult to find in any other source. Critically, the material
+ is presented in an incremental way so that the reader can
+ understand the precedents for each new development. This book
+ will be particularly useful to neuroscientists engaged in any
+ form of brain mapping; who have to contend with the real-world
+ problems of data analysis and understanding the techniques they
+ are using. It is primarily a scientific treatment and a didactic
+ introduction to the analysis of brain imaging data. It can be
+ used as both a textbook for students and scientists starting to
+ use the techniques, as well as a reference for practicing
+ neuroscientists. The book also serves as a companion to the
+ software packages that have been developed for brain imaging
+ data analysis. An essential reference and companion for users of
+ the SPM software Provides a complete description of the concepts
+ and procedures entailed by the analysis of brain images Offers
+ full didactic treatment of the basic mathematics behind the
+ analysis of brain imaging data Stands as a compendium of all the
+ advances in neuroimaging data analysis over the past decade
+ Adopts an easy to understand and incremental approach that takes
+ the reader from basic statistics to state of the art approaches
+ such as Variational Bayes Structured treatment of data analysis
+ issues that links different modalities and models Includes a
+ series of appendices and tutorial-style chapters that makes even
+ the most sophisticated approaches accessible",
+ publisher = "Elsevier",
+ month = apr,
+ year = 2011,
+ language = "en",
+ ISBN = {9780123725608}
+}
+
+@ARTICLE{Esteban2020-ul,
+ title = "Analysis of task-based functional {MRI} data preprocessed with
+ {fMRIPrep}",
+ author = "Esteban, Oscar and Ciric, Rastko and Finc, Karolina and Blair,
+ Ross W and Markiewicz, Christopher J and Moodie, Craig A and
+ Kent, James D and Goncalves, Mathias and DuPre, Elizabeth and
+ Gomez, Daniel E P and Ye, Zhifang and Salo, Taylor and
+ Valabregue, Romain and Amlien, Inge K and Liem, Franziskus and
+ Jacoby, Nir and Stoji{\'c}, Hrvoje and Cieslak, Matthew and
+ Urchs, Sebastian and Halchenko, Yaroslav O and Ghosh, Satrajit S
+ and De La Vega, Alejandro and Yarkoni, Tal and Wright, Jessey and
+ Thompson, William H and Poldrack, Russell A and Gorgolewski,
+ Krzysztof J",
+ abstract = "Functional magnetic resonance imaging (fMRI) is a standard tool
+ to investigate the neural correlates of cognition. fMRI
+ noninvasively measures brain activity, allowing identification of
+ patterns evoked by tasks performed during scanning. Despite the
+ long history of this technique, the idiosyncrasies of each
+ dataset have led to the use of ad-hoc preprocessing protocols
+ customized for nearly every different study. This approach is
+ time consuming, error prone and unsuitable for combining datasets
+ from many sources. Here we showcase fMRIPrep
+ (http://fmriprep.org), a robust tool to prepare human fMRI data
+ for statistical analysis. This software instrument addresses the
+ reproducibility concerns of the established protocols for fMRI
+ preprocessing. By leveraging the Brain Imaging Data Structure to
+ standardize both the input datasets (MRI data as stored by the
+ scanner) and the outputs (data ready for modeling and analysis),
+ fMRIPrep is capable of preprocessing a diversity of datasets
+ without manual intervention. In support of the growing popularity
+ of fMRIPrep, this protocol describes how to integrate the tool in
+ a task-based fMRI investigation workflow.",
+ journal = "Nature Protocols",
+ volume = 15,
+ number = 7,
+ pages = "2186--2202",
+ month = jul,
+ year = 2020,
+ language = "en",
+ doi = {10.1038/s41596-020-0327-3}
+}
+
+@ARTICLE{Cox1996-up,
+ title = "{AFNI}: software for analysis and visualization of functional
+ magnetic resonance neuroimages",
+ author = "Cox, R W",
+ abstract = "A package of computer programs for analysis and visualization of
+ three-dimensional human brain functional magnetic resonance
+ imaging (FMRI) results is described. The software can color
+ overlay neural activation maps onto higher resolution anatomical
+ scans. Slices in each cardinal plane can be viewed
+ simultaneously. Manual placement of markers on anatomical
+ landmarks allows transformation of anatomical and functional
+ scans into stereotaxic (Talairach-Tournoux) coordinates. The
+ techniques for automatically generating transformed functional
+ data sets from manually labeled anatomical data sets are
+ described. Facilities are provided for several types of
+ statistical analyses of multiple 3D functional data sets. The
+ programs are written in ANSI C and Motif 1.2 to run on Unix
+ workstations.",
+ journal = "Computers and Biomedical Research",
+ volume = 29,
+ number = 3,
+ pages = "162--173",
+ month = jun,
+ year = 1996,
+ language = "en",
+ doi = {10.1006/cbmr.1996.0014}
+}
+
+% The entry below contains non-ASCII chars that could not be converted
+% to a LaTeX equivalent.
+@ARTICLE{Gonzalez-Castillo2016-tj,
+ title = "Evaluation of multi-echo {ICA} denoising for task based {fMRI}
+ studies: Block designs, rapid event-related designs, and
+ cardiac-gated {fMRI}",
+ author = "Gonzalez-Castillo, Javier and Panwar, Puja and Buchanan, Laura C
+ and Caballero-Gaudes, Cesar and Handwerker, Daniel A and Jangraw,
+ David C and Zachariou, Valentinos and Inati, Souheil and
+ Roopchansingh, Vinai and Derbyshire, John A and Bandettini, Peter
+ A",
+ abstract = "Multi-echo fMRI, particularly the multi-echo independent
+ component analysis (ME-ICA) algorithm, has previously proven
+ useful for increasing the sensitivity and reducing false
+ positives for functional MRI (fMRI) based resting state
+ connectivity studies. Less is known about its efficacy for
+ task-based fMRI, especially at the single subject level. This
+ work, which focuses exclusively on individual subject results,
+ compares ME-ICA to single-echo fMRI and a voxel-wise T2(⁎)
+ weighted combination of multi-echo data for task-based fMRI under
+ the following scenarios: cardiac-gated block designs, constant
+ repetition time (TR) block designs, and constant TR rapid
+ event-related designs. Performance is evaluated primarily in
+ terms of sensitivity (i.e., activation extent, activation
+ magnitude, percent detected trials and effect size estimates)
+ using five different tasks expected to evoke neuronal activity in
+ a distributed set of regions. The ME-ICA algorithm significantly
+ outperformed all other evaluated processing alternatives in all
+ scenarios. Largest improvements were observed for the
+ cardiac-gated dataset, where ME-ICA was able to reliably detect
+ and remove non-neural T1 signal fluctuations caused by
+ non-constant repetition times. Although ME-ICA also outperformed
+ the other options in terms of percent detection of individual
+ trials for rapid event-related experiments, only 46\% of all
+ events were detected after ME-ICA; suggesting additional
+ improvements in sensitivity are required to reliably detect
+ individual short event occurrences. We conclude the manuscript
+ with a detailed evaluation of ME-ICA outcomes and a discussion of
+ how the ME-ICA algorithm could be further improved. Overall, our
+ results suggest that ME-ICA constitutes a versatile, powerful
+ approach for advanced denoising of task-based fMRI, not just
+ resting-state data.",
+ journal = "Neuroimage",
+ volume = 141,
+ pages = "452--468",
+ month = nov,
+ year = 2016,
+ keywords = "Block design; ME-ICA; Multi-echo fMRI; Rapid event related;
+ Sensitivity",
+ language = "en",
+ doi = {10.1016/j.neuroimage.2016.07.049}
+}
+
+@ARTICLE{Logothetis2002-af,
+ title = "The neural basis of the blood--oxygen--level--dependent
+ functional magnetic resonance imaging signal",
+ author = "Logothetis, Nikos K",
+ journal = "Philosophical Transactions of the Royal Society B: Biological Sciences",
+ publisher = "The Royal Society",
+ volume = 357,
+ number = 1424,
+ pages = "1003--1037",
+ year = 2002,
+ doi = {10.1098/rstb.2002.1114}
+}
+
+@ARTICLE{Posse1999-lt,
+ title = "Enhancement of {BOLD-contrast} sensitivity by single-shot
+ multi-echo functional {MR} imaging",
+ author = "Posse, S and Wiese, S and Gembris, D and Mathiak, K and Kessler,
+ C and Grosse-Ruyken, M L and Elghahwagi, B and Richards, T and
+ Dager, S R and Kiselev, V G",
+ 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.",
+ journal = "Magnetic Resonance in Medicine",
+ volume = 42,
+ number = 1,
+ pages = "87--97",
+ month = jul,
+ year = 1999,
+ language = "en",
+ doi = {10.1002/(sici)1522-2594(199907)42:1<87::aid-mrm13>3.0.co;2-o}
+}
+
+@ARTICLE{Cohen2021-ep,
+ title = "Improved resting state functional connectivity sensitivity and
+ reproducibility using a multiband multi-echo acquisition",
+ author = "Cohen, Alexander D and Yang, Baolian and Fernandez, Brice and
+ Banerjee, Suchandrima and Wang, Yang",
+ abstract = "Recent advances in functional MRI techniques include multiband
+ (MB) imaging and multi-echo (ME) imaging. In MB imaging multiple
+ slices are acquired simultaneously leading to significant
+ increases in temporal and spatial resolution. Multi-echo imaging
+ enables multiple echoes to be acquired in one shot, where the ME
+ images can be used to denoise the BOLD time series and increase
+ BOLD sensitivity. In this study, resting state fMRI (rs-fMRI)
+ data were collected using a combined MBME sequence and compared
+ to an MB single echo sequence. In total, 29 subjects were imaged,
+ and 18 of them returned within two weeks for repeat imaging.
+ Participants underwent one MBME scan with three echoes and one MB
+ scan with one echo. Both datasets were processed using standard
+ denoising and advanced denoising. Advanced denoising included
+ multi-echo independent component analysis (ME-ICA) for the MBME
+ data and ICA-AROMA for the MB data. Resting state functional
+ connectivity (RSFC) was evaluated using both selective seed-based
+ and whole grey matter (GM) region-of-interest (ROI) based
+ approaches. The reproducibility of connectivity metrics was also
+ analyzed in the repeat subjects. In addition, functional
+ connectivity density (FCD), a data-driven approach that counts
+ the number of significant connections, both within a local
+ cluster and globally, with each voxel was analyzed. Regardless of
+ the standard or advanced denoising technique, all seed-based RSFC
+ was significantly higher for MBME compared to MB. Much more GM
+ ROI combinations showed significantly higher RSFC for MBME vs.
+ MB. Reproducibility, evaluated using the dice coefficient was
+ significantly higher for MBME relative to MB data. Finally, FCD
+ was also higher for MBME vs. MB data. This study showed higher
+ RSFC for MBME vs. MB data using selected seed-based, whole GM
+ ROI-based, and data-driven approaches. Reproducibility found also
+ higher for MBME data. Taken together, these results indicate that
+ MBME is a promising technique for rs-fMRI.",
+ journal = "Neuroimage",
+ volume = 225,
+ pages = "117461",
+ month = jan,
+ year = 2021,
+ keywords = "Functional connectivity density; Multi-echo; Multi-echo
+ independent component analysis; Multiband; Reproducibility;
+ Resting state functional MRI",
+ language = "en",
+ doi = {10.1016/j.neuroimage.2020.117461}
+}
+
+@ARTICLE{Lynch2020-tz,
+ title = "Rapid Precision Functional Mapping of Individuals Using
+ {Multi-Echo} {fMRI}",
+ author = "Lynch, Charles J and Power, Jonathan D and Scult, Matthew A and
+ Dubin, Marc and Gunning, Faith M and Liston, Conor",
+ abstract = "Resting-state functional magnetic resonance imaging (fMRI) is
+ widely used in cognitive and clinical neuroscience, but
+ long-duration scans are currently needed to reliably characterize
+ individual differences in functional connectivity (FC) and brain
+ network topology. In this report, we demonstrate that multi-echo
+ fMRI can improve the reliability of FC-based measurements. In
+ four densely sampled individual humans, just 10 min of multi-echo
+ data yielded better test-retest reliability than 30 min of
+ single-echo data in independent datasets. This effect is
+ pronounced in clinically important brain regions, including the
+ subgenual cingulate, basal ganglia, and cerebellum, and is linked
+ to three biophysical signal mechanisms (thermal noise, regional
+ variability in the rate of T decay, and S-dependent artifacts)
+ with spatially distinct influences. Together, these findings
+ establish the potential utility of multi-echo fMRI for rapid
+ precision mapping using experimentally and clinically tractable
+ scan times and will facilitate longitudinal neuroimaging of
+ clinical populations.",
+ journal = "Cell Reports",
+ volume = 33,
+ number = 12,
+ pages = "108540",
+ month = dec,
+ year = 2020,
+ keywords = "functional brain networks; multi-echo fMRI; precision functional
+ mapping; test-retest reliability",
+ language = "en",
+ doi = {10.1016/j.celrep.2020.108540}
+}
+
+@ARTICLE{Chang2009-bj,
+ title = "Relationship between respiration, end-tidal {CO2}, and {BOLD}
+ signals in resting-state {fMRI}",
+ author = "Chang, Catie and Glover, Gary H",
+ abstract = "A significant component of BOLD fMRI physiological noise is
+ caused by variations in the depth and rate of respiration. It has
+ previously been demonstrated that a breath-to-breath metric of
+ respiratory variation (respiratory volume per time; RVT),
+ computed from pneumatic belt measurements of chest expansion, has
+ a strong linear relationship with resting-state BOLD signals
+ across the brain. RVT is believed to capture breathing-induced
+ changes in arterial CO(2), which is a cerebral vasodilator;
+ indeed, separate studies have found that spontaneous fluctuations
+ in end-tidal CO(2) (PETCO(2)) are correlated with BOLD signal
+ time series. The present study quantifies the degree to which RVT
+ and PETCO(2) measurements relate to one another and explain
+ common aspects of the resting-state BOLD signal. It is found that
+ RVT (particularly when convolved with a particular impulse
+ response, the ``respiration response function'') is highly
+ correlated with PETCO(2), and that both explain remarkably
+ similar spatial and temporal BOLD signal variance across the
+ brain. In addition, end-tidal O(2) is shown to be largely
+ redundant with PETCO(2). Finally, the latency at which PETCO(2)
+ and respiration belt measures are correlated with the time series
+ of individual voxels is found to vary across the brain and may
+ reveal properties of intrinsic vascular response delays.",
+ journal = "Neuroimage",
+ volume = 47,
+ number = 4,
+ pages = "1381--1393",
+ month = oct,
+ year = 2009,
+ language = "en",
+ doi = {10.1016/j.neuroimage.2009.04.048}
+}
+
+@ARTICLE{Peters2007-lc,
+ title = "T2* measurements in human brain at 1.5, 3 and 7 {T}",
+ author = "Peters, Andrew M and Brookes, Matthew J and Hoogenraad, Frank G
+ and Gowland, Penny A and Francis, Susan T and Morris, Peter G and
+ Bowtell, Richard",
+ abstract = "Measurements have been carried out in six subjects at magnetic
+ fields of 1.5, 3 and 7 T, with the aim of characterizing the
+ variation of T2* with field strength in human brain. Accurate
+ measurement of T2* in the presence of macroscopic magnetic field
+ inhomogeneity is problematic due to signal decay resulting from
+ through-slice dephasing. The approach employed here allowed the
+ signal decay due to through-slice dephasing to be characterized
+ and removed from data, thus facilitating an accurate measurement
+ of T2* even at ultrahigh field. Using double inversion recovery
+ turbo spin-echo images for tissue classification, an analysis of
+ T2* relaxation times in cortical grey matter and white matter was
+ carried out, along with an evaluation of the variation of T2*
+ with field strength in the caudate nucleus and putamen. The
+ results show an approximately linear increase in relaxation rate
+ R2* with field strength for all tissues, leading to a greater
+ range of relaxation times across tissue types at 7 T that can be
+ exploited in high-resolution T2*-weighted imaging.",
+ journal = "Magnetic Resonance Imaging",
+ volume = 25,
+ number = 6,
+ pages = "748--753",
+ month = jul,
+ year = 2007,
+ language = "en",
+ doi = {10.1016/j.mri.2007.02.014}
+}
+
+@ARTICLE{Jenkinson2012-eh,
+ title = "{FSL}",
+ author = "Jenkinson, Mark and Beckmann, Christian F and Behrens, Timothy E
+ J and Woolrich, Mark W and Smith, Stephen M",
+ abstract = "FSL (the FMRIB Software Library) is a comprehensive library of
+ analysis tools for functional, structural and diffusion MRI brain
+ imaging data, written mainly by members of the Analysis Group,
+ FMRIB, Oxford. For this NeuroImage special issue on ``20 years of
+ fMRI'' we have been asked to write about the history,
+ developments and current status of FSL. We also include some
+ descriptions of parts of FSL that are not well covered in the
+ existing literature. We hope that some of this content might be
+ of interest to users of FSL, and also maybe to new research
+ groups considering creating, releasing and supporting new
+ software packages for brain image analysis.",
+ journal = "Neuroimage",
+ volume = 62,
+ number = 2,
+ pages = "782--790",
+ month = aug,
+ year = 2012,
+ language = "en",
+ doi = {10.1016/j.neuroimage.2011.09.015}
+}
+
+@ARTICLE{Silvennoinen2003-kg,
+ title = "Comparison of the dependence of blood {R2} and R2* on oxygen
+ saturation at 1.5 and 4.7 Tesla",
+ author = "Silvennoinen, M J and Clingman, C S and Golay, X and Kauppinen, R
+ A and van Zijl, P C M",
+ abstract = "Gradient-echo (GRE) blood oxygen level-dependent (BOLD) effects
+ have both intra- and extravascular contributions. To better
+ understand the intravascular contribution in quantitative terms,
+ the spin-echo (SE) and GRE transverse relaxation rates, R(2) and
+ R(2)(*), of isolated blood were measured as a function of
+ oxygenation in a perfusion system. Over the normal oxygenation
+ saturation range of blood between veins, capillaries, and
+ arteries, the difference between these rates, R'(2) = R(2)(*) -
+ R(2), ranged from 1.5 to 2.1 Hz at 1.5 T and from 26 to 36 Hz at
+ 4.7 T. The blood data were used to calculate the expected
+ intravascular BOLD effects for physiological oxygenation changes
+ that are typical during visual activation. This modeling showed
+ that intravascular DeltaR(2)(*) is caused mainly by R(2)
+ relaxation changes, namely 85\% and 78\% at 1.5T and 4.7T,
+ respectively. The simulations also show that at longer TEs (>70
+ ms), the intravascular contribution to the percentual BOLD change
+ is smaller at high field than at low field, especially for GRE
+ experiments. At shorter TE values, the opposite is the case. For
+ pure parenchyma, the intravascular BOLD signal changes originate
+ predominantly from venules for all TEs at low field and for short
+ TEs at high field. At longer TEs at high field, the capillary
+ contribution dominates. The possible influence of partial volume
+ contributions with large vessels was also simulated, showing
+ large (two- to threefold) increases in the total intravascular
+ BOLD effect for both GRE and SE.",
+ journal = "Magnetic Resonance in Medicine",
+ volume = 49,
+ number = 1,
+ pages = "47--60",
+ month = jan,
+ year = 2003,
+ language = "en",
+ doi = {10.1002/mrm.10355}
+}
+
+@ARTICLE{Power2018-ca,
+ title = "Ridding {fMRI} data of motion-related influences: Removal of
+ signals with distinct spatial and physical bases in multiecho
+ data",
+ author = "Power, Jonathan D and Plitt, Mark and Gotts, Stephen J and Kundu,
+ Prantik and Voon, Valerie and Bandettini, Peter A and Martin,
+ Alex",
+ abstract = "``Functional connectivity'' techniques are commonplace tools for
+ studying brain organization. A critical element of these analyses
+ is to distinguish variance due to neurobiological signals from
+ variance due to nonneurobiological signals. Multiecho fMRI
+ techniques are a promising means for making such distinctions
+ based on signal decay properties. Here, we report that multiecho
+ fMRI techniques enable excellent removal of certain kinds of
+ artifactual variance, namely, spatially focal artifacts due to
+ motion. By removing these artifacts, multiecho techniques reveal
+ frequent, large-amplitude blood oxygen level-dependent (BOLD)
+ signal changes present across all gray matter that are also
+ linked to motion. These whole-brain BOLD signals could reflect
+ widespread neural processes or other processes, such as
+ alterations in blood partial pressure of carbon dioxide (pCO) due
+ to ventilation changes. By acquiring multiecho data while
+ monitoring breathing, we demonstrate that whole-brain BOLD
+ signals in the resting state are often caused by changes in
+ breathing that co-occur with head motion. These widespread
+ respiratory fMRI signals cannot be isolated from neurobiological
+ signals by multiecho techniques because they occur via the same
+ BOLD mechanism. Respiratory signals must therefore be removed by
+ some other technique to isolate neurobiological covariance in
+ fMRI time series. Several methods for removing global artifacts
+ are demonstrated and compared, and were found to yield fMRI time
+ series essentially free of motion-related influences. These
+ results identify two kinds of motion-associated fMRI variance,
+ with different physical mechanisms and spatial profiles, each of
+ which strongly and differentially influences functional
+ connectivity patterns. Distance-dependent patterns in covariance
+ are nearly entirely attributable to non-BOLD artifacts.",
+ journal = "Proceedings of the National Academy of Sciences of the United States of America",
+ volume = 115,
+ number = 9,
+ pages = "E2105--E2114",
+ month = feb,
+ year = 2018,
+ keywords = "fMRI; functional connectivity; motion artifact; multiecho;
+ respiration",
+ language = "en",
+ doi = {10.1073/pnas.1720985115}
+}
+
+% The entry below contains non-ASCII chars that could not be converted
+% to a LaTeX equivalent.
+@ARTICLE{Murphy2013-vo,
+ title = "Resting-state {fMRI} confounds and cleanup",
+ author = "Murphy, Kevin and Birn, Rasmus M and Bandettini, Peter A",
+ abstract = "The goal of resting-state functional magnetic resonance imaging
+ (fMRI) is to investigate the brain's functional connections by
+ using the temporal similarity between blood oxygenation level
+ dependent (BOLD) signals in different regions of the brain ``at
+ rest'' as an indicator of synchronous neural activity. Since this
+ measure relies on the temporal correlation of fMRI signal changes
+ between different parts of the brain, any non-neural
+ activity-related process that affects the signals will influence
+ the measure of functional connectivity, yielding spurious
+ results. To understand the sources of these resting-state fMRI
+ confounds, this article describes the origins of the BOLD signal
+ in terms of MR physics and cerebral physiology. Potential
+ confounds arising from motion, cardiac and respiratory cycles,
+ arterial CO₂ concentration, blood pressure/cerebral
+ autoregulation, and vasomotion are discussed. Two classes of
+ techniques to remove confounds from resting-state BOLD time
+ series are reviewed: 1) those utilising external recordings of
+ physiology and 2) data-based cleanup methods that only use the
+ resting-state fMRI data itself. Further methods that remove noise
+ from functional connectivity measures at a group level are also
+ discussed. For successful interpretation of resting-state fMRI
+ comparisons and results, noise cleanup is an often over-looked
+ but essential step in the analysis pipeline.",
+ journal = "Neuroimage",
+ volume = 80,
+ pages = "349--359",
+ month = oct,
+ year = 2013,
+ keywords = "Functional connectivity; Functional magnetic resonance imaging
+ (fMRI); Noise correction; Physiological noise; Resting-state",
+ language = "en",
+ doi = {10.1016/j.neuroimage.2013.04.001}
+}
+
+@ARTICLE{Caballero-Gaudes2017-ix,
+ title = "Methods for cleaning the {BOLD} {fMRI} signal",
+ author = "Caballero-Gaudes, C{\'e}sar and Reynolds, Richard C",
+ abstract = "Blood oxygen-level-dependent functional magnetic resonance
+ imaging (BOLD fMRI) has rapidly become a popular technique for
+ the investigation of brain function in healthy individuals,
+ patients as well as in animal studies. However, the BOLD signal
+ arises from a complex mixture of neuronal, metabolic and vascular
+ processes, being therefore an indirect measure of neuronal
+ activity, which is further severely corrupted by multiple
+ non-neuronal fluctuations of instrumental, physiological or
+ subject-specific origin. This review aims to provide a
+ comprehensive summary of existing methods for cleaning the BOLD
+ fMRI signal. The description is given from a methodological point
+ of view, focusing on the operation of the different techniques in
+ addition to pointing out the advantages and limitations in their
+ application. Since motion-related and physiological noise
+ fluctuations are two of the main noise components of the signal,
+ techniques targeting their removal are primarily addressed,
+ including both data-driven approaches and using external
+ recordings. Data-driven approaches, which are less specific in
+ the assumed model and can simultaneously reduce multiple noise
+ fluctuations, are mainly based on data decomposition techniques
+ such as principal and independent component analysis.
+ Importantly, the usefulness of strategies that benefit from the
+ information available in the phase component of the signal, or in
+ multiple signal echoes is also highlighted. The use of global
+ signal regression for denoising is also addressed. Finally,
+ practical recommendations regarding the optimization of the
+ preprocessing pipeline for the purpose of denoising and future
+ venues of research are indicated. Through the review, we
+ summarize the importance of signal denoising as an essential step
+ in the analysis pipeline of task-based and resting state fMRI
+ studies.",
+ journal = "Neuroimage",
+ volume = 154,
+ pages = "128--149",
+ month = jul,
+ year = 2017,
+ keywords = "BOLD fMRI; Denoising methods; Motion artifacts; Multi-echo;
+ Phase-based methods; Physiological noise",
+ language = "en",
+ doi = {10.1016/j.neuroimage.2016.12.018}
+}
+
+@MISC{asyraff2020stimulus,
+ title = "Stimulus-independent neural coding of event semantics:
+ Evidence from cross-sentence fMRI decoding",
+ author = "Asyraff, Aliff and Lemarchand, Rafael and Tamm, Andres and Hoffman, Paul",
+ journal = "bioRxiv",
+ year = 2020,
+ doi = {10.1101/2020.10.06.327817},
+ url = {https://www.biorxiv.org/content/10.1101/2020.10.06.327817v1}
+}
+
+@ARTICLE{Stocker2006-ae,
+ title = "Dependence of amygdala activation on echo time: results from
+ olfactory {fMRI} experiments",
+ author = "St{\"o}cker, Tony and Kellermann, Thilo and Schneider, Frank and
+ Habel, Ute and Amunts, Katrin and Pieperhoff, Peter and Zilles,
+ Karl and Shah, N Jon",
+ abstract = "Echo time dependence of the BOLD sensitivity is an important
+ topic in fMRI whenever brain regions are considered where the EPI
+ data quality suffers from susceptibility gradients. Here, an fMRI
+ study is presented showing that a reduced echo time EPI sequence
+ significantly enhances the statistical inference in subcortical
+ (limbic) brain regions, with special focus on the amygdala. As a
+ consequence, to facilitate whole-brain fMRI with optimal echo
+ times, a sequence with slice-dependent echo time is demonstrated
+ with a focus on structures suffering from susceptibility changes.
+ The applicability of this method is shown in a second fMRI study
+ aimed at both, cortical, and limbic brain regions. The results
+ are in good agreement with theoretical descriptions of the BOLD
+ sensitivity under the influence of susceptibility gradients.",
+ journal = "Neuroimage",
+ volume = 30,
+ number = 1,
+ pages = "151--159",
+ month = mar,
+ year = 2006,
+ language = "en",
+ doi = {10.1016/j.neuroimage.2005.09.050}
+}
+
+@ARTICLE{Moia2020-bb,
+ title = "Voxelwise optimization of hemodynamic lags to improve regional
+ {CVR} estimates in breath-hold {fMRI}",
+ author = "Moia, Stefano and Stickland, Rachael C and Ayyagari, Apoorva and
+ Termenon, Maite and Caballero-Gaudes, Cesar and Bright, Molly G",
+ abstract = "Cerebrovascular Reactivity (CVR), the responsiveness of blood
+ vessels to a vasodilatory stimulus, is an important indicator of
+ cerebrovascular health. Assessing CVR with fMRI, we can measure
+ the change in the Blood Oxygen Level Dependent (BOLD) response
+ induced by a change in CO2 pressure (\%BOLD/mmHg). However, there
+ exists a temporal offset between the recorded CO2 pressure and
+ the local BOLD response, due to both measurement and
+ physiological delays. If this offset is not corrected for,
+ voxel-wise CVR values will not be accurate. In this paper, we
+ propose a framework for mapping hemodynamic lag in breath-hold
+ fMRI data. As breath-hold tasks drive task-correlated head motion
+ artifacts in BOLD fMRI data, our framework for lag estimation
+ fits a model that includes polynomial terms and head motion
+ parameters, as well as a shifted variant of the CO2 regressor
+ ($\pm$9 s in 0.3 s increments), and the hemodynamic lag at each
+ voxel is the shift producing the maximum total model R2 within
+ physiological constraints. This approach is evaluated in 8
+ subjects with multi-echo fMRI data, resulting in robust maps of
+ hemodynamic delay that show consistent regional variation across
+ subjects, and improved contrast-to-noise compared to methods
+ where motion regression is ignored or performed earlier in
+ preprocessing.Clinical Relevance- We map hemodynamic lag using
+ breathhold fMRI, providing insight into vascular transit times
+ and improving the regional accuracy of cerebrovascular reactivity
+ measurements.",
+ journal = "Conference proceedings - IEEE engineering in medicine and biology society",
+ volume = 2020,
+ pages = "1489--1492",
+ month = jul,
+ year = 2020,
+ language = "en",
+ doi = {10.1109/EMBC44109.2020.9176225}
+}
+
+@ARTICLE{Moia2021-ti,
+ title = "{ICA-based} Denoising Strategies in {Breath-Hold} Induced
+ Cerebrovascular Reactivity Mapping with Multi Echo {BOLD} {fMRI}",
+ author = "Moia, Stefano and Termenon, Maite and Uru{\~n}uela, Eneko and
+ Chen, Gang and Stickland, Rachael C and Bright, Molly G and
+ Caballero-Gaudes, C{\'e}sar",
+ 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 subjectspecific CVR and
+ haemodynamic lag estimates. The evaluated approaches range from
+ conventional regression models including drifts and motion
+ timecourses as nuisance regressors applied on singleecho 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 demonstrate 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.
+ \#\#\# Competing Interest Statement The authors have declared no
+ competing interest.",
+ journal = {NeuroImage},
+ pages = {117914},
+ year = {2021},
+ issn = {1053-8119},
+ doi = {10.1016/j.neuroimage.2021.117914},
+ url = {https://www.sciencedirect.com/science/article/pii/S1053811921001919},
+ language = "en"
+}
diff --git a/paper/paper.md b/paper/paper.md
new file mode 100644
index 000000000..5f69cb840
--- /dev/null
+++ b/paper/paper.md
@@ -0,0 +1,128 @@
+---
+title: 'TE-dependent analysis of multi-echo fMRI with *tedana*'
+tags:
+ - Python
+ - fMRI
+ - neuroimaging
+authors:
+ - name: Elizabeth DuPre^[co-first author]
+ affiliation: 1
+ orcid: 0000-0003-1358-196X
+ - name: Taylor Salo^[co-first author]
+ affiliation: 2
+ orcid: 0000-0001-9813-3167
+ - name: Zaki Ahmed
+ affiliation: 3
+ orcid: 0000-0001-5648-0590
+ - name: Peter A. Bandettini
+ affiliation: 4
+ orcid: 0000-0001-9038-4746
+ - name: Katherine L. Bottenhorn
+ affiliation: 2
+ orcid: 0000-0002-7796-8795
+ - name: César Caballero-Gaudes
+ affiliation: 5
+ orcid: 0000-0002-9068-5810
+ - name: Logan T. Dowdle
+ affiliation: 6
+ orcid: 0000-0002-1879-705X
+ - name: Javier Gonzalez-Castillo
+ affiliation: 4
+ orcid: 0000-0002-6520-5125
+ - name: Stephan Heunis
+ affiliation: 7
+ orcid: 0000-0003-3503-9872
+ - name: Prantik Kundu
+ affiliation: 8
+ orcid: 0000-0001-9367-3068
+ - name: Angela R. Laird
+ affiliation: 2
+ orcid: 0000-0003-3379-8744
+ - name: Ross Markello
+ affiliation: 1
+ orcid: 0000-0003-1057-1336
+ - name: Christopher J. Markiewicz
+ affiliation: 9
+ orcid: 0000-0002-6533-164X
+ - name: Stefano Moia
+ affiliation: 5
+ orcid: 0000-0002-2553-3327
+ - name: Isla Staden
+ affiliation: 10
+ orcid: 0000-0002-0795-1154
+ - name: Joshua B. Teves
+ affiliation: 4
+ orcid: 0000-0002-7767-0067
+ - name: Eneko Uruñuela
+ affiliation: 5
+ orcid: 0000-0001-6849-9088
+ - name: Maryam Vaziri-Pashkam
+ affiliation: 4
+ orcid: 0000-0003-1830-2501
+ - name: Kirstie Whitaker
+ affiliation: 11
+ orcid: 0000-0001-8498-4059
+ - name: Daniel A. Handwerker^[corresponding author]
+ affiliation: 4
+ orcid: 0000-0001-7261-4042
+affiliations:
+- index: 1
+ name: McGill University
+- index: 2
+ name: Florida International University
+- index: 3
+ name: Mayo Clinic
+- index: 4
+ name: National Institutes of Health
+- index: 5
+ name: Basque Center on Cognition, Brain and Language
+- index: 6
+ name: Center for Magnetic Resonance Research, University of Minnesota
+- index: 7
+ name: Eindhoven University of Technology
+- index: 8
+ name: Mount Sinai Hospital
+- index: 9
+ name: Stanford University
+- index: 10
+ name: Thought Machine
+- index: 11
+ name: Alan Turing Institute
+date: 09 March 2021
+bibliography: paper.bib
+---
+
+# Summary
+
+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 [@Logothetis2002-af]. 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 [@Silvennoinen2003-kg]. 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.
+
+Even as optimal TE values vary by brain region, most whole-brain fMRI scans are "single-echo," where signal is collected at a fixed TE everywhere in the brain. This TE value is often based on either a value that is best on average across all brain regions or an optimised value for a specific region of interest [@Stocker2006-ae; @Peters2007-lc]. Generally, these choices reflect a tradeoff between BOLD weighting, overall signal-to-noise ratio (SNR), and signal loss due to magnetic susceptibility artifacts. Further, for any TE with BOLD signal there is also susceptibility to contamination from noise sources including head motion, respiration, and cardiac pulsation [@Chang2009-bj; @Power2018-ca; @Murphy2013-vo; @Caballero-Gaudes2017-ix].
+
+Rather than collect data at a single TE, an alternative approach is to collect multiple TEs (that is, multiple echos) for each time point. This approach, also known as multi-echo fMRI, has several benefits, including allowing researchers to estimate each voxel's T~2~^\*^ value, combining echos [@Posse1999-lt], recovering signal in regions typically not sampled at longer echo times [@Kundu2013-xm], and improving activation and connectivity mapping [@Gonzalez-Castillo2016-tj; @Caballero-Gaudes2019-lv; @Lynch2020-tz] even in real time fMRI [@Heunis2020-bd]. In addition, artifactual non-T~2~^\*^ changes (known as S~0~ in this context) may be identified and removed by leveraging the relationship between BOLD contrast and T~2~^\*^ obtained with multi-echo fMRI [@Kundu2012-bq]. Strategies to perform this efficiently and robustly are in active development.
+
+Continuing these efforts, we present *tedana* (TE-Dependent ANAlysis) as an open-source Python package for processing and denoising multi-echo fMRI data. *tedana* implements two approaches to multi-echo preprocessing: (1) estimating a T~2~^\*^ map and using these values to generate a weighted sum of individual echos, and (2) using echo-time dependent information in analysis and denoising [@Kundu2012-bq].
+
+# Statement of Need
+
+To date, multi-echo fMRI has not been widely adopted within the neuroimaging community. This is likely due to two constraints: (1) until recently, the lack of available multi-echo fMRI acquisition protocols, and (2) the lack of software for processing multi-echo fMRI data in a way that integrates with existing platforms, such as AFNI [@Cox1996-up], SPM [@Penny2011-vi], FSL [@Jenkinson2012-eh], and fMRIPrep [@Esteban2020-ul].
+
+*tedana* helps to address these gaps both as a software tool and as a community of practice. We have tightly scoped *tedana* processing to focus on those portions of the fMRI analysis workflow which are multi-echo specific in order to maximize their compatibility with other community tools. The primary interfaces for users are (1) a ``t2smap`` workflow, which estimates voxel-wise T~2~^\*^ and S~0~ and combines data across echos to increase temporal SNR, and (2) a full ``tedana`` workflow, which performs the same steps as the ``t2smap`` workflow and additionally performs ICA-based denoising to remove components exhibiting noise-like signal decay patterns across echos [@Kundu2012-bq]. The ``tedana`` workflow additionally generates interactive HTML reports through which users may visually inspect their denoising results and evaluate each component’s classification. An example report is presented in \autoref{fig:report}.
+
+The limited focus and modularity of each workflow allows for easy integration into existing fMRI processing platforms. Individual modules also allow researchers to flexibly perform T~2~^\*^/S~0~ estimation, combination across echos, decomposition with PCA or ICA, and component selection outside of a specific workflow call. As a community of practice, *tedana* serves as a resource for researchers looking to learn more about multi-echo fMRI, from theory to collection to analysis. To specifically increase the availability of multi-echo protocols, *tedana’s* documentation (available at https://tedana.readthedocs.io) consolidates acquisition guidelines for multi-echo sequences across a variety of field strengths and scanner vendors, as well as general recommendations for balancing relevant trade-offs in fMRI acquisition parameter choices. It further serves to consolidate community knowledge, including guides explaining the underlying principles of multi-echo fMRI and information on publicly available multi-echo datasets and general recommendations for balancing relevant trade-offs in sequence development.
+
+Although *tedana* is still in alpha release, it has already been incorporated into fMRIPrep and is supported by AFNI. *tedana* has additionally been used in a number of publications and conference presentations [@Lynch2020-tz; @Moia2021-ti; @Moia2020-bb; @asyraff2020stimulus; @Cohen2021-ep]. We further hope that *tedana* will serve as a testing bed for new multi-echo related methods. To this end, we have developed a detailed contributing process and explicit project governance to encourage a healthy community and encourage other multi-echo research groups to contribute.
+
+*tedana* is installable via PyPi (``pip install tedana``) and contains extensive documentation (https://tedana.readthedocs.io) to orient researchers to multi-echo fMRI acquisition and processing.
+
+# Figures
+
+
+
+# Acknowledgements
+
+We would like to thank the Mozilla Open Leaders program, and the NIMH intramural research program, including the Section on Functional Imaging Methods and the Statistical and Scientific Computing Core, which have all provided funding or resources for *tedana* development.
+
+Funding for ARL, KLB, and TS was provided by NIH R01-DA041353 and NIH U01-DA041156.
+Funding for KJW was provided through The Alan Turing Institute under the EPSRC grant EP/N510129/1.
+
+# References
diff --git a/tedana/__init__.py b/tedana/__init__.py
index 468049f2d..c53332fec 100644
--- a/tedana/__init__.py
+++ b/tedana/__init__.py
@@ -7,7 +7,7 @@
import warnings
-from .due import Doi, due
+from .due import BibTeX, Doi, due
from .info import (
__author__,
__copyright__,
@@ -24,6 +24,14 @@
# cmp is not used, so ignore nipype-generated warnings
warnings.filterwarnings("ignore", r"cmp not installed")
+# Citation for the package JOSS paper.
+due.cite(
+ Doi("10.21105/joss.03669"),
+ description="Publication introducing tedana.",
+ path="tedana",
+ cite_module=True,
+)
+
# Citation for the algorithm.
due.cite(
Doi("10.1016/j.neuroimage.2011.12.028"),
diff --git a/tedana/decay.py b/tedana/decay.py
index 914126d23..5b2fbe2e7 100644
--- a/tedana/decay.py
+++ b/tedana/decay.py
@@ -37,7 +37,11 @@ def _apply_t2s_floor(t2s, echo_times):
echo_times = echo_times[:, None]
eps = np.finfo(dtype=t2s.dtype).eps # smallest value for datatype
- temp_arr = np.exp(-echo_times / t2s) # (E x V) array
+ nonzerovox = t2s != 0
+ # Exclude values where t2s is 0 when dividing by t2s.
+ # These voxels are also excluded from bad_voxel_idx
+ temp_arr = np.zeros((len(echo_times), len(t2s)))
+ temp_arr[:, nonzerovox] = np.exp(-echo_times / t2s[nonzerovox]) # (E x V) array
bad_voxel_idx = np.any(temp_arr == 0, axis=0) & (t2s != 0)
n_bad_voxels = np.sum(bad_voxel_idx)
if n_bad_voxels > 0:
diff --git a/tedana/reporting/dynamic_figures.py b/tedana/reporting/dynamic_figures.py
index d32ef280b..87a987eab 100644
--- a/tedana/reporting/dynamic_figures.py
+++ b/tedana/reporting/dynamic_figures.py
@@ -17,11 +17,8 @@
// -----------------------------
var components = data['component']
var selected = components[selected_idx]
- var selected_padded = '' + selected;
- while (selected_padded.length < 2) {
- selected_padded = '0' + selected_padded;
- }
- var selected_padded_forIMG = '0' + selected_padded
+ var selected_padded = String(selected).padStart(3,0)
+ var selected_padded_forIMG = selected_padded
var selected_padded_C = 'ica_' + selected_padded
// Find color for selected component
diff --git a/tedana/workflows/t2smap.py b/tedana/workflows/t2smap.py
index fc6a9a522..f2cee3497 100644
--- a/tedana/workflows/t2smap.py
+++ b/tedana/workflows/t2smap.py
@@ -161,6 +161,8 @@ def t2smap_workflow(
"""
Estimate T2 and S0, and optimally combine data across TEs.
+ Please remember to cite [1]_.
+
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
@@ -219,6 +221,16 @@ def t2smap_workflow(
will have a NaN.
desc-optcom_bold.nii.gz Optimally combined timeseries.
============================= =================================================
+
+ References
+ ----------
+ .. [1] DuPre, E. M., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L.,
+ Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S.,
+ Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S.,
+ Staden, I., Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M.,
+ Whitaker, K., & Handwerker, D. A. (2021).
+ TE-dependent analysis of multi-echo fMRI with tedana.
+ Journal of Open Source Software, 6(66), 3669. doi:10.21105/joss.03669.
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py
index b290a17c6..60e77e8d3 100644
--- a/tedana/workflows/tedana.py
+++ b/tedana/workflows/tedana.py
@@ -344,6 +344,8 @@ def tedana_workflow(
"""
Run the "canonical" TE-Dependent ANAlysis workflow.
+ Please remember to cite [1]_.
+
Parameters
----------
data : :obj:`str` or :obj:`list` of :obj:`str`
@@ -426,6 +428,16 @@ def tedana_workflow(
This workflow writes out several files. For a complete list of the files
generated by this workflow, please visit
https://tedana.readthedocs.io/en/latest/outputs.html
+
+ References
+ ----------
+ .. [1] DuPre, E. M., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L.,
+ Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S.,
+ Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S.,
+ Staden, I., Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M.,
+ Whitaker, K., & Handwerker, D. A. (2021).
+ TE-dependent analysis of multi-echo fMRI with tedana.
+ Journal of Open Source Software, 6(66), 3669. doi:10.21105/joss.03669.
"""
out_dir = op.abspath(out_dir)
if not op.isdir(out_dir):
@@ -530,7 +542,20 @@ def tedana_workflow(
elif t2smap is not None:
raise IOError("Argument 't2smap' must be an existing file.")
- RepLGR.info("TE-dependence analysis was performed on input data.")
+ RepLGR.info(
+ "TE-dependence analysis was performed on input data using the tedana workflow "
+ "(DuPre, Salo et al., 2021)."
+ )
+ RefLGR.info(
+ "DuPre, E. M., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L., "
+ "Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S., "
+ "Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S., "
+ "Staden, I., Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M., "
+ "Whitaker, K., & Handwerker, D. A. (2021). "
+ "TE-dependent analysis of multi-echo fMRI with tedana. "
+ "Journal of Open Source Software, 6(66), 3669. doi:10.21105/joss.03669."
+ )
+
if mask and not t2smap:
# TODO: add affine check
LGR.info("Using user-defined mask")
