From 3a2da0c646a262beea4bb48d141d72fec2397980 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Thu, 18 May 2023 16:29:46 -0400 Subject: [PATCH 1/8] To close 683 adding fmrwhy --- docs/multi-echo.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/multi-echo.rst b/docs/multi-echo.rst index 11b581810..0fd969349 100644 --- a/docs/multi-echo.rst +++ b/docs/multi-echo.rst @@ -529,6 +529,11 @@ For more details, see the `fmriprep workflows page`_ and :ref:`collecting fMRIPr .. _fmriprep workflows page: https://fmriprep.readthedocs.io/en/stable/workflows.html +`fmrwhy`_ runs BIDS-compatible fMRI analysis with SPM12 and supports multi-echo data, +but it is no longer being actively maintained. + +.. _fmrwhy: https://fmrwhy.readthedocs.io + Currently SPM and FSL do not natively support multi-echo fmri data processing. From 290a7a853c483b7bcb4afc6ebfa03baab85a8256 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Mon, 22 May 2023 15:55:06 -0400 Subject: [PATCH 2/8] edited multi-echo.rst on distortion correction --- docs/multi-echo.rst | 50 ++++++++++++++++++++++++--------------------- 1 file changed, 27 insertions(+), 23 deletions(-) diff --git a/docs/multi-echo.rst b/docs/multi-echo.rst index e106bbc34..89687835f 100644 --- a/docs/multi-echo.rst +++ b/docs/multi-echo.rst @@ -440,8 +440,25 @@ For single-echo EPI data, that excitation time would be the same regardless of t and the same is true when one is collecting multiple echoes after a single excitation pulse. Therefore, we suggest using the same slice timing for all echoes in an ME-EPI series. +3. Apply spatial normalization and susceptibility distortion correction consistently across echoes +================================================================================================== -3. Perform distortion correction, spatial normalization, smoothing, and any rescaling or filtering **after** denoising +One key feature of susceptibility distortion is that it is primarily a factor of readout pattern and +total readout time, rather than echo time. This means that, for most multi-echo sequences, even though +dropout will increase with echo time, distortion will not (at least not to a noticeable/meaningful extent). + +For this reason, if you are applying TOPUP-style (blip-up/blip-down) "field maps", +we recommend using your first echo time, as this will exhibit the least dropout. +If your first echo time is very short, and exhibits poor gray/white contrast, then a later echo time may +be preferable. In any case, you should calculate the spatial transform from just one of your echoes and +apply it across all of them. + +Similarly, if spatial normalization to a template is done, the spatial transform should be calculated +once and the same transformation (ideally on transformation for motion correction, distortion correction, +and spatial normalization) should be applied to all echoes. + + +4. Perform smoothing, and any rescaling or filtering **after** denoising ====================================================================================================================== Any step that will alter the relationship of signal magnitudes between echoes should occur after denoising and combining @@ -452,32 +469,19 @@ and the subsequent calculation of voxelwise T2* values will be distorted or inco time point. .. note:: - We are assuming that spatial normalization and distortion correction, particularly non-linear normalization methods - with higher order interpolation functions, are likely to distort the relationship between echoes while rigid body - motion correction would linearly alter each echo in a similar manner. This assumption has not yet been empirically - tested and an affine normalzation with bilinear interpolation may not distort the relationship between echoes. - Additionally, there are benefits to applying only one spatial transform to data rather than applying one spatial - transform for motion correction and a later transform for normalization and distortion correction. Our advice - against doing normalization and distortion correction is a conservative choice and we encourage additional - research to better understand how these steps can be applied before denoising. - - -4. Apply susceptibility distortion correction consistently across echoes -======================================================================== - -One key feature of susceptibility distortion is that it is primarily a factor of readout pattern and total readout time, rather than echo time. -This means that, for most multi-echo sequences, even though dropout will increase with echo time, -distortion will not (at least not to a noticeable/meaningful extent). - -For this reason, if you are applying TOPUP-style (blip-up/blip-down) "field maps", -we recommend using your first echo time, as this will exhibit the least dropout. -If your first echo time is very short, and exhibits poor gray/white contrast, then a later echo time may be preferable. -In any case, you should calculate the spatial transform from just one of your echoes and apply it across all of them. - + Spatial normalization and distortion correction, particularly non-linear normalization methods + with higher order interpolation functions to regrid voxels in a new space, may distort the relationship between + echoes more than bilinear interpolation. This has the potential to distort the relationship between echoes and + there have been anecdotal cases where this might be an issue. Still, since serial spatial transforms spatially + smooth the data and most modern pipeline combine all spatial transforms into a single step, we recommend doing + these steps before running denoising. Particularly for data with high intensity heterogeneity between the surface + and center of the brain, we recommend checking if distoration correction and normalization adversely affect the + relationship between echoes. .. _fMRIPrep: https://fmriprep.readthedocs.io .. _afni_proc.py: https://afni.nimh.nih.gov/pub/dist/doc/program_help/afni_proc.py.html + ***************** General Resources ***************** From 0b2c43b853ef5ff2e5c922788d65697949ed068c Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Thu, 31 Aug 2023 12:03:53 -0400 Subject: [PATCH 3/8] Added docs for 898 --- docs/approach.rst | 19 ++++++++++++------- docs/faq.rst | 19 +++++++++++++++++++ 2 files changed, 31 insertions(+), 7 deletions(-) diff --git a/docs/approach.rst b/docs/approach.rst index ae59ef0bd..94688043e 100644 --- a/docs/approach.rst +++ b/docs/approach.rst @@ -49,10 +49,16 @@ have good signal for some echoes. In order to avoid using bad signal from affected echoes in calculating :math:`T_{2}^*` and :math:`S_{0}` for a given voxel, ``tedana`` generates an adaptive mask, where the value for each voxel is the number of echoes with -"good" signal. -When :math:`T_{2}^*` and :math:`S_{0}` are calculated below, each voxel's values -are only calculated from the first :math:`n` echoes, where :math:`n` is the -value for that voxel in the adaptive mask. +"good" signal. The voxel in the shortest echo with the 33rd percentile signal +is identified. The threshold for each echo is the signal in the same voxel +divided by 3. This is an arbitrary, but conservative threshold in that it only +excludes voxels where the signal is much lower than other measured signals in +each echo. When :math:`T_{2}^*` and :math:`S_{0}` are calculated below, each +voxel's valusesare only calculated from the first :math:`n` echoes, where +:math:`n` is the value for that voxel in the adaptive mask. By default, the +optimally combined and denoised time series will include voxels where there +is at least one good echo, but ICA and the fit maps require at least three +good echoes. .. note:: ``tedana`` allows users to provide their own mask. @@ -60,9 +66,8 @@ value for that voxel in the adaptive mask. it further based on the data. If a mask is not provided, ``tedana`` runs :func:`nilearn.masking.compute_epi_mask` on the first echo's data to derive a mask prior to adaptive masking. - The workflow does this because the adaptive mask generation function - sometimes identifies almost the entire bounding box as "brain", and - ``compute_epi_mask`` restricts analysis to a more reasonable area. + Some brain masking is required because the percile-based thresholding + in the adaptive mask will be flawed if it includes all out-of-brain voxels. .. image:: /_static/a03_adaptive_mask.png :width: 600 px diff --git a/docs/faq.rst b/docs/faq.rst index 46bbb53f1..f5e24b334 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -63,6 +63,25 @@ The standard space template in this example is "MNI152NLin2009cAsym", but will d +************************************************ +[tedana] Why are there so few voxels in my mask? +************************************************ + +Several steps in ``tedana`` require excluding voxels outside of the brain. If the +provided data are not already masked, ``tedana`` runs +:func:`nilearn.masking.compute_epi_mask` to create a mask. This often works, but +it is a fairly simple method and there are better masking tools in most fMRI +processing pipelines, such as `AFNI`_. If your full preprocessing pipeline already +generates a mask for fMRI data, we recommend inputting that mask to ``tedana`` using +the ``--mask`` option. ``tedana`` additionally creates an adaptive mask that will +exclude voxels from some steps that have large dropout in later echoes. The adaptive +mask will flag some voxels in common dropout regions, but should not radically alter +the inputted mask. Here is more information on the +`creation and use of the adaptive mask`_. + +.. _AFNI: http://afni.nimh.nih.gov +.. _creation and use of the adaptive mask: approach.html#adaptive-mask-generation + ************************************ [tedana] ICA has failed to converge. ************************************ From 0533ed38c7bce6a4430c1131b5d6388747842b32 Mon Sep 17 00:00:00 2001 From: handwerkerd Date: Thu, 31 Aug 2023 14:14:55 -0400 Subject: [PATCH 4/8] Added docs for 942 --- docs/_static/carpet_overview.png | Bin 363366 -> 695388 bytes docs/_static/rep01_betaMap.png | Bin 120994 -> 158435 bytes docs/_static/rep01_fftPlot.png | Bin 16692 -> 12281 bytes docs/_static/rep01_kappaScree.png | Bin 28644 -> 24596 bytes docs/_static/rep01_kapparhoScatter.png | Bin 35611 -> 26623 bytes docs/_static/rep01_overallview.png | Bin 657931 -> 330840 bytes docs/_static/rep01_rhoScree.png | Bin 29340 -> 24796 bytes docs/_static/rep01_tsPlot.png | Bin 30163 -> 29049 bytes docs/_static/rep01_varexpPie.png 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