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🧠 hackathonTo assess during the hackathonTo assess during the hackathonSPMflexible factorial designraw✨ goal: improvementImprovement to an existing featureImprovement to an existing feature
Description
Softwares
SPM
Input data
raw data
Additional context
see description below
List of tasks
Please tick the boxes below once the corresponding task is finished. 👍
- 👌 A maintainer of the project approved the issue, by assigning a 🏁
status: ready for devlabel to it. - 🌳 Create a branch on your fork to start the reproduction.
- 🌅 Create a file
team_{team_id}.pyinside thenarps_open/pipelines/directory. You can use a file insidenarps_open/pipelines/templatesfile as a template if needed. - 🧠 Write the code for the pipeline, using Nipype and the file architecture described in docs/pipelines.md.
- 📘 Make sure your code is documented enough.
- 🐍 Make sure your code is explicit and conforms with PEP8.
- 🔬 Create tests for your pipeline. You can use files in
tests/pipelines/test_team_*as examples. - 🔬 Make sure your code passes all the tests you created (see docs/testing.md).
- 📥 create a pull request from your code.
NARPS team description : 0ED6
General
teamID: 0ED6NV_collection_link: https://neurovault.org/collections/4994/results_comments: NApreregistered: Nolink_preregistration_form: NAregions_definition: Anatomical definition of ROIs
vmPFC: combination of Jülich cytoarchitectonic maps from the SPM Anatomy Toolbox (c 3), P >= 0.2: OFC_Fo1, OFC_Fo2, FP2, Cingul_s32 (left and right hemisphere)
amygdala: as described above including the following maps: SF, MF, IF, LB, CM (left and right)
- ventral striatum: from the striatum atlas included in FSL (5.0.10)
softwares: SPM12 v6685 www.fil.ion.ucl.ac.uk/spm/ ,
Matlab R2018a (9.4.0.813654) www.mathworks.com/products/matlab & Insight & Inference for DVARS http://www.nisox.org/Software/DSE/general_comments: NA
Exclusions
n_participants: 108exclusions_details: NA
Preprocessing
used_fmriprep_data: Nopreprocessing_order: 1. motion correction
- intersubject registration (normalization)
- spatial smoothing
brain_extraction: NAsegmentation: NAslice_time_correction: NAmotion_correction: SPM12, Realign & Unwarp using the Phase map created with SPM12 Fieldmap Toolbox v2.1 (default options): Other than defaults: Estimation: Quality 0.95, Speparation 3, Register to mean, Interpolation 7th Degree B-Spline; Unwarp Reslice: Interpolation 7th Degree B-Splinemotion:gradient_distortion_correction: The single-band reference EPI was also distortion corrected using the SPM12 Fieldmap Toolbox v2.1intra_subject_coreg: NAdistortion_correction: NAinter_subject_reg: SPM12: Within each run, the distortion corrected single-band reference EPI was co-registered to the mean EPI from Realignment & Warp using normalised mutual information. Then distortion corrected single-band reference EPI was co-registered to the gray matter probability map in the Old Segmentation toolbox in SPM using normalised mutual information and the distortion corrected EPI time-series as well as the mean EPI remained aligned. The single-band reference EPI was normalized to the SPM MNI152 template space using the classic Unified Segmentation approach in the Old Segment function in SPM, while mitigating overfitting by setting the warp frequency cutoff to 45 limiting the discrete cosine transform (DCT) bases and setting the sampling distance to 2. The resulting deformation field was applied to the distortion corrected EPI time-series, the mean EPI and the single-band reference EPI.intensity_correction: NAintensity_normalization: NAnoise_removal: NAvolume_censoring: To censor time-points significantly influences by noise the DVARS inference approach by Afyouni S. & Nichols T.E, (2017) was applied to each session for all subjects independently. Currupted time-points were identified using the DVARSCalc function.spatial_smoothing: SPM12, 5mm smoothing with a fixed kernel in MNI152 spacepreprocessing_comments: NA
Analysis
data_submitted_to_model: 4 sessions of 449 time points, 108 subjectsspatial_region_modeled: NAindependent_vars_first_level: We applied an event-related design with each trial modeled as epochs of 4 sec duration with 3 parametric modulators [gain, loss, reaction time] orthogonalized by demeaning against the task and the respective preceding modulator. The canonical HRF were used for convolution including the temporal derivative. Additionally 6 motion regressors as obtained by realignment were added as regressors of no interest. Also time-points significantly influences by noise as flagged by the DVARS inference approach by Afyouni S. & Nichols T.E, (2017) were censored via an additional regressor. For each participant, all 4 sessions were modeled in one 1st Level design and contrast images for each regressor of interest were computed: [task, gain, loss, raction time].RT_modeling: pmmovement_modeling: 1independent_vars_higher_level: We applied one flexible factorial design to examine the effects of the following 4 factors of interest for the two groups, equal Indifference and equal Range: [task, gain, loss, reaction time] resulting in 8 conditions on the 2nd level.model_type: Mass Univariatemodel_settings: 1st-level: with autocorrelation model in SPM [AR(1) + w] and a high-pass filter of128 s
2nd-level: random-effects GLM with weighted least squares in SPM (restricted maximum likelihood estimation) with both between-condition and between-group variances modeled as unequalinference_contrast_effect: We estimated linear T-contrasts for the two parametric modulators [gain, loss] in both groups to test for the effects of the 9 hypotheses.search_region: NAstatistic_type: peak-wisepval_computation: NAmultiple_testing_correction: Familywise Error correction via Random Field Theorycomments_analysis: NA
Categorized for analysis
region_definition_vmpfc: atlas Jülich cytoarchitectonicregion_definition_striatum: atlas Jülich cytoarchitectonicregion_definition_amygdala: atlas Jülich cytoarchitectonicanalysis_SW: SPManalysis_SW_with_version: SPM12smoothing_coef: 5testing: parametrictesting_thresh: adaptivecorrection_method: GRTFWE voxelwisecorrection_thresh_: p<0.05
Derived
n_participants: 108excluded_participants: n/afunc_fwhm: 5con_fwhm:
Comments
excluded_from_narps_analysis: Noexclusion_comment: N/Areproducibility: 2reproducibility_comment:
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🧠 hackathonTo assess during the hackathonTo assess during the hackathonSPMflexible factorial designraw✨ goal: improvementImprovement to an existing featureImprovement to an existing feature
Type
Projects
Status
Needs improvement