cobidas

cobidas#

You can see the full contents of this project on GitHub.

COBIDAS checklist#

<1-2 sentences describing the project>

Papers#

How the papers were obtained#

See the pubget query.txt file.

Annotation labels:#

Generated from the COBIDAS reproschema:

Created with /tools/create_labelbuddy_labels.py .

The labels contained here could be reused to label MRI, PET or eyetracking studies.

The labels for each subsection of the schema are stored in a separate file prefixed with a number: for example cobidas/5_preprocessing_labels.jsonl. But there is also a jsonl file that combines all labels from all sections.

Labels in this project#

toolbox (7 docs)

Example annotations:

…2 cm , number of averages = 1, data matrix = 64 × 64, voxel size = 3.4375 × 3.4375 × 4 mm , and 180 volumes lasting for 360 s. ### 2.3. Data preprocessing Based on MATLAB (MathWorks) platform, DPABI toolbox was used to preprocess rs-fMRI data. To allow magnetic saturation, the first five volumes of data were thrown away. Next, additional preprocessing was conducted with the following procedures: (1) sli…

…ownloaded on the website of Automated Human Habenula Segmentation Program according to a previously calculated functional magnetic resonance imaging study ( ). ### 2.5. Static FC analyses The Data Processing Assistant for Resting-State fMRI Advanced Edition (DPARSFA) software package in DPABI software is used for sFC analysis. The BOLD time series of the left and right habenula was extracted respectively, then the Pearson correlation coefficients with the whole brain’s time series were measured, an…

…on coefficients with the whole brain’s time series were measured, and finally the Fisher Z transformation values were used for subsequent statistical analysis. ### 2.6. Dynamic FC analyses The Temporary Dynamic Analysis software package in DPABI software is used for dFC analysis with the sliding window method ( ), which was employed to form dFC maps for each subject. According to the recommendations on dFC in previous researches ( ), in order to opti…

…uence with the following parameters: TR = 8.1 ms, TE = 3.2 ms, matrix size = 256 × 256, flip angle = 12°, FOV = 240 × 240 mm , 176 slices, and with 1.0 mm thickness (no gap). Data were processed in Matlab 2018b platform (MathWorks, Natick, MA) using the DPABI toolbox. For each subject, the first 10 functional images were discarded and were then corrected for differences in slice timing and head motion. The…

….2 ms, matrix size = 256 × 256, flip angle = 12°, FOV = 240 × 240 mm , 176 slices, and with 1.0 mm thickness (no gap). Data were processed in Matlab 2018b platform (MathWorks, Natick, MA) using the DPABI toolbox. For each subject, the first 10 functional images were discarded and were then corrected for differences in slice timing and head motion. The corrected images were coregistered to the T1-weighted ana…

…l, were regressed out. ### 2.5. Static functional connectivity analysis Based on the previous study ( ), the left TPJ seed was defined as a 10 mm radius sphere surrounding a central voxel ( ). Resting-State fMRI Data Analysis (REST) toolkit ( ) was used to calculate the seed-based resting-state SFC. The Pearson correlation coefficients between the time courses of the seed and the time series of each voxel of the whole brain were calcula…

…map with Fisher’s transformations to improve the normality of the data. Graphical representation of the prior defined region of interest (left temporoparietal junction area) was defined using the Marsbar toolbox (MNI: −50.7, −41.4, 22.7). ### 2.6. Dynamic functional connectivity analysis The DFC was computed by using a sliding window approach through the DynamicBC toolbox ( ). DFC calculates the corr…

…n area) was defined using the Marsbar toolbox (MNI: −50.7, −41.4, 22.7). ### 2.6. Dynamic functional connectivity analysis The DFC was computed by using a sliding window approach through the DynamicBC toolbox ( ). DFC calculates the correlation of time series according to a certain length of the sliding window. Based on the previous literature ( , ), the length of the sliding window was set a 50 TRs, and …

…ments. Whole-brain mask images were manually inspected and cleaned to remove excess parenchymal tissue using MRIcron ( ) to create a final whole-brain mask. BOLD fMRI images were preprocessed using SPM, FSL, and warping parameters from ANTs implemented in an automated pipeline run through Matlab (The MathWorks, Natick, MA, USA). The first 10 vol were dropped to allow for signal stabilization and th…

…. Whole-brain mask images were manually inspected and cleaned to remove excess parenchymal tissue using MRIcron ( ) to create a final whole-brain mask. BOLD fMRI images were preprocessed using SPM, FSL, and warping parameters from ANTs implemented in an automated pipeline run through Matlab (The MathWorks, Natick, MA, USA). The first 10 vol were dropped to allow for signal stabilization and the ‘to…

exclusions (1 docs)

Example annotations:

…n area) was defined using the Marsbar toolbox (MNI: −50.7, −41.4, 22.7). ### 2.6. Dynamic functional connectivity analysis The DFC was computed by using a sliding window approach through the DynamicBC toolbox ( ). DFC calculates the correlation of time series according to a certain length of the sliding window. Based on the previous literature ( , ), the length of the sliding window was set a 50 TRs, and …

interesting (0 docs)