subway_navigation_task

For opening all scripts we used in virtual subway navigation study. Here, we are grateful for all the supports given by Peter Zeidman in DCM analysis.

Introduction of Document Tree

Task Program

We used Psychopy v3.0 to present the whole task.

You can find the python scripts in Task_Program_Psychopy/.

Directory named "formal_experiment" contains the program used in formal experiment when fMRI scanning.

Directory named "train_stage" contains the program used in the training stage a day before scanning.

Linear Mixed Model Analysis

The linear mixed model which modeling the behavioral data of participants was built using R package lmerTest.

You can find the R scritps in Behavior_analysis/.

Step-1_Pre-analysis_data_resahpe.R is the script to shape the data recorded from the tasks which is located in behav_data_clean/. And subway_behaviordata_assumble_DA.csv is an example of the data after shaping by the pipeline.

Step-2_LMMs_Model_Construction.R is the script for two aims: (1) Estimating the effect of multiple planning complexities to the participants' RT (please see Fig.3a on our paper), and (2) testing the effect synchronicity of the planning complexities for influencing the RT and brain activity by Pearson's correlation (please see Fig.3c on our paper).

GLM1_roi_behavior/ contains the z-map of brain activity estimated by the GLM1, and the ROIs were identified by the DL modulation.

fMRI quality control

MRIqc_group/ contains the html report for the quality functional and structural images.

Universal Analysis of Neural Data

To detect the response of brain areas to computational costs, we built a general linear model 1 which contains four costs for parameteric modulation.

The GLM1 was conducted using FSL's feat. All *.fsf files in GLM1_related/ are design files using in run-level, subject-level and group-level.

The bash script BashRun_first_level_GLM1_Feat.sh is used for batching process of run-level GLM1.

The time series could be found in the directory named TimePointExtraction_GLM1.

Dynamic Causal Model Analysis

To investgate the neural architecture underlying hierarchical planning and test the connectivity in dorsomedial frontal cortext contributed to the individual difference in planning efficiency, DCM and PEB were performed. The scripts we ued are placed in the directory DCM_related/.

We first construct a subject-level GLM using SPM. Then we extracted the time series in each VOI, including the dorsomedial prefrontal cortex, the bilateral premotor cortex and the superior parietal cortex. The script used to build GLM and extract VOIs are Step1_GLM_all_pipeline_concate.m and Step2_GLM_pipeline_VOI_extraction.m.

The fist level DCM was specified using Step3_first_level_DCM.m.

The seond level DCM, including PEB analysis, auto-BMR, cross-validation and family-wise model comparison, was estimated using Step4_second_level_DCM.m.

DCM result visualization was conducted using R. The script is Step5_DCM_PEB_visualization.R.

All DCM pipeline was done using SPM12, following the guidance of part 1: First level analysis with DCM for fMRI and part 2: Second level analysis with PEB

QunjunLIANG / Subway navigational task Analysis Scripts

subway_navigation_task

Introduction of Document Tree

Task Program

Linear Mixed Model Analysis

fMRI quality control

Universal Analysis of Neural Data

Dynamic Causal Model Analysis

简介

发行版

贡献者

近期动态

QunjunLIANG / Subway navigational task Analysis Scripts .gitee-modal { width: 500px !important; }

subway_navigation_task

Introduction of Document Tree

Task Program

Linear Mixed Model Analysis

fMRI quality control

Universal Analysis of Neural Data

Dynamic Causal Model Analysis

简介

发行版

贡献者

近期动态

搜索帮助

QunjunLIANG / Subway navigational task Analysis Scripts