Effort_Less Processed fMRI

A) Overview This dataset includes preprocessed functional MRI (fMRI) data and force measurement data acquired during task-based paradigms investigating physical effort valuation. All data were collected, preprocessed, and organized for reuse in connectivity and time-series analyses. Data are stored in MATLAB file "matfiles_fMRI_Effort_Less.mat" format for compatibility with downstream processing workflows. Each subject folder contains approximately ~85 MB of data, and the full cohort remains within Zenodo’s 5 GB storage constraints.B) Participants Total subjects: 47 Missing / excluded: Subject 1, 9, 35: not included Subject 28: fMRI excluded due to intensity artifacts Subject-specific QC performed (see Section H) C) Experimental Tasks Task 1: Control condition Task 2: Ischemia (INB) condition Each task contains: 184 volumes per scan (preprocessed fMRI) D) fMRI Preprocessing Pipeline All fMRI data were preprocessed using FSL following standard pipelines and ICA-based denoising. Pipeline steps: A) Motion correctionB) Spatial smoothingC) Registration to the subject-specific T1-weighted anatomical imageD) Normalization to MNI-152 standard spaceE) ICA-based denoising (manual classification following Griffanti et al., 2016 / FIX framework) Additional notes: ICA-AROMA / FIX-based cleaning performed per subject Outputs include fully preprocessed functional images (Sub_XX_fMRI) All preprocessing scripts and analysis steps are documented in the associated GitHub repository E) Time-Series Extraction (ROI Data) fMRI time series were extracted using the Harvard-Oxford cortical atlas: Atlas: HarvardOxford-cort-maxprob-thr25-1mm.nii.gz Number of ROIs: 48 cortical regions Hemisphere handling: left/right averaged (no lateralization analysis) F) Data Format (IMPORTANT) Each subject file is stored in MATLAB .mat format: Structure: File naming:ROIs_sub_XX_taskn.mat Inside each file: MATLAB cell structure or matrix Dimensions: 48 ROIs × 184 time points Time-series properties: A) Sampling points: 184 per taskB) Repetition time (TR): 2.5 secondsC) Total scan duration: ~457.5 seconds per taskD) Signal type: preprocessed BOLD signal (denoised, normalized) G) Data Dictionary Variable Description ROI matrix 48 × 184 BOLD time series ROIs Harvard-Oxford cortical regions Time points 184 samples per task TR 2.5 s Space MNI-152 normalized Signal type ICA-denoised BOLD Format MATLAB cell/matrix Tasks Control (1), Ischemia (2) H) Force Data Force data were recorded using an MRI-compatible hand-grip device. Acquisition: in-house Python synchronization script Purpose: measure task-related effort exertion (control for activation profile ROIs) Stored per subject alongside fMRI data I) Quality Control (QC) Each subject folder includes a QC directory containing: ICA-AROMA outputs (or equivalent ICA-based QC) Default Mode Network visualization HTML reports for motion and artifact inspection Visual inspection of: Motion artifacts BOLD signal stability ICA component classification Ongoing QC procedure (planned/ongoing): Review of 184-volume × 44-subject dataset Identification of subjects requiring exlusion for motor artifacts Validation of normalization quality J) Data Validation Strategy Cross-validation is performed using: Connectivity structure Noise profiles across acquisition systems QC consistency across scanners LOSO K) Missing / Excluded Data Subjects 1, 9, 35: not included Subject 28: excluded due to intensity artifacts L) File Structure ROI_Task1Vs2/ subject-wise folders Task 1 & Task 2 ROI matrices Sub_XX_fMRI/ preprocessed NIfTI outputs QC/ ICA and motion reports .mat files final ROI time series M) Code & Reproducibility All preprocessing and analysis scripts are available at: https://github.com/diegomlombardo/Effort_Perception "behavioral_fMRIFD_data_Effort_Less.mat" includes the Physical Effort Scale (PES) total score in a subsample of 44 participants with complete rs-fMRI data, alongside framewise displacement metrics indexing in-scanner head motion N) Reference Griffanti L, et al. (2017)Hand classification of fMRI ICA noise components.NeuroImage, 154:188–205.https://doi.org/10.1016/j.neuroimage.2016.12.036 O) Suggested Citation If you use this dataset, please cite: Lombardo, D. et al. Effort_Less Processed fMRI Dataset. Zenodo.