The Neural mechanism of Nature-based intervention with Environmental information of Forest

By Chun-Yi, & Lee
Published on June 9, 2023

"Researchers will use fMRI to study how short-term exposure to nature affects the brain. They hypothesize that nature exposure will improve cognitive function and reduce noisy information processing in the VMPFC."

Project definition


Increasingly, research has shown that Nature-Based Interventions (NBIs) have positive effects on cognitive function and mental health. One study showed that participants who walked through an arboretum performed significantly better on memory and directed attention tasks, and increased positive affect more than did an urban-walking group. However, the different environmental preferences of individuals might moderate or confound the cognitive benefits of NBIs. It also remains unclear what specific mechanisms might link the neural processing of environmental information with cognitive benefits.


The “The Neural mechanism of Nature-based intervention with Environmental information of Forest” project will rely on the following technologies:

  • HeuDiConv for BIDS
  • fMRIPrep for preprocessing
  • Pliers package for automated feature extraction


Self-recruiting project Location: Taiwan Mind & Brain Imaging Center at National Chengchi University Participants: N = 60 (30 pro-nature, 30 pro-urban) Stimulus: 10 min video with 5 scenes for both natural and urban environment


At the end of this project, we will have:

  • Analyze workflow for BIDS conversion and fMRIPrep
  • Code script for Naturalistic paradigm analysis


Progress overview

The project was swiftly initiated by Chun-Yi Lee, about the naturalistic paradigm and nature-based intervention.

Tools I learned during this project

  • HeuDiConv for BIDS
  • fMRIPrep for preprocessing
  • Pliers package for automated feature extraction


Deliverable: results

  • Convert the raw dicom files into BIDS format by HeuDiConv tools
  • Create a container in server to isolate the process and manage packages
  • Preprocessed the nifti files with fMRIPrep as a Robust Preprocessing Pipeline for fMRI Data
  • Try to automated extract the features from environment stimuli with pliers package
  • Try to analyze the whole brain Intersubject Correlation and Functional Alignment between different participants with certain environmental preference

Conclusion and acknowledgement

I would like to thank the Brain Hack School 2023 for organizing this course and for their support. The course has been highly beneficial, and I would like to express my appreciation to organizer and TA for all your support!


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  • Nastase, S. A., Gazzola, V., Hasson, U., & Keysers, C. (2019). Measuring shared responses across subjects using intersubject correlation. Social Cognitive and Affective Neuroscience, Volume 14, Issue 6, June 2019, Pages 667–685,
  • Nummenmaa, L., Lahnakoski, J. M., and Glerean, E. (2018). Sharing the social world via intersubject neural synchronisation. Curr. Opin. Psychol. 24. doi:10.1016/j.copsyc.2018.02.021.
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