Prediction of Personality using Diffusion MRI Local Connectome Fingerprints

Project info

lcf Title: Prediction of Personality using Diffusion MRI Local Connectome Fingerprints

Project lead: Fang-Cheng Yeh

Project collaborators:

Registered Brainhack Global 2020 Event: The Pittsburgh Brainhack: DeBurghing 2020, Pittsburgh, PA,

Project Description: Local connectome fingerprints (LCF) are voxel-based metrics derived from diffusion MRI to provide a subject-specific quantification of brain connections. The task of this project is to predict personality using LCF.

Good first issues:

  1. Build a regression or classification model to predict the first answer to the personality question.
  2. Estimating predicting accuracy using 10-fold cross-validation

Data to use:

There are a total of 1062 subjects included in this data set. Each LCF of a subject has a total of 128894 brain fingerprint features. Each feature has an associated location (mni_location) and fiber orientation (fiber_direction) to allow plotting the spatial distribution of the feature. Please note that each voxel may have more than one feature (because there could be multiple fiber populations within the same voxel).


dimension: the image dimension of the original MRI data fiber_direction: the axonal fiber direction for each feature mni_location: the spatial location for each feature names: HCP serial number for each subject subjects: The LCFs of 1062 subjects (features) NEOFAC: subjects answers to 60 questions (variable to be predicted). The NEO-FFI variable can be 0.3: strongly disagree, 0.4 disagree, 0.5 neutral, 0.6: agree, 0.7: strongly agree.

Link to project repository/sources:

Goals for Brainhack Global 2020 The goal is to test whether fixed behaviors could be predicted from LCF.

Skills: Data regression using statistics or machine learning methods.

Tools/Software/Methods to Use: Python, Matlab, or R Any data analysis packages.

Communication channels: Twitter account: @FangChengYeh

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Jan 1, 0001 12:00 AM