Behavior and Neuroimaging Core User Manual
  • About
  • Infrastructure Overview
  • XNAT
    • Getting Started
    • Accessing XNAT
    • BIDS Ready Protocols
    • New XNAT projects
    • Uploading Data
    • Downloading Data
  • Demo Dataset
    • Introduction
    • How to access it
    • Protocol Information
    • Basic analysis example: checks task
  • XNAT to BIDS
    • Getting Started
    • XNAT2BIDS Software
    • Exporting to BIDS using Oscar
      • Oscar Utility Script
        • Running xnat2bids using default configuration
        • Running xnat2bids with a custom configuration
        • Syncing your XNAT project & Oscar data directory
        • Extra tools & features
      • Step-wise via Interact Session
    • BIDS Validation
      • Oscar
      • Docker
    • Converting non-MR data
      • Physiological data
      • EEG data
  • XNAT TO BIDS (Legacy)
    • Oscar SBATCH Scripts
  • BIDS and BIDS Containers
    • Introduction to BIDS
    • mriqc
    • fmriprep
    • BIDS to NIMH Data Archive (NDA)
  • Analysis Pipelines
    • Freesurfer
    • đźš§CONN Toolbox
    • FSL topup and eddy
    • Tractography: DSI Studio
    • Brown University MRS Data Collection and Preprocessing Protocol
    • LC Model
      • Installation
      • Example Run
      • Running LCModel on your own data
    • Quantitative Susceptibility Mapping (QSM)
  • Standalone Tools
    • Automated MR spectroscopy voxel placement with voxalign
      • Installation
      • Multi-session alignment
      • Center on MNI coordinate
      • Quantify voxel overlap
    • dicomsort: a tool to organize DICOM files
    • ironmap
    • convert enhanced multi-frame DICOMs to legacy single-frame
    • DICOM anonymization
  • MRF GUIDES
    • MRI simulator room
      • Motion Trainer: Balloon Task
      • Simulating scanner triggers
    • Stimulus display & response collection
    • Eyetracking at the scanner
    • Exporting data via scannershare
    • EEG in the scanner
    • Exporting spectroscopy RDA files
  • Community
    • MRF/BNC user community meetings
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  1. Standalone Tools
  2. Automated MR spectroscopy voxel placement with voxalign

Center on MNI coordinate

PreviousMulti-session alignmentNextQuantify voxel overlap

Last updated 3 days ago

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Running MNI lookup

  1. Activate the virtual environment with source /path/to/env/voxalign/bin/activate (on Tess, this will be source ~/Desktop/voxalign/bin/activate. You can tell that the environment is activated when it says (voxalign) before the command prompt.

  2. Launch the MNI lookup interface with mni-lookup

  3. Click "Create Output Folder" and choose an empty directory for your output (if you accidentally choose a non-empty directory, you'll be prompted to choose again).

  4. Enter your desired MNI coordinate(s). Click the button to add an additional coordinate, and delete any extra rows by clicking the X. Once coordinates are entered, you can hover over the brain icon as a sanity check that you're in the correct region, hemisphere, etc.

  5. If you have previously run mni-lookup on this same participant (for example, using a T1 that was collected at a previous session), you can click "Use existing MNI registration" and choose the output folder from the previous mni-lookup run. This will check that all necessary files are present and then allow you to skip the time-consuming nonlinear warping to MNI space.

  6. When your T1 has been collected and sent over via scannershare, you can click "Select T1 DICOM" and choose that file.

  7. Click "Calculate voxel position"

  8. Progress will be printed to the terminal; you'll find that the nonlinear warping to MNI space takes the longest - 4-6 mins or so on Tess, faster on a newer mac.


TIPS:

  • If you make a mistake entering MNI coordinates (enter them wrong, forget to include one), and the MNI lookup calculation has already finished, just run it again, click “Use existing MNI registration” and choose your output folder from the first time you ran it. You’ll need to specify a new output folder and input the T1 DICOM again, but when you click “calculate voxel position” this time it will be much faster because it will use the pre-run nonlinear registration to MNI space.

MNI lookup interface.