IRCCS San Camillo Hospital
Published August 24, 2021 | Version v1
Dataset Restricted

Dataset related to article "Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction"

Authors/Creators

  • 1. Research Center for Motor Control and Neuroplasticity, KU Leuven
  • 2. Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital

Description

SCORING SEGMENTATIONS

  • Qualitative segmentation scores by two raters (rater1; rater2).
  • Scale: excellent (4); good (3); doubtful (2) and failed (1).

 

DATABASES

 

SEGMENTATION METHODS

  • MR-TIM (Taberna et al., 2021), green rows
  • WTS (Liu et al., 2017), red rows

 

TABLES

IXI_young

  • 20 MRI from the IXI database, participants 20–35 years old;
  • MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G)

IXI_older

  • 20 MRI from the IXI database, participants 60–75 years old;
  • MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G)

ABIDE

  • 10 MRI from the ABIDE database, participants 18-25 years old;
  • MR scanner: Philips Achieva 3.0T

SchizConnect

  • 10 MRI from the SchizConnect database, participants 19-66 years old;
  • MR scanner: Siemens Trio Tim 3.0T

 

REFERENCES

Liu, Q., Farahibozorg, S., Porcaro, C., Wenderoth, N., & Mantini, D. (2017). Detecting large-scale networks in the human brain using high-density electroencephalography. Hum Brain Mapp, 38(9), 4631-4643. doi:10.1002/hbm.23688

Taberna, G. A., Samogin, J., & Mantini, D. (2021). Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction. Neuroinformatics. doi:10.1007/s12021-020-09504-5

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Additional details

Related works

Is documented by
Journal article: 10.1007/s12021-020-09504-5 (DOI)