MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-…

MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses.

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  "n": 0,
  "doi": "10.1038/s41597-022-01682-y",
  "claim": "MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses.",
  "cite_key": "Royer2022",
  "evidence": "MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses.",
  "effect_size": "50",
  "text_access": "fulltext",
  "study_system": "50 healthy adults (23 women; 29.54 ± 5.62 yr); 3T MRI",
  "source_cluster_id": "cluster_07",
  "replication_status": "replication_unknown",
  "claim_source_sentence": "Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla.",
  "replication_evidence_dois": [],
  "effect_size_source_sentence": "MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses."
}

[
  "paper:paper-c717abaed1d9"
]

Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla.

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MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses.