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Related Experiment Video

Updated: Nov 28, 2025

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Data-science ready, multisite, human diffusion MRI white-matter-tract statistics.

Garikoitz Lerma-Usabiaga^1,2,3, Pratik Mukherjee^4,5, Michael L Perry⁶

¹Department of Psychology, Stanford University, 450 Jane Stanford Way, Jordan Hall Building, 94305, Stanford, California, USA. garikoitz@gmail.com.

Scientific Data

|December 1, 2020

Summary

This summary is machine-generated.

We provide a data-science-ready dataset of human white matter tracts from diffusion MRI scans. This resource simplifies complex neuroimaging analysis for broader scientific collaboration and discovery.

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Last Updated: Nov 28, 2025

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Published on: August 11, 2016

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Area of Science:

Neuroscience
Medical Imaging
Data Science

Background:

White matter tracts are crucial for human brain function and behavior.
Diffusion MRI data analysis requires specialized computational neuroimaging and neuroanatomy expertise.
Accessibility of diffusion MRI data is limited for non-experts.

Purpose of the Study:

To create a curated, data-science-ready dataset of human white matter tract diffusion MRI measurements.
To facilitate analysis by non-diffusion MRI experts, accelerating research.
To share multisite data capturing inter-center variation.

Main Methods:

Acquisition of diffusion MRI data from three different MRI centers.
Processing raw data into a standardized, data-science-ready format.
Utilizing a computational system for reproducibility and provenance tracking.

Main Results:

A curated dataset of white matter tract diffusion MRI measurements is now available.
The dataset includes measures of within- and between-MRI center variation.
A reproducible computational framework ensures data integrity from scanner to analysis.

Conclusions:

The shared dataset lowers the barrier for data scientists to study white matter tracts.
This initiative promotes broader engagement and faster advancements in human white matter research.
Standardized, accessible data is key to unlocking new insights in neuroscience.