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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: May 5, 2026

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

Functional connectomics from resting-state fMRI.

Stephen M Smith¹, Diego Vidaurre, Christian F Beckmann

¹Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK.

Trends in Cognitive Sciences

|November 19, 2013

Summary

This summary is machine-generated.

Resting-state functional MRI (rfMRI) maps the brain's functional connectome by analyzing spontaneous activity. This review covers rfMRI methods, network analysis, and future directions in brain connectomics research.

Keywords:

connectomics network modelling resting-state fMRI

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Neuroscience
Medical Imaging
Network Science

Background:

Spontaneous brain activity fluctuations reflect functional brain networks.
Resting-state functional MRI (rfMRI) is a key tool for non-invasively studying these networks.
Understanding the macroscopic functional connectome is crucial for neuroscience.

Purpose of the Study:

To review the application of rfMRI in mapping the functional connectome.
To discuss methodologies for data acquisition, processing, and network analysis in connectomics.
To highlight advancements and challenges in the field.

Main Methods:

Review of established MRI acquisition protocols for resting-state fMRI.
Description of image-processing pipelines for connectomics data preparation.
Discussion of various computational approaches for estimating functional network structures.

Main Results:

rfMRI enables detailed mapping of the human brain's functional connectome.
Standardized processing facilitates robust network analysis.
High-quality data, like that from the Human Connectome Project, enhances connectome mapping.

Conclusions:

rfMRI is a powerful technique for understanding brain functional organization.
Advancements in data quality and analysis methods are expanding connectomics capabilities.
Future research requires addressing challenges in network interpretation and validation.