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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: Apr 10, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Spatial Patterns and Functional Profiles for Discovering Structure in fMRI Data.

Polina Golland¹, Danial Lashkari¹, Archana Venkataraman¹

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.

Conference Record. Asilomar Conference on Signals, Systems & Computers

|June 18, 2015

Summary

This summary is machine-generated.

This study introduces unsupervised machine learning methods for functional magnetic resonance imaging (fMRI) analysis. These techniques identify brain systems and responses, confirming known results and suggesting new research avenues for exploratory fMRI data analysis.

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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:

Neuroimaging
Computational Neuroscience
Machine Learning

Background:

Functional magnetic resonance imaging (fMRI) is a key tool for understanding brain function.
Unsupervised methods offer a hypothesis-free approach to analyzing complex fMRI data.
Identifying functionally homogeneous systems and brain responses to stimuli are critical challenges.

Purpose of the Study:

To explore unsupervised, hypothesis-free methods for fMRI data analysis.
To delineate large-scale functionally homogeneous brain systems using clustering.
To extend these methods to incorporate experimental protocol information for analyzing brain responses.

Main Methods:

Formulated a generative mixture model and derived the Expectation-Maximization (EM) algorithm for functional system delineation.
Applied spectral clustering to resting-state fMRI data for brain parcellation.
Developed a mixture model in the space of brain response profiles to stimuli.

Main Results:

Both generative mixture modeling and spectral clustering yielded similar brain partitions from resting-state fMRI data.
The extended approach successfully incorporated experimental protocol information.
The methods confirmed established findings in brain mapping.

Conclusions:

Unsupervised clustering methods are effective for identifying functional brain systems from fMRI data.
Integrating experimental information enhances the analysis of brain responses.
These exploratory approaches open new directions for fMRI research.