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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

Magnetic Resonance Imaging

Magnetic Resonance Imaging

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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Related Experiment Video

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Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Information theoretic approaches to functional neuroimaging.

Dirk Ostwald¹, Andrew P Bagshaw

¹School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. silvia.desantis@roma1.infn.it

Magnetic Resonance Imaging

|September 16, 2011

Summary

Information theory offers a model-free approach to quantify signal dependencies in brain imaging data. This method reveals insights missed by traditional analyses, advancing our understanding of brain function.

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

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Published on: November 8, 2012

Area of Science:

Neuroscience
Information Theory
Data Analysis

Background:

Information theory provides a probabilistic framework for quantifying statistical non-independence between signals.
Unlike classical methods, it is model-free, making no assumptions about functional forms or probability distributions.
This approach can uncover signal characteristics missed by traditional data analysis.

Purpose of the Study:

To review the application of information theoretic concepts in functional brain imaging analysis.
To demonstrate how these concepts address neurobiological questions.
To discuss limitations and future developments.

Main Methods:

Review of studies applying information theory to functional magnetic resonance imaging (fMRI) and magneto/electroencephalography (MEG/EEG).
Analysis of applications in brain functional specialization, integration, neurovascular coupling, and multimodal imaging.

Main Results:

Information theory successfully quantifies statistical non-independence in brain signals.
Applications span diverse neuroimaging domains, revealing complex brain dynamics.
The framework aids in answering specific neurobiological questions.

Conclusions:

Information theory is a powerful, model-free tool for analyzing functional brain imaging data.
It offers unique advantages over classical methods for uncovering hidden signal characteristics.
Further development of information theoretic frameworks will advance our understanding of brain function.