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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...

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

High-throughput neuro-imaging informatics.

Michael I Miller¹, Andreia V Faria², Kenichi Oishi²

¹Center for Imaging Science, Johns Hopkins Whiting School of Engineering, The Johns Hopkins University Baltimore, MD, USA ; Institute for Computational Medicine, Johns Hopkins School of Medicine and Whiting School of Engineering, The Johns Hopkins University Baltimore, MD, USA ; Department of Biomedical Engineering, The Johns Hopkins University Baltimore, MD, USA.

Frontiers in Neuroinformatics

|January 2, 2014

Summary

This study introduces neuroinformatics technologies for analyzing human brain imaging data. These methods enable advanced machine learning applications for diagnosing and understanding neuropsychiatric disorders.

Keywords:

computational anatomy functional imaging neuro-imaging neuroinformatics

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Area of Science:

Neuroscience
Medical Imaging
Bioinformatics

Background:

High-throughput 3D functional and structural imaging technologies are crucial for understanding the human brain.
Current radiological workflows require improved methods for medical image storage and utilization.
Machine learning is increasingly integrated into scientific fields like genomics and social networks.

Purpose of the Study:

To describe novel neuroinformatics technologies for analyzing human brain imaging data at a 1 mm anatomical scale.
To develop an abstract pipeline for converting brain imagery into high-dimensional neuroinformatic representations.
To demonstrate the integration of these representations with machine learning for clinical applications.

Main Methods:

Development of an abstract pipeline for converting functional and structural brain imagery into high-dimensional neuroinformatic representation indices (O(1000-10,000) dimensions).
Integration of advanced image analysis with digital knowledge representations, including dense atlases of the human brain.
Application of high-throughput machine learning methods to these neuroinformatic representations.

Main Results:

Demonstrated the integration of high-dimensional neuroinformatic representations with machine learning.
Showcased the pipeline's utility in cross-sectional and personalized analyses of neuropsychiatric illnesses.
Validated machine learning methods for evaluating subject health status against population data and integrating non-image medical records for diagnosis and prognosis.

Conclusions:

High-throughput neuroinformatics facilities have the potential to transform medical image storage and utilization in radiological workflows.
The described pipeline supports both cross-sectional and longitudinal studies for neuropsychiatric disorders.
Machine learning integration facilitates improved diagnosis and prognosis by combining imaging and non-imaging patient data.