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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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Machine learning in neuroimaging: from research to clinical practice.

Karl-Heinz Nenning^1,2, Georg Langs³

¹Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA.

Radiologie (Heidelberg, Germany)

|August 31, 2022

Summary

Machine learning enhances neuroimaging analysis for brain health and disease research. It aids in anatomical measurements, disease detection, and tracking changes, improving clinical care and understanding brain function.

Keywords:

Artificial intelligence Connectomics Deep learning Neuro imaging Prediction models

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Area of Science:

Neuroscience
Medical Imaging
Computational Neuroscience

Background:

Neuroimaging is vital for studying the brain in health and disease.
A complex relationship exists between brain structure, function, and imaging features.
Brain characteristics change across the lifespan, during disease, and recovery.

Purpose of the Study:

To review the application of machine learning in neuroimaging.
To highlight how machine learning exploits neuroimaging data for clinical care and research.
To provide an overview of clinical applications and contributions to computational neuroscience.

Main Methods:

Review of current machine learning techniques in neuroimaging analysis.
Focus on machine learning applications in anatomical measurements, lesion quantification, and disease pattern identification.
Examination of machine learning's role in identifying acute conditions like stroke and tracking imaging changes.

Main Results:

Machine learning contributes to anatomical measurements, segmentation, and quantification of lesions.
Rapid identification of acute conditions such as stroke is facilitated by machine learning.
Machine learning enables tracking of neuroimaging changes over time, aiding in understanding disease progression and recovery.

Conclusions:

Machine learning significantly advances the analysis of neuroimaging data.
The integration of machine learning enhances clinical decision-making and fundamental neuroscience research.
Continued advancements in neuroimaging and analysis techniques deepen our understanding of brain function and disease.