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Brain Imaging

Brain Imaging

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Overview of methods and available tools used in complex brain disorders.

Loukas Ilias¹, George Doukas¹, Michael Kontoulis¹

¹Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece.

Open Research Europe

|February 23, 2024

Summary

This study reviews methods and tools for diagnosing multiple complex brain disorders like Alzheimer's, sleep disorders, and epilepsy. It offers a comprehensive approach to analyzing diverse data types for better patient diagnosis.

Keywords:

data analytics data analytics tools epilepsy machine learning neurocognitive disorders sleep disorders

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Area of Science:

Neurology
Medical Informatics
Biomedical Engineering

Background:

Complex brain disorders, such as Alzheimer's dementia, sleep disorders, and epilepsy, pose significant health and socioeconomic challenges.
Existing literature reviews often focus on single disorders or data types, limiting comprehensive diagnostic support.
There is a need for integrated approaches to diagnose and manage these prevalent conditions.

Purpose of the Study:

To conduct a comprehensive literature review of methods for diagnosing multiple complex brain disorders.
To identify and review tools capable of processing diverse data types (EEG, actigraphy, MRI) for improved diagnostic accuracy.
To highlight the benefits and limitations of current diagnostic and data processing tools.

Main Methods:

Systematic literature search across multiple databases.
Inclusion of studies focusing on Alzheimer's dementia, sleep disorders, and epilepsy.
Analysis of tools for processing electroencephalography (EEG), actigraphy, and magnetic resonance imaging (MRI) data.

Main Results:

Identified a gap in comprehensive reviews covering multiple brain disorders and data types.
Cataloged existing methods and tools for diagnosing Alzheimer's dementia, sleep disorders, and epilepsy.
Evaluated the capabilities of tools in processing multimodal data for differential diagnosis.

Conclusions:

This review provides the first comprehensive overview of methods and tools for diagnosing multiple complex brain disorders using diverse data.
The findings can guide the development of integrated diagnostic platforms.
Further research is needed to address the limitations of existing tools and enhance diagnostic precision.