Evaluation of Usage fMRI in Alzheimer’s Disease Diagnose

Authors

Sohaila Yazdani1,*, Karim Khoshgard1, Hasan Norouzi1
1Kermanshah University of Medical Sciences, Kermanshah, Iran
*Corresponding Author: Kermanshah University of Medical Sciences, Kermanshah, Iran, E-mail: [email protected] Email: [email protected]

IJ Radiology:Vol. 14, issue Special Issue; e93691
Published online:Apr 13, 2017
Article type:Abstract
Received:May 14, 2019
Accepted:Feb 08, 2017
How to Cite:Yazdani S, Khoshgard K, Norouzi H. Evaluation of Usage fMRI in Alzheimer’s Disease Diagnose. I J Radiol. 2017;14(Special Issue):e93691. doi: https://doi.org/10.5812/iranjradiol.48279

Abstract

Background: Alzheimer’s disease (AD) is a state where neurons within the brain stop functioning, lose connection with other neurons and die. It’s the most common cause of dementia, a loss of brain function that can harmfully impact memory, thinking, language, judgment and behavior. Alzheimer’s is irreversible and progressive. Although the cause of Alzheimer’s disease is unknown, scientists believe that a build-up of beta-amyloid plaques and neurofibrillary tangles in the brain are associated with the disease. Medications that slow the progression of the disease and manage symptoms are available, but there is no cure for Alzheimer’s disease. Current diagnosis of AD is through clinical, neuropsychological, and neuroimaging assessments. Functional MRI (fMRI) measures brain activity during a cognitive, sensory, or motor task or at rest by measuring changes in blood oxygen level dependent (BOLD) MR signal.

Objectives: In this paper, we evaluate application of fMRI to detect AD.

Methods: The papers were searched in PubMed, Medline and Scopus databases with the relevant key words such as fMRI, Alzheimer disease and early detection.

Results: BOLD fMRI is considered to reflect the joined synaptic activity of neurons through MRI signal changes because of alterations in blood flow, blood volume, and the blood oxyhemoglobin/deoxyhemoglobin ratio. In patients with clinically diagnosed AD, the outcomes of fMRI have been quite consistent, showing reduced hippocampal activity during the encoding of new data. Several studies have reported increased prefrontal cortical activity in AD patients, suggesting that other networks may increase activity as an attempted compensatory mechanism during hippocampal failure. In particular, the event-related fMRI studies have found that hyperactivity was observed, which suggested that hyperactivity might represent a compensatory mechanism in the setting of early AD pathology. Also, fMRI of default mode network (DMN) brain activity during resting is lately achievement attention as a potential noninvasive biomarker to diagnose initial AD.

Conclusions: Both task-related and resting fMRI techniques have the potential to detect early brain dysfunction related to AD. However, the use of fMRI in AD populations thus far has been limited to a relatively small number of research groups.

Highlights

Copyright

© 2017, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.

Similar Articles

19
Aug
2024
Resting-State fMRI and Machine Learning as Diagnostic Tools for Alzheimer's Disease

Resting-State fMRI and Machine Learning as Diagnostic Tools for Alzheimer's Disease

Sajjad Iraji,
Fateme Darvishzadeh Mahani,
Hojjat M Dikdaragh,
Masoumeh Foroutan Koudehi,
Hamed Bageri,
Akram Nezhadi

Iraji S, Darvishzadeh Mahani F, M Dikdaragh H, Foroutan Koudehi M, Bageri H, et al. Resting-State fMRI and Machine Learning as Diagnostic Tools for Alzheimer's Disease. Ann Mil Health Sci Res. 2024;22(2):e149135. doi: https://doi.org/10.5812/amh-149135

10
Dec
2019

Classification of Brain MRI for Alzheimer’s Disease Detection Based on Ensemble Machine Learning

Soheil Ahmadzadeh Irandoost,
Fatemeh Asadi

Ahmadzadeh Irandoost S, Asadi F. Classification of Brain MRI for Alzheimer’s Disease Detection Based on Ensemble Machine Learning. I J Radiol. 2019;16(Special Issue):e99157. doi: https://doi.org/10.5812/iranjradiol.99157

2
Mar
2020

A survey on Alzheimer’s disease detection using gait analysis

Mahmoud Seyfollahi,
Hadi Soltanizadeh,
Afsoon Hasani Mehraban,
Fatemeh Khamseh

Seyfollahi M, Soltanizadeh H, Hasani Mehraban A, Khamseh F. A survey on Alzheimer’s disease detection using gait analysis. koomesh. 2020;22(1):e153144. doi:

1
Dec
2020

Application of magnetic resonance spectroscopy for evaluating metabolic alteration in anterior cingulate cortex in Alzheimer’s disease

Erfan Saatchian,
Sina Ehsani,
Alireza Montazerabadi

Saatchian E, Ehsani S, Montazerabadi A. Application of magnetic resonance spectroscopy for evaluating metabolic alteration in anterior cingulate cortex in Alzheimer’s disease. koomesh. 2020;22(4):e153228. doi:

31
Oct
2025
Machine Learning Approaches to Influential ROI Selection in Parkinson’s Disease: A Comparative Analysis of LASSO, Recursive Feature Elimination, and Random Forest

Machine Learning Approaches to Influential ROI Selection in Parkinson’s Disease: A Comparative Analysis of LASSO, Recursive Feature Elimination, and Random Forest

Keyvan Olazadeh,
Nasrin Borumandnia,
Hamid Alavi Majd

Olazadeh K, Borumandnia N, Alavi Majd H. Machine Learning Approaches to Influential ROI Selection in Parkinson’s Disease: A Comparative Analysis of LASSO, Recursive Feature Elimination, and Random Forest. Arch Neurosci. 2025;12(4):e165741. doi: https://doi.org/10.5812/ans-165741

More by these authors

Sohaila YazdaniPubMedScholar
Karim KhoshgardPubMedScholar
Hasan NorouziPubMedScholar
Share
Cited by
Metrics