Related Experiment Video
Updated: Dec 25, 2025
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
Neuroimaging and analytical methods for studying the pathways from mild cognitive impairment to Alzheimer's disease:
Maryam Ahmadzadeh1,2,3, Gregory J Christie1,2,3, Theodore D Cosco4,5
1Digital Health Hub, Simon Fraser University, 4190 Galleria 4, 250 - 13450 102 Ave, Surrey, BC, V3T 0A3, Canada.
Background:
Alzheimer's disease (AD) is a neurodegenerative disorder commonly associated with deficits of cognition and changes in behavior. Mild cognitive impairment (MCI) is the prodromal stage of AD that is defined by slight cognitive decline. Not all with MCI progress to AD dementia. Thus, the accurate prediction of progression to Alzheimer's, particularly in the stage of MCI could potentially offer developing treatments to delay or prevent the transition process. The objective of the present study is to investigate the most recent neuroimaging procedures in the domain of prediction of transition from MCI to AD dementia for clinical applications and to systematically discuss the machine learning techniques used for the prediction of MCI conversion.
Methods:
Electronic databases including PubMed, SCOPUS, and Web of Science will be searched from January 1, 2017, to the date of search commencement to provide a rapid review of the most recent studies that have investigated the prediction of conversion from MCI to Alzheimer's using neuroimaging modalities in randomized trial or observational studies. Two reviewers will screen full texts of included papers using predefined eligibility criteria. Studies will be included if addressed research on AD dementia and MCI, explained the results in a way that would be able to report the performance measures such as the accuracy, sensitivity, and specificity. Only studies addressed Alzheimer's type of dementia and its early-stage MCI using neuroimaging modalities will be included. We will exclude other forms of dementia such as vascular dementia, frontotemporal dementia, and Parkinson's disease. The risk of bias in individual studies will be appraised using an appropriate tool. If feasible, we will conduct a random effects meta-analysis. Sensitivity analyses will be conducted to explore the potential sources of heterogeneity.
Discussion:
The information gathered in our study will establish the extent of the evidence underlying the prediction of conversion to AD dementia from its early stage and will provide a rigorous and updated synthesis of neuroimaging modalities allied with the data analysis techniques used to measure the brain changes during the conversion process.
Systematic Review Registration:
PROSPERO,CRD42019133402.
Insights
Predicting Alzheimer's disease (AD) progression from mild cognitive impairment (MCI) is crucial. This review synthesizes recent neuroimaging and machine learning techniques to identify individuals likely to convert from MCI to AD dementia.
Area of Science:
- Neuroscience
- Medical Imaging
- Artificial Intelligence
Background:
- Alzheimer's disease (AD) is a neurodegenerative disorder impacting cognition and behavior.
- Mild cognitive impairment (MCI) is an early stage of AD, but not all individuals with MCI progress to dementia.
- Accurate prediction of MCI to AD progression is vital for developing interventions.
Purpose of the Study:
- To review recent neuroimaging procedures for predicting MCI to AD dementia conversion.
- To systematically discuss machine learning techniques used in predicting MCI conversion.
Main Methods:
- Rapid review of studies published from January 1, 2017, to the search date.
- Searched PubMed, SCOPUS, and Web of Science for relevant randomized or observational studies.
- Included studies on AD dementia and MCI using neuroimaging, excluding other dementia types.
Main Results:
- The study synthesizes evidence on neuroimaging modalities and data analysis techniques for predicting MCI to AD conversion.
- It provides an updated overview of methods used to detect brain changes during the conversion process.
Conclusions:
- The findings will clarify the extent of evidence for predicting MCI to AD dementia conversion.
- This synthesis aids in understanding the role of neuroimaging and machine learning in early AD detection.
