Advanced Imaging Techniques (PET, fMRI, DTI) in Early Detection of Neurodegenerative Diseases: A Systematic Review

Background

Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), and vascular and frontotemporal dementia (FTD), are characterized by progressive cognitive and motor decline. So, timely detection, especially early in the disease process, is crucial. Positron Emission Tomography (PET), Functional Magnetic Resonance Imaging (fMRI), and Diffusion Tensor Imaging (DTI) are advanced neuroimaging techniques that have shown promise for early diagnosis.

Objective

This review evaluates the diagnostic accuracy and clinical utility of PET, fMRI, and DTI in the early detection of neurodegenerative diseases.

Methods

A systematic search was conducted using PubMed, Google Scholar, and Cochrane Library for studies published between 2014 and 2024. Inclusion criteria focused on phase 2 and 3 clinical trials involving adult patients with AD, PD, and FTD. Studies were assessed for diagnostic accuracy, sensitivity, specificity, and identification of early biomarkers using PET, fMRI, and DTI. Data were extracted and analyzed from 14 selected studies.

Results

PET imaging with tracers like 18F-flortaucipir provided visualization of amyloid and tau aggregates in AD and dopaminergic changes in PD. PET showed a strong association with amyloid and tau pathology in AD, with up to 95% diagnostic performance. Another useful technique in identifying early changes in the brain networks was resting-state fMRI (rs-fMRI), with a diagnostic accuracy of 80%–95%. DTI offered essential data on white matter connectivity and showed microstructural alterations that pointed to early neurodegenerative processes. Integrating these neuroimaging modalities with machine learning models further enhanced diagnostic accuracy.

Conclusion

PET, fMRI, and DTI are valuable tools for the early diagnosis of neurodegenerative diseases. These techniques can identify structural and functional changes in the brain before the onset of clinical signs. Integrating these imaging techniques with machine learning improves diagnostic outcomes. Further large-scale studies with standardized methodologies are needed to validate these findings and implement these techniques in clinical practice.