Alzheimer's and Parkinson's are progressive neurodegenerative diseases marked by cognitive decline and motor dysfunction. Early diagnosis is crucial for management and slowing progression. Traditional methods detect changes only after significant neuronal loss.

fMRI offers a non-invasive approach to measuring brain activity by detecting blood oxygen level-dependent (BOLD) signals. This technique provides insights into functional connectivity and neural network dynamics, which are often disrupted in neurodegenerative diseases before structural changes become apparent.

In this study, we reviewed recent literature on fMRI applications in the early diagnosis of Alzheimer's disease (AD) and Parkinson's disease (PD). Key methodologies include resting-state fMRI (rs-fMRI) to assess disruptions in the default mode network (DMN) in AD and task-based fMRI to evaluate motor network dysfunctions in PD. Data from multiple studies were analyzed to identify consistent patterns of functional connectivity alterations. This approach aims to enhance the accuracy and early detection of these diseases by identifying disruptions in the brain’s functional networks. The findings from this study may provide valuable insights for future research and clinical practice, enabling more targeted interventions at earlier stages of the diseases.

Alzheimer's Disease

fMRI revealed significant disruptions in the Default Mode Network (DMN), which is associated with memory and self-referential thought. Key findings include:

• Reduced connectivity: The posterior cingulate cortex and hippocampus showed decreased communication, even in preclinical stages.
• Task-based deficits: Memory-related tasks elicited weaker activation in the medial temporal lobe.
• Beta-amyloid correlation: Amyloid deposition was linked to diminished functional connectivity, supporting fMRI’s role in tracking disease progression.

Parkinson's Disease

fMRI studies identified early functional changes in motor circuits and the basal ganglia:

• Motor network disruptions: Altered connectivity between the motor cortex, thalamus, and basal ganglia.
• Resting-state abnormalities: Decreased connectivity in motor and executive control networks.
• Dopaminergic dysfunction: Reduced activity in the substantia nigra correlated with disease severity.

Comparative Analysis

fMRI demonstrated the ability to differentiate:

• Alzheimer's patients from healthy controls with 85-90% accuracy.
• Parkinson's patients from controls based on resting-state connectivity metrics.

These comparative results highlight fMRI's superior sensitivity in detecting subtle neural alterations compared to traditional imaging techniques such as CT and structural MRI. Unlike structural imaging, which often identifies changes after extensive neuronal damage, fMRI can capture dynamic functional shifts at earlier stages. Moreover, the integration of fMRI with other biomarkers, such as cerebrospinal fluid (CSF) tau levels and amyloid PET imaging, significantly enhances diagnostic specificity.

Comparative data also suggest that machine learning algorithms applied to fMRI datasets improve diagnostic accuracy by identifying complex patterns invisible to conventional analysis methods. Furthermore, the use of fMRI to track early biomarkers of disease in pre-symptomatic patients holds significant promise for detecting Alzheimer’s and Parkinson’s at the earliest stages, even before the onset of visible clinical symptoms. Early identification of such biomarkers could enable more effective intervention and tailored treatment plans, potentially slowing or halting disease progression. The non-invasive nature of fMRI further supports its potential as a routine diagnostic tool in clinical settings, providing a cost-effective approach to early diagnosis and long-term monitoring of neurodegenerative diseases.

Study Design

This study involved a comprehensive literature review of recent fMRI applications, focusing on:

• Changes in DMN connectivity for Alzheimer’s detection.
• Alterations in motor-related networks for Parkinson’s.
• Early markers of neural dysfunction observed in at-risk populations (e.g., individuals with mild cognitive impairment or REM sleep behavior disorder).

Data from clinical trials and neuroimaging repositories were analyzed to assess the diagnostic performance of fMRI. The analysis explored:

• Temporal changes in functional connectivity over the disease course.
• Correlation of fMRI findings with established biomarkers, such as cerebrospinal fluid (CSF) tau levels and amyloid PET imaging.
• Use of machine learning algorithms to enhance the interpretation of fMRI data, improving sensitivity and specificity.