European Journal of Cardiovascular Medicine

Neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), are a growing concern due to an aging population. Early diagnosis is essential for slowing disease progression and improving patient outcomes. Traditional imaging methods, such as structural MRI, often detect changes only in later stages of these diseases. Diffusion MRI (dMRI) has emerged as a promising technique for detecting subtle microstructural changes in brain tissue, potentially allowing for earlier diagnosis.

dMRI measures the diffusion of water molecules in brain tissues, providing insights into the integrity of neural pathways. Diffusion Tensor Imaging (DTI), a form of dMRI, quantifies metrics such as Fractional Anisotropy (FA) and Mean Diffusivity (MD), which reflect the directional movement of water and overall diffusion, respectively. Changes in these metrics can indicate early neuronal damage before structural changes become apparent.

This study aims to investigate the potential of dMRI in detecting early signs of neurodegenerative diseases by comparing diffusion metrics in healthy individuals and those at high risk for AD and PD.

Study Design

This cross-sectional study included 100 participants divided into three groups: healthy controls (n=40), individuals at high risk for AD (n=30), and individuals at high risk for PD (n=30). High-risk individuals were identified based on genetic markers, family history, and early clinical symptoms.

MRI Acquisition

All participants underwent dMRI scanning using a 3T MRI scanner. The imaging protocol included:

• High-resolution T1-weighted images for anatomical reference.
• Diffusion-weighted images with 64 directions, b-value of 1000 s/mm².
• Acquisition of DTI metrics: FA, MD, Axial Diffusivity (AD), and Radial Diffusivity (RD).

Image Processing

Diffusion data were preprocessed using standard pipelines, including eddy current correction and motion correction. DTI metrics were calculated using FSL (FMRIB Software Library). Regions of interest (ROIs) included the hippocampus, corpus callosum, and substantia nigra, regions known to be affected in AD and PD.

Statistical Analysis

Group differences in diffusion metrics were assessed using ANOVA with post-hoc tests. Correlations between diffusion metrics and cognitive/clinical scores were evaluated using Pearson correlation coefficients.

Baseline Characteristics

Participant demographics and baseline characteristics are summarized in Table 1. There were no significant differences in age, sex, or education level among the groups.

Table 1: Baseline Characteristics of Study Participants

Diffusion Metrics

Significant differences in diffusion metrics were observed between healthy controls and high-risk groups (Table 2).

Table 2: Diffusion Metrics in Study Groups

Correlations

Significant correlations were found between diffusion metrics and cognitive/clinical scores, particularly in high-risk groups (Table 3).

Table 3: Correlations Between Diffusion Metrics and Cognitive/Clinical Scores

The findings of this study demonstrate that diffusion MRI (dMRI), particularly Diffusion Tensor Imaging (DTI), is a promising tool for detecting early signs of neurodegenerative diseases. Significant differences in diffusion metrics, such as Fractional Anisotropy (FA) and Mean Diffusivity (MD), were observed between healthy controls and individuals at high risk for Alzheimer's Disease (AD) and Parkinson's Disease (PD). These results are consistent with existing literature, supporting the utility of dMRI in identifying microstructural changes in the brain that precede clinical symptoms.

Comparison with Related Studies

Our results align with previous research indicating that dMRI can detect early microstructural changes in neurodegenerative diseases. For example, a study by Kantarci et al. (2017) found decreased FA and increased MD in the hippocampus of individuals with mild cognitive impairment (MCI), a precursor to AD. Similar to our findings, this study suggests that changes in diffusion metrics can serve as early indicators of AD, even before significant cognitive decline becomes apparent.¹

Zhang et al. (2015) reported reduced FA and increased MD in the substantia nigra of early-stage PD patients, consistent with our observation of significant diffusion metric changes in high-risk PD individuals. These alterations in the substantia nigra are thought to reflect early neurodegenerative processes, such as the loss of dopaminergic neurons, which are characteristic of PD.²

Additionally, a study by Acosta-Cabronero et al. (2010) observed widespread reductions in FA and increases in MD in the brains of individuals with early AD, particularly in white matter tracts. Our study's findings of significant changes in diffusion metrics in the corpus callosum and hippocampus further support the notion that dMRI can reveal early white matter abnormalities associated with neurodegeneration.³

Mechanisms of Action

The precise mechanisms underlying the observed changes in diffusion metrics are still being explored. However, it is widely accepted that alterations in FA and MD reflect disruptions in the microstructural integrity of brain tissues. In neurodegenerative diseases, these disruptions may be due to factors such as axonal degeneration, demyelination, and synaptic loss.

For example, in AD, the accumulation of amyloid plaques and neurofibrillary tangles can lead to neuronal death and loss of white matter integrity, which is detectable as decreased FA and increased MD on dMRI. In PD, the degeneration of dopaminergic neurons in the substantia nigra results in similar changes in diffusion metrics, reflecting the loss of neural pathways and overall brain connectivity.

Implications for Early Detection

Early detection of neurodegenerative diseases is crucial for effective intervention and management. The ability of dMRI to detect microstructural changes before clinical symptoms emerge offers a valuable opportunity for early diagnosis. This early detection capability is particularly important for conditions like AD and PD, where early intervention can potentially slow disease progression and improve quality of life.

The correlations between diffusion metrics and cognitive/clinical scores observed in our study further highlight the potential of dMRI as a biomarker for early neurodegenerative changes. These correlations suggest that changes in diffusion metrics are not only indicative of structural abnormalities but also have functional relevance, potentially reflecting the degree of cognitive impairment and disease severity.

Limitations and Future Directions

While our study provides compelling evidence for the utility of dMRI in early detection of neurodegenerative diseases, there are several limitations to consider. The cross-sectional design of the study does not allow for assessment of changes over time. Longitudinal studies are needed to confirm the utility of dMRI in tracking disease progression and to establish its prognostic value.

Additionally, our sample size, though adequate, should be expanded in future studies to include a more diverse population and larger cohort. This would enhance the generalizability of the findings and allow for more detailed subgroup analyses based on factors such as age, sex, and genetic risk.

Future research should also explore the integration of dMRI with other biomarkers, such as cerebrospinal fluid (CSF) analysis and positron emission tomography (PET), to enhance early diagnostic accuracy and provide a comprehensive understanding of neurodegenerative processes. Combining dMRI with these complementary techniques could offer a multimodal approach to early detection and monitoring of neurodegenerative diseases.

This study demonstrates the potential of diffusion MRI in detecting early signs of neurodegenerative diseases. Significant differences in diffusion metrics between healthy controls and high-risk individuals suggest that dMRI can identify microstructural changes associated with neurodegeneration. These findings are consistent with previous research and highlight the utility of dMRI as a non-invasive tool for early diagnosis. Further research is warranted to validate these findings, explore the mechanisms underlying diffusion metric changes, and establish the clinical utility of dMRI in early detection and monitoring of neurodegenerative diseases.

1. Kantarci K, Senjem ML, Avula R, et al. Diffusion tensor imaging and white matter abnormalities in patients with cognitive impairment. Neurology. 2017;69(3):308-316.
2. Zhang Y, Wu IW, Tosun D, et al. Progression of regional microstructural degeneration in Parkinson’s disease: a multicenter diffusion tensor imaging study. PLoS One. 2015;10(10).

3. Acosta-Cabronero J, Williams GB, Pengas G, et al. Diffusion tensor metrics as biomarkers in Alzheimer's disease. PLoS One. 2010;5(11).