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Updated: Aug 13, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Investigation of type 2 diabetes mellitus and mild cognitive impairment: a study based on diffusion tensor imaging
Shouqian Tian1, Tao Chen1, Qiuhuan Zhang2
1The Second Clinical Medical College of Beijing University of Chinese Medicine, Beijing, China.
Background:
Mild cognitive impairment (MCI) is a common complication of type 2 diabetes mellitus (T2DM); however, its underlying pathogenesis remains unclear. This study aimed to employ diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) and peak width of skeletonized mean diffusivity (PSMD) to investigate changes in the perivascular space (PVS) microenvironment in T2DM.
Methods:
Patients with T2DM (24 with MCI and 23 without MCI) and healthy controls (HCs) (n=26) were prospectively recruited. All participants underwent the Montreal cognitive assessment (MoCA) and diffusion tensor imaging (DTI). The analysis along the perivascular space (ALPS) index was calculated using the FMRIB software library (FSL), and PSMD was derived based on tract-based spatial statistics (TBSS). One-way analysis of variance (ANOVA) was used for comparisons among three groups. Correlations of the ALPS index and PSMD with MoCA scores, fasting blood glucose (FBG) levels, and disease duration were analyzed.
Results:
The ALPS index was significantly lower in the type 2 diabetes mellitus with mild cognitive impairment (T2DM-MCI) (mean ± standard deviation: 1.324±0.170) and type 2 diabetes mellitus without mild cognitive impairment (T2DM-nMCI) (1.362±0.142) groups than in the HC group (1.465±0.104) (P<0.05), while PSMD (×10-4 mm2/s) was significantly higher in the T2DM-MCI group [median (25th and 75th percentile): 2.139 (1.895-2.306)] than in the T2DM-nMCI [1.868 (1.777-2.026)] and HC [1.888 (1.776-2.111)] groups (P<0.05). PSMD was negatively correlated with MoCA scores (r=-0.425, P=0.003) and positively correlated with disease duration (r=0.433, P=0.002). The ALPS index was negatively correlated with FBG levels, disease duration, and PSMD (r=-0.382, P=0.008; r=-0.420, P=0.003; r=-0.333, P=0.022, respectively).
Conclusions:
The combined use of DTI-ALPS and PSMD provides a robust, non-invasive dual-biomarker framework that may advance understanding of the mechanisms underlying diabetic MCI and serve as a potential imaging biomarker.
Insights
Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) and peak width of skeletonized mean diffusivity (PSMD) reveal microenvironment changes in type 2 diabetes mellitus (T2DM). These imaging biomarkers may help understand diabetic mild cognitive impairment (MCI) mechanisms.
Area of Science:
- Neuroimaging
- Radiology
- Metabolic Disorders
Background:
- Mild cognitive impairment (MCI) is a frequent complication of type 2 diabetes mellitus (T2DM).
- The precise pathogenesis of MCI in T2DM remains incompletely understood.
- Investigating the perivascular space (PVS) microenvironment offers a potential avenue for understanding T2DM-related cognitive changes.
Purpose of the Study:
- To utilize Diffusion Tensor Imaging Analysis along the Perivascular Space (DTI-ALPS) and Peak Width of Skeletonized Mean Diffusivity (PSMD) to assess PVS microenvironment alterations in T2DM patients.
- To explore the relationship between these imaging markers and cognitive function, glycemic control, and disease duration.
Main Methods:
- Recruitment of T2DM patients with (T2DM-MCI) and without (T2DM-nMCI) mild cognitive impairment, and healthy controls (HCs).
- Performance of Montreal Cognitive Assessment (MoCA) and Diffusion Tensor Imaging (DTI) on all participants.
- Calculation of the ALPS index and PSMD using established neuroimaging software and statistical methods.
Main Results:
- The ALPS index was significantly lower in both T2DM-MCI and T2DM-nMCI groups compared to HCs.
- PSMD was significantly elevated in the T2DM-MCI group relative to T2DM-nMCI and HC groups.
- PSMD showed negative correlations with MoCA scores and positive correlations with disease duration, while ALPS index correlated negatively with fasting blood glucose and disease duration.
Conclusions:
- The combination of DTI-ALPS and PSMD offers a robust, non-invasive dual-biomarker approach.
- This framework may enhance the understanding of the mechanisms underlying diabetic MCI.
- These imaging markers show potential as biomarkers for T2DM-related cognitive impairment.
