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Updated: Apr 6, 2026
Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images
Published on: November 20, 2015
A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment
C Granziera1, A Daducci2, A Donati3
1Department of Clinical Neurosciences, CHUV, Lausanne, VD, Switzerland ; Advanced Clinical Imaging Technology, EPFL, Lausanne, VD, Switzerland.
Objectives:
The aim of this study was to investigate pathological mechanisms underlying brain tissue alterations in mild cognitive impairment (MCI) using multi-contrast 3 T magnetic resonance imaging (MRI).
Methods:
Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry as well as Magnetization Transfer (MT) MRI. Between-groups comparisons in MRI metrics were performed using permutation-based tests. Using MRI data, a generalized linear model (GLM) was computed to predict clinical performance and a support-vector machine (SVM) classification was used to classify MCI and HC subjects.
Results:
Multi-parametric MRI data showed microstructural brain alterations in MCI patients vs HC that might be interpreted as: (i) a broad loss of myelin/cellular proteins and tissue microstructure in the hippocampus (p ≤ 0.01) and global white matter (p < 0.05); and (ii) iron accumulation in the pallidus nucleus (p ≤ 0.05). MRI metrics accurately predicted memory and executive performances in patients (p ≤ 0.005). SVM classification reached an accuracy of 75% to separate MCI and HC, and performed best using both volumes and T1/T2*/MT metrics.
Conclusion:
Multi-contrast MRI appears to be a promising approach to infer pathophysiological mechanisms leading to brain tissue alterations in MCI. Likewise, parametric MRI data provide powerful correlates of cognitive deficits and improve automatic disease classification based on morphometric features.
Insights
Multi-contrast MRI reveals brain tissue changes in mild cognitive impairment (MCI), including myelin loss and iron accumulation. This imaging approach aids in understanding MCI pathology and predicting cognitive performance.
Area of Science:
- Neuroimaging
- Neurology
- Biomedical Engineering
Background:
- Mild cognitive impairment (MCI) is characterized by subtle brain tissue alterations.
- Understanding the pathological mechanisms of MCI is crucial for early diagnosis and intervention.
Purpose of the Study:
- To investigate pathological mechanisms underlying brain tissue alterations in MCI.
- To utilize multi-contrast 3 Tesla magnetic resonance imaging (MRI) for this investigation.
Main Methods:
- Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry and Magnetization Transfer (MT) MRI.
- Between-group comparisons of MRI metrics were performed using permutation-based tests.
- Generalized linear models (GLM) predicted clinical performance, and support-vector machine (SVM) classified MCI and HC subjects.
Main Results:
- MCI patients exhibited microstructural brain alterations compared to HC, including myelin/cellular protein loss in the hippocampus and white matter.
- Evidence of iron accumulation was found in the pallidus nucleus of MCI patients.
- MRI metrics accurately predicted memory and executive functions (p ≤ 0.005), with SVM achieving 75% accuracy in MCI/HC classification.
Conclusions:
- Multi-contrast MRI is a promising tool for inferring pathophysiological mechanisms in MCI.
- Parametric MRI data correlate strongly with cognitive deficits and enhance automated disease classification.
