Related Experiment Video
Updated: Jul 20, 2026
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
Cerebellar MRI-based radiomics models for identifying mild cognitive impairment: a retrospective multicenter study in
Jianping Lu1,2,3, Guoen Cai1,2,3, Naian Xiao4
1Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China.
Objective:
This study aimed to investigate the role of cerebellar magnetic resonance imaging (MRI) features in identifying mild cognitive impairment (MCI).
Methods:
This retrospective multicenter study included patients with MCI, patients with Alzheimer's disease (AD), and healthy controls (HCs) from three tertiary hospitals in China (January 2022-December 2023). Cerebellar and hippocampal radiomics features were extracted from T1-, T2-, and T2-FLAIR-weighted MRI. A sparse representation classifier was developed using 10-fold cross-validation and was validated on independent datasets. Diagnostic performance was assessed through sensitivity, specificity, and ROC-AUC values.
Results:
A total of 87 patients with MCI, 109 patients with AD, and 55 healthy controls (HCs) matched by gender and age were included for model construction and validation. Additionally, 13 patients with MCI and 26 patients with AD were included for external validation. The 10-fold cross-validation accuracy and ROC AUC for identifying cognitive impairment (CI) in the training set using a combination of cerebellar T1, T2, and T2-FLAIR weighted images were better than those of hippocampal models (91.0% vs. 86.8%, 0.943 vs. 0.931). The accuracy and ROC AUC in the independent test set were similar (89.3% vs. 89.3%, 0.908 vs. 0.906). The 10-fold cross-validation accuracy and ROC AUC for identifying MCI in the training set, using a combination of cerebellar T1, T2, and T2-FLAIR weighted images, were similar to those of hippocampal models (85.2% vs. 83.7%, 0.877 vs. 0.905). Furthermore, the results were consistent with the external validation set (89.7% vs. 93.1%, 0.962 vs. 0.974).
Conclusion:
Cerebellar MRI radiomics models exhibit diagnostic accuracy comparable to hippocampal models for identifying CI and MCI, supporting the cerebellum's role in detecting early cognitive dysfunction. These findings provide novel insights into cerebellar contributions to AD pathophysiology and offer potential biomarkers for clinical application.
Insights
Cerebellar MRI radiomics show promise in identifying mild cognitive impairment (MCI) and cognitive impairment (CI). These models perform comparably to hippocampal radiomics, highlighting the cerebellum's role in early cognitive dysfunction detection.
Area of Science:
- Neuroimaging
- Radiomics
- Cognitive Neurology
Background:
- Mild cognitive impairment (MCI) and Alzheimer's disease (AD) represent significant challenges in neurological diagnostics.
- Early identification of MCI is crucial for timely intervention and management.
- The role of the cerebellum in cognitive function and its potential as a biomarker for cognitive decline are increasingly recognized.
Purpose of the Study:
- To investigate the diagnostic utility of cerebellar magnetic resonance imaging (MRI) radiomics features for identifying mild cognitive impairment (MCI).
- To compare the performance of cerebellar radiomics models against hippocampal radiomics models in detecting cognitive impairment (CI) and MCI.
- To explore the cerebellum's contribution to the pathophysiology of cognitive decline.
Main Methods:
- A retrospective multicenter study involving patients with MCI, AD, and healthy controls (HCs).
- Extraction of radiomics features from cerebellar and hippocampal regions using T1-, T2-, and T2-FLAIR-weighted MRI sequences.
- Development and validation of a sparse representation classifier using 10-fold cross-validation and independent/external datasets.
Main Results:
- Cerebellar MRI radiomics models demonstrated diagnostic accuracy comparable to hippocampal models for identifying cognitive impairment (CI) and MCI.
- In the training set, cerebellar models showed higher accuracy (91.0%) and ROC AUC (0.943) for CI detection than hippocampal models.
- Both cerebellar and hippocampal models showed similar performance in independent and external validation sets for MCI detection.
Conclusions:
- Cerebellar MRI radiomics models offer comparable diagnostic accuracy to hippocampal models for identifying CI and MCI.
- These findings support the cerebellum's significant role in the early detection of cognitive dysfunction.
- The study suggests potential cerebellar biomarkers for clinical application in diagnosing cognitive impairment.
