Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning
Li Kang1, Jingwan Jiang1, Jianjun Huang1
1College of Information Engineering, Shenzhen University, Shenzhen, China.
Abstract:
Mild cognitive impairment (MCI) is a clinical state with a high risk of conversion to Alzheimer's Disease (AD). Since there is no effective treatment for AD, it is extremely important to diagnose MCI as early as possible, as this makes it possible to delay its progression toward AD. However, it's challenging to identify early MCI (EMCI) because there are only mild changes in the brain structures of patients compared with a normal control (NC). To extract remarkable features for these mild changes, in this paper, a multi-modality diagnosis approach based on deep learning is presented. Firstly, we propose to use structure MRI and diffusion tensor imaging (DTI) images as the multi-modality data to identify EMCI. Then, a convolutional neural network based on transfer learning technique is developed to extract features of the multi-modality data, where an L1-norm is introduced to reduce the feature dimensionality and retrieve essential features for the identification. At last, the classifier produces 94.2% accuracy for EMCI vs. NC on an ADNI dataset. Experimental results show that multi-modality data can provide more useful information to distinguish EMCI from NC compared with single modality data, and the proposed method can improve classification performance, which is beneficial to early intervention of AD. In addition, it is found that DTI image can act as an important biomarker for EMCI from the point of view of a clinical diagnosis.
Insights
Diagnosing early mild cognitive impairment (EMCI) is crucial for delaying Alzheimer's Disease (AD) progression. A new deep learning method using multi-modality MRI and DTI data achieved 94.2% accuracy in identifying EMCI.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Neurology
Background:
- Mild cognitive impairment (MCI) significantly increases the risk of progression to Alzheimer's Disease (AD).
- Early identification of early MCI (EMCI) is critical for timely intervention, yet challenging due to subtle brain changes.
- Current diagnostic methods struggle to detect subtle structural alterations indicative of early neurodegeneration.
Purpose of the Study:
- To develop and evaluate a multi-modality deep learning approach for accurate early MCI (EMCI) detection.
- To leverage structural MRI and diffusion tensor imaging (DTI) for enhanced EMCI classification.
- To identify key imaging biomarkers for early diagnosis of cognitive decline.
Main Methods:
- Utilized a multi-modality dataset combining structural MRI and DTI scans.
- Developed a deep learning model incorporating transfer learning for feature extraction.
- Implemented an L1-norm regularization technique to reduce feature dimensionality and enhance discriminative power.
- Validated the model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
Main Results:
- The proposed multi-modality approach achieved 94.2% accuracy in distinguishing EMCI from normal controls (NC).
- Multi-modality data provided superior diagnostic information compared to single-modality data.
- Diffusion tensor imaging (DTI) emerged as a significant biomarker for identifying EMCI.
Conclusions:
- The deep learning-based multi-modality approach effectively enhances the accuracy of early MCI detection.
- Integrating structural MRI and DTI offers a promising strategy for early Alzheimer's Disease intervention.
- DTI analysis holds potential as a valuable clinical tool for diagnosing early cognitive impairment.
