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Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks
Published on: December 2, 2015
Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional
Dalin Yang1, Ruisen Huang1, So-Hyeon Yoo1
1School of Mechanical Engineering, Pusan National University, Busan, South Korea.
Abstract:
Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at 13 specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20-60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.
Insights
Functional near-infrared spectroscopy (fNIRS) combined with deep learning accurately identifies mild cognitive impairment (MCI). This neuroimaging approach shows promise for early MCI detection in clinical settings.
Area of Science:
- Neuroscience
- Medical Imaging
- Artificial Intelligence
Background:
- Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), necessitating early diagnosis for timely intervention.
- Not all MCI patients progress to AD, highlighting the need for accurate diagnostic tools.
- Current diagnostic methods may not fully capture the nuances of early-stage cognitive decline.
Purpose of the Study:
- To develop and validate a novel neuroimaging approach for the early identification of MCI.
- To leverage functional near-infrared spectroscopy (fNIRS) and deep learning for MCI detection.
- To assess the efficacy of analyzing oxygenated hemoglobin changes (ΔHbO) during cognitive tasks.
Main Methods:
- Fifteen MCI subjects and nine healthy controls (HCs) performed N-back, Stroop, and verbal fluency (VF) tasks.
- fNIRS was used to measure ΔHbO changes in the prefrontal cortex.
- A four-layer convolutional neural network (CNN) was trained and evaluated using 5-fold cross-validation on ΔHbO maps and temporal feature maps.
Main Results:
- The CNN achieved high classification accuracies across tasks: 89.46% (N-back), 87.80% (Stroop), and 90.37% (VF).
- Temporal feature maps, particularly the ΔHbO slope map during the 20-60s interval of N-back tasks, yielded the highest accuracy of 98.61%.
- The fNIRS-based approach demonstrated significant potential in distinguishing MCI from healthy controls.
Conclusions:
- fNIRS combined with deep learning and temporal feature analysis offers a promising, non-invasive method for early MCI detection.
- This approach can serve as a valuable tool for clinicians to aid in the diagnosis of MCI.
- The study underscores the utility of neuroimaging biomarkers in understanding and diagnosing neurodegenerative disease precursors.
