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Hemodynamics Analysis of Patients With Mild Cognitive Impairment During Working Memory Tasks
Abstract:
Diagnosis of dementia in early stage is important to prevent progression of dementia in the aging society. Mild cognitive impairment (MCI) denotes an early stage of Alzheimer disease (AD). In this paper, we aim to classify MCI patients from healthy controls (HC) during working memory tasks using functional near-infrared spectroscopy (fNIRS). To achieve this objective, t-values and correlation coefficients are calculated to find the region of interest (ROI) channels and brain connectivity. From the ROI channels averaged over subjects, features (mean and slope) of hemodynamic responses were extracted for classification. Extracted features were labelled as two classes and classified via two classifiers, linear discriminant analysis (LDA) and support vector machine (SVM). The classification accuracies were 73.08 % with LDA and 71.15 % with SVM. The results show that there are significant differences in the hemodynamic responses (HR) between MCI patients and healthy controls. Therefore, these results suggest a possibility of using fNIRS as a diagnostic tool for MCI patients.
Insights
Functional near-infrared spectroscopy (fNIRS) can distinguish mild cognitive impairment (MCI) patients from healthy individuals during memory tasks. This neuroimaging technique shows potential as a diagnostic tool for early-stage Alzheimer's disease detection.
Area of Science:
- Neuroscience
- Medical Imaging
- Cognitive Science
Background:
- Early diagnosis of dementia is crucial for managing cognitive decline in aging populations.
- Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD).
- Distinguishing MCI from healthy aging is essential for timely intervention.
Purpose of the Study:
- To classify individuals with MCI from healthy controls (HC) using functional near-infrared spectroscopy (fNIRS).
- To investigate the utility of fNIRS during working memory tasks for MCI detection.
- To identify brain activity patterns indicative of MCI.
Main Methods:
- fNIRS data were collected during working memory tasks.
- Region of Interest (ROI) channels and brain connectivity were identified using t-values and correlation coefficients.
- Hemodynamic response (HR) features (mean and slope) were extracted from ROI channels.
- Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were employed for classification.
Main Results:
- Classification accuracy reached 73.08% with LDA and 71.15% with SVM.
- Significant differences in hemodynamic responses (HR) were observed between MCI patients and HC.
- fNIRS successfully differentiated between the two groups based on brain activity patterns.
Conclusions:
- fNIRS shows promise as a non-invasive diagnostic tool for identifying MCI.
- The study highlights the potential of neuroimaging in early AD detection.
- Hemodynamic responses during working memory tasks are valuable biomarkers for MCI.
