Dataset Information

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy.

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 (?HbO) in the region of interest, ?HbO 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 ?HbO 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 ?HbO 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.

SUBMITTER: Yang D

PROVIDER: S-EPMC7253632 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy.

Yang Dalin D Huang Ruisen R Yoo So-Hyeon SH Shin Myung-Jun MJ Yoon Jin A JA Shin Yong-Il YI Hong Keum-Shik KS

Frontiers in aging neuroscience 20200521

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, ...[more]

PMID: 32508627

Similar Datasets

Project description:BackgroundObstructive sleep apnea and nocturnal oxygen desaturations, which are prevalent in sickle cell disease (SCD) and chronic anemia disorders, have been linked to risks of stroke and silent cerebral infarcts (SCI). Cerebrovascular response to intermittent desaturations has not been well studied and may identify patients at greatest risk.PurposeTo investigate the cerebral dynamic response to induced desaturation in SCD patients with and without SCI, chronic anemia, and healthy subjects.Study typeProspective.SubjectsTwenty-six SCD patients (age = 21 ± 8.2, female 46.2%), including 15 subjects without SCI and nine subjects with SCI, 15 nonsickle anemic patients (age = 22 ± 5.8, female 66.7%), and 31 controls (age = 28 ± 12.3, female 77.4%).Field strength/sequence3T, gradient-echo echo-planar imaging.AssessmentA transient hypoxia challenge of five breaths of 100% nitrogen gas was performed with blood oxygen level-dependent (BOLD) MRI and near-infrared spectroscopy (NIRS) acquisitions. Hypoxia responses were characterized by desaturation depth, time-to-peak, return-to-baseline half-life, and posthypoxia recovery in the BOLD and NIRS time courses. SCI were documented by T2 fluid-attenuation inversion recovery (FLAIR).Statistical testsUnivariate and multivariate regressions were performed between hypoxic parameters and anemia predictors. Voxelwise two-sample t-statistic maps were used to assess the regional difference in hypoxic responses between anemic and control groups.ResultsCompared to controls, SCD and chronically anemic patients demonstrated significantly higher desaturation depth (P < 0.01) and shorter return-to-baseline timing response (P < 0.01). Patients having SCI had shorter time-to-peak (P < 0.01), return-to-baseline (P < 0.01), and larger desaturation depth (P < 0.01) in both white matter regions at risk and normal-appearing white matter than patients without infarcts. On multivariate analysis, desaturation depth and timing varied with age, sex, blood flow, white blood cells, and cell-free hemoglobin (r2 = 0.25 for desaturation depth; r2 = 0.18 for time-to-peak; r2 = 0.37 for return-to-baseline).Data conclusionTransient hypoxia revealed global and regional response differences between anemic and healthy subjects. SCI was associated with extensive heterogeneity of desaturation dynamics, consistent with extensive underlying microvascular remodeling.

Dataset Information

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy.

Publications

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets