Mild cognitive impairment (MCI), a condition characterizing poor cognition, is associated with aging and depicts early symptoms of severe cognitive impairment, known as Alzheimer’s disease (AD). Meanwhile, early detection of MCI can prevent progression to AD. A great deal of research has been performed in the past decade on MCI detection. However, availability of biomarkers for MCI detection requires greater attention. In our study, we evaluated putative and reliable biomarkers for diagnosing MCI by performing different mental tasks (i.e., N-back task, Stroop task, and verbal fluency task) using functional near-infrared spectroscopy (fNIRS) signals on a group of 15 MCI patients and 9 healthy control (HC). The 15 digital biomarkers (i.e., five means, seven slopes, peak, skewness, and kurtosis) and two image biomarkers (t-map, correlation map) in the prefrontal cortex (PFC) (i.e., left PFC, middle PFC, and right PFC) between the MCI and HC groups were investigated by the statistical analysis, linear discriminant analysis (LDA), and convolutional neural network (CNN) individually. The results reveal that the statistical analysis using digital biomarkers (with a p-value < 0.05) could not distinguish the MCI patients from the HC over 60% accuracy. Therefore, the current statistical analysis needs to be improved to be used for diagnosing the MCI patients. The best accuracy with LDA was 76.67% with the N-back and Stroop tasks. However, the CNN classification results trained by image biomarkers showed a high accuracy. In particular, the CNN results trained via t-maps revealed the best accuracy (90.62%) with the N-back task, whereas the CNN result trained by the correlation maps was 85.58% with the N-back task. Also, the results illustrated that investigating the sub-regions (i.e., right, middle, left) of the PFC for detecting MCI would be better than examining the whole PFC. The t-map (or/and the correlation map) is conclusively recommended as an image biomarker for early detection of AD. The combination of CNN and image biomarkers can provide a reliable clinical tool for diagnosing MCI patients.

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.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS–EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS–EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS–EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS–EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS–EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS–EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS–EEG data analyses in future research.

BackgroundBrain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.ObjectiveThis study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.MethodA systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.ResultsSixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.ConclusionAn appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.