Evaluation of Neural Degeneration Biomarkers in the Prefrontal Cortex for Early Identification of Patients With Mild Cognitive Impairment: An fNIRS Study

Abstract

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.

Evidence of Neuroplasticity Due to Acupuncture: An fNIRS Study

“…By using images of brain activation and connectivity higher classi cation accuracies were obtained. These results are in line with a recently published work by [36]. Overall, the results indicate that acupuncture is useful for enhancing the mental state of patients.…”

“…After the pre-processing, the next step was to classify the data. For the purpose of classi cation, two different methods were used: Classi cation based on temporal features and classi cation using images [36]. [37].…”

“…Again, vefold cross-validation was used. Additional details regarding the method were presented in a previous work [36]. For detecting cortical activation, the time-series signal for each trial was tted to the desired hemodynamic response (dHRF) signal [38][39][40].…”

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

“…To avoid the surgical procedures, there are four popular non-invasive measurement methods for acquiring the brain information: functional magnetic resonance imaging (fMRI) [ 6 ], magnetoencephalography (MEG) [ 7 ], electroencephalogram (EEG) [ 8 ], and functional near-infrared spectroscopy (fNIRS) [ 9 , 10 ]. Due to the advantage of the good temporal resolution, portable, and low cost, EEG shows unique usability superiority for the BCI-based control system in comparison to the other type of brain techniques (i.e., MEG, fMRI, and fNIRS) [ 11 ].…”

Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

“…It offers several advantages, including acceptable temporal and spatial resolution , portability, and low cost (Ferrari and Quaresima, 2012). With these advantages, fNIRS has successfully demonstrated its potential as a viable neuroimaging tool for applications to the health care industry (Hong and Yaqub, 2019), neurological disorders (Obrig, 2014;Ghafoor et al, 2019;Yang et al, 2019), psychiatric disorders (Ohi et al, 2017), behavioral and cognitive development (Watanabe et al, 2017;Yaqub et al, 2018), and brain-computer interfaces (BCIs) (Nicolas-Alonso and Gomez-Gil, 2012;Naseer and Hong, 2015;Schudlo and Chau, 2018;Shin and Im, 2018).…”