We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta band of AD subjects shows an increase in the power, whereas the alpha band shows a decreased activity (P-value <0.05). In the right brain hemisphere of AD subjects, a decreased activity is observed in all frequency bands. It was also noticed that the right temporal region shows a significant difference between the two groups in all frequency bands. Using a support vector machine, control and patient groups are discriminated with an accuracy of 84.4%, sensitivity 75.0%, and specificity of 93.7%.

Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

We investigated the use of a multimodal functional neuroimaging system in quantifying mental workload of healthy human volunteers. We recorded behavioral performance measures as well as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously from subjects performing n-back tasks. The EEG and fNIRS signals were used in feature generation and classification offline using support vector machines. We examined the classification accuracy of three distinct systems: EEG based; fNIRS based; and Hybrid, which contained features from the first two systems as based on their interactions. The classification accuracy of the Hybrid system was observed to be greater than that of either system, indicating the synergistic role played by multimodal signals and by neurovascular coupling in quantifying mental workload.

The aim of this study was to determine the effects of cyclic adenosine monophosphate (cAMP) and its dependent pathway on thermal nociception in a mouse model of acute pain. Here, we studied the effect of H-89 (protein kinase A inhibitor), bucladesine (Db-cAMP) (membrane-permeable analog of cAMP), and pentoxifylline (PTX; nonspecific phosphodiesterase (PDE) inhibitor) on pain sensation. Different doses of H-89 (0.05, 0.1, and 0.5 mg/100 g), PTX (5, 10, and 20 mg/100 g), and Db-cAMP (50, 100, and 300 nm/mouse) were administered intraperitoneally (I.p.) 15 min before a tail-flick test. In combination groups, we injected the first and the second compounds 30 and 15 min before the tail-flick test, respectively. I.p. administration of H-89 and PTX significantly decreased the thermal-induced pain sensation in their low applied doses. Db-cAMP, however, decreased the pain sensation in a dose-dependent manner. The highest applied dose of H-89 (0.5 mg/100 g) attenuated the antinociceptive effect of Db-cAMP in doses of 50 and 100 nm/mouse. Surprisingly, Db-cAMP decreased the antinociceptive effect of the lowest dose of H-89 (0.05 mg/100 g). All applied doses of PTX reduced the effect of 0.05 mg/100 g H-89 on pain sensation; however, the highest dose of H-89 compromised the antinociceptive effect of 20 mg/100 g dose of PTX. Co-administration of Db-cAMP and PTX increased the antinociceptive effect of each compound on thermal-induced pain. In conclusion, PTX, H-89, and Db-cAMP affect the thermal-induced pain by probably interacting with intracellular cAMP and cGMP signaling pathways and cyclic nucleotide-dependent protein kinases.