Single-Trial Classification of fNIRS Signals in Four Directions Motor Imagery Tasks Measured From Prefrontal Cortex.

As a promising non-invasive technique, functional near-infrared spectroscopy (fNIRS) can easily detect the hemodynamic responses of cortical brain activities. This paper investigated the multiclass classification of motor imagery (MI) based on fNIRS; ten healthy individuals were recruited to move an object using their imagination. A multi-channel continuous-wave fNIRS equipment was applied to obtain the signals from the prefrontal cortex. A combination of ensemble empirical mode decomposition and independent component analysis method was used to solve the signal-noise frequency spectrum aliasing issues caused by the Mayer wave (0.1 Hz), then the signal means features were extracted as an input of linear discriminant analysis and support vector machine (SVM) classifier. The SVM classifier shows better classification results, and the average accuracies of four directions, up-down and left-right were 40.55%, 73.05%, and 70.7%, respectively, using oxygenated hemoglobin (8-21 s). This paper demonstrated that Brodmann area 4 was activated, which is consistent with previous conclusions. Furthermore, we found that the orbitofrontal cortex is also involved in MI and O2sat can also serve as a classified index.

Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study.

This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.

Frequency-Specific Functional Connectivity Density as an Effective Biomarker for Adolescent Generalized Anxiety Disorder.

Several neuropsychiatric diseases have been found to influence the frequency-specific spontaneous functional brain organization (SFBO) in resting state, demonstrating that the abnormal brain activities of different frequency bands are associated with various physiological and psychological dysfunctions. However, little is known about the frequency specificities of SFBO in adolescent generalized anxiety disorder (GAD). Here, a novel complete ensemble empirical mode decomposition with adaptive noise method was applied to decompose the time series of each voxel across all participants (31 adolescent patients with GAD and 28 matched healthy controls; HCs) into four frequency-specific bands with distinct intrinsic oscillation. The functional connectivity density (FCD) of different scales (short-range and long-range) was calculated to quantify the SFBO changes related to GAD within each above frequency-specific band and the conventional frequency band (0.01-0.08 Hz). Support vector machine classifier was further used to examine the discriminative ability of the frequency-specific FCD values. The results showed that adolescent GAD patients exhibited abnormal alterations of both short-range and long-range FCD (S-FCD and L-FCD) in widespread brain regions across three frequency-specific bands. Positive correlation between the State Anxiety Inventory (SAI) score and increased L-FCD in the fusiform gyrus in the conventional frequency band was found in adolescents with GAD. Both S-FCD and L-FCD in the insula in the lower frequency band (0.02-0.036 Hz) had the highest classification performance compared to all other brain regions with inter-group difference. Furthermore, a satisfactory classification performance was achieved by combining the discrepant S-FCD and L-FCD values in all frequency bands, with the area under the curve (AUC) value of 0.9414 and the corresponding sensitivity, specificity, and accuracy of 87.15, 92.92, and 89.83%, respectively. This study indicates that the alterations of SFBO in adolescent GAD are frequency dependence and the frequency-specific FCD can potentially serve as a valuable biomarker in discriminating GAD patients from HCs. These findings may provide new insights into the pathophysiological mechanisms of adolescent GAD.

An Effective Method to Identify Adolescent Generalized Anxiety Disorder by Temporal Features of Dynamic Functional Connectivity.

Generalized anxiety disorder (GAD) is one of common anxiety disorders in adolescents. Although adolescents with GAD are thought to be at high risk for other mental diseases, the disease-specific alterations have not been adequately explored. Recent studies have revealed the abnormal functional connectivity (FC) in adolescents with GAD. Most previous researches have investigated the static FC which ignores the fluctuations of FC over time and focused on the structures of "fear circuit". To figure out the alterations of dynamic FC caused by GAD and the possibilities of dynamic FC as biomarkers, we propose an effective approach to identify adolescent GAD using temporal features derived from dynamic FC. In our study, the instantaneous synchronization of pairwise signals was estimated as dynamic FC. The Hurst exponent (H) and variance, indicating regularity and variable degree of a time series respectively, were calculated as temporal features of dynamic FC. By leave-one-out cross-validation (LOOCV), a relatively high accuracy of 88.46% could be achieved when H and variance of dynamic FC were combined as features. In addition, we identified the disease-related regions, including regions belonging to default mode (DM) and cerebellar networks. The results suggest that temporal features of dynamic FC could achieve a clinically acceptable diagnostic power and serve as biomarkers of adolescent GAD. Furthermore, our work could be helpful in understanding the pathophysiological mechanism of adolescent GAD.

Emotion Regulating Attentional Control Abnormalities In Major Depressive Disorder: An Event-Related Potential Study.

Major depressive disorders (MDD) exhibit cognitive dysfunction with respect to attention. The deficiencies in cognitive control of emotional information are associated with MDD as compared to healthy controls (HC). However, the brain mechanism underlying emotion that influences the attentional control in MDD necessitates further research. The present study explores the emotion-regulated cognitive competence in MDD at a dynamic attentional stage. Event-Related Potentials (ERPs) were recorded from 35 clinical MDD outpatients and matched HCs by applying a modified affective priming dot-probe paradigm, which consisted of various emotional facial expression pairs. From a dynamic perspective, ERPs combined with sLORETA results showed significant differences among the groups. In compared to HC, 100 ms MDD group exhibited a greater interior-prefrontal N100, sensitive to negative-neutral faces. 200 ms MDD showed an activated parietal-occipital P200 linked to sad face, suggesting that the attentional control ability concentrated on sad mood-congruent cognition. 300 ms, a distinct P300 was observed at dorsolateral parietal cortex, representing a sustained attentional control. Our findings suggested that a negatively sad emotion influenced cognitive attentional control in MDD in the early and late attentional stages of cognition. P200 and P300 might be predictors of potential neurocognitive mechanism underlying the dysregulated attentional control of MDD.

An Internet-based program for depression using activity and physiological sensors: efficacy, expectations, satisfaction, and ease of use.

PURPOSE: Computerized cognitive behavioral therapy (CCBT) has been shown to be efficacious. Moreover, CCBT can be enhanced by using physiological and activity sensors, but there is no evidence about the acceptability of all these tools. The objective of this study is to examine the efficacy, expectations, satisfaction, and ease of use of an Internet-based CCBT program for preventing depression, with and without sensors (electroencephalography, electrocardiograhpy ECG, and actigraphy), in a high-risk population (unemployed men). PATIENTS AND METHODS: Sixty participants at risk of depression (unemployed men) were randomly assigned to three experimental conditions: 1) intervention program (N=22), 2) intervention program plus sensors (N=19), and 3) control group (N=19). Participants completed depression, anxiety, positive and negative affect, and perceived stress measures. Furthermore, they also completed the measures for expectation, satisfaction, and the ease of use of the program. RESULTS: Results showed that the two intervention groups improved significantly more than the control group on the clinical variables, and the improvements were greater in the group that used sensors than in the group that did not use them. Furthermore, participants in both intervention groups scored high on expectations and satisfaction with the CCBT program (with and without sensors). The mean score for usability was 88 out of 100 (standard deviation =12.32). No significant differences were found between groups on any of these variables. CONCLUSION: This is the first study to analyze the efficacy, expectations, satisfaction, and ease of use of an Internet-based program using physiological and activity sensors. These results suggest that an Internet program for depression with or without physiological and activity sensors is effective, satisfactory, and easy to use.

Signal Quality Assessment Model for Wearable EEG Sensor on Prediction of Mental Stress.

Electroencephalogram (EEG) plays an important role in E-healthcare systems, especially in the mental healthcare area, where constant and unobtrusive monitoring is desirable. In the context of OPTIMI project, a novel, low cost, and light weight wearable EEG sensor has been designed and produced. In order to improve the performance and reliability of EEG sensors in real-life settings, we propose a method to evaluate the quality of EEG signals, based on which users can easily adjust the connection between electrodes and their skin. Our method helps to filter invalid EEG data from personal trials in both domestic and office settings. We then apply an algorithm based on Discrete Wavelet Transformation (DWT) and Adaptive Noise Cancellation (ANC) which has been designed to remove ocular artifacts (OA) from the EEG signal. DWT is applied to obtain a reconstructed OA signal as a reference while ANC, based on recursive least squares, is used to remove the OA from the original EEG data. The newly produced sensors were tested and deployed within the OPTIMI framework for chronic stress detection. EEG nonlinear dynamics features and frontal asymmetry of theta, alpha, and beta bands have been selected as biological indicators for chronic stress, showing relative greater right anterior EEG data activity in stressful individuals. Evaluation results demonstrate that our EEG sensor and data processing algorithms have successfully addressed the requirements and challenges of a portable system for patient monitoring, as envisioned by the EU OPTIMI project.