Aim: This study aims to develop new approaches to characterize brain networks to potentially contribute to a better understanding of mechanisms involved in depression. Method and subjects: We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed investigating functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were developed at a functional network level, using a false discovery rate method. Linear discriminant analysis was used to differentiate between the two groups. Results and discussion: Significant differences in functional connectivity (FC) between depressed patients vs. healthy controls was demonstrated, with brain regions including the lingual gyrus, cerebellum, midcingulate cortex and thalamus more prominent in healthy subjects as compared to depression where the orbitofrontal cortex emerged as a key node. Linear discriminant analysis demonstrated that full-connectivity matrices were the most precise in differentiating between depression vs. health subjects. Conclusion: The study provides supportive evidence for impaired functional connectivity networks in MDE patients.

We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks’ global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm. This approach revealed that the groups are indistinguishable in the univariate feature space but become distinguishable in a three-dimensional feature space formed by the identified most important features: mean node strength, clustering coefficient, and the number of edges. LDA achieves the highest accuracy when considering the network with all connections or only the strongest ones. Our approach allowed us to analyze the separability of classes in the multidimensional feature space, which is critical for interpreting the results of machine learning models. We demonstrated that the parametric planes of the control and MDD groups rotate in the feature space with increasing the thresholding parameter and that their intersection increases with approaching the threshold of 0.45, for which classification accuracy is minimal. Overall, the combined approach for feature selection provides an effective and interpretable scenario for discriminating between MDD patients and healthy controls using measures of functional connectivity networks. This approach can be applied to other machine learning tasks to achieve high accuracy while ensuring the interpretability of the results.

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

Neuroeducation seeks to implement knowledge about neural mechanisms of learning into educational practice and to understand the impact of learning itself. The crucial tasks in this field are to evaluate and to enhance cognitive abilities, that are used in monitoring educational performance, but also known to greatly impact learning process. Contemporary neuroscience achieved significant progress in measuring brain cognitive abilities through mental state assessment. Popular approach to this task based on brain-computer interface can be difficult to implement in the context of education, but general concept of neuroadaptation is still plausible. In this study, we propose open-loop neuroadaptive system for enhancing student’s cognitive abilities in learning. Assessment of cognitive abilities is based on the concept of executive functions. We design EEG study with special tests and use combined analysis of behavioral and brain activity to assess the level of development of cognitive abilities. Feedback in this system is implemented in the form of recommendations aimed to develop and enhance underdeveloped cognitive abilities and skills. Recommendations have form of various types of extracurricular activities and are based on extensive literature search. This is the system with open-loop adaptation, as it can assess cognitive abilities, provide feedback aimed to enhance these abilities and then after a period of time it can assess cognitive abilities again as a part of the next loop. We believe that developed neuroadaptive system has a potential to be used in educational institutes.

We present a novel method for analyzing brain functional networks using functional magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to a standard group-based method for patients diagnosed with major depressive disorder (MDD) and a healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the consensus network approach uncovers distinct characteristics in network measures and degree distributions when considering connection strengths. In the healthy control group, as connection strengths increase, we observe a transition in the network topology from a combination of scale-free and random topologies to a small-world topology. Conversely, the MDD group exhibits uncertainty in weak connections, while strong connections display small-world properties. In contrast, the group-based approach does not exhibit significant differences in behavior between the two groups. However, it does indicate a transition in topology from a scale-free-like structure to a combination of small-world and scale-free topologies. The use of the consensus network approach also holds immense potential for the classification of MDD patients, as it unveils substantial distinctions between the two groups.

The problem of maintaining cognitive performance under fatigue is crucial in fields requiring high concentration and efficiency to successfully complete critical tasks. In this context, the study of compensatory mechanisms that help the brain overcome fatigue is particularly important. This research investigates the correlations between physiological, behavioral, and subjective measures while considering the impact of fatigue on the performance of working memory tasks. A combined approach of functional near-infrared spectroscopy (fNIRS) and eye-tracking was used to reconstruct brain functional networks based on fNIRS data and analyze them in terms of network characteristics such as global clustering coefficient and global efficiency. Results showed a significant increase in subjective fatigue but no significant change in performance during the experiment. The study confirmed that despite fatigue, subjects can maintain performance through compensatory mechanisms, increasing mental effort, with the level of compensation depending on the task’s complexity. Furthermore, the study showed that compensatory effort maintains the efficiency of the frontoparietal network, and the degree of compensatory effort is related to the difference in response times between high- and low-complexity tasks.