Topological Changes in the Brain Network Induced by the Training on a Piloting Task: An EEG-Based Functional Connectome Approach

Neuromodulation of brain power topography and network topology by prefrontal transcranial photobiomodulation

Objective.Transcranial photobiomodulation (tPBM) has shown promising benefits, including cognitive improvement, in healthy humans and in patients with Alzheimer's disease. In this study, we aimed to identify key cortical regions that present significant changes caused by tPBM in the electroencephalogram (EEG) oscillation powers and functional connectivity in the healthy human brain.Approach. A 64-channel EEG was recorded from 45 healthy participants during a 13 min period consisting of a 2 min baseline, 8 min tPBM/sham intervention, and 3 min recovery. After pre-processing and normalizing the EEG data at the five EEG rhythms, cluster-based permutation tests were performed for multiple comparisons of spectral power topographies, followed by graph-theory analysis as a topological approach for quantification of brain connectivity metrics at global and nodal/cluster levels.Main results. EEG power enhancement was observed in clusters of channels over the frontoparietal regions in the alpha band and the centroparietal regions in the beta band. The global measures of the network revealed a reduction in synchronization, global efficiency, and small-worldness of beta band connectivity, implying an enhancement of brain network complexity. In addition, in the beta band, nodal graphical analysis demonstrated significant increases in local information integration and centrality over the frontal clusters, accompanied by a decrease in segregation over the bilateral frontal, left parietal, and left occipital regions.Significance.Frontal tPBM increased EEG alpha and beta powers in the frontal-central-parietal regions, enhanced the complexity of the global beta-wave brain network, and augmented local information flow and integration of beta oscillations across prefrontal cortical regions. This study sheds light on the potential link between electrophysiological effects and human cognitive improvement induced by tPBM.

Aviation and neurophysiology: A systematic review

This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the current state-of-art of (neuro)ergonomic design and practice in flight training. We identified multiple candidate neurometrics of training progress and workload, such as frontal theta power, the EEG Engagement Index and the Cognitive Stability Index. Furthermore, we discovered that several types of classifiers could be used to accurately detect mental states, such as the detection of drowsiness and mental fatigue. The paper advances practical guidelines on terminology usage, simulator fidelity, and multimodality, as well as future research ideas including the potential of Virtual Reality flight simulations for training, and a brain-computer interface for flight training.

The Enhanced Interhemispheric Functional Connectivity in the Striatum Is Related to the Cognitive Impairment in Individuals With White Matter Hyperintensities

Objective

The cognitive performance of individuals with white matter hyperintensities (WMH) tends to vary considerably. This study aimed to explore the relationship of the synchronous spontaneous activities in homotopic areas across hemispheres, named as voxel-mirrored homotopic connectivity (VMHC), with the cognitive performance of individuals with WMH.

Materials and Methods

Eighty-two WMH subjects without cognitive impairment (CI), 56 WMH subjects with CI, and 92 healthy subjects (HS) underwent neuropsychological tests and multimodal magnetic resonance imaging scans. VMHC maps were analyzed among the three groups. Correlative analyses were performed between VMHC values and cognitive function.

Results

No significant difference in WMH volume, brain volume, or gray matter atrophy rate was shown between WMH subjects with and without CI. In contrast, those with CI displayed lower VMHC in the bilateral cuneus and calcarine and higher VMHC in the lentiform nucleus and caudate nucleus (LNCN) than those without CI. Furthermore, the VMHC in the LNCN was negatively associated with the global function and the memory function in WMH subjects.

Conclusion

The enhanced VMHC in the LNCN was associated with the development of CI in individuals with WMH. This finding may contribute to the exploration of surrogate markers for the CI caused by WMH.

An EEG-based systematic explainable detection framework for probing and localizing abnormal patterns in Alzheimer's disease

OBJECTIVE

Electroencephalography (EEG) is a potential source of downstream biomarkers for the early diagnosis of Alzheimer's disease (AD) due to its low-cost, non-invasive, and portable advantages. Accurately detecting AD-induced patterns from EEG signals is essential for understanding AD-related neurodegeneration at the EEG level and further evaluating the risk of AD at an early stage. This paper proposes a deep learning-based, functional explanatory framework that probes AD abnormalities from short-sequence EEG data.

APPROACH

The framework is a learning-based automatic detection system consisting of three encoding pathways that analyze EEG signals in frequency, complexity, and synchronous domains. We integrated the proposed EEG descriptors with the neural network components into one learning system to detect AD patterns. A transfer learning-based model was used to learn the deep representations, and a modified generative adversarial module was attached to the model to overcome feature sparsity. Furthermore, we utilized activation mapping to obtain the AD-related neurodegeneration at brain rhythm, dynamic complexity, and functional connectivity levels.

MAIN RESULTS

The proposed framework can accurately (100%) detect AD patterns based on our raw EEG recordings without delicate preprocessing. Meanwhile, the system indicates that 1) the power of different brain rhythms exhibits abnormal in the frontal lobes of AD patients, and such abnormality spreads to central lobes in the alpha and beta rhythms, 2) the difference in nonlinear complexity varies with the temporal scales, and 3) all the connections of pair-wise brain regions except bilateral temporal connectivity are weak in AD patterns. The proposed method outperforms other related methods in detection performance.

SIGNIFICANCE

We provide a new method for revealing abnormalities and corresponding localizations in different feature domains of EEG from AD patients. This study is a significant foundation for our future work on identifying individuals at high risk of AD at an early stage.

EEG Based Dynamic Functional Connectivity Analysis in Mental Workload Tasks with Different Types of Information

The accurate evaluation of operators' mental workload in human-machine systems plays an important role in ensuring the correct execution of tasks and the safety of operators. However, the performance of cross-task mental workload evaluation based on physiological metrics remains unsatisfactory. To explore the changes in dynamic functional connectivity properties with varying mental workload in different tasks, four mental workload tasks with different types of information were designed and a newly proposed dynamic brain network analysis method based on EEG microstate was applied in this paper. Six microstate topographies labeled as Microstate A-F were obtained to describe the task-state EEG dynamics, which was highly consistent with previous studies. Dynamic brain network analysis revealed that 15 nodes and 68 pairs of connectivity from the Frontal-Parietal region were sensitive to mental workload in all four tasks, indicating that these nodal metrics had potential to effectively evaluate mental workload in the cross-task scenario. The characteristic path length of Microstate D brain network in both Theta and Alpha bands decreased whereas the global efficiency increased significantly when the mental workload became higher, suggesting that the cognitive control network of brain tended to have higher function integration property under high mental workload state. Furthermore, by using a SVM classifier, an averaged classification accuracy of 95.8% for within-task and 80.3% for cross-task mental workload discrimination were achieved. Results implies that it is feasible to evaluate the cross-task mental workload using the dynamic functional connectivity metrics under specific microstate, which provided a new insight for understanding the neural mechanism of mental workload with different types of information.

Basal ganglia stroke is associated with altered functional connectivity of the left inferior temporal gyrus

BACKGROUND AND PURPOSE

Acute cerebral infarction in the basal ganglia is associated with an increased risk of cognitive impairment, suggesting that cognitive networks might be involved in neural plasticity after ischemic stroke. This study was conducted to explore the abnormalities in functional and causal connectivity of the brain network in patients with acute ischemic stroke (AIS) in the basal ganglia.

METHODS

Resting-state functional magnetic resonance imaging was performed in 27 patients with AIS in the basal ganglia and 27 healthy controls (HCs). Brain regions with statistically different degree centrality (DC) values between groups were selected as seed points for granger causality analysis (GCA) analysis. The effective connectivity values of GCA were extracted, and the correlation between them and the Montreal Cognitive Assessment (MoCA) score was analyzed.

RESULTS

Compared with HCs group, AIS patients displayed increased DC value in the left inferior temporal gyrus (ITG) and hippocampus head, reduced effective connectivity from the left ITG to the left precentral and postcentral gyri, increased effective connectivity from the left precentral and postcentral gyri to the left ITG, and reduced effective connectivity from the left anterior cingulate gyrus to the left ITG. The MoCA score of the AIS group was lower than that of the HCs group (t = -7.33, p < .05).

CONCLUSION

Alterations of functional and causal connectivity among multiple brain regions suggest that patients with AIS in the basal ganglia have impairment of multifunctional networks in the whole brain.

A complex-valued functional brain connectivity descriptor amenable to Riemannian geometry

OBJECTIVE

We introduce a novel, phase-based, functional connectivity descriptor that encapsulates not only the synchronization strength between distinct brain regions, but also the time-lag between the involved neural oscillations. The new estimator employs complex-valued measurements and results in a brain network sketch that lives on the smooth manifold of Hermitian Positive Definite (HPD) matrices.

APPROACH

Leveraging the HPD property of the proposed descriptor, we adapt a recently introduced dimensionality reduction methodology that is based on Riemannian Geometry and discriminatively detects the recording sites which best reflect the differences in network organization between contrasting recording conditions in order to overcome the problem of high-dimensionality, usually encountered in the connectivity patterns derived from multisite encephalographic recordings.

MAIN RESULTS

The proposed framework is validated using an EEG dataset that refers to the challenging problem of differentiating between attentive and passive visual responses. We provide evidence that the reduced connectivity representation facilitates high classification performance and caters for neuroscientific explorations.

SIGNIFICANCE

Our paper is the very first that introduces an advanced connectivity descriptor that can take advantage of Riemannian geometry tools. The proposed descriptor, that inherently and simultaneously captures both the strength and the corresponding time-lag of the phase synchronization, is the first phase-based descriptor tailored to leverage the benefits of Riemannian geometry.

Biological Relevance of Network Architecture

Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.