Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.

Different Modes of Low-Frequency Focused Ultrasound-Mediated Attenuation of Epilepsy Based on the Topological Theory

Epilepsy is common brain dysfunction, where abnormal synchronized activities can be observed across multiple brain regions. Low-frequency focused pulsed ultrasound has been proven to modulate the epileptic brain network. In this study, we used two modes of low-intensity focused ultrasound (pulsed-wave and continuous-wave) to sonicate the brains of KA-induced epileptic rats, analyzed the EEG functional brain connections to explore their respective effect on the epileptic brain network, and discuss the mechanism of ultrasound neuromodulation. By comparing the brain network characteristics before and after sonication, we found that two modes of ultrasound both significantly affected the functional brain network, especially in the low-frequency band below 12 Hz. After two modes of sonication, the power spectral density of the EEG signals and the connection strength of the brain network were significantly reduced, but there was no significant difference between the two modes. Our results indicated that the ultrasound neuromodulation could effectively regulate the epileptic brain connections. The ultrasound-mediated attenuation of epilepsy was independent of modes of ultrasound.

Identifying super-feminine, super-masculine and sex-defining connections in the human braingraph

For more than a decade now, we can discover and study thousands of cerebral connections with the application of diffusion magnetic resonance imaging (dMRI) techniques and the accompanying algorithmic workflow. While numerous connectomical results were published enlightening the relation between the braingraph and certain biological, medical, and psychological properties, it is still a great challenge to identify a small number of brain connections closely related to those conditions. In the present contribution, by applying the 1200 Subjects Release of the Human Connectome Project (HCP) and Support Vector Machines, we identify just 102 connections out of the total number of 1950 connections in the 83-vertex graphs of 1064 subjects, which—by a simple linear test—precisely, without any error determine the sex of the subject. Next, we re-scaled the weights of the edges—corresponding to the discovered fibers—to be between 0 and 1, and, very surprisingly, we were able to identify two graph edges out of these 102, such that, if their weights are both 1, then the connectome always belongs to a female subject, independently of the other edges. Similarly, we have identified 3 edges from these 102, whose weights, if two of them are 1 and one is 0, imply that the graph belongs to a male subject—again, independently of the other edges. We call the former 2 edges superfeminine and the first two of the 3 edges supermasculine edges of the human connectome. Even more interestingly, the edge, connecting the right Pars Triangularis and the right Superior Parietal areas, is one of the 2 superfeminine edges, and it is also the third edge, accompanying the two supermasculine connections if its weight is 0; therefore, it is also a “switching” edge. Identifying such edge-sets of distinction is the unprecedented result of this work.

Social synchronisation of brain activity by eye-contact

Humans make eye-contact to extract information about other people’s mental states, recruiting dedicated brain networks that process information about the self and others. Recent studies show that eye-contact increases the synchronization between two brains. We investigated how eye-contact affects the frequency and direction of the synchronization within and between brains and the characteristics of the dual brain network (i.e. hyperbrain). Eye-contact was associated with higher coherence in the gamma frequency band (30-45Hz) for between and within brain connections. Network analysis revealed that some brain areas served as hubs which linked within- and between- brain networks (midparietal, midfrontal and right parietal areas). Friends showed more efficient eye-contact hyperbrain networks than strangers. During eye-contact, some dyads spontaneously adopted leader/follower roles, resulting in an increase in synchronization from leader to follower (interbrain) in the alpha frequency band. Eye-contact affected directed and undirected synchronization between brains more than within brains, offering support to the interactive brain hypothesis.

A taxonomy of the brain’s white matter: Twenty-one major tracts for the twenty-first century

The functional and computational properties of brain areas are determined, in large part, by their connectivity profiles. Advances in neuroimaging and network neuroscience allow us to characterize the human brain noninvasively and in vivo, but a comprehensive understanding of the human brain demands an account of the anatomy of brain connections. Long-range anatomical connections are instantiated by white matter and organized into tracts. Here, we aim to characterize the connections, morphology, traversal, and functions of the major white matter tracts in the brain. It is clear that there are significant discrepancies across different accounts of white matter tract anatomy, hindering our attempts to accurately map the connectivity of the human brain. We thoroughly synthesize accounts from multiple methods, but especially nonhuman primate tract-tracing and human diffusion tractography. Ultimately, we suggest that our synthesis provides an essential reference for neuroscientists and clinicians interested in brain connectivity and anatomy, allowing for the study of the association of white matter’s macro and microstructural properties with behavior, development, and disordered processes.

CHARACTERISTICS OF THE BRAIN ELECTRIC ACTIVITY IN COGNITIVE IMPAIRMENT

Based on the analysis of joint recurrence plots of the brain’s response to functional load in the form of rhythmic photostimulation, quantitative indicators of changes in the bioelectrical activity of the brain of patients with moderate cognitive impairments were revealed before and after the method of therapy associated with the formation of stable functional brain connections. It has been shown that such therapy, leading to an improvement in the condition of patients, correlates with changes in the indicators of joint recurrence plots of the bioelectrical activity of the brain and photostimulus.

Electroencephalographic Microstates in Schizophrenia and Bipolar Disorder

Schizophrenia (SCH) and bipolar disorder (BD) are characterized by many types of symptoms, damaged cognitive function, and abnormal brain connections. The microstates are considered to be the cornerstones of the mental states shown in EEG data. In our study, we investigated the use of microstates as biomarkers to distinguish patients with bipolar disorder from those with schizophrenia by analyzing EEG data measured in an eyes-closed resting state. The purpose of this article is to provide an electron directional physiological explanation for the observed brain dysfunction of schizophrenia and bipolar disorder patients.Methods: We used microstate resting EEG data to explore group differences in the duration, coverage, occurrence, and transition probability of 4 microstate maps among 20 SCH patients, 26 BD patients, and 35 healthy controls (HCs).Results: Microstate analysis revealed 4 microstates (A–D) in global clustering across SCH patients, BD patients, and HCs. The samples were chosen to be matched. We found the greater presence of microstate B in BD patients, and the less presence of microstate class A and B, the greater presence of microstate class C, and less presence of D in SCH patients. Besides, a greater frequent switching between microstates A and B and between microstates B and A in BD patients than in SCH patients and HCs and less frequent switching between microstates C and D and between microstates D and C in BD patients compared with SCH patients.Conclusion: We found abnormal features of microstate A, B in BD patients and abnormal features of microstate A, B, C, and D in SCH patients. These features may indicate the potential abnormalities of SCH patients and BD patients in distributing neural resources and influencing opportune transitions between different states of activity.

Less Is Better: Single-Digit Brain Functional Connections Predict T2DM and T2DM-Induced Cognitive Impairment

Type 2 diabetes mellitus (T2DM) leads to a higher risk of brain damage and adversely affects cognition. The underlying neural mechanism of T2DM-induced cognitive impairment (T2DM-CI) remains unclear. This study proposes to identify a small number of dysfunctional brain connections as imaging biomarkers, distinguishing between T2DM-CI, T2DM with normal cognition (T2DM-NC), and healthy controls (HC). We have recruited 22 T2DM-CI patients, 31 T2DM-NC patients, and 39 HCs. The structural Magnetic Resonance Imaging (MRI) and resting state fMRI images are acquired, and neuropsychological tests are carried out. Amplitude of low frequency fluctuations (ALFF) is analyzed to identify impaired brain regions implicated with T2DM and T2DM-CI. The functional network is built and all connections connected to impaired brain regions are selected. Subsequently, L1-norm regularized sparse canonical correlation analysis and sparse logistic regression are used to identify discriminative connections and Support Vector Machine is trained to realize three two-category classifications. It is found that single-digit dysfunctional connections predict T2DM and T2DM-CI. For T2DM-CI versus HC, T2DM-NC versus HC, and T2DM-CI versus T2DM-NC, the number of connections is 6, 7, and 5 and the area under curve (AUC) can reach 0.912, 0.901, and 0.861, respectively. The dysfunctional connection is mainly related to Default Model Network (DMN) and long-distance links. The strength of identified connections is significantly different among groups and correlated with cognitive assessment score (p < 0.05). Via ALFF analysis and further feature selection algorithms, a small number of dysfunctional brain connections can be identified to predict T2DM and T2DM-CI. These connections might be the imaging biomarkers of T2DM-CI and targets of intervention.

Corpus Callosum and Cerebellum Anomaly in a Puppy

This paper is aimed to present a corpus callosum and cerebellar anomaly with pathological findings in a 40-day-old, male, Golden Retriever puppy. It was stated that the dog rapidly deteriorated and died. On necropsy, after opening the skull, it was observed that the brain and cerebellum hemispheres were separated. In the detailed macroscopic examination, it was observed that the corpus callosum, interthalamic connections, which connect the brain hemispheres, were completely separated from each other. It was observed that the corpus callosum was more prominent in the right hemisphere but the anatomical structures of the left hemisphere were not evident. It was also observed that the hemispheres of the cerebellum were almost completely separated from the vermis region. It has been observed that brain connections can be achieved only by attachment between the midbrain and pons and continuing with the pons. Microscopic examination revealed no inflammatory reactions in the brain and cerebellum. Corpus callosum and cerebellar vermis anomalies in dogs have been reported before. However, split brain syndrome characterized by the loss of almost all connections of the brain and cerebellum in such severity that was observed in this case has not been previously reported.