OUP accepted manuscript

Integrated sentence-level speech perception evokes strengthened language networks and facilitates early speech development

Natural poetic speeches (i.e., proverbs, nursery rhymes, and commercial ads) with strong prosodic regularities are easily memorized by children and the harmonious acoustic patterns are suggested to facilitate their integrated sentence processing. Do children have specific neural pathways for perceiving such poetic utterances, and does their speech development benefit from it? We recorded the task-induced hemodynamic changes of 94 children aged 2 to 12 years using functional near-infrared spectroscopy (fNIRS) while they listened to poetic and non-poetic natural sentences. Seventy-three adult as controls were recruited to investigate the developmental specificity of children group. The results indicated that poetic sentences perceiving is a highly integrated process featured by a lower brain workload in both groups. However, an early activated large-scale network was induced only in the child group, coordinated by hubs for connectivity diversity. Additionally, poetic speeches evoked activation in the phonological encoding regions in the children's group rather than adult controls which decreases with children's ages. The neural responses to poetic speeches were positively linked to children's speech communication performance, especially the fluency and semantic aspects. These results reveal children's neural sensitivity to integrated speech perception which facilitate early speech development by strengthening more sophisticated language networks and the perception-production circuit.

Forgive or complain: Interpersonal distance modulates reactive attitudes and neural responses toward wrongdoers

While the effect of interpersonal distance on forgiveness has been investigated over the past few years, it remains unclear whether this facilitating effect holds even when measured implicitly. Meanwhile, though cognitive control and the corresponding prefrontal cortex play a prominent role in forgiveness processing, the neural mechanism underlying forgiveness toward varied wrongdoers is largely unexplored. Here, forty-two participants initially underwent noise offense either from their friend or stranger, followed by a word identification test to examine their implicit attitude, during which they were presented with word-name combinations and required to categorize forgive- or complain-label words while ignoring the names of their friends or strangers below. A shorter reaction time reflects more congruence with one's implicit attitude. Electroencephalogram was recorded during the word identification test. Behaviorally, while individuals reacted faster to forgive-friend relative to complain-friend pairings, no such reaction bias was found for the stranger-wrongdoer, which suggests that individuals were more inclined to forgive someone close. Regarding the EEG/ERP results, forgive-friend elicited lower alpha oscillation and more negative frontal alpha asymmetry (FAA) value than complain-friend combinations, suggesting increased and dominant activity in the right prefrontal network during forgiveness toward friends. Whereas complain- relative to forgive-stranger combinations elicited larger P3 amplitudes, suggesting a neural encoding bias to information associated with complaints about stranger-wrongdoer. These multimodal findings provide evidence for the benefits of closeness in forgiveness and shed light on the neural mechanisms underlying forgiveness toward different types of wrongdoers.

Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study

Background

It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking.

Objectives

We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy.

Methods

Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts.

Results

We included 41 patients (21 men), with a mean age of 60.1 years (range 23-82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p1-30Hz = 0.002, pγ = 0.002, pβ = 0.002, pα = 0.002, pθ = 0.024, and pδ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p1-30Hz = 0.031, pδ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (pθ = 0.048) and decrease in WB node degree (pα = 0.039) in PSEs versus PNSEs at the uncorrected level.

Conclusion

Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings.

Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities

In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.

Screening of Brain Tumors Using Functional Connectivity Patterns of Steady-State Visually Evoked Potentials

Abstract Objective: The irregular growth and proliferation of cells in the brain and skull is named brain tumor, which is a special serious type of tumor due to its location. Nowadays, brain tumor is diagnosed by using imaging techniques such as computed tomography, positron emission tomography, single photon emission computed tomography, infrared imaging, magnetic resonance imaging (MRI), and functional MRI. However, these methods are not affordable to be used as screening tests. In contrast, electroencephalography has the ability of measuring biological signals, and it is a cost-effective and non-dangerous tool, being feasible to be used for screening purposes. The aim of this research was to evaluate the possibility of brain tumor detection, using functional connectivity features extracted from steady-state visually evoked potentials of eight brain tumor patients and four healthy control participants. Methods: For this purpose, after preprocessing, phase lag index was calculated as a functional connectivity measure. Afterward, four of the channels were chosen as the selected nodes, based on the highest number of strong connectivity (top 5%) as well as the most significant ones. The selected nodes were O1, O2, P3, and P4. As a final step, node strength was calculated for these selected nodes, which was used to classify the samples in the two groups, using Naïve Bayes, discriminant analysis (DA), k-nearest neighbors, support vector machines, and logistic regression methods. Results: The highest accuracy of 89.6% was obtained by using a DA classifier. This result shows that, in fact, brain tumor has the ability of changing brain functional connectivity. Conclusion: As the physiological alterations may occur sooner than the anatomical ones in tumor onset, detection of these alterations may be a useful measure for early diagnosis of this disease. This is still a primary study, but with the possibility of leading to further research, which can lead to the development of a method for the screening and early detection of brain tumor. Impact statement Imaging modalities are often used to diagnose brain tumors. However, these approaches are cost prohibitive for screening tests. Electroencephalography, a method that measures biological signals from the brain, on the other hand, is cost-effective, non-dangerous, and non-invasive, making it an ideal screening tool. The goal of this study is to analyze whether functional connectivity patterns taken from Steady-State Visually Evoked Potentials may be used to detect brain tumors at early stages. Since physiological changes occur before anatomical ones in tumor development, monitoring these alterations could be a useful tool to screen high-risk individuals, and also to an early diagnosis of the disease.