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Luo Q, Gao L, Yang Z, Chen S, Yang J, Lu S. Integrated sentence-level speech perception evokes strengthened language networks and facilitates early speech development. Neuroimage 2024; 289:120544. [PMID: 38365164 DOI: 10.1016/j.neuroimage.2024.120544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 12/23/2023] [Accepted: 02/14/2024] [Indexed: 02/18/2024] Open
Abstract
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.
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Affiliation(s)
- Qinqin Luo
- Neurolinguistics Laboratory,College of International Studies, Shenzhen University, Shenzhen, China; Department of Chinese Language and Literature, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Leyan Gao
- Neurolinguistics Laboratory,College of International Studies, Shenzhen University, Shenzhen, China
| | - Zhirui Yang
- Neurolinguistics Laboratory,College of International Studies, Shenzhen University, Shenzhen, China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Sihui Chen
- Department of Chinese Language and Literature, Sun Yat-sen University, Guangzhou, China
| | - Jingwen Yang
- Neurolinguistics Laboratory,College of International Studies, Shenzhen University, Shenzhen, China
| | - Shuo Lu
- Neurolinguistics Laboratory,College of International Studies, Shenzhen University, Shenzhen, China; Department of Clinical Neurolinguistics Research, Mental and Neurological Diseases Research Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
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Li S, Cheng S, Shangguan C, Su X, Li X. Forgive or complain: Interpersonal distance modulates reactive attitudes and neural responses toward wrongdoers. Biol Psychol 2023; 183:108653. [PMID: 37536652 DOI: 10.1016/j.biopsycho.2023.108653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/23/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023]
Abstract
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.
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Affiliation(s)
- Sijin Li
- School of Psychology, Shenzhen University, Shenzhen 518060, China
| | - Si Cheng
- School of Psychology, Shenzhen University, Shenzhen 518060, China
| | - Chenyu Shangguan
- College of Education Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Xianling Su
- College of Education, Shanghai Normal University, Shanghai 200234, China
| | - Xu Li
- College of Education, Shanghai Normal University, Shanghai 200234, China.
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Ladisich B, Rampp S, Trinka E, Weisz N, Schwartz C, Kraus T, Sherif C, Marhold F, Demarchi G. Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study. Ther Adv Neurol Disord 2023; 16:17562864231190298. [PMID: 37655227 PMCID: PMC10467269 DOI: 10.1177/17562864231190298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/07/2023] [Indexed: 09/02/2023] Open
Abstract
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.
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Affiliation(s)
- Barbara Ladisich
- Department of Neurosurgery, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Department of Neurosurgery, University Hospital St. Poelten, Dunant-Platz 1, St Polten 3100 Austria
- Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Stefan Rampp
- Department of Neurosurgery, Department of Neuroradiology, University Hospital Erlangen, Germany
- Department of Neurosurgery, University Hospital Halle (Saale), Germany
| | - Eugen Trinka
- Department of Neurology, Center for Cognitive Neuroscience Salzburg, Member of the European Reference Network, EpiCARE, Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Karl Landsteiner Institute of Neurorehabilitation and Space Neurology, Salzburg, Austria
| | - Nathan Weisz
- Neuroscience Institute, Christian Doppler University Hospital, Salzburg, Austria
- Center for Cognitive Neuroscience & Department of Psychology, Paris Lodron University, Salzburg, Austria
| | - Christoph Schwartz
- Department of Neurosurgery, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Theo Kraus
- Institute of Pathology, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Camillo Sherif
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Franz Marhold
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Gianpaolo Demarchi
- Neuroscience Institute, Christian Doppler University Hospital, Salzburg, Austria
- Center for Cognitive Neuroscience & Department of Psychology, Paris Lodron University, Salzburg, Austria
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Boerger TF, Pahapill P, Butts AM, Arocho-Quinones E, Raghavan M, Krucoff MO. Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities. Front Hum Neurosci 2023; 17:1170419. [PMID: 37520929 PMCID: PMC10372448 DOI: 10.3389/fnhum.2023.1170419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/16/2023] [Indexed: 08/01/2023] Open
Abstract
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.
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Affiliation(s)
- Timothy F. Boerger
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Peter Pahapill
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Alissa M. Butts
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, United States
- Mayo Clinic, Rochester, MN, United States
| | - Elsa Arocho-Quinones
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Max O. Krucoff
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Biomedical Engineering, Medical College of Wisconsin, Marquette University, Milwaukee, WI, United States
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Tabanfar Z, Firoozabadi M, Shankayi Z, Sharifi G. Screening of Brain Tumors Using Functional Connectivity Patterns of Steady-State Visually Evoked Potentials. Brain Connect 2022; 12:883-891. [PMID: 35473402 DOI: 10.1089/brain.2021.0170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
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.
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Affiliation(s)
- Zahra Tabanfar
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.,Computational Neuroscience Lab, Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Mohammad Firoozabadi
- Department of Medical Physics, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran
| | - Zeinab Shankayi
- Department of Medical Physics, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran
| | - Guive Sharifi
- Department of Neurosurgery, Loghman e Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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