Hemisphere Network Research Articles

The Expressivity Dimension of Speech is the basis of the Expression Dimension. Evidence from Behavioural and Neuroimaging Studies

The modalities of communication are the sum of the expression dimension (linguistics) and the expressivity dimension (prosody), both being equally important in language communication. The expressivity dimension which comes first in the act of speech, is the basis on which phonemes, syllables, words, grammar, and morphosyntax, i.e., the expression dimension of speech is superimposed. We will review evidence (1) revealing the importance of prosody in language acquisition and (2) showing that prosody triggers the involvement of specific brain areas dedicated to sentences and word-list processing. To support the first point, we will not only rely on experimental psychology studies conducted in newborns and young children but also on neuroimaging studies that have helped to validate these behavioral experiments. Then, neuroimaging data on adults will allow for the conclusion that the expressivity dimension of speech modulates both the right hemisphere prosodic areas and the left hemisphere network in charge of the expression dimension.

Remapping and Reconnecting the Language Network after Stroke.

Here, we review the literature on neurotypical individuals and individuals with post-stroke aphasia showing that right-hemisphere regions homologous to language network and other regions, like the right cerebellum, are activated in language tasks and support language even in healthy people. We propose that language recovery in post-stroke aphasia occurs largely by potentiating the right hemisphere network homologous to the language network and other networks that previously supported language to a lesser degree and by modulating connection strength between nodes of the right-hemisphere language network and undamaged nodes of the left-hemisphere language network. Based on this premise (supported by evidence we review), we propose that interventions should be aimed at potentiating the right-hemisphere language network through Hebbian learning or by augmenting connections between network nodes through neuroplasticity, such as non-invasive brain stimulation and perhaps modulation of neurotransmitters involved in neuroplasticity. We review aphasia treatment studies that have taken this approach. We conclude that further aphasia rehabilitation with this aim is justified.

Structural neural connectivity correlates with pre-reading abilities in preschool children

Pre-reading abilities are predictive of later reading ability and can be assessed before reading begins. However, the neural correlates of pre-reading abilities in young children are not fully understood. To address this, we examined 246 datasets collected in an accelerated longitudinal design from 81 children aged 2–6 years (age = 4.6 ± 0.98 years, 47 males). Children completed pre-reading assessments (NEPSY-II Phonological Processing and Speeded Naming) and underwent a diffusion magnetic resonance imaging (MRI) scan to assess white matter connectivity. We defined a core neural network of reading and language regions based on prior literature, and structural connections within this network were assessed using graph theory analysis. Linear mixed models accounting for repeated measures were used to test associations between children’s pre-reading performance and graph theory measures for the whole bilateral reading network and each hemisphere separately. Phonological Processing scores were positively associated with global efficiency, local efficiency, and clustering coefficient in the bilateral and right hemisphere networks, as well as local efficiency and clustering coefficient in the left hemisphere network. Our findings provide further evidence that structural neural correlates of Phonological Processing emerge in early childhood, before and during early reading instruction.

Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog

Data-driven approaches to identify geophysical signals have proven beneficial in high dimensional environments where model-driven methods fall short. GNSS offers a source of unsaturated ground motion observations that are the data currency of ground motion forecasting and rapid seismic hazard assessment and alerting. However, these GNSS-sourced signals are superposed onto hardware-, location- and time-dependent noise signatures influenced by the Earth’s atmosphere, low-cost or spaceborne oscillators, and complex radio frequency environments. Eschewing heuristic or physics based models for a data-driven approach in this context is a step forward in autonomous signal discrimination. However, the performance of a data-driven approach depends upon substantial representative samples with accurate classifications, and more complex algorithm architectures for deeper scientific insights compound this need. The existing catalogs of high-rate (≥1Hz) GNSS ground motions are relatively limited. In this work, we model and evaluate the probabilistic noise of GNSS velocity measurements over a hemispheric network. We generate stochastic noise time series to augment transferred low-noise strong motion signals from within 70 kilometers of strong events (≥ MW 5.0) from an existing inertial catalog. We leverage known signal and noise information to assess feature extraction strategies and quantify augmentation benefits. We find a classifier model trained on this expanded pseudo-synthetic catalog improves generalization compared to a model trained solely on a real-GNSS velocity catalog, and offers a framework for future enhanced data driven approaches.

Brain asymmetry: a novel perspective on hemispheric network

Brain asymmetry, involving structural and functional differences between the two hemispheres, is a major organizational principle of the human brain. The structural and functional connectivity within each hemisphere defines the hemispheric network or connectome. Elucidating left-right differences of the hemispheric network provides opportunities for brain asymmetry exploration. This review examines the asymmetry in the hemispheric white matter and functional network to assess health and brain disorders. In this article, the brain asymmetry in structural and functional connectivity including network topologies of healthy individuals, involving brain cognitive systems and the development trend, is highlighted. Moreover, the abnormal asymmetry of the hemispheric network related to cognition changes in brain disorders, such as Alzheimer’s disease, schizophrenia, autism spectrum disorder, attention deficit hyperactivity disorder, and bipolar disorder, is presented. This review suggests that the hemispheric network is highly conserved for measuring human brain asymmetries and has potential in the study of the cognitive system and brain disorders.

Intra– and inter–hemispheric network dynamics supporting object recognition and speech production

We built normative brain atlases that animate millisecond-scale intra- and inter-hemispheric white matter-level connectivity dynamics supporting object recognition and speech production. We quantified electrocorticographic modulations during three naming tasks using event-related high-gamma activity from 1,114 nonepileptogenic intracranial electrodes (i.e., non-lesional areas unaffected by epileptiform discharges). Using this electrocorticography data, we visualized functional connectivity modulations defined as significant naming-related high-gamma modulations occurring simultaneously at two sites connected by direct white matter streamlines on diffusion-weighted imaging tractography. Immediately after stimulus onset, intra- and inter-hemispheric functional connectivity enhancements were confined mainly across modality-specific perceptual regions. During response preparation, left intra-hemispheric connectivity enhancements propagated in a posterior-to-anterior direction, involving the left precentral and prefrontal areas. After overt response onset, inter- and intra-hemispheric connectivity enhancements mainly encompassed precentral, postcentral, and superior-temporal (STG) gyri. We found task-specific connectivity enhancements during response preparation as follows. Picture naming enhanced activity along the left arcuate fasciculus between the inferior-temporal and precentral/posterior inferior-frontal (pIFG) gyri. Nonspeech environmental sound naming augmented functional connectivity via the left inferior longitudinal and fronto-occipital fasciculi between the medial-occipital and STG/pIFG. Auditory descriptive naming task enhanced usage of the left frontal U-fibers, involving the middle-frontal gyrus. Taken together, the commonly observed network enhancements include inter-hemispheric connectivity optimizing perceptual processing exerted in each hemisphere, left intra-hemispheric connectivity supporting semantic and lexical processing, and inter-hemispheric connectivity for symmetric oral movements during overt speech. Our atlases improve the currently available models of object recognition and speech production by adding neural dynamics via direct intra- and inter-hemispheric white matter tracts.

Functional connectivity and gray matter deficits within the auditory attention circuit in first-episode psychosis.

Selective attention deficits in first episode of psychosis (FEP) can be indexed by impaired attentional modulation of auditory M100. It is unknown if the pathophysiology underlying this deficit is restricted to auditory cortex or involves a distributed attention network. We examined the auditory attention network in FEP. MEG was recorded from 27 FEP and 31 matched healthy controls (HC) while alternately ignoring or attending tones. A whole-brain analysis of MEG source activity during auditory M100 identified non-auditory areas with increased activity. Time-frequency activity and phase-amplitude coupling were examined in auditory cortex to identify the attentional executive carrier frequency. Attention networks were defined by phase-locking at the carrier frequency. Spectral and gray matter deficits in the identified circuits were examined in FEP. Attention-related activity was identified in prefrontal and parietal regions, markedly in precuneus. Theta power and phase coupling to gamma amplitude increased with attention in left primary auditory cortex. Two unilateral attention networks were identified with precuneus seeds in HC. Network synchrony was impaired in FEP. Gray matter thickness was reduced within the left hemisphere network in FEP but did not correlate with synchrony. Several extra-auditory attention areas with attention-related activity were identified. Theta was the carrier frequency for attentional modulation in auditory cortex. Left and right hemisphere attention networks were identified, with bilateral functional deficits and left hemisphere structural deficits, though FEP showed intact auditory cortex theta phase-gamma amplitude coupling. These novel findings indicate attention-related circuitopathy early in psychosis potentially amenable to future non-invasive interventions.

Disconnectomics to unravel the network underlying deficits of spatial exploration and attention

Spatial attention and exploration are related to a predominantly right hemispheric network structure. However, the areas of the brain involved and their exact role is still debated. Spatial neglect following right hemispheric stroke lesions has been frequently viewed as a model to study these processes in humans. Previous investigations on the anatomical basis on spatial neglect predominantly focused on focal brain damage and lesion-behaviour mapping analyses. This approach might not be suited to detect remote areas structurally spared but which might contribute to the behavioural deficit. In the present study of a sample of 203 right hemispheric stroke patients, we combined connectome lesion-symptom mapping with multivariate support vector regression to unravel the complex and disconnected network structure in spatial neglect. We delineated three central nodes that were extensively disconnected from other intrahemispheric areas, namely the right superior parietal lobule, the insula, and the temporal pole. Additionally, the analysis allocated central roles within this network to the inferior frontal gyrus (pars triangularis and opercularis), right middle temporal gyrus, right temporal pole and left and right orbitofrontal cortices, including interhemispheric disconnection. Our results suggest that these structures—although not necessarily directly damaged—might play a role within the network underlying spatial neglect in humans.

White matter brain structure predicts language performance and learning success.

Individual differences in the ability to process language have long been discussed. Much of the neural basis of these, however, is yet unknown. Here we investigated the relationship between long-range white matter connectivity of the brain, as revealed by diffusion tractography, and the ability to process syntactically complex sentences in the participants' native language as well as the improvement thereof by multiday training. We identified specific network motifs by singular value decomposition that indeed related white matter structural connectivity to individual language processing performance. First, for two such motifs, one in the left and one in the right hemisphere, their individual prevalence significantly predicted the individual language performance, suggesting an anatomical predisposition for the individual ability to process syntactically complex sentences. Both motifs comprise a number of cortical regions, but seem to be dominated by areas known for the involvement in working memory rather than the classical language network itself. Second, we identified another left hemispheric network motif, whose change of prevalence over the training period significantly correlated with the individual change in performance, thus reflecting training induced white matter plasticity. This motif comprises diverse cortical areas including regions known for their involvement in language processing, working memory and motor functions. The present findings suggest that individual differences in language processing and learning can be explained, in part, by individual differences in the brain's white matter structure. Brain structure may be a crucial factor to be considered when discussing variations in human cognitive performance, more generally.

Attentional impairment in Parkinson’s disease is modulated by side of onset: Neurophysiological evidence

ObjectiveNeurophysiological studies exploring involuntary attention have reported that electroencephalographic (EEG) measures can indicate impaired neural processing from initial stages of Parkinson’s disease (PD). Since involuntary attention is regulated by right hemisphere networks and PD generally initiates its motor symptomatology unilaterally, whether involuntary attention is impaired depending on the onset side of PD remains unknown. MethodsWe compared the neurophysiological correlates of involuntary attention among a PD group with left-side onset (L-PD), a PD group with right-side onset (R-PD) symptomatology, and a healthy control group (HC). All participants performed an auditory involuntary attention task while a digital EEG was recorded. ResultsOur main finding was a reduction both in the P3a amplitude and evoked delta-theta phase alignment in the L-PD group compared to the HC. Further, there was a significant correlation between P3a amplitude and disease duration in the R-PD, but not in the L-PD group. Behaviorally, both clinical groups, and in particular L-PD, showed reduced orientation towards novel stimuli, and no reduction of distraction effects during the experiment. ConclusionsOur results indicate that involuntary attention is differentially impaired in patients with left side onset of symptoms. Involuntary attention impairment might be present from initial stages of left onset PD and become progressively compromised in patients with right onset PD. SignificanceThe onset side of symptomatology should be considered for attentional impairment in PD.

Complex network view to solar flare asymmetric activity

Asymmetric behavior of the solar hemispheres is an essential aspect of the activity. This paper uses a complex network approach to study the solar flare asymmetric activity in two hemispheres. We construct a network based on the locations and flare flux (GOES flux classification) from 2008 December to 2019 December for 4033 and 4725 flares on the north and south hemispheres, respectively. We apply the visibility graph analysis to construct the networks for different spatial (HEALPix pixelization) constraints. The HEALPix Mollweide projection maps show the nodes with a considerable degree and clustering coefficients in low latitudes in the southern hemisphere. The northern hemisphere network exhibits more hubs (the nodes with the highest connectivity, closeness, betweenness, and Pagerank) than the south. The locations of identified hubs can indicate regions with high magnetic activity. Identifying active areas of the sun is one of the essential issues in space weather science. We explore the small-world and scale-free characteristic of the flare complex network that indicates the flares form a system of self-organized criticality and each flare has an influential role in occurring subsequent flares on the solar surface.

Brain Functional Connectivity in Low- and High-Grade Gliomas: Differences in Network Dynamics Associated with Tumor Grade and Location.

Simple SummaryThis study investigates brain network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging and graph theory. We demonstrated that low-grade gliomas (LGG) lead to increased efficiency of the surrounding functional network, while high-grade gliomas (HGG) seem to disrupt brain connectivity in remote areas. Tumor location appears to influence the pattern of reorganization, including the recruitment of the contralateral hemisphere. Overall, LGG may show more favorable connectivity changes than HGG. If confirmed by future studies, the ability to discriminate between ‘maladaptive’ (detrimental) and ‘adaptive’ (beneficial) functional reorganization based on graph theory metrics may provide biomarkers to select patients for surgery and monitor recovery. Brain tumors lead to modifications of brain networks. Graph theory plays an important role in clarifying the principles of brain connectivity. Our objective was to investigate network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging (fMRI) and graph theory. We retrospectively studied 30 low-grade (LGG), 30 high-grade (HGG) left-hemispheric glioma patients and 20 healthy controls (HC) with rs-fMRI. Tumor location was labeled as: frontal, temporal, parietal, insular or occipital. We collected patients’ clinical data from records. We analyzed whole-brain and hemispheric networks in all patients and HC. Subsequently, we studied lobar networks in subgroups of patients divided by tumor location. Seven graph-theoretical metrics were calculated (FDR p < 0.05). Connectograms were computed for significant nodes. The two-tailed Student t-test or Mann–Whitney U-test (p < 0.05) were used to compare graph metrics and clinical data. The hemispheric network analysis showed increased ipsilateral connectivity for LGG (global efficiency p = 0.03) and decreased contralateral connectivity for HGG (degree/cost p = 0.028). Frontal and temporal tumors showed bilateral modifications; parietal and insular tumors showed only local effects. Temporal tumors led to a bilateral decrease in all graph metrics. Tumor grade and location influence the pattern of network reorganization. LGG may show more favorable network changes than HGG, reflecting fewer clinical deficits.

Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis.

Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.

Cortical dynamics underlying speech planning in rapid conversational turn-taking

During conversation, silent gaps between speakers are typically 200ms or less—suggesting that speech planning often occurs while speakers listen to their partners’ turns. Though the psycholinguistic mechanisms of planning during turn-taking are well-studied, its neural underpinnings are largely unknown. Using intracranial electrocorticography in neurosurgical patient-volunteers, we delineated planning-related activity from dynamics underlying sensorimotor processes using an interactive question–answer paradigm (adapted from Bögels et al., 2015) and found that planning activity is largely restricted to a left hemisphere network centered on caudal inferior and middle frontal gyri. In a separate task, participants performed both speech and non-speech actions, and we found that this planning network is most active while planning spoken output. We then examined neural activity during unconstrained conversation and found that the identified planning circuit is active prior to participant turn onset and while participants listen to the turns of conversational partners. Finally, in preliminary direct cortical stimulation experiments, we found that perturbation of this planning network results in significantly longer reaction times and lexical errors but not gross articulatory disruptions. In conclusion, using controlled interactive language tasks, we uncovered a speech-selective cortical planning circuit that is active during natural conversation and required to execute rapid turn-taking.

Benefit of Action Naming Over Object Naming for Visualization of Subcortical Language Pathways in Navigated Transcranial Magnetic Stimulation-Based Diffusion Tensor Imaging-Fiber Tracking.

Visualization of functionally significant subcortical white matter fibers is needed in neurosurgical procedures in order to avoid damage to the language network during resection. In an effort to achieve this, positive cortical points revealed during preoperative language mapping with navigated transcranial magnetic stimulation (nTMS) can be employed as regions of interest (ROIs) for diffusion tensor imaging (DTI) fiber tracking. However, the effect that the use of different language tasks has on nTMS mapping and subsequent DTI-fiber tracking remains unexplored. The visualization of ventral stream tracts with an assumed lexico-semantic role may especially benefit from ROIs delivered by the lexico-semantically demanding verb task, Action Naming. In a first step, bihemispheric nTMS language mapping was administered in 18 healthy participants using the standard task Object Naming and the novel task Action Naming to trigger verbs in a small sentence context. Cortical areas in which nTMS induced language errors were identified as language-positive cortical sites. In a second step, nTMS-based DTI-fiber tracking was conducted using solely these language-positive points as ROIs. The ability of the two tasks’ ROIs to visualize the dorsal tracts Arcuate Fascicle and Superior Longitudinal Fascicle, the ventral tracts Inferior Longitudinal Fascicle, Uncinate Fascicle, and Inferior Fronto-Occipital Fascicle, the speech-articulatory Cortico-Nuclear Tract, and interhemispheric commissural fibers was compared in both hemispheres. In the left hemisphere, ROIs of Action Naming led to a significantly higher fraction of overall visualized tracts, specifically in the ventral stream’s Inferior Fronto-Occipital and Inferior Longitudinal Fascicle. No difference was found between tracking with Action Naming vs. Object Naming seeds for dorsal stream tracts, neither for the speech-articulatory tract nor the inter-hemispheric connections. While the two tasks appeared equally demanding for phonological-articulatory processes, ROI seeding through the task Action Naming seemed to better visualize lexico-semantic tracts in the ventral stream. This distinction was not evident in the right hemisphere. However, the distribution of tracts exposed was, overall, mirrored relative to those in the left hemisphere network. In presurgical practice, mapping and tracking of language pathways may profit from these findings and should consider inclusion of the Action Naming task, particularly for lesions in ventral subcortical regions.

Lateral Reading Lyric Testimony; or, The Difficult Miracle of Black Poetry in the Americas

Canon, tradition, and origin anchor developmental accounts of Black literary history, describing the forward movement from a singular beginning in terms of birth, maturation, and inheritance. This model delimits a specialized field of study, but also obscures texts, practices, and archives that do not cohere with it. In the study of slave testimony, the canonization of Anglo-American poets like Phillis Wheatley and Jupiter Hammon has eclipsed the reception and translation of other enslaved poets across the Americas. This essay proposes a method of lateral reading to remedy this lopsided historiography. First, it tracks how conceptions of "American Negro Poetry" shifted throughout the twentieth century, from initially describing a multilingual, hemispheric network of Black writers to ultimately signifying an Anglophone, nationally bounded African American canon. Second, it considers the temporalities of lyric, which move outward rather than forward in time, to suggest how we might read enslaved poets without expecting that their works reflect the "experience" of enslavement. And third, it demonstrates how a cohort of enslaved Afro-Cuban poets together established and elaborated a "writing community" through lyric form, overlapping social networks, and shared participation in an urban periodical culture. Taken together, these insights enable us to glimpse a wider, hemispheric corpus of enslaved poetics in the Americas.

The role of a critical left fronto-temporal network with its right-hemispheric homologue in syntactic learning based on word category information

Word category information (WCI) is proposed to be fundamental for syntactic learning and processing. However, it remains largely unclear how left-hemispheric key regions for language, including BA 44 in the inferior frontal gyrus (IFG) and superior temporal gyrus (STG), interact with their right-hemispheric homologues to support the WCI-based syntactic learning. To address this question, this study employed a unified structural equation modeling (uSEM) approach to explore both the intra- and inter-hemispheric effective connectivity among these areas, to specify the neural underpinnings of handling WCI for syntactic learning. Modeling results identified a distinctive intra-left hemispheric connection from left BA 44 to left STG, a more integrated intra-right hemispheric network, and a particular frontal right-to-left hemispheric connectivity pattern for WCI-based syntactic learning. Further analyses revealed a selective positive correlation between task performance and the lagged effect in left BA 44. These results converge on a critical left fronto-temporal language network with left BA 44 and its connectivity to left STG for WCI-based syntactic learning, which is also facilitated in a domain-general fashion by the right homologues. Together, these results provide novel insights into crucial neural network(s) for syntactic learning on the basis of WCI.

Hemispheric Network Expression in Parkinson's Disease: Relationship to Dopaminergic Asymmetries.

Parkinson's disease (PD) is characterized by brain metabolic networks, specifically associated with motor and cognitive manifestations. Few studies have investigated network changes in cerebral hemispheres ipsilateral and contralateral to the clinically more affected body side. We examined hemispheric network abnormalities and their relationship to striatal dopaminergic deficits in PD patients at different stages. 45 PD patients underwent dual-tracer positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) and 18F-fluorodopa (FDOPA) in a high-resolution PET scanner. In all patients, we computed expression levels for the PD-related motor/cognition metabolic patterns (PDRP/PDCP) as well as putamen/caudate FDOPA uptake values in both hemispheres. Resulting hemispheric measures in the PD group were compared with corresponding healthy control values and assessed across disease stages. Hemispheric PDRP and PDCP expression was significantly elevated contralateral and ipsilateral to the more affected body side in patients with unilateral symptoms (H&Y 1: p < 0.01) and in patients with bilateral limb involvement (H&Y 2-3: p < 0.001; H&Y 4: p < 0.003). Elevations in pattern expression were symmetrical at all disease stages. By contrast, FDOPA uptake in the caudate and putamen was reduced bilaterally (p < 0.002), with lower values on both sides at more advanced disease stages. Hemispheric uptake was asymmetrical in both striatal regions, with lower contralateral values at all disease stages. The magnitude of hemispheric uptake asymmetry was smaller with more advanced disease, reflecting greater change ipsilaterally. Symmetrical network expression in PD represents bilateral functional effects unrelated to nigrostriatal dopaminergic asymmetries.

The Effect of Blindness on Spatial Asymmetries.

The human cerebral cortex is asymmetrically organized with hemispheric lateralization pervading nearly all neural systems of the brain. Whether the lack of normal visual development affects hemispheric specialization subserving the deployment of visuospatial attention asymmetries is controversial. In principle, indeed, the lack of early visual experience may affect the lateralization of spatial functions, and the blind may rely on a different sensory input compared to the sighted. In this review article, we thus present a current state-of-the-art synthesis of empirical evidence concerning the effects of visual deprivation on the lateralization of various spatial processes (i.e., including line bisection, mirror symmetry, and localization tasks). Overall, the evidence reviewed indicates that spatial processes are supported by a right hemispheric network in the blind, hence, analogously to the sighted. Such a right-hemisphere dominance, however, seems more accentuated in the blind as compared to the sighted as indexed by the greater leftward bias shown in different spatial tasks. This is possibly the result of the more pronounced involvement of the right parietal cortex during spatial tasks in blind individuals compared to the sighted, as well as of the additional recruitment of the right occipital cortex, which would reflect the cross-modal plastic phenomena that largely characterize the blind brain.

Hemispheric Asymmetry of Functional Brain Networks under Different Emotions Using EEG Data.

Despite many studies reporting hemispheric asymmetry in the representation and processing of emotions, the essence of the asymmetry remains controversial. Brain network analysis based on electroencephalography (EEG) is a useful biological method to study brain function. Here, EEG data were recorded while participants watched different emotional videos. According to the videos’ emotional categories, the data were divided into four categories: high arousal high valence (HAHV), low arousal high valence (LAHV), low arousal low valence (LALV) and high arousal low valence (HALV). The phase lag index as a connectivity index was calculated in theta (4–7 Hz), alpha (8–13 Hz), beta (14–30 Hz) and gamma (31–45 Hz) bands. Hemispheric networks were constructed for each trial, and graph theory was applied to quantify the hemispheric networks’ topological properties. Statistical analyses showed significant topological differences in the gamma band. The left hemispheric network showed significantly higher clustering coefficient (Cp), global efficiency (Eg) and local efficiency (Eloc) and lower characteristic path length (Lp) under HAHV emotion. The right hemispheric network showed significantly higher Cp and Eloc and lower Lp under HALV emotion. The results showed that the left hemisphere was dominant for HAHV emotion, while the right hemisphere was dominant for HALV emotion. The research revealed the relationship between emotion and hemispheric asymmetry from the perspective of brain networks.