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Individual contralesional recruitment in the context of structural reserve in early motor reorganization after stroke

The concept of structural reserve in stroke reorganization assumes that the relevance of the contralesional hemisphere strongly depends on the brain tissue spared by the lesion in the affected hemisphere. Recent studies, however, have indicated that the contralesional hemisphere's impact exhibits region-specific variability with concurrently existing maladaptive and supportive influences. This challenges traditional views, necessitating a nuanced investigation of contralesional motor areas and their interaction with ipsilesional networks.Our study focused on the functional role of contralesional key motor areas and lesion-induced connectome disruption early after stroke.Online TMS data of twenty-five stroke patients was analyzed to disentangle interindividual differences in the functional roles of contralesional primary motor cortex (M1), dorsal premotor cortex (dPMC), and anterior interparietal sulcus (aIPS) for motor function. Connectome-based lesion symptom mapping and corticospinal tract lesion quantification were used to investigate how TMS effects depend on ipsilesional structural network properties.At group and individual levels, TMS interference with contralesional M1 and aIPS but not dPMC led to improved performance early after stroke. At the connectome level, a more disturbing role of contralesional M1 was related to a more severe disruption of the structural integrity of ipsilesional M1 in the affected motor network. In contrast, a detrimental influence of contralesional aIPS was linked to less disruption of the ipsilesional M1 connectivity.Our findings indicate that contralesional areas distinctively interfere with motor performance early after stroke depending on ipsilesional structural integrity, extending the concept of structural reserve to regional specificity in recovery of function.

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Semi-analytic three-shell forward calculation for magnetoencephalography

In previous studies, the magnetic lead field theorem in the quasi-static approximation was derived and used for the development of a method for the forward problem of MEG. It was applied and tested on a single-shell model of the human head and the question whether one shell is adequate enough for the calculation of the magnetic field is the main reason for this study. This forward method is based on the fundamental concept that one can calculate the lead field for MEG by decomposing it into two parts: the lead field of an arbitrary volume conductor that is already known and the gradient of basis functions that have to be harmonic, here derived from spherical harmonics. The problem then is reduced to evaluating the coefficients found in the basis functions. In this research we aim to improve the accuracy of the forward model, hence improving the localization accuracy in inverse methods by introducing a more detailed realistic head model. We here generalize the algorithm developed for a single-shell volume conductor to a three-shell volume conductor representing the brain, the skull and the skin with homogenous and isotropic conductivities in realistic ratios. The expansion to three shells could be tested as the three-shell algorithm is approaching the single-shell with high accuracy in special cases where three-shell solutions can also be calculated using a single-shell solution, especially for higher levels of expansion. The deviation in the calculation of the lead field is also evaluated when using three shells with realistic conductivities. The magnetic field turned out to differ to an important measurable extend in particular for deeper sources, making the three-shell algorithm substantially more accurate for these dipole locations.

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Relationships between brain structure-function coupling in normal aging and cognition: A cross-ethnicity population-based study

Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.

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Source imaging method based on diagonal covariance bases and its applications to OPM-MEG

Magnetoencephalography (MEG) is a noninvasive imaging technique used in neuroscience and clinical research. The source estimation of MEG involves solving a highly underdetermined inverse problem, which requires additional constraints to restrict the solution space. Traditional methods tend to obscure the extent of the sources. However, an accurate estimation of the source extent is important for studying brain activity or preoperatively estimating pathogenic regions. To improve the estimation accuracy of the extended source extent, the spatial constraint of sources is employed in the Bayesian framework. For example, the source is decomposed into a linear combination of validated spatial basis functions, which is proved to improve the source imaging accuracy. In this work, we further construct the spatial properties of the source using the diagonal covariance bases (DCB), which we summarize as the source imaging method SI-DCB. In this approach, specifically, the covariance matrix of the spatial coefficients is modeled as a weighted combination of diagonal covariance basis functions. The convex analysis is used to estimate noise and model parameters under the Bayesian framework. Extensive numerical simulations showed that SI-DCB outperformed five benchmark methods in accurately estimating the location and extent of patch sources. The effectiveness of SI-DCB was verified through somatosensory stimulation experiments performed on a 31-channel OPM-MEG system. The SI-DCB correctly identified the source area where each brain response occurred. The superior performance of SI-DCB suggests that it can provide a template approach for improving the accuracy of source extent estimations under a sparse Bayesian framework.

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The association between social rewards and anxiety: Links from neurophysiological analysis in virtual reality and social interaction game

Individuals’ affective experience can be intricate, influenced by various factors including monetary rewards and social factors during social interaction. However, within this array of factors, divergent evidence has been considered as potential contributors to social anxiety. To gain a better understanding of the specific factors associated with anxiety during social interaction, we combined a social interaction task with neurophysiological recordings obtained through an anxiety-elicitation task conducted in a Virtual Reality (VR) environment. Employing inter-subject representational similarity analysis (ISRSA), we explored the potential linkage between individuals’ anxiety neural patterns and their affective experiences during social interaction. Our findings suggest that, after controlling for other factors, the influence of the partner's emotional cues on individuals’ affective experiences is specifically linked to their neural pattern of anxiety. This indicates that the emergence of anxiety during social interaction may be particularly associated with the emotional cues provided by the social partner, rather than individuals’ own reward or prediction errors during social interaction. These results provide further support for the cognitive theory of social anxiety and extend the application of VR in future cognitive and affective studies.

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The promoting effect of the absence of second-party's punishment power on third-party punishment in maintaining social fairness norms: An EEG hyper-scanning study

Third-party punishment (TPP) plays an irreplaceable role in maintaining social fairness. Punishment power is a significant area of study within economic games. However, the impact of whether or not the second-party possesses punishment power on TPP remains unexplored. The present study utilizes the high temporal resolution of EEG and time-frequency analysis, intra-barin functional connectivity analysis, inter-brain synchronization (IBS) analysis, and granger causality analysis(GCA) to comprehensively explore the neural mechanism of TPP from the perspective of third-party individual's decision-making and IBS in the real-time social interaction. Time-frequency results found that, the absence of the punishment power activated more theta-band and alpha-band power compare to when second-party has punishment power. When second-party has no punishment power, functional connection results observed stronger functional connectivity in theta band for medium unfair offers between rTPJ and PFC. Dual-brain analysis revealed that when the second-party has no punishment power, there is a significantly higher IBS in the alpha band between the frontal and frontal-central lobes of the second-party and the parietal and parietal occipital lobes of the third-party. GCA results further showed that the direction of IBS from third-party to second-party was significantly stronger than from second-party to third-party. This study demonstrates that the absence of the second-party's punishment power promote TPP, and similar cognitive process of thinking on how to maintain social fairness enhances IBS. The current study emphasizes the influence of punishment power on TPP, broadens the research perspective and contributes crucial insights into maintain social fairness.

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Plastic reorganization of the topological asymmetry of hemispheric white matter networks induced by congenital visual experience deprivation

Congenital blindness offers a unique opportunity to investigate human brain plasticity. The influence of congenital visual loss on the asymmetry of the structural network remains poorly understood. To address this question, we recruited 21 participants with congenital blindness (CB) and 21 age-matched sighted controls (SCs). Employing diffusion and structural magnetic resonance imaging, we constructed hemispheric white matter (WM) networks using deterministic fiber tractography and applied graph theory methodologies to assess topological efficiency (i.e., network global efficiency, network local efficiency, and nodal local efficiency) within these networks. Statistical analyses revealed a consistent leftward asymmetry in global efficiency across both groups. However, a different pattern emerged in network local efficiency, with the CB group exhibiting a symmetric state, while the SC group showed a leftward asymmetry. Specifically, compared to the SC group, the CB group exhibited a decrease in local efficiency in the left hemisphere, which was caused by a reduction in the nodal properties of some key regions mainly distributed in the left occipital lobe. Furthermore, interhemispheric tracts connecting these key regions exhibited significant structural changes primarily in the splenium of the corpus callosum. This result confirms the initial observation that the reorganization in asymmetry of the WM network following congenital visual loss is associated with structural changes in the corpus callosum. These findings provide novel insights into the neuroplasticity and adaptability of the brain, particularly at the network level.

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The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task

Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.

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Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia

Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l2,1 sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders.

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