NeuroImage最新文献

Partnership investment is a common form of business where investors have different levels of power and need to persuade each other to reach a consensus. This study investigated the neural mechanisms underlying the impact of decision-making power on persuasive communication in partnership investment, aiming to provide neural evidence to test two competing hypotheses: the power-responsibility hypothesis and the power-overconfidence hypothesis. Using functional near-infrared spectroscopy (fNIRS), we recorded brain activity from persuader-receiver dyads as they engaged in a partnership investment task. Behavioral results showed that receivers' decisions were more affected by persuaders' persuasive messages when receivers had dominant decision-making power. Neurally, the functional connectivity (FC) between the left and right temporo-parietal junctions (lTPJ and rTPJ) of the receiver was significantly increased by their decision-making power. Additionally, we identified four pairs of interpersonal neural synchronization (INS) that exhibited significant enhancement when persuaders used numeric persuasion rather than non-numeric persuasion: lTPJ-rTPJ, left superior temporal gyrus (lSTG)-rTPJ, left middle temporal gyrus (lMTG)-rTPJ, and medial prefrontal cortex (mPFC)-lTPJ. The decision-making power amplified the INS difference in the last three pairs. Furthermore, using a support vector machine (SVM) algorithm, the INS could accurately predict receivers' adoption of persuasive messages when they held dominant decision-making power. Finally, we found that FC at lTPJ-rTPJ and INS at lSTG-rTPJ were positively associated with receivers' adoption of persuasive messages as well. Our study clarifies how decision-making power alters the way individuals process persuasive messages in partnership investment, providing insights into the neural basis of persuasion in group decision-making contexts and supporting the power-responsibility hypothesis.

The processing of kinematic information embedded in observed actions is an essential ability for understanding others' behavior. Previous research showed that the action observation network (AON) may encode some action kinematic features. However, our understanding of how direction and velocity are encoded within the AON is still limited. In this study, we employed event-related fMRI to investigate the neural substrates specifically activated during observation of hand grasping actions presented as point-light displays, performed with different directions (right, left) and velocities (fast, slow). Twenty-three healthy adult participants took part in the study. To identify brain regions differentially recruited by grasping direction and velocity, univariate and multivariate pattern analysis (MVPA) were performed. The results of univariate analysis demonstrate that direction is encoded in occipito-temporal and posterior visual areas, while velocity recruits lateral occipito-temporal, superior parietal and intraparietal areas. Results of MVPA further show: a) a significant decoding accuracy of both velocity and direction at the network level; b) the possibility to decode within lateral occipito-temporal and parietal areas both direction and velocity; c) a contribution of bilateral premotor areas to velocity decoding models. These results indicate that posterior parietal nodes of the AON are mainly involved in coding grasping direction and that premotor regions are crucial for coding grasping velocity, while lateral occipito-temporal cortices play a key role in encoding both parameters. The current findings could have implications for observational-based rehabilitation treatments of patients with motor disorders and artificial intelligence-based hand action recognition models.

Introduction: Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this extent, SynthSeg is a robust deep learning model designed for automatic brain segmentation across various contrasts and resolutions. This study validates the SynthSeg robust brain segmentation model on computed tomography (CT), using a multi-center dataset.

Methods: An open access dataset of 260 paired CT and magnetic resonance imaging (MRI) from radiotherapy patients treated in 5 centers was collected. Brain segmentations from CT and MRI were obtained with SynthSeg model, a component of the Freesurfer imaging suite. These segmentations were compared and evaluated using Dice scores and Hausdorff 95 distance (HD95), treating MRI-based segmentations as the ground truth. Brain regions that failed to meet performance criteria were excluded based on automated quality control (QC) scores.

Results: Dice scores indicate a median overlap of 0.76 (IQR: 0.65-0.83). The mean volume difference is 7.79% (CI: 6.41%-9.18%), with CT segmentations typically smaller than MRI-based. The median HD95 is 2.95 mm (IQR: 1.73-5.39). QC score based thresholding improves median dice by 0.1 and median HD95 by 0.05 mm. Morphological differences related to sex and age, as detected by MRI, were also replicated with CT, with an approximate 17% difference between the CT and MRI results for sex and 10% difference between the results for age.

Conclusion: SynthSeg can be utilized for CT-based automatic brain segmentation, but only in applications where precision is not essential. CT performance is lower than MRI based on the integrated QC scores, but low-quality segmentations can be excluded with QC-based thresholding. Additionally, performing CT-based neuroanatomical studies is encouraged, as the results show correlations in sex- and age-based analyses similar to those found with MRI.

The left lateralization of language has been attributed to hemispheric specialization for processing rapidly changing information. While interhemispheric differences in auditory cortex organization support this view, the macrostructure of the entire cerebral cortex has not been thoroughly examined from this perspective. This study investigated hemispheric asymmetries in cortical surface area and thickness and their relationship to pronunciation scores from oral reading using the Human Connectome Project Young Adult dataset (N=1113). Heschl's gyrus had the most left-lateralized surface area, while the temporal pole showed the strongest right-lateralization in thickness. These areas correspond to the core components of speech: sound and meaning. Notably, their structural features were the only ones also yielding a significant correlation with pronunciation scores. Additionally, Broca's area's posterior region (pars opercularis), involved in articulatory phonological processing, showed leftward lateralization, contrasting with the right-lateralized anterior portions. Left-hemisphere language areas were largely thinner and more extended than their right-sided homologs with a larger white-to-gray matter ratio. Cortical thickness was inversely related to surface area. The lateralization of auditory-related language areas and their structure's correlation with pronunciation in oral reading supports a genetically based auditory foundation for language. A thinner, more efficient cortex with larger surface areas and increased myelination likely underlies the left-hemispheric dominance of language. Thinner, more extended brain areas have been linked to more myelination and wider cortical columns and intercolumnar space. This provides the potential for a fast network of interconnected, discrete information units able to support language's demands of rapid categorical processing.

In accelerated MRI, the robust artificial-neural-network for k-space interpolation (RAKI) method is an attractive learning-based reconstruction that does not require additional training data. This study was focused on obtaining high quality MR images from regular under-sampled multi-coil k-space data using a high-pass filtered RAKI (HP-RAKI) reconstruction without any extra training data. MRI scan from human subjects was under-sampled with a regular pattern using skipped phase encoding and a fully sampled k-space center. A high-pass (HP) filter was applied in k-space to reduce image support to facilitate linear prediction. The HP filtered k-space center was used to train the RAKI network without any extra training data. The unacquired k-space data can be predicted from a trained RAKI network with optimized parameters. Final reconstruction was obtained after performing an inverse HP filtering for the predicted k-space data. This HP-RAKI method can be extended to corresponding residual structure (HP-rRAKI). HP-RAKI was compared with GRAPPA, HP-GRAPPA, RAKI and MW-RAKI algorithms, and HP-rRAKI was compared with corresponding residual extensions, including rRAKI and MW-rRAKI, all qualitatively and quantitatively using visual inspection and such metrics as SSIM and PSNR. HP-RAKI and HP-rRAKI were found to be effective in reconstructing MR images even at high acceleration factors. HP-RAKI and HP-rRAKI compared favorably with other algorithms. Using high-pass filtered central k-space data for training, HP-RAKI offers higher reconstruction quality for regularly under-sampled multi-coil k-space data without any extra training data. It has shown promising capabilities for fast MRI applications, especially those lacking fully sampled training data.

The steady-state visual evoked potentials (SSVEPs), evoked by dual-frequency or multi-frequency stimulation, likely contains intermodulation frequency components (IMs). Visual IMs are products of nonlinear integration of neural signals and can be evoked by various paradigms that induce neural interaction. IMs have demonstrated many interesting and important characteristics in cognitive psychology, clinical neuroscience, brain-computer interface and other fields, and possess substantial research potential. In this paper, we first review the definition of IMs and summarize the stimulation paradigms capable of inducing them, along with the possible neural origins of IMs. Subsequently, we describe the characteristics and derived applications of IMs in previous studies, and then introduced three signal processing methods favored by researchers to enhance the signal-to-noise ratio of IMs. Finally, we summarize the characteristics of IMs, and propose several potential future research directions related to IMs.

Accumulation of pathological tau is one of the primary causes of Primary Progressive Aphasia (PPA). The glymphatic system is crucial for removing metabolite waste from the brain whereas impairments in glymphatic clearance in PPA are poorly understood. Thus, this study aims to investigate the role of dysregulated macroscopic cerebrospinal fluid (CSF) movement in PPA. Fifty-six PPA individuals and ninety-four healthy controls were included in our analysis after excluding those with excessive head motions during the scan. The coupling strength between blood-oxygen-level-dependent (BOLD) signals in the gray matter and CSF flow was calculated using Pearson correlation and compared between the groups. Its associations with clinical characteristics including scores from Clinical Dementia Rating (CDR), Mini-Mental State Exam, Geriatric Depression Scale and with morphological measures in the hippocampus and entorhinal cortex were examined. PPA subjects exhibited weaker global BOLD-CSF coupling compared to HCs, indicating impairments in glymphatic function in the patients (p = 0.01). In the PPA but not HC group, global BOLD-CSF coupling correlated with the CDR scores (p = 0.04) and hippocampal volume (p = 0.009). The observed decoupling between global brain activity and CSF flow and its association with symptomatology and brain structural changes in PPA converges with previous reports on the same measure in other neurodegenerative diseases. These findings support the potential role of global BOLD-CSF coupling as a noninvasive marker for glymphatic dysregulation in PPA.

Objective: Electroencephalography (EEG) is widely used for assessing infant's brain activity, and multi-channel recordings support studies on functional cortical networks. Here, we aimed to assess how the number of recording electrodes affects the quality and level of details accessible in studying infant's cortical networks.

Methods: Dense array EEG recordings with 124 channels from N=20 infants were used as the reference, and lower electrode numbers were subsampled to simulate recording setups with 63, 31, and 19 electrodes, respectively. Cortical activity networks were computed for each recording setup and different frequencies using amplitude and phase correlation measures. The effects of the recording setup were systematically assessed on global, nodal, and edge levels.

Results: Compared to the reference 124-channel recording setup, lowering electrode density affected network measures in a modality- and frequency-specific manner. The global network features were essentially comparable with 63 or 31 channels. However, the analytic reliability of the local network measures, both at nodal and edge levels, was proportional to the electrode density. The low-frequency amplitude correlations were most robust to the number of recording electrodes, whereas higher frequency phase correlation networks were most sensitive to the density of recording electrodes.

Conclusions: Our findings suggest strong and predictable effects of recording setup on the network analyses. Higher electrode number supports studies on networks with phase correlations, higher frequency, and finer spatial details.

Significance: The relationship between the recording setup and reliability of network analyses is essential for the prospective design of research data collection, as well as for guiding analytic strategies when using already collected EEG data from infants.