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The dataset provided in this article comprises frequencies of task-related thoughts, task-unrelated thoughts, involuntary autobiographical memories (IAMs), and involuntary future thoughts (IFTs) reported by adult participants during a laboratory vigilance task. Participants completed a vigilance task that included incidental cue words intended to trigger IAMs and IFTs, whose frequency was measured using random thought probes. The data were collected from two studies (n = 240 per study) in which working memory load and cue-presentation were manipulated. In both studies, participants completed an unexpected cue-recognition task after completing the vigilance task, which allowed for gathering additional data about noticing and remembering specific categories of cues (positive, neutral or negative). The dataset includes not only the frequencies of specific categories of thoughts but also data from numerous follow-up questions related to how participants perceived their performance in the task, such as their concentration level or perceived task difficulty. In conclusion the dataset contains three categories of variables: (1) variables related to participants and the conditions of the experimental sessions (i.e., age, gender, working memory load condition, etc.); (2) variables related to control questions (i.e., perceived task difficulty, emotional states, fatigue, etc.); and (3) variables related to performance in the vigilance task and the occurrence of thoughts (i.e., number of task-unrelated thoughts, number of involuntary memories, percentage of successfully recognized cues, etc.). This dataset could be reused to investigate many interesting relationships between cognitively engaging computer task characteristics and various parameters of task performance. Additionally, it could be used to conduct alternative or replication analyses to gain a deeper understanding of the relationship between working memory load and the experience of involuntary thoughts.

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Brain development from infancy through childhood involves complex structural and functional changes influenced by both internal and external factors. This review provides a comprehensive analysis of event and task-related brain oscillations, focusing on developmental changes across different frequency bands, including delta, theta, alpha, beta, and gamma. Electroencephalography (EEG) studies highlight that these oscillations serve as functional building blocks for sensory and cognitive processes, with significant variations observed across different developmental stages. Delta oscillations, primarily associated with deep sleep and early cognitive demands, gradually diminish as children age. Theta rhythms, crucial for attention and memory, display a distinct pattern in early childhood, evolving with cognitive maturation. Alpha oscillations, reflecting thalamocortical interactions and cognitive performance, increase in complexity with age. Beta rhythms, linked to active thinking and problem-solving, show developmental differences in motor and cognitive tasks. Gamma oscillations, associated with higher cognitive functions, exhibit notable changes in response to sensory stimuli and cognitive tasks. This review underscores the importance of understanding oscillatory dynamics to elucidate brain development and its implications for sensory and cognitive processing in childhood. The findings provide a foundation for future research on developmental neuroscience and potential clinical applications.

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Earlier research has suggested gender differences in event-related potentials/oscillations (ERPs/EROs). Yet, the alteration in event-related oscillations (EROs) in the delta and theta frequency bands have not been explored between genders across the three age groups of adulthood, i.e., 18-50, 51-65, and >65 years. Data from 155 healthy elderly participants who underwent a neurological examination, comprehensive neuropsychological assessment (including attention, memory, executive function, language, and visuospatial skills), and magnetic resonance imaging (MRI) from past studies were used. The delta and theta ERO powers across the age groups and between genders were compared and correlational analyses among the ERO power, age, and neuropsychological tests were performed. The results indicated that females displayed higher theta ERO responses than males in the frontal, central, and parietal regions but not in the occipital location between 18 and 50 years of adulthood. The declining theta power of EROs in women reached that of men after the age of 50 while the theta ERO power was more stable across the age groups in men. Our results imply that the cohorts must be recruited at specified age ranges across genders, and clinical trials using neurophysiological biomarkers as an intervention endpoint should take gender into account in the future.

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The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.

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OBJECTIVE: The aim of this study was to explore differences in brain activity and connectivity using simultaneous electroencephalography and near-infrared spectroscopy in patients with focal dystonia during handwriting and finger-tapping tasks. METHODS: Patients with idiopathic right upper limb focal dystonia and controls were assessed by simultaneous near-infrared spectroscopy and electroencephalography during the writing and finger-tapping tasks in terms of the mu-alpha, mu-beta, beta and low gamma power and effective connectivity, as well as relative changes in oxyhemoglobin (oxy-Hb) and deoxyhemoglobin using a channel-wise approach with a mixed-effect model. RESULTS: Patients exhibited higher oxy-Hb levels in the right and left motor cortex and supplementary motor area during writing, but lower oxy-Hb levels in the left sensorimotor and bilateral somatosensory area during finger-tapping compared to controls. During writing, patients showed increased low gamma power in the bilateral sensorimotor cortex and less mu-beta and beta attenuation compared to controls. Additionally, patients had reduced connectivity between the supplementary motor area and the left sensorimotor cortex during writing. No differences were observed in terms of effective connectivity in either task. Finally, patients failed to attenuate the mu-alpha, mu-beta, and beta rhythms during the finger-tapping task. CONCLUSIONS: Cortical blood flow and EEG spectral power differ between controls and dystonia patients, depending on the task. Writing increased blood flow and altered connectivity in dystonia patients, and it also decreased slow-band attenuation. Finger-tapping decreased blood flow and slow-band attenuation. SIGNIFICANCE: Simultaneous fNIRS and EEG may show relevant information regarding brain dynamics in movement disorders patients in unconstrained environments.

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Episodic memory decline is an early symptom of Alzheimer's disease (AD) - a neurodegenerative disease that has a higher prevalence rate in older females compared to older males. However, little is known about why these sex differences in prevalence rate exist. In the current longitudinal task fMRI study, we explored whether there were sex differences in the patterns of memory decline and brain activity during object-location (spatial context) encoding and retrieval in a large sample of cognitively unimpaired older adults from the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD) program who are at heightened risk of developing AD due to having a family history (+FH) of the disease. The goal of the study was to gain insight into whether there are sex differences in the neural correlates of episodic memory decline, which may advance knowledge about sex-specific patterns in the natural progression to AD. Our results indicate that +FH females performed better than +FH males at both baseline and follow-up on neuropsychological and task fMRI measures of episodic memory. Moreover, multivariate data-driven task fMRI analysis identified generalized patterns of longitudinal decline in medial temporal lobe activity that was paralleled by longitudinal increases in lateral prefrontal cortex, caudate and midline cortical activity during successful episodic retrieval and novelty detection in +FH males, but not females. Post-hoc analyses indicated that higher education had a stronger effect on +FH females neuropsychological scores compared to +FH males. We conclude that higher educational attainment may have a greater neuroprotective effect in older +FH females compared to +FH males.

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Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations.Approach. This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment.Main results. The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 s to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits min-1with a peak of 242.6 bits min-1.Significance. This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.

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Integrating the underlying brain circuit's structural and functional architecture is required to explore the functional organization of cognitive networks. In that regard, we recently introduced the Functionnectome. This structural-functional method combines an fMRI acquisition with tractography-derived white matter connectivity data to map cognitive processes onto the white matter. However, this multimodal integration faces three significant challenges: (1) the necessarily limited overlap between tractography streamlines and the grey matter, which may reduce the amount of functional signal associated with the related structural connectivity; (2) the scrambling effect of crossing fibers on functional signal, as a single voxel in such regions can be structurally connected to several cognitive networks with heterogeneous functional signals; and (3) the difficulty of interpretation of the resulting cognitive maps, as crossing and overlapping white matter tracts can obscure the organization of the studied network. In the present study, we tackled these problems by developing a streamline-extension procedure and dividing the white matter anatomical priors between association, commissural, and projection fibers. This approach significantly improved the characterization of the white matter involvement in the studied cognitive processes. The new Functionnectome priors produced are now readily available, and the analysis workflow highlighted here should also be generalizable to other structural-functional approaches. We improved the Functionnectome approach to better study the involvement of white matter in brain function by separating the analysis of the three classes of white matter fibers (association, commissural, and projection fibers). This step successfully clarified the activation maps and increased their statistical significance.

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Similar to a camera aperture, pupil size adjusts to the surrounding luminance. Unlike a camera, pupil size is additionally modulated both by stimulus properties and by cognitive processes, including attention and arousal, though the interdependence of these factors is unclear. We hypothesized that different stimulus properties interact to jointly modulate pupil size while remaining independent from the impact of arousal. We measured pupil responses from human observers to equiluminant stimuli during a demanding rapid serial visual presentation (RSVP) task at fixation and tested how response amplitude depends on contrast, spatial frequency, and reward level. We found that under constant luminance, unattended stimuli evoke responses that are separable from changes caused by general arousal or attention. We further uncovered a double-dissociation between task-related responses and stimulus-evoked responses, suggesting that different sources of pupil size modulation are independent of one another. Our results shed light on neural pathways underlying pupillary response.

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BACKGROUND AND HYPOTHESES: Impaired executive control is a potential prognostic and endophenotypic marker of schizophrenia (SZ) and bipolar disorder (BP). Assessing children with familial high-risk (FHR) of SZ or BP enables characterization of early risk markers and we hypothesize that they express impaired executive control as well as aberrant brain activation compared to population-based control (PBC) children. STUDY DESIGN: Using a flanker task, we examined executive control together with functional magnetic resonance imaging (fMRI) in 11- to 12-year-old children with FHR of SZ (FHR-SZ) or FHR of BP (FHR-BP) and PBC children as part of a register-based, prospective cohort-study; The Danish High Risk and Resilience study-VIA 11. STUDY RESULTS: We included 85 (44% female) FHR-SZ, 63 (52% female) FHR-BP and 98 (50% female) PBC in the analyses. Executive control effects, caused by the spatial visuomotor conflict, showed no differences between groups. Bayesian ANOVA of reaction time (RT) variability, quantified by the coefficient of variation (CVRT), revealed a group effect with similarly higher CVRT in FHR-BP and FHR-SZ compared to PBC (BF10 = 6.82). The fMRI analyses revealed no evidence for between-group differences in task-related brain activation. Post hoc analyses excluding children with psychiatric illness yielded same results. CONCLUSION: FHR-SZ and FHR-BP at age 11-12 show intact ability to resolve a spatial visuomotor conflict and neural efficacy. The increased variability in RT may reflect difficulties in maintaining sustained attention. Since variability in RT was independent of existing psychiatric illness, it may reflect a potential endophenotypic marker of risk.

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Objective.Currently, steady-state visual evoked potentials (SSVEPs)-based brain-computer interfaces (BCIs) have achieved the highest interaction accuracy and speed among all BCI paradigms. However, its decoding efficacy depends deeply on the number of training samples, and the system performance would have a dramatic drop when the training dataset decreased to a small size. To date, no study has been reported to incorporate the unsupervised learning information from testing trails into the construction of supervised classification model, which is a potential way to mitigate the overfitting effect of limited samples.Approach.This study proposed a novel method for SSVEPs detection, i.e. cyclic shift trials (CSTs), which could combine unsupervised learning information from test trials and supervised learning information from train trials. Furthermore, since SSVEPs are time-locked and phase-locked to the onset of specific flashes, CST could also expand training samples on the basis of its regularity and periodicity. In order to verify the effectiveness of CST, we designed an online SSVEP-BCI system, and tested this system combined CST with two common classification algorithms, i.e. extended canonical correlation analysis and ensemble task-related component analysis.Main results.CST could significantly enhance the signal to noise ratios of SSVEPs and improve the performance of systems especially for the condition of few training samples and short stimulus time. The online information transfer rate could reach up to 236.19 bits min-1using 36 s calibration time of only one training sample for each category.Significance.The proposed CST method can take full advantages of supervised learning information from training samples and unsupervised learning information of testing samples. Furthermore, it is a data expansion technique, which can enhance the SSVEP characteristics and reduce dependence on sample size. Above all, CST is a promising method to improve the performance of SSVEP-based BCI without any additional experimental burden.

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Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high-pass filtering. However, these standard steps do not remove many sources of noise. Thus, noise-reduction techniques, for example, CompCor, FIX, and ICA-AROMA have been developed to further improve the ability to draw meaningful conclusions from the data. The ability of these techniques to minimize noise while conserving signals of interest has been tested almost exclusively in resting-state fMRI and, only rarely, in task-related fMRI. Application of noise-reduction techniques to task-related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. Little remains known about the impact of these techniques on the retention of signal in tasks that may be associated with systemic physiological changes. In this paper, we compared two ICA-based, that is FIX and ICA-AROMA, two CompCor-based noise-reduction techniques, that is aCompCor, and tCompCor, and standard preprocessing using a large (n = 101) fMRI dataset including noxious heat and non-noxious auditory stimulation. Results show that preprocessing using FIX performs optimally for data obtained using noxious heat, conserving more signals than CompCor-based techniques and ICA-AROMA, while removing only slightly less noise. Similarly, for data obtained during non-noxious auditory stimulation, FIX noise-reduction technique before analysis with a covariate of interest outperforms the other techniques. These results indicate that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise during task-related fMRI.

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[This corrects the article DOI: 10.3389/fnimg.2022.983324.].

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Background: The activation patterns and functional network characteristics between stroke survivors and healthy individuals based on resting-or task-state neuroimaging and neurophysiological techniques have been extensively explored. However, the discrepancy between stroke patients at different recovery stages remains unclear. Objective: To investigate the changes in brain connectivity and network topology between subacute and chronic patients, and hope to provide a basis for rehabilitation strategies at different stages after stroke. Methods: Fifteen stroke survivors were assigned to the subacute group (SG, N = 9) and chronic group (CG, N = 6). They were asked to perform hand grasping under active, passive, and MI conditions when recording EEG. The Fugl-Meyer Assessment Upper Extremity subscale (FMA_UE), modified Ashworth Scale (MAS), Manual Muscle Test (MMT), grip and pinch strength, modified Barthel Index (MBI), and Berg Balance Scale (BBS) were measured. Results: Functional connectivity analyses showed significant interactions on frontal, parietal and occipital lobes connections in each frequency band, particularly in the delta band. The coupling strength of premotor cortex, M1, S1 and several connections linked to frontal, parietal, and occipital lobes in subacute subjects were lower than in chronic subjects in low alpha, high alpha, low beta, and high beta bands. Nodal clustering coefficient (CC) analyses revealed that the CC in chronic subjects was higher than in subacute subjects in the ipsilesional S1 and occipital area, contralesional dorsolateral prefrontal cortex and parietal area. Characteristic path length (CPL) analyses showed that CPL in subacute subjects was lower than in chronic subjects in low beta, high beta, and gamma bands. There were no significant differences between subacute and chronic subjects for small-world property. Conclusion: Subacute stroke survivors were characterized by higher transfer efficiency of the entire brain network and weak local nodal effects. Transfer efficiency was reduced, the local nodal role was strengthened, and more neural resources needed to be mobilized to perform motor tasks for chronic survivors. Overall, these results may help to understand the remodeling pattern of the brain network for different post-stroke stages on task conditions and the mechanism of spontaneous recovery.

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Objective.The steady-state visual evoked potential (SSVEP)-based brain-computer interface has received extensive attention in research due to its simple system, less training data, and high information transfer rate. There are currently two prominent methods dominating the classification of SSVEP signals. One is the knowledge-based task-related component analysis (TRCA) method, whose core idea is to find the spatial filters by maximizing the inter-trial covariance. The other is the deep learning-based approach, which directly learns a classification model from data. However, how to integrate the two methods to achieve better performance has not been studied before.Approach.In this study, we develop a novel algorithm named TRCA-Net (TRCA-Net) to enhance SSVEP signal classification, which enjoys the advantages of both the knowledge-based method and the deep model. Specifically, the proposed TRCA-Net first performs TRCA to obtain spatial filters, which extract task-related components of data. Then the TRCA-filtered features from different filters are rearranged as new multi-channel signals for a deep convolutional neural network (CNN) for classification. Introducing the TRCA filters to a deep learning-based approach improves the signal-to-noise ratio of input data, hence benefiting the deep learning model.Main results.We evaluate the performance of TRCA-Net using two publicly available large-scale benchmark datasets, and the results demonstrate the effectiveness of TRCA-Net. Additionally, offline and online experiments separately testing ten and five subjects further validate the robustness of TRCA-Net. Further, we conduct ablation studies on different CNN backbones and demonstrate that our approach can be transplanted into other CNN models to boost their performance.Significance.The proposed approach is believed to have a promising potential for SSVEP classification and promote its practical applications in communication and control. The code is available athttps://github.com/Sungden/TRCA-Net.

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Whether remitted major depressive disorder (rMDD) and MDD present common or distinct neuropathological mechanisms remains unclear. We performed a meta-analysis of task-related whole-brain functional magnetic resonance imaging (fMRI) using anisotropic effect-size signed differential mapping software to compare brain activation between rMDD/MDD patients and healthy controls (HCs). We included 18 rMDD studies (458 patients and 476 HCs) and 120 MDD studies (3746 patients and 3863 HCs). The results showed that MDD and rMDD patients shared increased neural activation in the right temporal pole and right superior temporal gyrus. Several brain regions, including the right middle temporal gyrus, left inferior parietal, prefrontal cortex, left superior frontal gyrus and striatum, differed significantly between MDD and rMDD. Meta-regression analyses revealed that the percentage of females with MDD was positively associated with brain activity in the right lenticular nucleus/putamen. Our results provide valuable insights into the underlying neuropathology of brain dysfunction in MDD, developing more targeted and efficacious treatment and intervention strategies, and more importantly, providing potential neuroimaging targets for the early screening of MDD.

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BACKGROUND AND OBJECTIVE: The simultaneous execution of a motor and cognitive dual task may lead to the deterioration of task performance in one or both tasks due to cognitive-motor interference (CMI). Neuroimaging techniques are promising ways to reveal the underlying neural mechanism of CMI. However, existing studies have only explored CMI from a single neuroimaging modality, which lack built-in validation and comparison of analysis results. This work is aimed to establish an effective analysis framework to comprehensively investigate the CMI by exploring the electrophysiological and hemodynamic activities as well as their neurovascular coupling. METHODS: Experiments including an upper limb single motor task, single cognitive task, and cognitive-motor dual task were designed and performed with 16 healthy young participants. Bimodal signals of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded simultaneously during the experiments. A novel bimodal signal analysis framework was proposed to extract the task-related components for EEG and fNIRS signals respectively and analyze their correlation. Indicators including within-class similarity and between-class distance were utilized to validate the effectiveness of the proposed analysis framework compared to the canonical channel-averaged method. Statistical analysis was performed to investigate the difference in the behavior and neural correlates between the single and dual tasks. RESULTS: Our results revealed that the extra cognitive interference caused divided attention in the dual task, which led to the decreased neurovascular coupling between fNIRS and EEG in all theta, alpha, and beta rhythms. The proposed framework was demonstrated to have a better ability in characterizing the neural patterns than the canonical channel-averaged method with significantly higher within-class similarity and between-class distance indicators. CONCLUSIONS: This study proposed a method to investigate CMI by exploring the task-related electrophysiological and hemodynamic activities as well as their neurovascular coupling. Our concurrent EEG-fNIRS study provides new insight into the EEG-fNIRS correlation analysis and novel evidence for the mechanism of neurovascular coupling in the CMI.

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Cognitive impairment and affective disorders are common in patients with Cushing's syndrome (CS). In fact, as an effect of prolonged cortisol excess on the brain, patients with CS often have memory problems, concentration difficulties, impaired attention and executive function, that are not always reversible following successful treatment. Neuroimaging is essential for understanding the deleterious effects of hypercortisolism on the brain. In CS, structural alterations have been observed, including reduction of hippocampal volume, amygdala and the prefrontal cortex. The aim of this article is to summarize results from studies that have used functional magnetic resonance imaging (fMRI) to study functional brain alterations in patients with CS. In these studies, alterations in brain areas and networks essential for cognitive function, emotional processing, and executive function have been observed, both in patients with active CS as well as following treatment. Nevertheless, longitudinal studies with a comprehensive evaluation of functional brain alterations and neurocognitive evaluation are still needed to determine whether the apparent deleterious effects of hypercortisolism on the brain are reversible or not.

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Introduction: Integrating faultline theory and the attention-based view, this paper explores the impact and process mechanisms of two types of faultlines (i.e., task-related faultlines and relationship-related faultlines) within top management teams (TMTs), specifically on corporate industrial diversification. Methods: Based on the unbalanced panel data of Chinese A-share non-financial listed firms from 2008-2021, this study uses the fixed-effects model for hypothesis testing. Results: First, task-related TMT faultlines promote corporate industrial diversification, while conversely, relationship-related TMT faultlines inhibit corporate industrial diversification. Second, task-related TMT faultlines promote firms' strategic attentional breadth, while conversely, relationship-related TMT faultlines inhibit firms' strategic attentional breadth. Third, strategic attentional breadth plays a partially-mediating role in the relationship between both types of TMT faultlines and firms' industrial diversification. Discussion: This study extends the research related to corporate industrial diversification based on micro-level explanatory mechanisms, and also provides implications and guidance for the rational allocation of TMT and firms' industrial diversification management practices.

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Much of the thought content and mind-wandering literature examines self-reported thought content's relationship with performance criteria in limited ways. Furthermore, retrospective reports about thought content may be influenced by the quality of one's performance. We explored these method issues in a cross-sectional study of individuals competing in a trail race and an equestrian event. Our results demonstrated that self-reports of thought content differed based on the performance context: whereas runners' task-related and task-unrelated thoughts were negatively correlated, equestrians' thought content showed no relationship. Moreover, equestrians in general reported fewer task-related and task-unrelated thoughts than runners. Finally, objective performance predicted task-unrelated thought (but not task-related thought) among runners, and an exploratory mediation test suggested the effect was partially mediated by performance awareness. We discuss the applied implications of this research for human performance practitioners.

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