Theta Signal Transfer from Parietal to Prefrontal Cortex Ignites Conscious Awareness of Implicit Knowledge during Sequence Learning.

Unconscious acquisition of sequence structure from experienced events can lead to explicit awareness of the pattern through extended practice. Although the implicit-to-explicit transition has been extensively studied in humans using the serial reaction time (SRT) task, the subtle neural activity supporting this transition remains unclear. Here, we investigated whether frequency-specific neural signal transfer contributes to this transition. A total of 208 participants (107 females) learned a sequence pattern through a multisession SRT task, allowing us to observe the transitions. Session-by-session measures of participants' awareness for sequence knowledge were conducted during the SRT task to identify the session when the transition occurred. By analyzing time course RT data using switchpoint modeling, we identified an increase in learning benefit specifically at the transition session. Electroencephalogram (EEG)/magnetoencephalogram (MEG) recordings revealed increased theta power in parietal (precuneus) regions one session before the transition (pretransition) and a prefrontal (superior frontal gyrus; SFG) one at the transition session. Phase transfer entropy (PTE) analysis confirmed that directional theta transfer from precuneus → SFG occurred at the pretransition session and its strength positively predicted learning improvement at the subsequent transition session. Furthermore, repetitive transcranial magnetic stimulation (TMS) modulated precuneus theta power and altered transfer strength from precuneus to SFG, resulting in changes in both transition rate and learning benefit at that specific point of transition. Our brain-stimulation evidence supports a role for parietal → prefrontal theta signal transfer in igniting conscious awareness of implicitly acquired knowledge.SIGNIFICANCE STATEMENT There exists a pervasive phenomenon wherein individuals unconsciously acquire sequence patterns from their environment, gradually becoming aware of the underlying regularities through repeated practice. While previous studies have established the robustness of this implicit-to-explicit transition in humans, the refined neural mechanisms facilitating conscious access to implicit knowledge remain poorly understood. Here, we demonstrate that prefrontal activity, known to be crucial for conscious awareness, is triggered by neural signal transfer originating from the posterior brain region, specifically the precuneus. By employing brain stimulation techniques, we establish a causal link between neural signal transfer and the occurrence of awareness. Our findings unveil a mechanism by which implicit knowledge becomes consciously accessible in human cognition.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

The neural and genetic underpinnings of different developmental trajectories of Attention-Deficit/Hyperactivity Symptoms in children and adolescents.

BACKGROUND: The trajectory of attention-deficit hyperactivity disorder (ADHD) symptoms in children and adolescents, encompassing descending, stable, and ascending patterns, delineates their ADHD status as remission, persistence or late onset. However, the neural and genetic underpinnings governing the trajectory of ADHD remain inadequately elucidated. METHODS: In this study, we employed neuroimaging techniques, behavioral assessments, and genetic analyses on a cohort of 487 children aged 6-15 from the Children School Functions and Brain Development project at baseline and two follow-up tests for 1 year each (interval 1: 1.14 ± 0.32 years; interval 2: 1.14 ± 0.30 years). We applied a Latent class mixed model (LCMM) to identify the developmental trajectory of ADHD symptoms in children and adolescents, while investigating the neural correlates through gray matter volume (GMV) analysis and exploring the genetic underpinnings using polygenic risk scores (PRS). RESULTS: This study identified three distinct trajectories (ascending-high, stable-low, and descending-medium) of ADHD symptoms from childhood through adolescence. Utilizing the linear mixed-effects (LME) model, we discovered that attention hub regions served as the neural basis for these three developmental trajectories. These regions encompassed the left anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC), responsible for inhibitory control; the right inferior parietal lobule (IPL), which facilitated conscious focus on exogenous stimuli; and the bilateral middle frontal gyrus/precentral gyrus (MFG/PCG), accountable for regulating both dorsal and ventral attention networks while playing a crucial role in flexible modulation of endogenous and extrinsic attention. Furthermore, our findings revealed that individuals in the ascending-high group exhibited the highest PRS for ADHD, followed by those in the descending-medium group, with individuals in the stable-low group displaying the lowest PRS. Notably, both ascending-high and descending-medium groups had significantly higher PRS compared to the stable-low group. CONCLUSIONS: The developmental trajectory of ADHD symptoms in the general population throughout childhood and adolescence can be reliably classified into ascending-high, stable-low, and descending-medium groups. The bilateral MFG/PCG, left ACC/mPFC, and right IPL may serve as crucial brain regions involved in attention processing, potentially determining these trajectories. Furthermore, the ascending-high pattern of ADHD symptoms exhibited the highest PRS for ADHD.

A generalized QUCESOP method with evaluating CEST peak overlap.

The overlapping peaks of the target chemical exchange saturation transfer (CEST) solutes and other unknown CEST solutes affect the quantification results and accuracy of the chemical exchange parameters-the fractional concentration, f b , exchange rate, k b , and transverse relaxation rate, R 2 b -for the target solutes. However, to date, no method has been established for assessing the overlapping peaks. This study aimed to develop a method for quantifying the f b , k b , and R 2 b values of a specific CEST solute, as well as assessing the overlap between the CEST peaks of the specific solute(s) and other unknown solutes. A simplified R 1 ρ model was proposed, assuming linear approximation of the other solutes' contributions to R 1 ρ . A CEST data acquisition scheme was applied with various saturation offsets and saturation powers. In addition to fitting the f b , k b , and R 2 b values of the specific solute, the overlapping condition was evaluated based on the root mean square error (RMSE) between the trajectories of the acquired and synthesized data. Single-solute and multi-solute phantoms with various phosphocreatine (PCr) concentrations and pH values were used to calculate the f b and k b of PCr and the corresponding RMSE. The feasibility of RMSE for evaluating the overlapping condition, and the accurate fitting of f b and k b in weak overlapping conditions, were verified. Furthermore, the method was employed to quantify the nuclear Overhauser effect signal in rat brains and the PCr signal in rat skeletal muscles, providing results that were consistent with those reported in previous studies. In summary, the proposed approach can be applied to evaluate the overlapping condition of CEST peaks and quantify the f b , k b , and R 2 b values of specific solutes, if the weak overlapping condition is satisfied.

Common and distinct neural representations of imagined and perceived speech.

Humans excel at constructing mental representations of speech streams in the absence of external auditory input: the internal experience of speech imagery. Elucidating the neural processes underlying speech imagery is critical to understanding this higher-order brain function in humans. Here, using functional magnetic resonance imaging, we investigated the shared and distinct neural correlates of imagined and perceived speech by asking participants to listen to poems articulated by a male voice (perception condition) and to imagine hearing poems spoken by that same voice (imagery condition). We found that compared to baseline, speech imagery and perception activated overlapping brain regions, including the bilateral superior temporal gyri and supplementary motor areas. The left inferior frontal gyrus was more strongly activated by speech imagery than by speech perception, suggesting functional specialization for generating speech imagery. Although more research with a larger sample size and a direct behavioral indicator is needed to clarify the neural systems underlying the construction of complex speech imagery, this study provides valuable insights into the neural mechanisms of the closely associated but functionally distinct processes of speech imagery and perception.

Learning to read may help promote attention by increasing the volume of the left middle frontal gyrus and enhancing its connectivity to the ventral attention network.

Attention and reading are essential skills for successful schooling and in adult life. While previous studies have documented that attention development supports reading acquisition, whether and how learning to read may improve attention among school-age children and the brain structural and functional development that may be involved remain unknown. In this prospective longitudinal study, we examined bidirectional and longitudinal predictions between attention and reading development and the neural mediators of attention and reading development among school-age children using cross-lagged panel modeling. The results showed that better baseline reading performance significantly predicted better attention performance one year later after controlling for baseline attention performance. In contrast, after controlling for baseline reading performance, attention did not significantly predict reading performance one year later, while more attention problems also significantly predicted worse reading performance. Both the increasing gray matter volume of the left middle frontal gyrus and the increasing connectivity between the left middle frontal gyrus and the ventral attention network mediated the above significant longitudinal predictions. This study, directly revealed that reading skills may predict the development of important cognitive functions, such as attention, in school-age children. Therefore, learning to read is not only a challenge for school-age children but is also an important way to optimize attention and brain development.

Development of brain state dynamics involved in working memory.

Human functional brain networks are dynamically organized to enable cognitive and behavioral flexibility to meet ever-changing environmental demands. Frontal-parietal network (FPN) and default mode network (DMN) are recognized to play an essential role in executive functions such as working memory. However, little is known about the developmental differences in the brain-state dynamics of these two networks involved in working memory from childhood to adulthood. Here, we implemented Bayesian switching dynamical systems approach to identify brain states of the FPN and DMN during working memory in 69 school-age children and 51 adults. We identified five brain states with rapid transitions, which are characterized by dynamic configurations among FPN and DMN nodes with active and inactive engagement in different task demands. Compared with adults, children exhibited less frequent brain states with the highest activity in FPN nodes dominant to high demand, and its occupancy rate increased with age. Children preferred to attain inactive brain states with low activity in both FPN and DMN nodes. Moreover, children exhibited lower transition probability from low-to-high demand states and such a transition was positively correlated with working memory performance. Notably, higher transition probability from low-to-high demand states was associated with a stronger structural connectivity across FPN and DMN, but with weaker structure-function coupling of these two networks. These findings extend our understanding of how FPN and DMN nodes are dynamically organized into a set of transient brain states to support moment-to-moment information updating during working memory and suggest immature organization of these functional brain networks in childhood, which is constrained by the structural connectivity.

Global diversity in individualized cortical network topography.

Individualized cortical network topography (ICNT) varies between people and exhibits great variability in the association networks in the human brain. However, these findings were mainly discovered in Western populations. It remains unclear whether and how ICNT is shaped by the non-Western populations. Here, we leveraged a multisession hierarchical Bayesian model to define individualized functional networks in White American and Han Chinese populations with data from both US and Chinese Human Connectome Projects. We found that both the size and spatial topography of individualized functional networks differed between White American and Han Chinese groups, especially in the heteromodal association cortex (including the ventral attention, control, language, dorsal attention, and default mode networks). Employing a support vector machine, we then demonstrated that ethnicity-related ICNT diversity can be used to identify an individual's ethnicity with high accuracy (74%, pperm < 0.0001), with heteromodal networks contributing most to the classification. This finding was further validated through mass-univariate analyses with generalized additive models. Moreover, we reveal that the spatial heterogeneity of ethnic diversity in ICNT correlated with fundamental properties of cortical organization, including evolutionary cortical expansion, brain myelination, and cerebral blood flow. Altogether, this case study highlights a need for more globally diverse and publicly available neuroimaging datasets.

Default mode network scaffolds immature frontoparietal network in cognitive development.

The default mode network (DMN) is a workspace for convergence of internal and external information. The frontal parietal network (FPN) is indispensable to executive functioning. Yet, how they interplay to support cognitive development remains elusive. Using longitudinal developmental fMRI with an n-back paradigm, we show a heterogeneity of maturational changes in multivoxel activity and network connectivity among DMN and FPN nodes in 528 children and 103 young adults. Compared with adults, children exhibited prominent longitudinal improvement but still inferior behavioral performance, which paired with less pronounced DMN deactivation and weaker FPN activation in children, but stronger DMN coupling with FPN regions. Children's DMN reached an adult-like level earlier than FPN at both multivoxel activity pattern and intranetwork connectivity levels. Intrinsic DMN-FPN internetwork coupling in children mediated the relationship between age and working memory-related functional coupling of these networks, with posterior cingulate cortex (PCC)-dorsolateral prefrontal cortex (DLPFC) coupling emerging as most prominent pathway. Coupling of PCC-DLPFC may further work together with task-invoked activity in PCC to account for longitudinal improvement in behavioral performance in children. Our findings suggest that the DMN provides a scaffolding effect in support of an immature FPN that is critical for the development of executive functions in children.

State-dependent and region-specific alterations of cerebellar connectivity across stable human wakefulness and NREM sleep states.

Sleep regulation and functioning may rely on systematic coordination throughout the whole brain, including the cerebellum. However, whether and how interactions between the cerebellum and other brain regions vary across sleep stages remain poorly understood. Here, using simultaneous EEG-fMRI recordings captured from 73 participants during wakefulness and non-rapid eye movement (NREM) sleep, we constructed cerebellar connectivity among intrinsic functional networks with intra-cerebellar, neocortical and subcortical regions. We uncovered that cerebellar connectivity exhibited sleep-dependent alterations: slight differences between wakefulness and N1/N2 sleep and greater changes in N3 sleep than other states. Region-specific cerebellar connectivity changes between N2 sleep and N3 sleep were also revealed: general breakdown of intra-cerebellar connectivity, enhancement of limbic-cerebellar connectivity and alterations of cerebellar connectivity with spatially specific neocortices. Further correlation analysis showed that functional connectivity between the cerebellar Control II network and regions (including the insula, hippocampus, and amygdala) correlated with delta power during N3 and beta power during N2 sleep. These findings systematically reveal altered cerebellar connectivity among intrinsic networks from wakefulness to deep sleep and highlight the potential role of the cerebellum in sleep regulation and functioning.

Long-term ANT-DBS effects in pilocarpine-induced epileptic rats: A combined 9.4T MRI and histological study.

Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) appears to be effective against seizures in animals and humans however, its therapeutic mechanisms remain elusive. This study aimed to combine 9.4T multimodal magnetic resonance imaging (MRI) with histology to investigate the longitudinal effects of long-term ANT-DBS in pilocarpine-induced epileptic rats. Status epilepsy (SE) was induced by LiCl-pilocarpine injection in 11 adult male Sprague-Dawley rats. Four weeks after SE, chronic epileptic rats underwent either ANT-DBS (n = 6) or sham-DBS (n = 5) surgery. Electroencephalography (EEG) and spontaneous recurrent seizures (SRS) were recorded for 1 week. The T2-weighted image and images from resting-state functional MRI (rs-fMRI) were acquired at three states: before SE, at 4 weeks post-SE, and at 5 weeks post-DBS. Volumes of the hippocampal subregions and hippocampal-related functional connectivity (FC) were compared longitudinally. Finally, antibodies against neuronal nuclei (NeuN) and glial fibrillary acidic proteins were used to evaluate neuronal loss and astrogliosis in the hippocampus. Long-term ANT-DBS significantly reduced seizure generalization in pilocarpine-induced epileptic rats. By analyzing the gray matter volume using T2-weighted images, long-term ANT-DBS displayed morphometric restoration of the hippocampal subregions. Neuronal protection of the hippocampal subregions and inhibition of astrogliosis in the hippocampal subregions were observed in the ANT-DBS group. ANT-DBS caused reversible regulation of FC in the insula-hippocampus and subthalamic nucleus-hippocampus. Long-term ANT-DBS provides comprehensive protection of hippocampal histology, hippocampal morphometrics, and hippocampal-related functional networks.

Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence.

Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.

A Heteromodal Word-Meaning Binding Site in the Visual Word Form Area under Top-Down Frontoparietal Control.

The integral capacity of human language together with semantic memory drives the linkage of words and their meaning, which theoretically is subject to cognitive control. However, it remains unknown whether, across different language modalities and input/output formats, there is a shared system in the human brain for word-meaning binding and how this system interacts with cognitive control. Here, we conducted a functional magnetic resonance imaging experiment based on a large cohort of subjects (50 females, 50 males) to comprehensively measure the brain responses evoked by semantic processing in spoken and written word comprehension and production tasks (listening, speaking, reading, and writing). We found that heteromodal word input and output tasks involved distributed brain regions within a frontal-parietal-temporal network and focally coactivated the anterior lateral visual word form area (VWFA), which is located in the basal occipitotemporal area. Directed connectivity analysis revealed that the VWFA was invariably under significant top-down modulation of the frontoparietal control network and interacts with regions related to attention and semantic representation. This study reveals that the VWFA is a key site subserving general semantic processes linking words and meaning, challenging the predominant emphasis on this area's specific role in reading or more general visual processes. Our findings also suggest that the dynamics between semantic memory and cognitive control mechanisms during word processing are largely independent of the modalities of input or output.SIGNIFICANCE STATEMENT Binding words and their meaning into a coherent whole during retrieval requires accessing semantic memory and cognitive control, allowing our thoughts to be expressed and comprehended through mind-external tokens in multiple modalities, such as written or spoken forms. However, it is still unknown whether multimodal language comprehension and production share a common word-meaning binding system in human brains and how this system is connected to a cognitive control mechanism. By systematically measuring brain activity evoked by spoken and written verbal input and output tasks tagging word-meaning binding processes, we demonstrate a general word-meaning binding site within the visual word form area (VWFA) and how this site is modulated by the frontal-parietal control network.

Disrupted neural tracking of sound localization during non-rapid eye movement sleep.

Spatial hearing in humans is a high-level auditory process that is crucial to rapid sound localization in the environment. Both neurophysiological models with animals and neuroimaging evidence from human subjects in the wakefulness stage suggest that the localization of auditory objects is mainly located in the posterior auditory cortex. However, whether this cognitive process is preserved during sleep remains unclear. To fill this research gap, we investigated the sleeping brain's capacity to identify sound locations by recording simultaneous electroencephalographic (EEG) and magnetoencephalographic (MEG) signals during wakefulness and non-rapid eye movement (NREM) sleep in human subjects. Using the frequency-tagging paradigm, the subjects were presented with a basic syllable sequence at 5 Hz and a location change that occurred every three syllables, resulting in a sound localization shift at 1.67 Hz. The EEG and MEG signals were used for sleep scoring and neural tracking analyses, respectively. Neural tracking responses at 5 Hz reflecting basic auditory processing were observed during both wakefulness and NREM sleep, although the responses during sleep were weaker than those during wakefulness. Cortical responses at 1.67 Hz, which correspond to the sound location change, were observed during wakefulness regardless of attention to the stimuli but vanished during NREM sleep. These results for the first time indicate that sleep preserves basic auditory processing but disrupts the higher-order brain function of sound localization.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

Informed MEG/EEG source imaging reveals the locations of interictal spikes missed by SEEG.

Determining the accurate locations of interictal spikes has been fundamental in the presurgical evaluation of epilepsy surgery. Stereo-electroencephalography (SEEG) is able to directly record cortical activity and localize interictal spikes. However, the main caveat of SEEG techniques is that they have limited spatial sampling (covering <5% of the whole brain), which may lead to missed spikes originating from brain regions that were not covered by SEEG. To address this problem, we propose a SEEG-informed minimum-norm estimates (SIMNE) method by combining SEEG with magnetoencephalography (MEG) or EEG. Specifically, the spike locations determined by SEEG offer as a priori information to guide MEG source reconstruction. Both computer simulations and experiments using data from five epilepsy patients were conducted to evaluate the performance of SIMNE. Our results demonstrate that SIMNE generates more accurate source estimation than a traditional minimum-norm estimates method and reveals the locations of spikes missed by SEEG, which would improve presurgical evaluation of the epileptogenic zone.

Multimodal neuroimaging with optically pumped magnetometers: A simultaneous MEG-EEG-fNIRS acquisition system.

Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in the brain to be precisely and comprehensively depicted, providing an effective and advanced platform to study brain function. Noncryogenic optically pumped magnetometer (OPM) MEG has high signal power due to its on-scalp sensor layout and enables more flexible configurations than traditional commercial superconducting MEG. Here, we integrate OPM-MEG with EEG and fNIRS to develop a multimodal neuroimaging system that can simultaneously measure brain electrophysiology and hemodynamics. We conducted a series of experiments to demonstrate the feasibility and robustness of our MEG-EEG-fNIRS acquisition system. The complementary neural and physiological signals simultaneously collected by our multimodal imaging system provide opportunities for a wide range of potential applications in neurovascular coupling, wearable neuroimaging, hyperscanning and brain-computer interfaces.

Myeloarchitectonic Asymmetries of Language Regions in the Human Brain.

One prominent theory in neuroscience and psychology assumes that cortical regions for language are left hemisphere lateralized in the human brain. In the current study, we used a novel technique, quantitative magnetic resonance imaging (qMRI), to examine interhemispheric asymmetries in language regions in terms of macromolecular tissue volume (MTV) and quantitative longitudinal relaxation time (T1) maps in the living human brain. These two measures are known to reflect cortical myeloarchitecture from the microstructural perspective. One hundred and fifteen adults (55 male, 60 female) were examined for their myeloarchitectonic asymmetries of language regions. We found that the cortical myeloarchitecture of inferior frontal areas including the pars opercularis, pars triangularis, and pars orbitalis is left lateralized, while that of the middle temporal gyrus, Heschl's gyrus, and planum temporale is right lateralized. Moreover, the leftward lateralization of myelination structure is significantly correlated with language skills measured by phonemic and speech tone awareness. This study reveals for the first time a mixed pattern of myeloarchitectonic asymmetries, which calls for a general theory to accommodate the full complexity of principles underlying human hemispheric specialization.

Neural representations of imagined speech revealed by frequency-tagged magnetoencephalography responses.

Speech mental imagery is a quasi-perceptual experience that occurs in the absence of real speech stimulation. How imagined speech with higher-order structures such as words, phrases and sentences is rapidly organized and internally constructed remains elusive. To address this issue, subjects were tasked with imagining and perceiving poems along with a sequence of reference sounds with a presentation rate of 4 Hz while magnetoencephalography (MEG) recording was conducted. Giving that a sentence in a traditional Chinese poem is five syllables, a sentential rhythm was generated at a distinctive frequency of 0.8 Hz. Using the frequency tagging we concurrently tracked the neural processing timescale to the top-down generation of rhythmic constructs embedded in speech mental imagery and the bottom-up sensory-driven activity that were precisely tagged at the sentence-level rate of 0.8 Hz and a stimulus-level rate of 4 Hz, respectively. We found similar neural responses induced by the internal construction of sentences from syllables with both imagined and perceived poems and further revealed shared and distinct cohorts of cortical areas corresponding to the sentence-level rhythm in imagery and perception. This study supports the view of a common mechanism between imagery and perception by illustrating the neural representations of higher-order rhythmic structures embedded in imagined and perceived speech.

Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study.

The default-mode network (DMN) is a set of functionally connected regions that play crucial roles in internal cognitive processing. Previous resting-state fMRI studies have demonstrated that the intrinsic functional organization of the DMN undergoes remarkable reconfigurations during childhood and adolescence. However, these studies have mainly focused on cross-sectional designs with small sample sizes, limiting the consistency and interpretations of the findings. Here, we used a large sample of longitudinal resting-state fMRI data comprising 305 typically developing children (6-12 years of age at baseline, 491 scans in total) and graph theoretical approaches to delineate the developmental trajectories of the functional architecture of the DMN. For each child, the DMN was constructed according to a prior parcellation with 32 brain nodes. We showed that the overall connectivity increased in strength from childhood to adolescence and became spatially similar to that in the young adult group (N = 61, 18-28 years of age). These increases were primarily located in the midline structures. Global and local network efficiency in the DMN also increased with age, indicating an enhanced capability in parallel information communication within the brain system. Based on the divergent developmental rates of nodal centrality, we identified three subclusters within the DMN, with the fastest rates in the cluster mainly comprising the anterior medial prefrontal cortex and posterior cingulate cortex. Together, our findings highlight the developmental patterns of the functional architecture in the DMN from childhood to adolescence, which has implications for the understanding of network mechanisms underlying the cognitive development of individuals.