Cerebral Cortex

The early development of the prefrontal cortex is crucial for higher cognitive functions. However, current research presents inconsistent findings regarding whether intra-prefrontal connectivity increases or decreases in infants younger than six months. Do dynamic changes in connection strength across different states over time carry information about prefrontal maturation? This study used functional near-infrared spectroscopy (fNIRS) to record prefrontal brain activity in 48 healthy infants aged 1-8 months during natural sleep and auditory stimulation. By analyzing the fluctuations in frequency-domain characteristics of functional connectivity (FC) and various brain network properties, we found that: under auditory stimulation, the intensity of FC fluctuations in the ultra-low frequency range was positively correlated with age; while in the resting state, the fluctuation intensity of network properties in relatively higher frequency bands decreased with age. Furthermore, auditory stimulation reconfigured the energy distribution of network fluctuations, shifting it towards higher frequency bands. These results suggest that the early development of the infant prefrontal internal network is characterized by state-dependent optimization of its dynamic fluctuation properties, shedding light on the developmental tuning of functional network dynamics in infancy.

A central question in psycholinguistics in visual word recognition is whether morphologically complex words are obligatorily decomposed into stems and affixes during visual word recognition or whether whole-word access can occur when forms are frequent and familiar. The present study investigated how morphological complexity and lexical frequency jointly shape neural responses by leveraging Korean nominal inflection, whose transparent stem-suffix structure permits a clean dissociation between base (stem) frequency and surface (whole-word) frequency. Twenty-five native Korean speakers completed a rapid event-related fMRI lexical decision task involving simple and inflected nouns that varied parametrically in both frequency measures. Representational similarity analysis (RSA) revealed robust encoding of surface frequency--but not base frequency--in the inferior frontal gyrus (IFG) pars opercularis and supramarginal gyrus (SMG), with significantly stronger correlations for inflected than simple nouns. Univariate analyses converged with this result: surface frequency selectively increased activation for inflected nouns in inferior parietal regions, whereas base frequency showed no reliable effects in any ROI. These findings challenge models positing obligatory pre-lexical decomposition, instead supporting accounts in which morphological processing is shaped by post-lexical, usage-driven lexical statistics. Taken together, our findings shed light on a distributed perspective on morphological processing, suggesting that structural and statistical factors jointly constrain access to morphologically complex forms.

Visual spatial attention deployed in advance of sensory stimulation enhances the processing of the stimuli at an attended location. While it is understood that the attention control signals are established even before the stimulus occurs, how these signals help achieve stimulus selection is still not clear. Here, we investigated the neural mechanisms of spatial attention control and subsequent stimulus selection by recording fMRI data from participants performing a cued visual spatial attention task. At the beginning of each trial, participants were cued to covertly attend either the left or the right visual field. Following a random cue-target period, a target stimulus appeared either at the attended location or at the unattended location. Participants discriminated the stimulus appearing at the attended location and ignored the stimulus appearing at the unattended location. Using MVPA decoding and multivariate functional connectivity techniques, we investigated the nature of the information in visual cortex during both the cue and target periods, and further probed how cue-related information was related to target-related sensory processing. The following results were found: (1) attend-left vs. attend-right conditions could be decoded from the cue-period neural activity in visual cortex, (2) target position (target-left vs. target-right) could also be decoded from the target-evoked activity in visual cortex, (3) classifiers built on cue-period neural activity could cross-decode attended target-evoked neural activity in visual cortex and vice versa, (4) higher pattern similarity across cue and target periods, as indexed by cross-decoding accuracy, was correlated with better behavioral performance, (5) the strength of cue-evoked multivariate functional connectivity patterns in visual cortex was positively correlated with behavioral performance, and (6) cue-evoked multivariate functional connectivity patterns were similar to those evoked by the attended targets, and higher connectivity pattern similarity across cue and target periods was correlated with better behavioral performance. These results suggest that top-down attention control enables the formation of (1) a spatial attention template at the level of individual visual cortical areas and (2) an attention network template across visual areas, and these neural patterns support stimulus selection likely via a template matching mechanism at both area and pathway levels.

Play is a fundamental aspect of childhood and plays a crucial role in the development of creativity, yet its neural mechanisms remain poorly understood. We tested the hypothesis that more frequent play is associated with stronger functional integration among the default mode network (DMN), executive control network (CN), and salience network (SAL), as these cortical networks have been implicated in creativity in adults. In a preregistered study of infants and toddlers (Study 1; N = 143, 10 months-3 years, 67 boys, Baby Connectome Project), parent-reported play and imitation behaviors increased sharply from 1 to 2 years, and were associated with stronger within-DMN connectivity and DMN-CN coupling, controlling for age, sex, and head motion. In middle childhood (Study 2; N = 108, ages 4-11 years, 52 boys), parent-reported play frequency declined with age, as did cross-network coupling involving SAL. However, children who engaged more frequently in play showed higher DMN-SAL and CN-SAL connectivity. Finally, in a quasi-experimental comparison (Study 3; N = 45; ages 4-12 years, 20 boys), children enrolled in a curriculum that includes guided play (Montessori) showed higher DMN-SAL and DMN-CN connectivity than peers in traditional schools, suggesting that pedagogies that center child-led exploration might enable protracted brain network integration. Across these three studies, play was consistently associated with greater integration among DMN, SAL, and CN, a pattern previously linked to creativity in adults. Our findings offer a potential mechanism linking childhood play to later creativity through its role in supporting brain integration during development. Public Significant StatementO_LIPlay is widely believed to nurture childrens creativity, yet the brain mechanisms behind this link are not well understood. C_LIO_LIAcross three studies from infancy to middle childhood, we found that more frequent play was associated with stronger integration among brain networks tied to imagination, attention, and control. C_LIO_LIThese findings suggest that play may help build the neural foundation for later creative thinking. C_LI

Salient sensory stimuli are known to evoke neural activations across distributed brain regions. However, the temporal dynamics of these responses over sub-second timescales remain poorly understood, in part due to limitations in the temporal resolution of non-invasive neuroimaging methods. We examined the spatiotemporal dynamics of neural activations evoked by salient sensory stimuli (rare sounds) using 1,194 widely distributed intracranial electrodes in 5 neurosurgical patients. Salient stimuli preferentially activated 263 of 1,194 electrodes (22%), with responses segregating into two largely distinct spatiotemporal patterns: (1) phasic activation in sensorimotor regions, and (2) sustained activation within the salience network. Cross-correlation analysis revealed that phasic sensorimotor activation preceded sustained salience network activation on a trial-by-trial basis. These findings support an updated view of salience processing in the human brain, revealing that salient stimuli evoke two sequential stages of neural activation--phasic sensorimotor responses followed by sustained salience network activity--rather than simultaneous widespread activation.

The relationship between neonatal brain activity patterns and later cognitive development has become a central topic in developmental neuroscience. Addressing this question requires whole-brain analytical approaches capable of identifying which large-scale functional systems carry stable and generalizable predictive signals. However, most existing studies remain focused on specific brain regions or localized functional circuits, such as thalamocortical pathways and amygdala-centered emotional networks. While these region-specific investigations have provided important insights, they are inherently limited in terms of robustness and cross-sample generalizability. As a result, systematic evidence identifying which large-scale functional systems reliably support stable and generalizable predictive signals remains scarce. Overcoming the methodological constraints of conventional whole-brain analytical paradigms has therefore become a key bottleneck in advancing our understanding of how early brain activity patterns relate to subsequent cognitive development. Here, using data from 402 infants in the developing Human Connectome Project (278 term-born; 124 preterm-born), we introduce a region-of-interest (ROI)-constrained variant of Connectome-Based Predictive Modeling (CPM) that incorporates ROI-degree-guided feature selection to predict 18-month Bayley-III cognitive, language, and motor outcomes. Model performance declined as progressively lower-degree regions were included, indicating that conventional whole-connectome CPM may obscure robust predictive signals by incorporating low signal-to-noise (SNR) features. Our models robustly predicted cognitive, language, and motor outcomes at 18 months of age. Cohort-specific connectivity patterns emerged. In term-born infants, dominant predictive features were concentrated in visual-auditory interactions, as well as connections between visual and auditory networks and other cortical regions. Interhemispheric and intrahemispheric connections contributed in roughly equal proportions. In contrast, among preterm infants, predictive features were primarily concentrated in connectivity involving auditory and temporoparietal networks, with interhemispheric connections comprising approximately twice the number of intrahemispheric connections. The whole-cohort model (term + preterm) reflected the combined contributions of both term- and preterm-associated connectivity patterns. Predictions generalized across Bayley composite and subscale scores and were supported by permutation testing and held-out validation. These findings identify early sensory hubs--particularly visual and auditory regions--as promising early biomarkers for later neurodevelopmental outcomes. Furthermore, they demonstrate that ROI-constrained CPM can reveal meaningful predictive signals that may be obscured by conventional connectome-wide approaches.

Most evidence on age-related network topology derives from resting-state paradigms, leaving unclear how aging alters brain organization during naturalistic processing and whether graph-theoretical metrics relate to emotional and cognitive functioning in ecologically valid contexts. We analyzed movie-fMRI and behavioral data from 72 younger and 68 older adults, examining global (small-worldness, clustering coefficient, characteristic path length), network (participation coefficient), and nodal (degree centrality, betweenness centrality, nodal efficiency) properties. Regression models were used to test associations between nodal measures and both the Emotional Resilience Index (ERI) and the Cognitive Function Index (CFI), while mediation analyses were conducted to test whether nodal measures mediate the relationship between age and ERI. Older adults exhibited increased characteristic path length and clustering coefficient, indicating reduced global integration and greater local segregation. Although small-world organization was preserved in two groups, there was less pronounced small-world architecture in older adults compared to younger adults, suggesting a shift toward more regularized, locally clustered networks and reduced long-range connections during dynamic stimuli. Participation coefficient values were higher in the somatomotor, frontoparietal, and default mode networks, and lower in the subcortical network, among older adults reflecting greater between-network integration in cortical networks but diminished subcortical coordination in aging. Five key nodes, two thalamic regions, hippocampus, and two insular regions, showed reduced centrality and efficiency in older adults during the negative movie, indicating weakened dominance of subcortical hubs under emotional salience condition. Right thalamic nodal properties were negatively associated with ERI and CFI and served as mediators in the relationship between age and emotional resilience. These findings suggest that reduced thalamic hub centrality may reflect adaptive recalibration of salience emotional processing, linking network reorganization to improved emotional resilience in aging. Key pointsO_LIOlder adults showed higher path length and clustering, suggesting reduced integration. C_LIO_LIReduced small-worldness reflects weaker balance of segregation and integration with age. C_LIO_LIOlder adults showed higher cortical but lower subcortical participation coefficients. C_LIO_LIKey nodes showed reduced centrality during negative stimuli, indicating weaker hubs. C_LIO_LIRight thalamus changes linked to resilience, mediating age-emotion relationships. C_LI

Previous studies exploring the connection between early language development and brain anatomy have shown that cortical areas relating to individual differences in language skills are diverse and vary depending on the age of child. However, due to lack of large longitudinal samples, current literature is limited in answering the extent to which individual differences in language development prior to school age are reflected in areas of the cortex. To fill this gap, we compared gray matter density between participants that belonged to different longitudinally defined language profiles from 14 months to five years of age in a large population-based sample. Participants were 166 children from the FinnBrain Birth Cohort Study who had longitudinal language data from 14 months to five years of age and magnetic resonance imaging data at five years of age. Three groups of language development were used as per our prior study: persistent low, stable average, and stable high. Voxel-based morphometry metrics were calculated using SPM12 and the three language profile groups were compared to one another. Covariates included sex and age at brain scan. The statistics were thresholded at p < 0.01 and false discovery rate corrected at the cluster level. Of the three longitudinal language profiles, the stable high group had higher gray matter density than the persistent low group in the right superior frontal gyrus. No differences were found between the stable average and stable high groups, nor persistent low and stable average groups. The identified superior frontal cortical area belongs to executive functions neural network. This finding adds to the cumulating evidence that individual differences in language development are reflected in growth of gray matter supporting general processing ability rather than specialized language regions. The results suggest that cognitive development and early language development are linked through shared principles of neural growth, identifiable already at age five. Key pointsO_LIAn association between early language development from 14 months to five years of age and gray matter density differences of the right superior frontal gyrus was found at the age of five years. Children following the strongest language trajectory were more likely to exhibit higher gray matter density of the right superior frontal gyrus than children following the weakest trajectory. C_LIO_LIAs the superior frontal gyrus is part of executive functions network, we propose that individual differences in early language development are more defined by general learning mechanisms supported by those networks, rather than language specific pathways. C_LI

Human attention is inherently transient and limited in span to only a few moments without lapsing. The intrinsic dynamics of large-scale neurocognitive networks are thought to contribute to these lapses and result in the unavoidable fluctuations in attention that constrain its span. However, it remains unclear how the millisecond temporal dynamics of specific electrophysiological brain states contribute to the endogenous maintenance of attention or the onset of attentional lapses. In the present study, we investigated whether the strength and millisecond dynamics of brain electric microstates differentiate states of focus from inattention and contribute to the endogenous maintenance of attention over short and long timescales. We recorded 128-channel EEG while participants maintained their attention during the wait time delay of trials in the Sustained Attention to Cue Task (SACT) and segmented the EEG into a categorized time series of microstates based on data-driven clustering of topographic voltage patterns. The findings revealed that the prevalence and rate of occurrence of microstates C and E in the wait time delay of trials differentiated trials in which the target stimulus was correctly detected from incorrectly detected. These same microstates were also implicated in the maintenance of attention over short and long timescales, with their time-varying dynamics changing systematically during the wait time delay of trials and over the course of the task session. Together, these findings demonstrate the sensitivity of microstates to variation in attentional states and suggest that the millisecond dynamics of these brain states contribute to the maintenance of attention over time.

Glutamatergic neuronal synapses in the mouse neocortex mature during the first two months after birth. A key event during synaptic maturation is a change in short-term synaptic plasticity (STP), i.e. a switch from strong synaptic depression to a weaker depression or even facilitation. Glutamatergic pyramidal neurons located in the cortical layers II/III, layer V, and layer VI project axons through the corpus callosum where they release glutamate along their shafts and form glutamatergic synapses with oligodendrocyte precursor cells (OPCs). Here, we used single-cell electrophysiological recordings in brain slices to investigate synaptic plasticity at neuron-OPC synapses along axonal shafts in the white matter, and applied computation approaches to pinpoint the mechanisms of this plasticity. We found that during postnatal development of mice, there is a switch from short-term synaptic depression to short-term synaptic facilitation at glutamatergic neuron-OPC synapses in the corpus callosum. Synaptic delay of phasic neuron-OPC excitatory postsynaptic current shortens, and the amount of asynchronous release at neuron-OPC synapses decrease as animals mature, indicating that glutamate release becomes more synchronized. Our computational modelling suggests that both pre- and postsynaptic changes may contribute to the functional development and changes of plasticity at neuron-OPC synapses in the white matter. Taking together, our findings indicate that synaptic release machineries located at different sites along the same axon (i.e. axonal shaft in the white matter vs synaptic boutons in the grey matter) mature in a very similar fashion, STP occurs at both synaptic sites, and STP dynamics represent an important event during brain maturation.

Our daily experiences unfold as a continuous stream, yet we perceive and remember them as discrete events. Event boundaries, the moments of transition between these events, are known to elicit increases in hippocampal activity believed to reflect memory encoding. However, it remains unknown how this hippocampal response relates to large-scale brain dynamics. Here, using fMRI data from two independent datasets (Sherlock and StudyFor-rest), we applied the Greedy State Boundary Search (GSBS) algorithm to whole-brain activity patterns and identified two recurring global brain states corresponding to the Default Mode Network (DMN) and Task-Positive Network (TPN). We found that event boundaries were associated with an increased probability of being in the TPN state, and that hippocampal activity was generally higher during TPN states. The hippocampal response to event boundaries appeared predominantly during TPN states. When overall state-related differences in baseline hippocampal activity were controlled for, event boundaries elicited a hippocampal response regardless of the concurrent global state. Critically, individual differences in the tendency to shift toward the TPN state at event boundaries; as well as overall time spent at the DMN state predicted subsequent memory for narrative content, whereas univariate hippocampal activity at boundaries did not. These findings demonstrate that hippocampal event boundary responses are modulated by global brain state dynamics, and suggest that the interplay between large-scale network configurations and event segmentation plays a key role in how continuous experience is encoded into memory.

Pain captures attention and interferes with executive and motor processes but task performance may be preserved at the cost of more effort. In a preregistered fMRI study, 40 participants performed a visuomotor force-matching task at two force levels under individually calibrated painful or non-painful thermal stimulation, while reporting the intensity of perceived effort. Maintaining task performance under pain was associated with increased perceived effort and recruited brain regions involved in pain modulation and cognitive control. Region-of-interest analysis showed perceived effort was consistently linked to decreased anterior midcingulate cortex activity, whereas supplementary motor area contributions varied depending on its role in motor execution or pain processing. Across experimental condition, motor, pain-modulatory and cognitive-control regions were associated with effort perception. Independently of condition, effort perception was modulated by ventromedial prefrontal cortex and ventral striatum. These findings indicate that effort perception reflects brain activity within areas involved in motor, executive and valuation processes.

Reading is a complex skill with a well-characterized neural basis. Multivariate fMRI analyses have deepened our neuroscientific understanding of literacy by linking neural patterns to behavioral traits. Although task-based fMRI often outperforms resting-state fMRI in predicting cognitive traits, few studies have applied it to continuous measures of childrens reading ability. To identify neural markers of literacy, we compared predictive performance across multiple fMRI tasks and reading-related measures. In this data-driven study, we predicted literacy skills in school-aged children (6.7-10.3 years) from eleven behavioral scores grouped into Reading (fluency and comprehension), Verbal (vocabulary knowledge and verbal intelligence), and Naming (object naming speed). Predictive performance was examined across four fMRI tasks completed by subgroups of children (n = 73-97): two active tasks - phonological-lexical decisions (PhonLex) and audiovisual character learning (Learn) - and two passive tasks - word and face viewing (Localizer) and character processing (CharProc). Individual activation contrast maps, categorized as simple (single condition) or subtractive (condition contrasts), were analyzed using a machine learning model with whole-brain predictors derived from principal component analysis. Results showed the highest predictive performance for Reading and Naming with PhonLex > Learn > Localizer = CharProc, and for Verbal with PhonLex = Learn > Localizer = CharProc. Simple contrasts generally outperformed subtractive contrasts in predicting behavioral scores. Key neural predictors, identified through whole-brain and region-of-interest analyses, included the left inferior frontal gyrus, supramarginal gyrus, ventral occipitotemporal cortex, insula, and default mode network regions. Together, these findings indicate that, for predicting literacy traits in children, active tasks and tasks that engage brain systems involved in multisensory learning tend to outperform both passive paradigms and simple subtractive task contrasts. This study provides a methodological benchmark for brain-based prediction of reading ability and highlights the value of activation heterogeneity across distributed regions as a potential marker for tracking literacy development over time.

Children with developmental language disorder (DLD) have persistent language learning difficulties and often perform poorly on pseudoword repetition, a task that probes phonological, memory, and speech-motor processes that support vocabulary acquisition. Research on the neural basis of pseudoword repetition in DLD is limited. We used whole-brain functional MRI (fMRI) to examine pseudoword repetition and repetition-based learning in 46 children with DLD (ages 10-15 years) and 71 age-matched children with typical language development. During scanning, children heard and repeated pseudowords paired with visual referents, allowing us to track learning-related changes in neural activity across repetitions. Repeated pseudoword production yielded comparable behavioural learning across groups, with faster productions by later repetitions. Post-scan, form-referent recognition was comparable across groups, whereas pseudoword repetition accuracy was lower in DLD. Pseudoword repetition engaged a distributed neural network, including inferior frontal cortex bilaterally (greater on the left), premotor and sensorimotor cortex, and posterior temporal and occipital regions. Group differences emerged primarily in regions where activity was task negative (i.e., below baseline or deactivated): lateral occipito-parietal cortex (posterior angular gyrus), medial parieto-occipital cortex (retrosplenial), and right posterior cingulate cortex. Learning-related decreases in activity were similar across groups, but region-of-interest analyses showed reduced leftward lateralisation of activity in inferior frontal gyrus in DLD. These findings suggest weaker disengagement of the default mode network during a linguistically demanding task in DLD. Although repetition-based pseudoword learning recruited similar neural mechanisms in both groups, these mechanisms may operate less efficiently in DLD, alongside reduced hemispheric specialisation in inferior frontal cortex. HighlightsO_LISimilar repetition-related neural attenuation across groups during pseudoword learning. C_LIO_LIReduced default-mode network suppression during pseudoword repetition in DLD. C_LIO_LIReduced left-hemisphere specialisation of inferior frontal cortex in DLD. C_LIO_LIRepetition-based learning in DLD supported by less efficient neural networks. C_LI

A central goal of neuroscience is to understand how neural circuit architecture supports learning. While recent work has clarified the computational role of depth in sensory cortical hierarchies, it remains unclear why predominantly feedforward, non-convolutional circuits such as the cerebellum and olfactory system also contain multiple processing layers. Theoretical work in deep learning has shown that two-hidden-layer networks can achieve classification capacity that scales quadratically with the number of intermediate neurons, but these results rely on nonlocal synaptic optimization and are therefore difficult to reconcile with biological learning rules. Here, we show analytically and numerically that a two-hidden-layer network with feedforward gating can achieve quadratic capacity using local three-factor Hebbian learning when intermediate activity is sparse. This architecture supports efficient one-shot learning and, in settings where backpropagation requires many repeated weight updates, offers an advantage in learning speed. Beyond random perceptron tasks, the model also performs well on structured cerebellum-related tasks, including reinforcement-learning-based motor control. Mapping the model onto cerebellar microcircuitry further suggests functional roles for dendritic compartmentalization, branch-specific inhibition, and disinhibitory interneuron pathways. Together, these results extend the Marr-Albus-Ito framework by showing how the presence of multiple intermediate layers in cerebellum-like circuits can support fast, local, and high-capacity learning.

A fundamental goal in neuroscience is to understand how the brains physical architecture supports complex functional dynamics. While the relationship between structural connectivity and pairwise functional connectivity has been extensively studied, the anatomical basis of higher-order interactions remains poorly understood. In this study, we use multivariate information theory -specifically the O-information- to investigate how the human connectome constrains subsets of brain regions characterized by predominantly redundant or synergistic information sharing. By analyzing the topology and community embedding of these subsets, we reveal two different structural profiles. Redundant subsets are characterized by high internal connection density and strong weights. Their nodes have high clustering and occupy globally less central positions. In contrast, synergistic subsets consist of globally central nodes with high betweenness centrality. We further demonstrate that leveraging these structural features, in particular node centrality, significantly improves the identification of synergistic subsets compared to random sampling. Together, these results demonstrate that the human connectome imposes specific constraints on higher-order information sharing, extending structure-function relationships beyond pairwise interactions and providing new insight into the structural origins of multivariate functional organization.

Recent advances have shown that the complexity of neural signals tracks global states of consciousness, such as wakefulness versus sleep. However, it is still unclear to what extent neural complexity reflects fine-grained changes in conscious content within the same global state. Here, we investigate how the complexity of brain signals is affected by increased perceptual clarity of a stimulus. To this end, we estimated neural signal complexity using Complexity via State-space Entropy Rate (CSER) to EEG recordings from an auditory discrimination task. In this paradigm, auditory stimuli were presented at varying signal-to-noise ratios (SNRs), with higher SNRs corresponding to greater subjective audibility and perceptual clarity, enabling us to relate neural complexity to graded perceptual awareness within a constant global state of consciousness. Our results showed that, while broadband CSER remains constant across SNRs, its spectral decomposition displays frequency-specific effects, with higher SNRs associated with a decreased complexity in and {beta} bands, increased complexity in{delta} , and no significant changes in{gamma} . Additionally, a temporal investigation of CSER exhibited a significant increase in complexity with stimulus clarity, with deviations from baseline peaking approximately 30 ms before the ERP. Extending this analysis to pairs of brain regions, mutual information rate uncovered a sudden post-stimulus breakdown in long-range information transmission relative to baseline. Taken together, these results reveal that while aggregated complexity measures track global states of consciousness, time- and frequency-resolved information-theoretic measures can capture variations in perceptual awareness, demonstrating their sensitivity as estimators of the level of conscious experience.

Response inhibition is a critical cognitive process that is not fully mature at the time of puberty but continues to improve during adolescence. To understand the neural basis of the maturation process, we obtained longitudinal behavioral, neurophysiological, and imaging data in macaque monkeys as they aged through adolescence. Behavioral performance in several variants of the antisaccade task improved markedly through this period. Neural activity in the prefrontal cortex generally increased, particularly when synchronized to the saccade generation. Trajectories of neural activity and cognitive performance were well predicted by maturation of long-distance white matter tracts connecting the frontal lobe with other brain areas. Our results link the maturation of response inhibition and prefrontal neural activity changes to white matter maturation.

Short-interval intracortical inhibition (SICI) is the most widely used neurophysiological index of GABAergic inhibition in the human cortex. However, it is an indirect measure, inferring synaptic inhibition from suppression of peripherally recorded motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS). In the standard protocol, a subthreshold conditioning pulse suppresses the MEP evoked by a suprathreshold test pulse delivered 1-5 ms later. Interpretation is further complicated by temporal overlap with short-interval intracortical facilitation (SICF), reflecting excitatory interactions at interstimulus intervals of [~]1.5 and 2.7 ms. To overcome these limitations, we recorded immediate TMS-evoked EEG potentials (iTEPs; 1-10 ms post-stimulus) as a more direct measure of motor cortical activity in 16 healthy volunteers (20-35 years; 7 male). The conventional SICI protocol suppressed only later components of the iTEP, likely corresponding to late corticospinal volleys previously identified in epidural spinal recordings after suprathreshold TMS, while the earliest iTEP component was unaffected. Importantly, later iTEPs were suppressed to a similar extent whether conditioning-test intervals coincided with SICF peaks or troughs, and the magnitude of iTEP suppression correlated with concurrently recorded paired-pulse MEP suppression. SICI also reduced an early TEP component (N15; 10-20 ms), but paired-pulse N15 suppression showed a different dependence on stimulus intensity and did not correlate with MEP suppression. These findings demonstrate that SICI measured via MEPs does not reflect a global index of cortical GABAergic motor cortical inhibition but instead reflects inhibition within specific cortical circuits that can be investigated directly with iTEPs.

Human behavior is highly flexible, allowing efficient performance across a wide range of task contexts. A distributed set of frontal and parietal regions, commonly termed the multiple-demand network (MDN), is consistently engaged during diverse cognitively demanding tasks and is thought to support this flexibility. However, it remains unclear how patterns of MDN engagement relate to the qualitative features of ongoing cognition experienced during task performance. To address this issue, we examined the reliability of self-reported experiential features sampled during performance of a broad range of tasks. Across tasks, we found little evidence that particular patterns of thought were intrinsically more reliable than others, nor that individual tasks were associated with stable, characteristic thought profiles. Instead, the reliability of specific experiential features varied systematically across task contexts, with the same patterns showing high stability in some tasks and low stability in others. We next asked whether stable patterns of thought were associated with distinct neural signatures. We found that patterns of brain activity resembling the MDN tended to be present for tasks in which deliberate task focus was high, and when distraction was lower, adding to an emerging body of research suggesting that coordinated activity within frontal and parietal regions helps to establish a stable goal-focused mode of thoughts and actions.