Learning environments are associated with developmental trajectories of thalamocortical attention circuits in childhood

Learning environments are associated with developmental trajectories of thalamocortical attention circuits in childhood | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Learning environments are associated with developmental trajectories of thalamocortical attention circuits in childhood Tamara Mino-Matot, Hamza Kebiri, João Jorge, Jean-Baptiste Ledoux, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9269326/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Educational environments place sustained demands on attention and self-regulation during a period of rapid brain development. Yet it remains unclear whether everyday schooling contexts are associated with differences in the maturation of neural systems supporting these processes. The thalamus plays a central role in sensory gating and large-scale cortical communication, but its developmental organization in relation to real-world learning environments has rarely been examined. Here, we analyzed high-temporal resolution resting-state fMRI (TR = 500 ms) from 72 children aged 4–15 years enrolled in Montessori or traditional classrooms. Using a pediatric data-driven parcellation approach based on voxelwise functional connectivity, we identified five spatially coherent thalamic territories with distinct large-scale connectivity profiles, primarily linked to visual, sensorimotor, and attention networks. Connectivity analyses revealed selective associations with educational context. In a thalamic territory preferentially coupled to the ventral attention system, Montessori enrollment was associated with stronger thalamocortical connectivity and a steeper age-related increase across development. No clusters showed enhanced connectivity with executive-control or default-mode networks, consistent with their later maturation. These effects were robust to motion and demographic controls. Together, these findings suggest that schooling context co-varies with the developmental refinement of thalamic circuits supporting salience detection and the selective prioritization of information during learning. While causal direction cannot be inferred, the results identify a tractable neural pathway through which everyday learning environments may relate to developing brain systems underlying attention and adaptive behaviour. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Research Highlights Pediatric thalamic parcellation reveals distinct functional connectivity profiles across thalamocortical networks. Educational environments are associated with differences in thalamocortical connectivity during childhood. Montessori schooling shows a steeper developmental trajectory of connectivity within attention-related circuits. Results suggest that everyday learning environments may influence maturation of neural systems supporting attention. Introduction The thalamus is a central regulator of cortical computation in development, shaping how information is prioritized, routed, and sustained across large-scale networks (Hwang et al., 2017 ; Shine et al., 2023 ). Rather than a passive relay, thalamic circuits dynamically gate sensory and cortico-cortical signals, supporting the emergence of executive control, goal-directed behavior, and sustained attention. These channels are present early, yet undergo protracted refinement through childhood and adolescence, tracking the maturation of higher-order cognition (de Bourbon-Teles et al., 2014 ; Hwang et al., 2022 ). This maturational window coincides with the school years, i.e., an intensive, socially embedded context with distinct attentional demands, sensory contingencies, and autonomy structures, yet links between everyday pedagogy and thalamocortical organization remain unclear. Given the thalamus’s role in sensory integration and attentional gating (Bandiera & Molnár, 2022 ; Wolff et al., 2021 ), its connectivity may be especially sensitive to practices that differentially emphasize self-paced exploration (e.g., Montessori education) versus externally imposed structure (e.g., traditional education). Anatomically, the thalamus comprises multiple nuclei with distinct projections to cortex, subcortex, and cerebellum (Hwang et al., 2022 ), including First-Order relays and Higher-Order nuclei that mediate cortico-cortical communication (Guillery, 1995 ; Wolff et al., 2021 ). This architecture is sculpted by the thalamic reticular nucleus (TRN) and by cerebello-basal ganglia loops (Li et al., 2020 ; Shine et al., 2023 ), enabling coordination of sensorimotor processing, learning, memory consolidation, and cognitive control (Najdenovska et al., 2018 ; Nakajima & Halassa, 2017 ). Perturbations to these circuits carry developmental costs in both animal models and humans, implicating thalamic organization as a bottleneck for downstream cognition: TRN disruption produces hyperactivity and attentional dysregulation in rodents (Wells et al., 2016 ; Li et al., 2020 ), and atypical thalamic structure/connectivity is associated with dyslexia, ADHD, and learning difficulties in humans (Ivanov et al., 2010 ; Müller-Axt et al., 2024 ). Converging evidence also indicates that higher-order thalamic nuclei (e.g., pulvinar, mediodorsal) modulate inter-areal communication during attention and learning (Halassa & Kastner, 2017 ), and that thalamocortical coupling shows experience-related reweighting across development (de Bourbon-Teles et al., 2014 ). Within this framework, schooling contexts that differentially engage attentional, memory, and sensorimotor demands, such as Montessori’s multisensory, embodied, child-led approach (Marshall, 2017 ), are associated with enhancements in multisensory integration, semantic memory, creativity, and functional stability in attention and sensorimotor networks (Denervaud et al., 2020 , 2021; Zanchi et al., 2024 ), motivating the hypothesis that pedagogy may be associated with distinct thalamic connectivity profile during development. Progress on experience-related differences has been limited by methods that transfer adult atlases to children and may obscure age-specific thalamic heterogeneity. We therefore adopt a pediatric, data-driven functional parcellation of the thalamus for resting-state fMRI, allowing the data to determine parcel boundaries. Building on an infant-derived k-means framework (KNIT; Kebiri et al., 2025 ), we (i) derive voxelwise thalamus-to–parcel connectivity by correlating each thalamic voxet with parcel-wise mean time series from 506 brain regions (cortex + subcortex; Lausanne2018 atlas), using a child-optimized acquisition and robust preprocessing; (ii) allow the data to determine the number and boundaries of thalamic territories via an a priori multi-granularity search over K with objective selection criteria (spatial coherence, hemispheric symmetry, functional specificity); and (iii) link each subregion’s large-scale “fingerprint” to behaviorally relevant context via cluster-resolved modeling of pedagogy, age, and gender with multiple-comparison control. This approach moves beyond adult-atlas transfer and yields an empirically grounded map of pediatric thalamic organization suitable for testing experience-related hypotheses. We analyzed high-temporal resolution resting-state fMRI from children aged 4 to 15 years enrolled in Montessori or traditional schools. Using the pediatric-adapted KNIT pipeline, we test three hypotheses: (1) the pediatric thalamus can be robustly and interpretably subdivided using data-driven clustering; (2) thalamocortical profiles differ by schooling context in a demographically matched sample; and (3) age tracks shift in connectivity consistent with known developmental trajectories. Materials and Methods Participants This study is part of an ongoing longitudinal investigation (initiated in 2018) examining how school environments shape neurocognitive development in childhood. Participants were recruited from either Montessori or traditional school systems within the region. Inclusion criteria required active enrollment in one of these educational settings. Children with reported learning disabilities or developmental disorders, as disclosed by parents or legal guardians, were excluded. All procedures were approved by the local ethics committee (Commission d’Ethique Romande CER-VD 2018 − 00244) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from legal guardians, and verbal assent was provided by all participating children. Participation was voluntary, and children were free to withdraw at any time without consequence. As compensation, participants received a gift voucher. Ninety-six children had available resting-state functional MRI (rs-fMRI) data. We restricted the sample to participants aged 4 to15 years (M = 9.78, SD = 2.58), resulting in the exclusion of 6 individuals outside this range. Following rigorous quality control procedures (see Neuroimaging Preprocessing section), 18 additional participants were excluded due to incomplete imaging data (e.g., presence of dental braces, n = 1) or excessive head motion during scanning (n = 17). The final sample comprised 72 participants (see Table 1 for demographics and group distributions). Behavioral Measures and Demographics All behavioral data were collected on the day of the MRI scan. Children completed assessments either before or after imaging in a quiet room, under the supervision of an experimenter. Variables included for this study were chronological age, gender, and fluid intelligence, measured using Raven's Progressive Matrices PM47 (Raven et al., 1998 ). While children were in the scanner, parents or guardians completed questionnaires assessing, among others, socioeconomic status (SES; Genoud, 2011 ) and interest in pedagogical practices based on a tailor-made questionnaire. These additional measures served to identify and control for potential confounding variables, especially important given that Montessori school enrollment is often associated with higher SES backgrounds (Marshall, 2017 ). All data were anonymized and checked for accuracy and completeness prior to analysis. Missing values were addressed following standard imputation procedures when appropriate. Statistical Analysis of Behavioral Measures and Demographics Group differences in age, SES, and fluid intelligence were assessed using independent samples t-tests. Gender distribution and parental interest for pedagogy were compared via chi-square analysis. All statistical analyses were conducted using jamovi ( https://www.jamovi.org/about.html , Version 2.6). Neuroimaging Acquisition All MRI data were acquired on a 3 T Siemens Prisma-Fit scanner (VE11E) with a 64-channel head coil at the University Hospital. Structural images used a 3D T1-weighted MP-RAGE sequence (sagittal; TR/TE/TI = 2000/2.47/900 ms; flip angle = 8°; FOV = 256 × 256 mm²; voxel size = 1 × 1 × 1 mm³). Resting-state fMRI consisted of a 6-min multiband EPI run (720 volumes; TR = 500 ms; TE = 33 ms; flip angle = 80°; 48 contiguous axial slices; multiband factor = 8; matrix = 94 × 94; FOV = 224 × 224 mm²; bandwidth = 2418 Hz/Px; EPI factor = 94), yielding 2.4-mm isotropic voxels. To limit motion, foam padding stabilized the head. During the rs-fMRI, participants kept their eyes open, fixated a central cross, remained still, and let their minds wander without falling asleep. Neuroimaging Preprocessing Image preprocessing and quality control were performed using fMRIPrep 20.2.1 (Esteban et al., 2019 ), a robust pipeline based on Nipype 1.5.1 (Gorgolewski et al., 2011 ). The preprocessing pipeline included skull stripping, intensity normalization, bias field correction, motion correction, coregistration to T1-weighted anatomical scans, and normalization to standard MNI space (MNI152NLin2009cAsym). To account for potential confounding effects of head motion, we included both automated and manual quality control procedures. Functional MRI quality was evaluated using FD and DVARS metrics (Power et al., 2012 ). Participants were excluded if their mean FD exceeded 0.5 mm, if DVARS indicated excessive signal variability, or if there were visible artifacts (e.g., due to dental implants or failed anatomical-functional registration). To examine whether residual motion varied systematically by demographic or experimental factors, we conducted separate ANOVAs on FD and DVARS with Pedagogy, Age, and Gender as predictors (excluding higher-order three-way interactions). Results indicated no significant effects of pedagogy, age, or gender on DVARS (all ps > .46; Table S1 ). In contrast, FD was marginally lower with increasing age (F(1,63) = 4.29, p = .043) but was not influenced by pedagogy or gender (Table S2). These findings support the interpretation that group-level differences in functional connectivity are unlikely to be driven by systematic motion artifacts. Thalamic Clustering Procedure using KNIT Model Functional parcellation of the thalamus was performed using an adaptation of the KNIT (K-means for Nuclei in Infants’ Thalamus) model (Kebiri et al., 2025 ), originally developed for neonatal populations. All analyses were implemented in Python (PyCharm 2025.1.1.1). Anatomical regions were defined according to the Lausanne2018 parcellation (506 ROIs) as implemented in Connectome Mapper v3 (Tourbier et al., 2022 ); the T1 parcellation was rigidly registered to each participant’s averaged resting-state BOLD image using ITK-SNAP (Yushkevich et al., 2006 ). A thalamic mask was generated per subject based on the parcellation. Then, functional connectivity (FC) was computed between each thalamic voxel and all parcels of the Lausanne2018-508 atlas (cortex+subcortex) by correlating voxel time series with parcel-wise mean time series. This produced individual voxel-wise FC maps. These individual maps were then Fisher z-transformed to normalize correlation values and averaged across the full cohort to generate a population-level thalamic FC map. A mean thalamic mask was derived from this group map, retaining only voxels consistently exhibiting reliable connectivity across subjects, thereby improving signal specificity. To identify distinct functional territories within the thalamus, the resulting group-level FC maps were submitted to k-means clustering. Each thalamic voxel was assigned to one of K clusters based on the similarity of its whole-brain connectivity profile. Clustering was conducted iteratively across a range of values (K = [6,7,8]) to evaluate segmentation stability and functional interpretability. This range was motivated by the known anatomical organization reporting seven major subdivisions (Najdenovska et al., 2018 ). Cluster Validation and Network Characterization The functional connectivity profile of each thalamic cluster was characterized by examining its whole-brain connectivity pattern in relation to the canonical seven-network cortical parcellation proposed by Yeo et al. (2011). Connectivity values were Fisher z-transformed to normalize the distribution and then thresholded to enhance interpretability of network showing strong connectivity (z > + 1) or strong dysconnectivity (z < -1). Clustering solutions were systematically evaluated according to three criteria: spatial coherence (including bilateral symmetry and compactness), functional specificity (defined by the distinctiveness of each cluster’s cortical connectivity signature), and anatomical plausibility (cluster size and topography). Clusters that were spatially fragmented or extremely small (voxel count < 60) were excluded from further analysis, as these likely reflected noise or unstable solutions. Functional Connectivity Analyses Functional connectivity (FC) values were aggregated within each thalamic cluster by averaging connectivity to cortical networks previously identified as significant in the voxel-wise analysis (e.g., Visual, Limbic). Separate general linear models were estimated for each cluster with Pedagogy (Traditional vs. Montessori; between-subject), Gender (Male vs. Female; between-subject), and Age (continuous, in years) as predictors. The full model included Pedagogy × Age and Pedagogy × Gender interactions; the Age × Gender and Pedagogy × Age × Gender terms were excluded to reduce overparameterization. Models were evaluated with Type III ANOVAs using the car package in R. For post hoc inference, estimated marginal means were obtained via the emmeans package, with FDR corrections applied per cluster to account for multiple effects. Data visualization was performed with ggplot2 and patchwork . To further ensure that observed FC differences were not confounded by residual motion artifacts, we conducted a control analysis on global mean FC including FD and DVARS as covariates. Neither motion metric explained significant variance in FC (both FDR-adjusted ps > .70), indicating minimal influence of head motion on the main findings (Figure S1 ). Results Behavioral Measures and Demographics Demographic and behavioral data are summarized in Table 1 . No significant differences were observed between children enrolled in Montessori versus traditional school systems with respect to gender distribution, chronological age, fluid intelligence, or socioeconomic status (all p > 0.12), indicating a matched sample across groups. Table 1. Behavioral and Demographic Characteristics of the Study Sample. Demographic and cognitive measures are presented for children enrolled in Montessori and traditional school settings. SES stands for socioeconomic status, SD stands for standard deviation. Thalamic Clustering Procedure using KNIT Model Thalamic functional parcellation was first conducted across the full sample using k-means clustering. The initial K = 7 solution (Figure S3) yielded seven distinct clusters, two of which (Clusters 1 and 2) were excluded due to low voxel count (< 35 voxels), suggestive of spurious or unstable segmentations. The remaining clusters exhibited clearly differentiated whole-brain connectivity profiles. Cluster 5 in this solution, however, displayed poor spatial coherence, appearing scattered and asymmetrical across hemispheres. To assess the robustness and granularity of the parcellation, additional clustering solutions were evaluated at K = 6 and K = 8 (Figures S2 and S4, respectively). In the K = 6 solution, two clusters were excluded due to insufficient size (< 60 voxels), whereas the K = 8 segmentation yielded the most spatially coherent and functionally interpretable configuration. The K = 8 solution reproduced the major connectivity patterns observed in K = 6, with the addition of a distinct (eighth) cluster contributing further specificity. Based on spatial compactness, hemispheric symmetry, and functional distinctiveness, the K = 8 solution was retained for all subsequent analyses (Fig. 1 ). Three clusters were excluded from subsequent analyses: clusters 3 and 6 were considered noise due to their small size (< 60 voxels); cluster 7, although slightly larger (108 voxels), appeared visually scattered and asymmetric, and was therefore also regarded as noise and removed from further analyses. Cluster Validation and Network Characterization In the K = 8 solution, five thalamic cluster exhibited a distinct and interpretable functional connectivity profile with large-scale brain networks (Fig. 1 C). Cluster 1 showed strong positive connectivity with the visual network and significant negative correlations with the limbic system, suggesting a role in sensory filtering or visual-limbic dissociation. Clusters 2 and 5 demonstrated robust connectivity with both subcortical structures and the ventral attention network, indicative of integrated thalamo-cortical involvement in salience detection and attentional control. Notably, Cluster 2 displayed anti-correlated activity with the dorsal attention system. Cluster 5 exhibited concurrent anti-correlations with both the visual and limbic networks, reflecting a potentially distinct regulatory role in balancing external sensory and internal emotional inputs. Finally, Cluster 4 showed preferential connectivity with the somatomotor and dorsal attention networks, along with negative correlations with limbic regions, suggesting a sensorimotor-attentional integration pathway modulated by affective gating. Functional Connectivity Analyses For Cluster 2, a significant main effect of Pedagogy was observed, F (1, 66) = 8.26, p FDR = .027, indicating greater FC in Montessori students compared to Traditional students. In addition, a significant Pedagogy × Age interaction emerged, F (1, 66) = 6.45, p FDR = .034. Follow-up analyses suggested that the group difference increased with age, with Montessori students showing a steeper developmental trajectory of FC in this cluster. For Cluster 5, trends were observed for both the main effect of Pedagogy, F (1, 66) = 5.69, p FDR = .082, and the Pedagogy × Age interaction, F (1, 66) = 4.75, p FDR = .082, but these did not survive FDR correction (see Fig. 2 B and 2 C). Finally, for Cluster 8, a Pedagogy × Gender interaction was observed at the uncorrected level, F (1, 66) = 4.55, p = .037, but did not remain significant after correction ( p FDR = .163). No other main effects or interactions reached significance ( ps FDR > .25) in the other Clusters. Full statistics are reported in Table S3. DISCUSSION Our data-driven parcellation and connectivity analyses suggest that thalamo-cortical organization in childhood is developmentally differentiated and varies by educational context in a cluster-specific manner. After evaluating multiple granularities, we retained the K = 8 solution because it maximized spatial compactness and hemispheric symmetry while yielding functionally interpretable profiles for 5 clusters. The K = 7 solution, although aligned with several adult reports, included a spatially scattered cluster of low anatomical plausibility, whereas K = 6 reduced specificities. This pattern is compatible with age-related shifts in optimal parcellation grain (e.g., Fan et al., 2015 ) and with elevated variability in network organization between ages 4–15 (Fair et al., 2010 ), with early pediatric cohorts often favoring coarser solutions (e.g., K ≈ 5; Kebiri et al., 2025 ) and adults finer ones (e.g., K ≈ 7). To contextualize the cluster-wise results, the K = 8 segmentation yielded dissociable, interpretable connectivity fingerprints across large-scale networks (Fig. 1 C) that align with established thalamic-cortical motifs (Segobin et al., 2024 ). Cluster 1 coupled with the visual network and anticorrelated with limbic regions, consistent with pulvinar-linked sensory selection and visual-limbic dissociation (Saalmann et al., 2012 ; Arcaro et al., 2015 ; Halassa & Kastner, 2017 ). Clusters 2, 3, and 5 jointly engaged subcortical structures and the ventral attention network, a profile associated with salience detection and reorienting (Corbetta & Shulman, 2002 , 2011; Vossel et al., 2014 ). Within this set, Cluster 2 showed anticorrelation with dorsal attention, aligning with complementary bottom-up vs. top-down control (Fox et al., 2006 ), and Cluster 3 showed negative coupling with limbic regions, consistent with models in which higher-order thalamic nuclei, particularly the pulvinar and mediodorsal complex, gate affectively salient input and regulate amygdala-cortical interactions to bias attentional routing (Halassa & Kastner, 2017 ; Saalmann et al., 2012 ; Zhou et al., 2016 ; Kragel et al., 2021 ; Mitchell, 2015 ). Cluster 5 showed concurrent anticorrelations with visual and limbic networks, a pattern consistent with balancing exteroceptive input and internal emotional context (Cortes et al., 2024 ). By contrast, Cluster 4 preferentially coupled with somatomotor and dorsal attention networks and anticorrelated with limbic regions, matching ventrolateral/ventroanterior motor thalamus involvement in sensorimotor-attentional integration (Sherman & Guillery, 2006; Hwang et al., 2017 ). Collectively, these fingerprints support a view of the pediatric thalamus as a set of specialized modulatory units rather than a unitary hub. Within this parcellation, several clusters displayed associations with pedagogy, age, and gender. The most robust effect localized to Cluster 2 (subcortical/ventral-attention coupling with dorsal-attention anticorrelation): Montessori enrollment was associated with stronger functional coupling (F(1,66) = 8.26, p FDR = .027), and the between-group difference increased with age (Pedagogy × Age: F(1,66) = 6.45, p FDR = .034). A similar, uncorrected trend appeared for Cluster 5 (dorsal attention-somatomotor), where trajectories differed by pedagogy (decreasing FC with age in traditionally schooled children, increasing in Montessori). These associations are compatible with the idea that classroom experiences co-vary with thalamic channels implicated in salience and sensorimotor–attentional integration, but they do not establish directional effects. We also observed a Pedagogy × Gender interaction in Cluster 8 (ventral thalamus) at the uncorrected level (F(1,66) = 4.55, p = .037; p FDR = .163): gender differences were evident in the traditional group and not apparent in the Montessori group. Given correction thresholds and sample size, this pattern should be considered provisional and hypothesis-generating. Absences were informative. Across solutions, resting-state thalamic coupling with default-mode (DMN) and executive-control (ECN) networks was not prominent. Adult work often reports thalamo-DMN and thalamo-prefrontal interactions (e.g., Hwang et al., 2017 ; Schmitt et al., 2017), whereas DMN specialization and ECN integration strengthen later in development (Fair et al., 2010 ; Satterthwaite et al., 2013). The present pattern is consistent with this timetable and with stronger thalamic links to sensorimotor and attention systems in childhood (Park et al., 2024 ). Future task-based studies could test whether DMN/ECN-related thalamic interactions emerge under cognitive load despite weak resting-state associations. Methodologically, three features strengthen interpretability. First, the parcellation was data-driven and pediatric-adapted, with the choice of K guided by pre-specified anatomical and functional criteria rather than adult atlas transfer. Second, clusters with low voxel counts or poor spatial coherence were excluded to prioritize biological plausibility. Third, effects were estimated at the cluster level rather than collapsed across the thalamus, respecting known heterogeneity. Still, several limitations circumscribe inference. The design is cross-sectional; thus, associations between pedagogy and connectivity cannot be interpreted as directional influences. Data were collected between 2018–2021; newer multiband acquisitions and denoising approaches (e.g., field map inclusion) could further improve small-nuclei fidelity. Resting-state measures may underestimate condition-dependent dynamics; tasks probing exogenous orienting, set reconfiguration, and affective conflict could provide complementary tests. Finally, some interactions (e.g., Pedagogy × Gender in Cluster 8; Cluster 5 trends) did not survive FDR correction and warrant confirmation in larger, preregistered samples. By combining pediatric data-driven thalamic mapping with variation in real-world educational environments, the present study identifies selective associations between schooling context and the maturation of thalamocortical circuits supporting attention and sensorimotor integration. Rather than reflecting global differences in brain organization, these effects appear localized to specific attention-related thalamic channels, consistent with models in which the thalamus dynamically regulates information flow across cortical networks during learning. These findings suggest that everyday learning environments may relate to the developmental refinement of neural systems that prioritize and route information during cognition. Longitudinal and task-based studies will be necessary to determine whether educational experiences contribute causally to these developmental trajectories. Declarations FUNDING STATEMENT This work was supported by grants from the Swiss National Science Foundation (grants 182602, 215641) and the Société Académique Vaudoise. Imaging was supported in part by the Centre d’Imagerie Biomédicale (CIBM) of the Université de Lausanne (UNIL), Université de Genève (UNIGE), Hôpitaux Universitaires de Genève (HUG), Centre Hospitalier Universitaire Vaudois (CHUV), Ecole Polytechnique Fédérale de Lausanne (EPFL), and the Leenaards and Jeantet Foundations. The funding sources had no role in study design, data collection, analysis, interpretation, or the decision to submit the manuscript. DATA AND CODE AVAILABILITY All analysis scripts and preprocessed data supporting the findings of this study will be made publicly available on a GitLab repository upon publication. COMPETING INTERESTS The authors declare no competing interests. LICENSE The authors opt for the Open Access Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) license. 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Journal of Neurophysiology , 76 (3), 1367–1395. https://doi.org/10.1152/jn.1996.76.3.1367 Shine, J. M., Lewis, L. D., Garrett, D. D., & Hwang, K. (2023). The impact of the human thalamus on brain-wide information processing. Nature Reviews Neuroscience , 24 (7), 416–430. https://doi.org/10.1038/s41583-023-00701-0 The jamovi project (2025). jamovi (Version 2.6) [Computer Software]. Retrieved from https://www.jamovi.org Tourbier, S., Rue-Queralt, J., Glomb, K., Aleman-Gomez, Y., Mullier, E., Griffa, A., Schöttner, M., Wirsich, J., Tuncel, M. A., Jancovic, J., Cuadra, M. B., & Hagmann, P. (2022). Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome Mapping. Journal of Open Source Software , 7 (74), 4248. https://doi.org/10.21105/joss.04248 Vossel, S., Geng, J. J., & Fink, G. R. (2014). Dorsal and Ventral Attention Systems. The Neuroscientist , 20 (2), 150–159. https://doi.org/10.1177/1073858413494269 Wells, M. F., Wimmer, R. D., Schmitt, L. I., Feng, G., & Halassa, M. M. (2016). Thalamic reticular impairment underlies attention deficit in Ptchd1(Y/-) mice. Nature , 532 (7597), 58–63. https://doi.org/10.1038/nature17427 Wolff, M., Morceau, S., Folkard, R., Martin-Cortecero, J., & Groh, A. (2021). A thalamic bridge from sensory perception to cognition. Neuroscience & Biobehavioral Reviews , 120 , 222–235. https://doi.org/10.1016/j.neubiorev.2020.11.013 Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011 Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage , 31 (3), 1116–1128. https://doi.org/10.1016/j.neuroimage.2006.01.015 Zanchi, P., Mullier, E., Fornari, E., Guerrier de Dumast, P., Alemán-Gómez, Y., Ledoux, J.-B., Beaty, R., Hagmann, P., & Denervaud, S. (2024). Differences in spatiotemporal brain network dynamics of Montessori and traditionally schooled students. NPJ Science of Learning , 9 (1), 45. https://doi.org/10.1038/s41539-024-00254-6 Zhou, H., Schafer, R. J., & Desimone, R. (2016). Pulvinar-Cortex Interactions in Vision and Attention. Neuron, 89(1), 209–220. https://doi.org/10.1016/j.neuron.2015.11.034 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9269326","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633640495,"identity":"09bfc9b0-b22e-4db2-869e-70d90f8ee72e","order_by":0,"name":"Tamara Mino-Matot","email":"","orcid":"","institution":"Swiss Federal Institute of Technology Lausanne (EPFL)","correspondingAuthor":false,"prefix":"","firstName":"Tamara","middleName":"","lastName":"Mino-Matot","suffix":""},{"id":633640496,"identity":"0217af88-88d1-45bb-94d3-4f9669d746eb","order_by":1,"name":"Hamza Kebiri","email":"","orcid":"","institution":"Lausanne University Hospital and University of Lausanne (CHUV-UNIL)","correspondingAuthor":false,"prefix":"","firstName":"Hamza","middleName":"","lastName":"Kebiri","suffix":""},{"id":633640499,"identity":"29ceba26-ce6b-4cbb-b36f-8813403df429","order_by":2,"name":"João Jorge","email":"","orcid":"","institution":"CSEM – Swiss Center for Electronics and Microtechnology","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"","lastName":"Jorge","suffix":""},{"id":633640501,"identity":"bdd1af60-7cdc-4a58-8d63-d42d031435c1","order_by":3,"name":"Jean-Baptiste Ledoux","email":"","orcid":"","institution":"Lausanne University Hospital and University of Lausanne (CHUV-UNIL)","correspondingAuthor":false,"prefix":"","firstName":"Jean-Baptiste","middleName":"","lastName":"Ledoux","suffix":""},{"id":633640503,"identity":"410bf17b-0b54-4903-86a5-331306cb3f12","order_by":4,"name":"Eleonora Fornari","email":"","orcid":"","institution":"Lausanne University Hospital and University of Lausanne (CHUV-UNIL)","correspondingAuthor":false,"prefix":"","firstName":"Eleonora","middleName":"","lastName":"Fornari","suffix":""},{"id":633640504,"identity":"8b1cf91f-0d55-4e8e-a42d-884e59fd1f7d","order_by":5,"name":"Meritxell Bach Cuadra","email":"","orcid":"","institution":"Lausanne University Hospital and University of Lausanne (CHUV-UNIL)","correspondingAuthor":false,"prefix":"","firstName":"Meritxell","middleName":"Bach","lastName":"Cuadra","suffix":""},{"id":633640505,"identity":"9449f787-a42c-4c1a-b932-1f569eb4ef0d","order_by":6,"name":"Solange Denervaud","email":"data:image/png;base64,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","orcid":"","institution":"Swiss Federal Institute of Technology Lausanne (EPFL)","correspondingAuthor":true,"prefix":"","firstName":"Solange","middleName":"","lastName":"Denervaud","suffix":""}],"badges":[],"createdAt":"2026-03-30 16:02:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9269326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9269326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108942721,"identity":"d1fcea00-e2c6-43f9-b49f-519ec8589a74","added_by":"auto","created_at":"2026-05-11 05:42:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":424682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Study Design. Diagram illustrating from left to right the different steps of the study protocol. B. Functional Segmentation of the Thalamus Using K = 8 Clustering Across the Full Cohort. This panel shows the eight thalamic clusters identified through k-means clustering and their spatial distribution within the thalamus, presented as a 32-rendering visualization. Five clusters (number 1, 2, 4, 5, and 8) yielded \u003c/strong\u003espatial compactness, hemispheric symmetry, and functional distinctiveness criterion and were further used for the Functional Connectivity profile analyses. Clusters number 2, 6, and 7 were discarded.\u003cstrong\u003e C. Functional Connectivity Profiles of the 5 Thalamic Clusters resulting from the K = 8 Segmentation. \u003c/strong\u003eEach plot depicts the z-scored functional connectivity (FC) values for each of the five thalamic cluster across the seven canonical functional cortical networks and the subcortex.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9269326/v1/33a7718b2b0246a570c7c6cd.png"},{"id":108942733,"identity":"b350ecad-00f7-434d-90aa-8fde7d765395","added_by":"auto","created_at":"2026-05-11 05:42:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":572879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Yeo’s main network FC profiles per cluster. B. Estimated marginal means by Pedagogy and Gender, \u003c/strong\u003eillustrating a significant group difference in Cluster 2. \u003cstrong\u003eC.\u003c/strong\u003e \u003cstrong\u003eDevelopmental trajectories (Age × Pedagogy) per cluster, \u003c/strong\u003eillustrating the significant increasing divergence between groups in Cluster 2 only.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9269326/v1/2283d5899f2e57df85c48d15.png"},{"id":108977972,"identity":"fd552a9e-cc76-4a0d-a2a9-1987817f2d76","added_by":"auto","created_at":"2026-05-11 11:33:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1356393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9269326/v1/ca32c665-80c8-48b5-9b91-0c1de4be16d3.pdf"},{"id":108942730,"identity":"d57729d8-6d7c-491f-a7b3-5b1d07f9a644","added_by":"auto","created_at":"2026-05-11 05:42:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1552710,"visible":true,"origin":"","legend":"","description":"","filename":"20260402ThalamusnpjSciLearnSupp.docx","url":"https://assets-eu.researchsquare.com/files/rs-9269326/v1/e78dbf92fca0c4af425f9c5f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Learning environments are associated with developmental trajectories of thalamocortical attention circuits in childhood","fulltext":[{"header":"Research Highlights","content":"\u003cul\u003e\n \u003cli\u003ePediatric thalamic parcellation reveals distinct functional connectivity profiles across thalamocortical networks.\u003c/li\u003e\n \u003cli\u003eEducational environments are associated with differences in thalamocortical connectivity during childhood.\u003c/li\u003e\n \u003cli\u003eMontessori schooling shows a steeper developmental trajectory of connectivity within attention-related circuits.\u003c/li\u003e\n \u003cli\u003eResults suggest that everyday learning environments may influence maturation of neural systems supporting attention.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe thalamus is a central regulator of cortical computation in development, shaping how information is prioritized, routed, and sustained across large-scale networks (Hwang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shine et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rather than a passive relay, thalamic circuits dynamically gate sensory and cortico-cortical signals, supporting the emergence of executive control, goal-directed behavior, and sustained attention. These channels are present early, yet undergo protracted refinement through childhood and adolescence, tracking the maturation of higher-order cognition (de Bourbon-Teles et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hwang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This maturational window coincides with the school years, i.e., an intensive, socially embedded context with distinct attentional demands, sensory contingencies, and autonomy structures, yet links between everyday pedagogy and thalamocortical organization remain unclear. Given the thalamus\u0026rsquo;s role in sensory integration and attentional gating (Bandiera \u0026amp; Moln\u0026aacute;r, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wolff et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), its connectivity may be especially sensitive to practices that differentially emphasize self-paced exploration (e.g., Montessori education) versus externally imposed structure (e.g., traditional education).\u003c/p\u003e \u003cp\u003eAnatomically, the thalamus comprises multiple nuclei with distinct projections to cortex, subcortex, and cerebellum (Hwang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), including First-Order relays and Higher-Order nuclei that mediate cortico-cortical communication (Guillery, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Wolff et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This architecture is sculpted by the thalamic reticular nucleus (TRN) and by cerebello-basal ganglia loops (Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shine et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), enabling coordination of sensorimotor processing, learning, memory consolidation, and cognitive control (Najdenovska et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nakajima \u0026amp; Halassa, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Perturbations to these circuits carry developmental costs in both animal models and humans, implicating thalamic organization as a bottleneck for downstream cognition: TRN disruption produces hyperactivity and attentional dysregulation in rodents (Wells et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and atypical thalamic structure/connectivity is associated with dyslexia, ADHD, and learning difficulties in humans (Ivanov et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; M\u0026uuml;ller-Axt et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Converging evidence also indicates that higher-order thalamic nuclei (e.g., pulvinar, mediodorsal) modulate inter-areal communication during attention and learning (Halassa \u0026amp; Kastner, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and that thalamocortical coupling shows experience-related reweighting across development (de Bourbon-Teles et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Within this framework, schooling contexts that differentially engage attentional, memory, and sensorimotor demands, such as Montessori\u0026rsquo;s multisensory, embodied, child-led approach (Marshall, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), are associated with enhancements in multisensory integration, semantic memory, creativity, and functional stability in attention and sensorimotor networks (Denervaud et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, 2021; Zanchi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), motivating the hypothesis that pedagogy may be associated with distinct thalamic connectivity profile during development.\u003c/p\u003e \u003cp\u003eProgress on experience-related differences has been limited by methods that transfer adult atlases to children and may obscure age-specific thalamic heterogeneity. We therefore adopt a pediatric, data-driven functional parcellation of the thalamus for resting-state fMRI, allowing the data to determine parcel boundaries. Building on an infant-derived k-means framework (KNIT; Kebiri et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we (i) derive voxelwise thalamus-to\u0026ndash;parcel connectivity by correlating each thalamic voxet with parcel-wise mean time series from 506 brain regions (cortex\u0026thinsp;+\u0026thinsp;subcortex; Lausanne2018 atlas), using a child-optimized acquisition and robust preprocessing; (ii) allow the data to determine the number and boundaries of thalamic territories via an a priori multi-granularity search over K with objective selection criteria (spatial coherence, hemispheric symmetry, functional specificity); and (iii) link each subregion\u0026rsquo;s large-scale \u0026ldquo;fingerprint\u0026rdquo; to behaviorally relevant context via cluster-resolved modeling of pedagogy, age, and gender with multiple-comparison control. This approach moves beyond adult-atlas transfer and yields an empirically grounded map of pediatric thalamic organization suitable for testing experience-related hypotheses.\u003c/p\u003e \u003cp\u003eWe analyzed high-temporal resolution resting-state fMRI from children aged 4 to 15 years enrolled in Montessori or traditional schools. Using the pediatric-adapted KNIT pipeline, we test three hypotheses: (1) the pediatric thalamus can be robustly and interpretably subdivided using data-driven clustering; (2) thalamocortical profiles differ by schooling context in a demographically matched sample; and (3) age tracks shift in connectivity consistent with known developmental trajectories.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study is part of an ongoing longitudinal investigation (initiated in 2018) examining how school environments shape neurocognitive development in childhood. Participants were recruited from either Montessori or traditional school systems within the region. Inclusion criteria required active enrollment in one of these educational settings. Children with reported learning disabilities or developmental disorders, as disclosed by parents or legal guardians, were excluded. All procedures were approved by the local ethics committee (Commission d\u0026rsquo;Ethique Romande CER-VD 2018\u0026thinsp;\u0026minus;\u0026thinsp;00244) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from legal guardians, and verbal assent was provided by all participating children. Participation was voluntary, and children were free to withdraw at any time without consequence. As compensation, participants received a gift voucher.\u003c/p\u003e \u003cp\u003eNinety-six children had available resting-state functional MRI (rs-fMRI) data. We restricted the sample to participants aged 4 to15 years (M\u0026thinsp;=\u0026thinsp;9.78, SD\u0026thinsp;=\u0026thinsp;2.58), resulting in the exclusion of 6 individuals outside this range. Following rigorous quality control procedures (see Neuroimaging Preprocessing section), 18 additional participants were excluded due to incomplete imaging data (e.g., presence of dental braces, n\u0026thinsp;=\u0026thinsp;1) or excessive head motion during scanning (n\u0026thinsp;=\u0026thinsp;17). The final sample comprised 72 participants (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for demographics and group distributions).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBehavioral Measures and Demographics\u003c/h3\u003e\n\u003cp\u003eAll behavioral data were collected on the day of the MRI scan. Children completed assessments either before or after imaging in a quiet room, under the supervision of an experimenter. Variables included for this study were chronological age, gender, and fluid intelligence, measured using Raven's Progressive Matrices PM47 (Raven et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). While children were in the scanner, parents or guardians completed questionnaires assessing, among others, socioeconomic status (SES; Genoud, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and interest in pedagogical practices based on a tailor-made questionnaire. These additional measures served to identify and control for potential confounding variables, especially important given that Montessori school enrollment is often associated with higher SES backgrounds (Marshall, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll data were anonymized and checked for accuracy and completeness prior to analysis. Missing values were addressed following standard imputation procedures when appropriate.\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis of Behavioral Measures and Demographics\u003c/h3\u003e\n\u003cp\u003eGroup differences in age, SES, and fluid intelligence were assessed using independent samples t-tests. Gender distribution and parental interest for pedagogy were compared via chi-square analysis. All statistical analyses were conducted using \u003cem\u003ejamovi\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jamovi.org/about.html\u003c/span\u003e\u003cspan address=\"https://www.jamovi.org/about.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Version 2.6).\u003c/p\u003e\n\u003ch3\u003eNeuroimaging Acquisition\u003c/h3\u003e\n\u003cp\u003eAll MRI data were acquired on a 3 T Siemens Prisma-Fit scanner (VE11E) with a 64-channel head coil at the University Hospital. Structural images used a 3D T1-weighted MP-RAGE sequence (sagittal; TR/TE/TI\u0026thinsp;=\u0026thinsp;2000/2.47/900 ms; flip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;; FOV\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm\u0026sup2;; voxel size\u0026thinsp;=\u0026thinsp;1 \u0026times; 1 \u0026times; 1 mm\u0026sup3;). Resting-state fMRI consisted of a 6-min multiband EPI run (720 volumes; TR\u0026thinsp;=\u0026thinsp;500 ms; TE\u0026thinsp;=\u0026thinsp;33 ms; flip angle\u0026thinsp;=\u0026thinsp;80\u0026deg;; 48 contiguous axial slices; multiband factor\u0026thinsp;=\u0026thinsp;8; matrix\u0026thinsp;=\u0026thinsp;94 \u0026times; 94; FOV\u0026thinsp;=\u0026thinsp;224 \u0026times; 224 mm\u0026sup2;; bandwidth\u0026thinsp;=\u0026thinsp;2418 Hz/Px; EPI factor\u0026thinsp;=\u0026thinsp;94), yielding 2.4-mm isotropic voxels. To limit motion, foam padding stabilized the head. During the rs-fMRI, participants kept their eyes open, fixated a central cross, remained still, and let their minds wander without falling asleep.\u003c/p\u003e\n\u003ch3\u003eNeuroimaging Preprocessing\u003c/h3\u003e\n\u003cp\u003eImage preprocessing and quality control were performed using fMRIPrep 20.2.1 (Esteban et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a robust pipeline based on Nipype 1.5.1 (Gorgolewski et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The preprocessing pipeline included skull stripping, intensity normalization, bias field correction, motion correction, coregistration to T1-weighted anatomical scans, and normalization to standard MNI space (MNI152NLin2009cAsym).\u003c/p\u003e \u003cp\u003eTo account for potential confounding effects of head motion, we included both automated and manual quality control procedures. Functional MRI quality was evaluated using FD and DVARS metrics (Power et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Participants were excluded if their mean FD exceeded 0.5 mm, if DVARS indicated excessive signal variability, or if there were visible artifacts (e.g., due to dental implants or failed anatomical-functional registration). To examine whether residual motion varied systematically by demographic or experimental factors, we conducted separate ANOVAs on FD and DVARS with Pedagogy, Age, and Gender as predictors (excluding higher-order three-way interactions). Results indicated no significant effects of pedagogy, age, or gender on DVARS (all ps \u0026gt; .46; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In contrast, FD was marginally lower with increasing age (F(1,63)\u0026thinsp;=\u0026thinsp;4.29, p = .043) but was not influenced by pedagogy or gender (Table S2). These findings support the interpretation that group-level differences in functional connectivity are unlikely to be driven by systematic motion artifacts.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThalamic Clustering Procedure using KNIT Model\u003c/h2\u003e \u003cp\u003eFunctional parcellation of the thalamus was performed using an adaptation of the KNIT (K-means for Nuclei in Infants\u0026rsquo; Thalamus) model (Kebiri et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), originally developed for neonatal populations. All analyses were implemented in Python (PyCharm 2025.1.1.1).\u003c/p\u003e \u003cp\u003eAnatomical regions were defined according to the Lausanne2018 parcellation (506 ROIs) as implemented in Connectome Mapper v3 (Tourbier et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); the T1 parcellation was rigidly registered to each participant\u0026rsquo;s averaged resting-state BOLD image using ITK-SNAP (Yushkevich et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A thalamic mask was generated per subject based on the parcellation. Then, functional connectivity (FC) was computed between each thalamic voxel and all parcels of the Lausanne2018-508 atlas (cortex+subcortex) by correlating voxel time series with parcel-wise mean time series. This produced individual voxel-wise FC maps. These individual maps were then Fisher z-transformed to normalize correlation values and averaged across the full cohort to generate a population-level thalamic FC map. A mean thalamic mask was derived from this group map, retaining only voxels consistently exhibiting reliable connectivity across subjects, thereby improving signal specificity.\u003c/p\u003e \u003cp\u003eTo identify distinct functional territories within the thalamus, the resulting group-level FC maps were submitted to k-means clustering. Each thalamic voxel was assigned to one of \u003cem\u003eK\u003c/em\u003e clusters based on the similarity of its whole-brain connectivity profile. Clustering was conducted iteratively across a range of values (K = [6,7,8]) to evaluate segmentation stability and functional interpretability. This range was motivated by the known anatomical organization reporting seven major subdivisions (Najdenovska et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCluster Validation and Network Characterization\u003c/h3\u003e\n\u003cp\u003eThe functional connectivity profile of each thalamic cluster was characterized by examining its whole-brain connectivity pattern in relation to the canonical seven-network cortical parcellation proposed by Yeo et al. (2011). Connectivity values were Fisher z-transformed to normalize the distribution and then thresholded to enhance interpretability of network showing strong connectivity (z\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;1) or strong dysconnectivity (z \u0026lt; -1).\u003c/p\u003e \u003cp\u003eClustering solutions were systematically evaluated according to three criteria: spatial coherence (including bilateral symmetry and compactness), functional specificity (defined by the distinctiveness of each cluster\u0026rsquo;s cortical connectivity signature), and anatomical plausibility (cluster size and topography). Clusters that were spatially fragmented or extremely small (voxel count\u0026thinsp;\u0026lt;\u0026thinsp;60) were excluded from further analysis, as these likely reflected noise or unstable solutions.\u003c/p\u003e\n\u003ch3\u003eFunctional Connectivity Analyses\u003c/h3\u003e\n\u003cp\u003eFunctional connectivity (FC) values were aggregated within each thalamic cluster by averaging connectivity to cortical networks previously identified as significant in the voxel-wise analysis (e.g., Visual, Limbic). Separate general linear models were estimated for each cluster with Pedagogy (Traditional vs. Montessori; between-subject), Gender (Male vs. Female; between-subject), and Age (continuous, in years) as predictors. The full model included Pedagogy \u0026times; Age and Pedagogy \u0026times; Gender interactions; the Age \u0026times; Gender and Pedagogy \u0026times; Age \u0026times; Gender terms were excluded to reduce overparameterization. Models were evaluated with Type III ANOVAs using the \u003cem\u003ecar\u003c/em\u003e package in R.\u003c/p\u003e \u003cp\u003eFor post hoc inference, estimated marginal means were obtained via the \u003cem\u003eemmeans\u003c/em\u003e package, with FDR corrections applied per cluster to account for multiple effects. Data visualization was performed with \u003cem\u003eggplot2\u003c/em\u003e and \u003cem\u003epatchwork\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTo further ensure that observed FC differences were not confounded by residual motion artifacts, we conducted a control analysis on global mean FC including FD and DVARS as covariates. Neither motion metric explained significant variance in FC (both FDR-adjusted ps \u0026gt; .70), indicating minimal influence of head motion on the main findings (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":" \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eBehavioral Measures and Demographics\u003c/h2\u003e\n \u003cp\u003eDemographic and behavioral data are summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed between children enrolled in Montessori versus traditional school systems with respect to gender distribution, chronological age, fluid intelligence, or socioeconomic status (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.12), indicating a matched sample across groups.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Behavioral and Demographic Characteristics of the Study Sample.\u003c/strong\u003e Demographic and cognitive measures are presented for children enrolled in Montessori and traditional school settings. SES stands for socioeconomic status, SD stands for standard deviation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n\n\u003c/div\u003eThalamic Clustering Procedure using KNIT Model\u003c/h2\u003e \u003cp\u003eThalamic functional parcellation was first conducted across the full sample using k-means clustering. The initial K\u0026thinsp;=\u0026thinsp;7 solution (Figure S3) yielded seven distinct clusters, two of which (Clusters 1 and 2) were excluded due to low voxel count (\u0026lt;\u0026thinsp;35 voxels), suggestive of spurious or unstable segmentations. The remaining clusters exhibited clearly differentiated whole-brain connectivity profiles. Cluster 5 in this solution, however, displayed poor spatial coherence, appearing scattered and asymmetrical across hemispheres. To assess the robustness and granularity of the parcellation, additional clustering solutions were evaluated at K\u0026thinsp;=\u0026thinsp;6 and K\u0026thinsp;=\u0026thinsp;8 (Figures S2 and S4, respectively). In the K\u0026thinsp;=\u0026thinsp;6 solution, two clusters were excluded due to insufficient size (\u0026lt;\u0026thinsp;60 voxels), whereas the K\u0026thinsp;=\u0026thinsp;8 segmentation yielded the most spatially coherent and functionally interpretable configuration. The K\u0026thinsp;=\u0026thinsp;8 solution reproduced the major connectivity patterns observed in K\u0026thinsp;=\u0026thinsp;6, with the addition of a distinct (eighth) cluster contributing further specificity. Based on spatial compactness, hemispheric symmetry, and functional distinctiveness, the K\u0026thinsp;=\u0026thinsp;8 solution was retained for all subsequent analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three clusters were excluded from subsequent analyses: clusters 3 and 6 were considered noise due to their small size (\u0026lt;\u0026thinsp;60 voxels); cluster 7, although slightly larger (108 voxels), appeared visually scattered and asymmetric, and was therefore also regarded as noise and removed from further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCluster Validation and Network Characterization\u003c/h2\u003e \u003cp\u003eIn the K\u0026thinsp;=\u0026thinsp;8 solution, five thalamic cluster exhibited a distinct and interpretable functional connectivity profile with large-scale brain networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Cluster 1 showed strong positive connectivity with the visual network and significant negative correlations with the limbic system, suggesting a role in sensory filtering or visual-limbic dissociation. Clusters 2 and 5 demonstrated robust connectivity with both subcortical structures and the ventral attention network, indicative of integrated thalamo-cortical involvement in salience detection and attentional control. Notably, Cluster 2 displayed anti-correlated activity with the dorsal attention system. Cluster 5 exhibited concurrent anti-correlations with both the visual and limbic networks, reflecting a potentially distinct regulatory role in balancing external sensory and internal emotional inputs. Finally, Cluster 4 showed preferential connectivity with the somatomotor and dorsal attention networks, along with negative correlations with limbic regions, suggesting a sensorimotor-attentional integration pathway modulated by affective gating.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Connectivity Analyses\u003c/h2\u003e \u003cp\u003eFor Cluster 2, a significant main effect of Pedagogy was observed, \u003cem\u003eF\u003c/em\u003e(1, 66)\u0026thinsp;=\u0026thinsp;8.26, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = .027, indicating greater FC in Montessori students compared to Traditional students. In addition, a significant Pedagogy \u0026times; Age interaction emerged, \u003cem\u003eF\u003c/em\u003e(1, 66)\u0026thinsp;=\u0026thinsp;6.45, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = .034. Follow-up analyses suggested that the group difference increased with age, with Montessori students showing a steeper developmental trajectory of FC in this cluster. For Cluster 5, trends were observed for both the main effect of Pedagogy, \u003cem\u003eF\u003c/em\u003e(1, 66)\u0026thinsp;=\u0026thinsp;5.69, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = .082, and the Pedagogy \u0026times; Age interaction, \u003cem\u003eF\u003c/em\u003e(1, 66)\u0026thinsp;=\u0026thinsp;4.75, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = .082, but these did not survive FDR correction (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Finally, for Cluster 8, a Pedagogy \u0026times; Gender interaction was observed at the uncorrected level, \u003cem\u003eF\u003c/em\u003e(1, 66)\u0026thinsp;=\u0026thinsp;4.55, \u003cem\u003ep\u003c/em\u003e = .037, but did not remain significant after correction (\u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = .163). No other main effects or interactions reached significance (\u003cem\u003eps\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026gt; .25) in the other Clusters. Full statistics are reported in Table S3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur data-driven parcellation and connectivity analyses suggest that thalamo-cortical organization in childhood is developmentally differentiated and varies by educational context in a cluster-specific manner. After evaluating multiple granularities, we retained the K\u0026thinsp;=\u0026thinsp;8 solution because it maximized spatial compactness and hemispheric symmetry while yielding functionally interpretable profiles for 5 clusters. The K\u0026thinsp;=\u0026thinsp;7 solution, although aligned with several adult reports, included a spatially scattered cluster of low anatomical plausibility, whereas K\u0026thinsp;=\u0026thinsp;6 reduced specificities. This pattern is compatible with age-related shifts in optimal parcellation grain (e.g., Fan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and with elevated variability in network organization between ages 4\u0026ndash;15 (Fair et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), with early pediatric cohorts often favoring coarser solutions (e.g., K\u0026thinsp;\u0026asymp;\u0026thinsp;5; Kebiri et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and adults finer ones (e.g., K\u0026thinsp;\u0026asymp;\u0026thinsp;7).\u003c/p\u003e \u003cp\u003eTo contextualize the cluster-wise results, the K\u0026thinsp;=\u0026thinsp;8 segmentation yielded dissociable, interpretable connectivity fingerprints across large-scale networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) that align with established thalamic-cortical motifs (Segobin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cluster 1 coupled with the visual network and anticorrelated with limbic regions, consistent with pulvinar-linked sensory selection and visual-limbic dissociation (Saalmann et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Arcaro et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Halassa \u0026amp; Kastner, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Clusters 2, 3, and 5 jointly engaged subcortical structures and the ventral attention network, a profile associated with salience detection and reorienting (Corbetta \u0026amp; Shulman, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, 2011; Vossel et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Within this set, Cluster 2 showed anticorrelation with dorsal attention, aligning with complementary bottom-up vs. top-down control (Fox et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and Cluster 3 showed negative coupling with limbic regions, consistent with models in which higher-order thalamic nuclei, particularly the pulvinar and mediodorsal complex, gate affectively salient input and regulate amygdala-cortical interactions to bias attentional routing (Halassa \u0026amp; Kastner, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saalmann et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kragel et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mitchell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Cluster 5 showed concurrent anticorrelations with visual and limbic networks, a pattern consistent with balancing exteroceptive input and internal emotional context (Cortes et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By contrast, Cluster 4 preferentially coupled with somatomotor and dorsal attention networks and anticorrelated with limbic regions, matching ventrolateral/ventroanterior motor thalamus involvement in sensorimotor-attentional integration (Sherman \u0026amp; Guillery, 2006; Hwang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Collectively, these fingerprints support a view of the pediatric thalamus as a set of specialized modulatory units rather than a unitary hub.\u003c/p\u003e \u003cp\u003eWithin this parcellation, several clusters displayed associations with pedagogy, age, and gender. The most robust effect localized to Cluster 2 (subcortical/ventral-attention coupling with dorsal-attention anticorrelation): Montessori enrollment was associated with stronger functional coupling (F(1,66)\u0026thinsp;=\u0026thinsp;8.26, p\u003csub\u003eFDR\u003c/sub\u003e = .027), and the between-group difference increased with age (Pedagogy \u0026times; Age: F(1,66)\u0026thinsp;=\u0026thinsp;6.45, p\u003csub\u003eFDR\u003c/sub\u003e = .034). A similar, uncorrected trend appeared for Cluster 5 (dorsal attention-somatomotor), where trajectories differed by pedagogy (decreasing FC with age in traditionally schooled children, increasing in Montessori). These associations are compatible with the idea that classroom experiences co-vary with thalamic channels implicated in salience and sensorimotor\u0026ndash;attentional integration, but they do not establish directional effects. We also observed a Pedagogy \u0026times; Gender interaction in Cluster 8 (ventral thalamus) at the uncorrected level (F(1,66)\u0026thinsp;=\u0026thinsp;4.55, p = .037; p\u003csub\u003eFDR\u003c/sub\u003e = .163): gender differences were evident in the traditional group and not apparent in the Montessori group. Given correction thresholds and sample size, this pattern should be considered provisional and hypothesis-generating.\u003c/p\u003e \u003cp\u003eAbsences were informative. Across solutions, resting-state thalamic coupling with default-mode (DMN) and executive-control (ECN) networks was not prominent. Adult work often reports thalamo-DMN and thalamo-prefrontal interactions (e.g., Hwang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schmitt et al., 2017), whereas DMN specialization and ECN integration strengthen later in development (Fair et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Satterthwaite et al., 2013). The present pattern is consistent with this timetable and with stronger thalamic links to sensorimotor and attention systems in childhood (Park et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Future task-based studies could test whether DMN/ECN-related thalamic interactions emerge under cognitive load despite weak resting-state associations.\u003c/p\u003e \u003cp\u003eMethodologically, three features strengthen interpretability. First, the parcellation was data-driven and pediatric-adapted, with the choice of K guided by pre-specified anatomical and functional criteria rather than adult atlas transfer. Second, clusters with low voxel counts or poor spatial coherence were excluded to prioritize biological plausibility. Third, effects were estimated at the cluster level rather than collapsed across the thalamus, respecting known heterogeneity. Still, several limitations circumscribe inference. The design is cross-sectional; thus, associations between pedagogy and connectivity cannot be interpreted as directional influences. Data were collected between 2018\u0026ndash;2021; newer multiband acquisitions and denoising approaches (e.g., field map inclusion) could further improve small-nuclei fidelity. Resting-state measures may underestimate condition-dependent dynamics; tasks probing exogenous orienting, set reconfiguration, and affective conflict could provide complementary tests. Finally, some interactions (e.g., Pedagogy \u0026times; Gender in Cluster 8; Cluster 5 trends) did not survive FDR correction and warrant confirmation in larger, preregistered samples.\u003c/p\u003e \u003cp\u003eBy combining pediatric data-driven thalamic mapping with variation in real-world educational environments, the present study identifies selective associations between schooling context and the maturation of thalamocortical circuits supporting attention and sensorimotor integration. Rather than reflecting global differences in brain organization, these effects appear localized to specific attention-related thalamic channels, consistent with models in which the thalamus dynamically regulates information flow across cortical networks during learning. These findings suggest that everyday learning environments may relate to the developmental refinement of neural systems that prioritize and route information during cognition. Longitudinal and task-based studies will be necessary to determine whether educational experiences contribute causally to these developmental trajectories.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Swiss National Science Foundation (grants 182602, 215641) and the Soci\u0026eacute;t\u0026eacute; Acad\u0026eacute;mique Vaudoise. Imaging was supported in part by the Centre d\u0026rsquo;Imagerie Biom\u0026eacute;dicale (CIBM) of the Universit\u0026eacute; de Lausanne (UNIL), Universit\u0026eacute; de Gen\u0026egrave;ve (UNIGE), H\u0026ocirc;pitaux Universitaires de Gen\u0026egrave;ve (HUG), Centre Hospitalier Universitaire Vaudois (CHUV), Ecole Polytechnique F\u0026eacute;d\u0026eacute;rale de Lausanne (EPFL), and the Leenaards and Jeantet Foundations. The funding sources had no role in study design, data collection, analysis, interpretation, or the decision to submit the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AND CODE AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analysis scripts and preprocessed data supporting the findings of this study will be made publicly available on a GitLab repository upon publication. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLICENSE\u003c/strong\u003e\u003cbr\u003e The authors opt for the Open Access Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) license.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAI USE STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo artificial intelligence-generated content was used in the creation of the data, analyses, or scientific interpretations presented in this manuscript. Large language model tools were used only for limited editorial assistance in language refinement. All scientific content, analyses, interpretations, and conclusions were developed and verified by the authors. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAUTHORS CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.D., M.B.C., H.K. designed the study, H.K. adjusted the pipeline, T.M.M, H.K., S.D, analyzed the data and wrote the main manuscript text. All authors reviewed the manuscript.\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArcaro, M. J., Pinsk, M. A., \u0026amp; Kastner, S. (2015). The Anatomical and Functional Organization of the Human Visual Pulvinar. 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Neuron, 89(1), 209\u0026ndash;220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuron.2015.11.034\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2015.11.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-science-of-learning","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjscilearn","sideBox":"Learn more about [npj Science of Learning](http://www.nature.com/npjscilearn/)","snPcode":"41539","submissionUrl":"https://mts-npjscilearn.nature.com/cgi-bin/main.plex","title":"npj Science of Learning","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9269326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9269326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEducational environments place sustained demands on attention and self-regulation during a period of rapid brain development. Yet it remains unclear whether everyday schooling contexts are associated with differences in the maturation of neural systems supporting these processes. The thalamus plays a central role in sensory gating and large-scale cortical communication, but its developmental organization in relation to real-world learning environments has rarely been examined. Here, we analyzed high-temporal resolution resting-state fMRI (TR\u0026thinsp;=\u0026thinsp;500 ms) from 72 children aged 4\u0026ndash;15 years enrolled in Montessori or traditional classrooms. Using a pediatric data-driven parcellation approach based on voxelwise functional connectivity, we identified five spatially coherent thalamic territories with distinct large-scale connectivity profiles, primarily linked to visual, sensorimotor, and attention networks. Connectivity analyses revealed selective associations with educational context. In a thalamic territory preferentially coupled to the ventral attention system, Montessori enrollment was associated with stronger thalamocortical connectivity and a steeper age-related increase across development. No clusters showed enhanced connectivity with executive-control or default-mode networks, consistent with their later maturation. These effects were robust to motion and demographic controls. Together, these findings suggest that schooling context co-varies with the developmental refinement of thalamic circuits supporting salience detection and the selective prioritization of information during learning. While causal direction cannot be inferred, the results identify a tractable neural pathway through which everyday learning environments may relate to developing brain systems underlying attention and adaptive behaviour.\u003c/p\u003e","manuscriptTitle":"Learning environments are associated with developmental trajectories of thalamocortical attention circuits in childhood","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:40:42","doi":"10.21203/rs.3.rs-9269326/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T15:46:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246824287748683520551748398047512835654","date":"2026-05-02T20:58:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218309953230236180247814587192352788379","date":"2026-04-28T20:20:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T12:49:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T18:37:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T16:44:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Science of Learning","date":"2026-03-30T15:35:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-science-of-learning","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjscilearn","sideBox":"Learn more about [npj Science of Learning](http://www.nature.com/npjscilearn/)","snPcode":"41539","submissionUrl":"https://mts-npjscilearn.nature.com/cgi-bin/main.plex","title":"npj Science of Learning","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91d5c5b9-488a-4e40-8f38-8aa733bee945","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T15:46:07+00:00","index":33,"fulltext":""},{"type":"reviewerAgreed","content":"246824287748683520551748398047512835654","date":"2026-05-02T20:58:49+00:00","index":30,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67439183,"name":"Biological sciences/Neuroscience"},{"id":67439184,"name":"Biological sciences/Psychology"},{"id":67439185,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-11T05:40:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:40:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9269326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9269326","identity":"rs-9269326","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}