Professional Go (Baduk) is a rare model of lifelong cognitive expertise that taxes strategic planning and working memory. Prior Go studies examined younger or amateur players and isolated regions (Jung et al., 2013); whether professional practice leaves a network-level structural signature in older experts is unknown. To our knowledge, this is the first characterization in an older professional cohort, extending beyond regional measures to network morphometry and inter-hemispheric covariance. We used T1-weighted structural MRI to compare 29 older male professional Go players (PGP; mean career 54.6 years; dan 5-9) with 50 male controls (CTRL) recruited at baseline of a Go-training trial (KCT0010704). Groups were comparable in age and vascular risk, although CTRL had more education (15.9 vs 12.4 years). Regional gray-matter volume, thickness, and texture were aggregated into six Yeo functional-network regions, and inter-hemispheric structural covariance was computed. PGP showed larger executive control network (ECN) volume (η²p = 0.153; PFDR = 0.006), driven by the right middle frontal gyrus (MFG; η²p = 0.392; PFDR < 0.001). The right MFG effect held across education-adjusted ANCOVA, propensity-score matching, and a cognitively-normal subgroup (n = 62); ECN persisted in the subgroup but did not survive strict matching in the full sample. Covariate-adjusted inter-hemispheric covariance was higher in PGP in five of six networks (all PFDR < 0.05). Cognition was domain-specific rather than globally superior: PGP excelled at working memory (+0.71 SD) but underperformed at naming (-0.82 SD; Group × Domain P < 0.001). The enlarged right MFG, robust to education adjustment, is the principal, association-level finding, motivating longitudinal follow-up.

Numerical human models are essential to advance medical device design, safety assessment, and to study how anatomical development influences physiological processes. Despite increasing availability of pediatric models, a critical gap remains in high-resolution, non-morphed whole-body models representing children around one year of age. Existing pediatric models are often derived from morphing older anatomies or lack sufficient tissue segmentation to accurately capture early developmental anatomy. This study introduces Thalia, a non-morphed, high-resolution numerical model of a healthy 10-month-old female. The model was constructed by segmenting 442 tissues from Magnetic Resonance Imaging data. Brain tissues were automatically segmented using an infant-specific FreeSurfer framework, followed by semi-automated and manual refinement in 3DSlicer. The model was validated by expert review and quantitative comparison with age-matched anatomical values reported in the literature. The resulting whole-body model provides detailed anatomical representation across the brain, musculoskeletal system, vasculature, and internal organs, enabling realistic assignment of tissue-specific properties for computational studies. It provides a versatile platform for pediatric medical device development, dosimetry, safety assessment, and bioelectromagnetic simulations. This pediatric numerical anatomical model is openly available as an open-source resource.

Intergroup emotions regulate the intergroup interactions, often leading to attitudes and behaviors that favor the ingroup. Theoretical frameworks in social psychology suggest that group identities, perceived threats, stereotypes, and context shape intergroup outcomes, including intergroup emotions. Hence, studying emotions in intergroup dynamics can enhance our understanding of the mechanisms underlying prejudice and emotional bias. In this research, we aimed to investigate how emotional expressions and physical proximity of group members modulate brain patterns of emotions during intra- and intergroup encounters. We presented a series of 360-degree videos showing ingroup and outgroup members either looking at the Finnish White majority participants (N = 40) from a distance or approaching them while their BOLD activity was measured with fMRI. The protagonists in the videos expressed neutral, happy, or angry facial expressions. Evoked emotions were measured by decoding emotion-related neural patterns while watching the videos. The results suggest that the valence of emotional expressions modified the perception of warmth of ingroup and outgroup members, as happy-looking outgroup members evoked emotions associated with the paternalistic stereotype, whereas angry-looking ingroup members elicited emotions associated with the envious stereotype. Moreover, physical proximity enhanced intergroup emotional bias, with close-proximity ingroup members evoking neural patterns of anger and fear, while close-proximity outgroup members evoked patterns of contempt and discomfort. The results are discussed in terms of their contribution to neuroscientifically informed models of intergroup emotions.

Lesion-symptom mapping is widely used to identify causal relationships between brain structures and behaviour, and has played a central role in neuropsychologically informed network models of cognition. However, even recent approaches remain constrained by a one-to-one mapping framework, which oversimplifies the complex relationships between network-level damage and cognitive deficits. In addition, the non-orthogonality of cortical and white matter damage makes it difficult to disentangle their distinct contributions. Here, we used graph-based multilayer network analysis to address these limitations and evaluate clinical relevance. Using neuroanatomical and longitudinal neuropsychological data from 252 patients who underwent awake neurosurgery for low-grade glioma, we constructed interactive, three-layer networks for each hemisphere. Layer 1 comprised neuropsychological tasks (NT), layer 2 structural disconnections (SD), and layer 3 cortical damage (CD). Nodes represented tasks, white matter tracts, and cortical parcels, respectively, whereas within-layer edges captured correlations in performance or co-occurring damage patterns. Multilayer community detection identified domain- and hemisphere-specific brain-behaviour motifs linking executive, language, and spatial functions to distinct combinations of cortical and white matter disruption, a pattern confirmed by two spatial embedding approaches. Centrality analyses revealed a continuum of mapping relationships, ranging from one-to-one to one-to-many associations, indicating that tasks such as verbal fluency are better explained by multiple disconnection mechanisms. Additional analyses uncovered many-to-one and many-to-many relationships and highlighted tracts and cortical regions with domain-general relevance. Together, these findings support a neurobiologically grounded, network-oriented account of how structural brain damage gives rise to cognitive deficits, with implications for clinical care.

Human memory consolidation involves the gradual stabilization and reorganization of memory traces over time. Despite numerous empirical and computational accounts emphasizing different aspects of this process, an integrated framework for evaluating consolidation theories against brain data remains limited. We propose a biologically informed, data-driven framework based on Koopman operator analysis to examine latent dynamical structure in fMRI signals associated with memory consolidation. The Koopman framework lifts nonlinear brain dynamics into a linear function space, enabling spectral characterization of persistence and stability. In practice, we employ Dynamic Mode Decomposition (DMD) together with an observability-aware extension tailored to consolidation-related neural dynamics. We organize existing theories into three functional clusters: standard consolidation, episodic replay during rest, and distributed long-term storage, and then align open-access fMRI datasets with each cluster to assess their dynamical plausibility. Across datasets, delayed or repeated retrieval conditions generally tend to show greater spectral persistence than early encoding-related conditions. Among the three analyses, Cluster 3 yielded the clearest statistically reliable subject-level contrast, with semantically abstracted autobiographical content exhibiting higher mean eigenvalue magnitude and more near-unit modes than event-specific episodic content. This finding is compatible with transformation-oriented and distributed-storage accounts but does not constitute a direct temporal test of consolidation. Replay-related conditions show strong spectral differentiation across task states, although part of this separation likely reflects task structure in addition to consolidation-related dynamics. For the standard consolidation cluster, effects are directionally consistent with theory but remain small and not statistically significant at the subject level. Overall, the proposed framework provides an interpretable operator-theoretic approach for linking memory consolidation theory to latent brain dynamics and for comparing competing accounts in a common spectral language.

Binocular rivalry occurs when two eyes compete for perceptual dominance, leading to alternating awareness of incompatible images over time. Previous studies have linked GABA+ concentrations in the visual cortex to rivalry dynamics and perceptual suppression, but it remains unclear whether perceptual changes are better explained by shifts in excitatory/inhibitory (E/I) balance or by neurochemical responses within specific pathways. Here, we use magnetic resonance spectroscopy to measure concentrations of Glx (glutamine + glutamate) and GABA+ in the visual cortex and a binocular rivalry paradigm to evaluate the effect of 60-min dark exposure on mixed perception and corresponding shifts in neurochemical concentrations in 14 normally sighted observers. Dark exposure increased the proportion of mixed perception and elevated Glx concentrations but did not produce significant changes in GABA+ concentrations or net E/I balance, as indexed by the Glx/GABA ratio. Dark exposure also did not significantly alter ocular dominance, phase durations, rivalry dynamics or perceptual alternation rates. Despite the absence of a group-level GABA+ effect, individual differences in mixed-perception changes were strongly associated with the magnitude of GABA+ fluctuations, whereas no comparable relationship was observed for Glx. These findings suggest that perceptual changes following dark exposure are more closely related to individual differences in GABA+ responses than to mean shifts in E/I balance. More broadly, the results suggest that binocular rivalry may be influenced by specific neurochemical responses that are not captured by directional measures of E/I balance alone, revealing a more nuanced relationship between neurochemistry and binocular perception.

Patients with brain tumors involving language-critical regions face surgical limitations when balancing resection with preservation of function. Non-invasive neuromodulation-induced prehabilitation (NIP) aims to guide preoperative neuroplastic reorganization, potentially facilitating larger resections while preserving function. We investigated whether NIP selectively modulates the targeted language network, in our case defined as the language production network, compared with control networks, and whether such modulation is behaviorally safe. We enrolled 26 patients (mean age = 55.9 ± 11.8 years) from the Prehabilita project (Clinical Trial: NCT05844605) with operable brain tumors affecting language or motor regions. Eleven received language-targeted NIP, combining transcranial magnetic stimulation and/or transcranial direct current stimulation with intensive language training. Fourteen patients with NIP targeting non-language networks, primarily motor networks, served controls. Assessments included task-based functional magnetic resonance imaging (tb-fMRI) and a neuropsychological battery assessing language and cognitive domains before and after prehabilitation. Results indicated a group-specific NIP effect on the language production network. In the language-targeted group, tb-fMRI revealed reduced overlap between a region of interest centered on the stimulation target and fMRI-derived language activation maps, whereas no comparable changes were observed in controls. No significant modulation effects were detected in the motor network in either group. These findings indicate that NIP can selectively reorganize the language production network, with modulation patterns differing in sensorimotor networks. Importantly, language production network modulation occurred while preserving language and cognitive performance. These results support NIP targeting higher-order functions such as language as a safe preoperative strategy that may reduce functional constraints on surgery and enable larger and safer resections in patients with tumors involving language-critical regions.

•Leakage-free nested CV-RFE identifies a reproducible connectivity signature that characterizes the PTE-diseased state in our cohort.•Data-driven feature selection is essential for classification performance, with only four stable functional connections (from an original feature space of 4950) able to distinguish PTE from TBI with an AUC of 0.95.•We identify a specific connectivity phenotype in our PTE cohort which includes the Visual network as a hub of PTE-related changes, left-hemisphere overrepresentation and hyper-coupling, and right-hemisphere un-coupling.•The resting-state network disruptions identified by our ML paradigm went unrecognized by direct comparison between groups and traditional statistics, supporting the use of advanced data-driven methodologies for exploring functional connectivity in PTE.

Olfactory adaptation, the progressive reduction of neural responses to repeated odor stimulation, is closely linked to cognitive function and is altered in Alzheimer's disease (AD). However, its evolution across biomarker-defined stages and relationship with plasma p-tau217 remain unclear. We studied 168 participants classified by plasma p-tau217: cognitively normal (p-tau217-NC, n = 37; p-tau217+NC, n = 8), subjective cognitive decline (p-tau217-SCD, n = 57; p-tau217+SCD, n = 16), and mild cognitive impairment (p-tau217-MCI, n = 40; p-tau217+MCI, n = 10). Odor-induced fMRI with four concentrations (0.032%, 0.1%, 0.32%, 1.0%) presented in a fixed ascending order, although concentration effects could not be fully separated from time-related factors and other confounders, to assess activation in the primary olfactory cortex (POC), hippocampus (HP), and parietal lobe (PL). Receiver operating characteristic (ROC) analyses were performed using logistic regression models. In NC groups, adaptation emerged at 0.1% and 0.32% odor conditions. In p-tau217-SCD, POC adaptation was delayed to 1.0% odor condition, HP was largely preserved, and PL was dysregulated. p-tau217+SCD and MCI groups showed delayed and dysregulated adaptation across all regions. Odor adptations were associated with plasma p-tau217 levels and olfactory memory (p_unc 〈 0.05, p_FDR 〉 0.05). Furthermore, plasma p-tau217 partially mediated the relationship between adaptation-related alterations and olfactory memory. ROC analyses indicated that olfactory adaptation may distinguished individuals across disease stages, require further confirmation in independent cohorts. These findings reveal that impaired olfactory adaptation may represent an early signature associated with AD continuum, particularly in SCD and p-tau217-positive stages.

In vivo myelin imaging often requires long and dedicated MRI scanning techniques. Combining conventional T1-weighted and FLAIR images into the T1w/FLAIR-ratio has been proposed as a myelin-sensitive proxy, however its sensitivity is debated. In this study, we explore the sensitivity to the myelin content of an improved combination of T1w and FLAIR contrasts. A training dataset (N = 23) with reference standard myelin water fraction (MWF) values was used to fit both the T1w and FLAIR contrasts to the MWF using a second order polynomial surface fitting method. This combination was evaluated in an independent dataset (N = 30) with reference MWF values relative to the T1w/FLAIR-ratio by partial correlation analysis adjusting for age and sex. Moreover, the performance of the improved combination was demonstrated in a population-based dataset (N = 4652) by assessing its relation with age. The polynomial fit showed a 27% increase in correlation strength with the MWF compared to the standard T1w/FLAIR-ratio (r = .72 vs. r = .56, p < .01) in the white matter. In the external validation set the polynomial fit also increased the correlation strength compared to the T1w/FLAIR-ratio (r=.64 vs. r = .51, p = .07). Furthermore, in the large population-based dataset, the polynomial fit explained 9.5% more variance in a model with age and age squared, compared to the T1w/FLAIR ratio (R[2] = .48 vs. R[2] = .43, p < .001). A second-order polynomial combination of T1w and FLAIR images provides an empirically optimized marker with stronger associations with the reference MWF compared to the conventional T1w/FLAIR-ratio, without dedicated time-consuming imaging.