Brain Connectivity

Resting-state functional MRI has been widely used to study brain connectivity, yet the test-retest reliability of commonly used metrics remains a concern. To improve reliability, extended scan lengths and larger subject cohorts are often recommended. However, these solutions can be impractical and pose challenges, particularly in studies of clinical populations. Here, we systematically assess the reliability of two main types of functional connectivity measures: node-based connectome metrics (edge-level intraclass correlation coefficient [ICC], connectome-level ICC, functional connectivity fingerprinting, and discriminability); and voxel-based resting-state networks (RSNs) (spatial similarity of independent component analysis [ICA]-derived RSN maps quantified using the Dice coefficient). Using data from the Human Connectome Project, we evaluated the effects of scan length (3.6, 7.2, 10.8, and 14.4 minutes) and number of participants (n = 10, 20, 50, and 100), on both within-session and between-session reliability. We found that multivariate connectome metrics demonstrated greater reliability than edge-level measures, and that scan length had stronger influence on test-retest reliability than the number of participants. For connectome metrics, 14 minutes of scanning and a cohort of approximately 20 participants were sufficient to achieve reliable estimates. In contrast, RSN measures benefited from larger cohort sizes. Our findings provide practical guidelines for designing resting-state fMRI studies in terms of scan length and number of participants, balancing reliability and feasibility. Ultimately, protocol choices should be guided by the specific study objectives and the functional connectivity metric of interest.

BackgroundIn multiple sclerosis (MS), demyelination and degeneration of transcallosal pathways impair interhemispheric communication. While white matter damage is well documented, the impact of cortical lesions on transcallosal conduction remains unclear. ObjectiveTo determine whether cortical lesions in the sensorimotor hand area (SM1{square}HAND) contribute to impaired transcallosal motor interaction using ultra{square}high{square}field MRI and transcranial magnetic stimulation (TMS). MethodsTwenty healthy controls (HCs) and 38 MS patients underwent 7T structural and diffusion{square}weighted MRI. Structural scans were used to identify cortical lesions in SM1{square}HAND, while diffusion tensor imaging (DTI) quantified microstructural properties in the transcallosal tract connecting left and right SM1{square}HAND. Single{square}pulse TMS was delivered to each SM1{square}HAND during tonic first dorsal interosseous contraction to measure the ipsilateral silent period (iSP). Corticospinal conduction was measured with contralateral motor{square}evoked potentials (MEPs), while the iSP was used to compute transcallosal conduction time (TCT). ResultsAmong MS patients, 41 of 76 hemispheres contained an SM1{square}HAND lesion. TCT was significantly prolonged in MS relative to HCs (P<0.001). In patients, cortical lesions delayed transcallosal conduction from the non{square}lesion{square}bearing to the lesion{square}bearing hemisphere (P=0.026). This direction-specific delay was associated with an intracortical lesion type (P<0.001), but not with DTI{square}derived microstructural measures (P>0.05). ConclusionsThe presence of cortical lesions in the sensorimotor cortex affects transcallosal inhibition between homologous sensorimotor regions in MS, slowing the build-up of inhibitory influence on the corticospinal output in the lesioned cortex. This delayed inhibitory buildlup appears to be associated with an intracortical lesion type. HighlightsO_LIIpsilateral silent period reveals delayed transcallosal motor interaction in multiple sclerosis C_LIO_LICortical lesions in sensorimotor cortex delay the onset of transcallosal motor inhibition C_LIO_LIDelayed transcallosal inhibition is only present toward the lesioned cortex C_LIO_LIIntracortical lesions, not callosal microstructure, is linked to this directionlspecific delay C_LI

ObjectiveFinding indicators of early response to antidepressant treatment in EEG signals recorded from patients suffering from major depressive disorder. MethodsFunctional brain connectivity networks based on weighted imaginary coherence and weighted imaginary mean phase coherence were computed for 176 patients for 6 different EEG frequency bands. Cross-hemispheric connectivity (CH) and lateral asymmetry (LA) were estimated from these networks based on EEG signals recorded before the beginning of treatment (V is1) and one week after the start of the treatment (V is2). Repeated measures ANOVA was used to check for statistically significant changes in connectivity based on these measures at V is2 w.r.t. V is1. Post-hoc analysis was performed with multiple pairwise comparison tests to determine which group means were significantly different. ResultsIt was found that CHV is2 was significantly reduced w.r.t. CHV is1 in the {beta}1 [12.5 - 17.5 Hz] frequency band for the responders to treatment. Also, LAV is2 was significantly increased w.r.t. LAV is1 in the {beta}1 frequency band for the responders. No such significant changes were observed for the non-responders. Brain networks constructed using both weighted imaginary coherence and weighted imaginary mean phase coherence were found to exhibit these results. For the CH connectivity changes, binarized networks and for the LA connectivity changes, weighted networks were found to be more reliable. ConclusionsResponders were found to show a reduction in cross-hemispheric connectivity and an increase in lateral asymmetry, both in the {beta}1 band while no such change was observed for the non-responders. SignificanceDecrease in cross-hemispheric connectivity and increase in lateral asymmetry in the {beta}1 band may represent candidate neurophysiological indicators of early treatment response, but they require independent replication before any clinical application can be considered.

Background and purpose: People after mild traumatic brain injury (mTBI) show persistent deficits in reactive balance. Cortical processes engaged during preparation and execution of balance reactions are reflected in distinct cortical activity signatures that can be measured with electroencephalography (EEG). The purpose of this study was to 1) compare preparatory cortical beta activity and evoked cortical N1 responses during balance recovery in people with mTBI and controls, and 2) explore relationships between preparatory and evoked cortical activity. Methods: Participants (age 21-35 years) with symptomatic mTBI (n=5, 27 +/- 13 days post-injury) and controls (n=5) completed the instrumented and modified push & release tests of reactive balance. Cortical activity was recorded using encephalography (EEG). Main outcome measures were 1) preparatory sensorimotor cortical beta-bust power and duration prior to balance perturbation onset (-1s-0s), and 2) cortical N1 response amplitude and latency during the post-perturbation balance recovery (50-250ms). Results: People with mTBI exhibited lower preparatory beta-burst power compared to controls (p=0.044, g=1.18). During balance recovery, cortical N1 responses occurred earlier in people with mTBI compared to controls (p=0.045, g=3.28). Relationships between preparatory and evoked cortical activity were altered after mTBI compared to controls; people after mTBI with greater beta-burst power and longer duration elicited shorter N1 latencies (r's>0.77, p's<0.010). Discussion and conclusion: The results serve as preliminary, hypothesis-generating observations to guide future research directions investigating neural signatures of reactive balance deficits in people after mTBI. The preparatory brain state before reactive balance recovery should be explored as a potential target for post-mTBI balance rehabilitation.

Background: Neurite orientation dispersion and density imaging (NODDI) shows promise in providing specific insights into the neurite morphology underlying white matter (WM) damage in neurodegenerative diseases. This study aimed to advance the currently limited knowledge by characterizing NODDI-derived microstructural WM alterations in Wilson disease (WD) and examining their relationships with clinical symptoms. Methods: 30 WD patients, including 19 with predominant neurological involvement (neuro-WD) and 11 with hepatic manifestation (hep-WD), and 30 matched healthy controls underwent multi-shell diffusion-weighted magnetic resonance imaging. NODDI metrics, including neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF), and diffusion tensor imaging-based fractional anisotropy (FA) were estimated. Group differences in diffusion parameters across the WM skeleton were determined using tract-based spatial statistics. Additionally, voxel-wise correlations with neurological and cognitive scores were investigated. Results: We observed widespread NDI and ODI reductions in neuro-WD patients and ISOVF increases in hep-WD patients compared with healthy controls, particularly involving the corpus callosum, corona radiata, superior longitudinal fasciculus, external and internal capsule, and superior fronto-occipital fasciculus. A comparable yet more subtle pattern was found when comparing phenotypes. Distinct NDI and ODI constellations were identified as the microstructural determinants of FA alterations. Decreased NDI in the aforementioned fibers were correlated with neurological impairment, processing speed, and visual attention. Conclusions: Phenotype-specific microstructural WM alterations were identified, characterized by globally reduced axonal density and fiber organization in neuro-WD and excess free water in hep-WD. NODDI could be useful as an imaging biomarker for forecasting conversion to neurological WD manifestations and monitoring of disease progression.

Language impairments in aphasia are characterized by various representational disruptions that may be reflected in discourse production. This research examines the capacity of transformer-based language models, particularly GPT-2, to serve as a computational framework for analyzing variations in aphasic narrative speech. A longitudinal dataset of narrative speech samples collected at six time points from individuals with aphasia (N = 47) was utilized as part of an intervention study. All transcripts were processed via the GPT-2 language model to obtain activation values from each of the 12 transformer layers. Statistically significant differences in activation magnitude across aphasia subtypes were found at every layer (all p < .001), with the most pronounced effects in the deeper layers. Pairwise Tukey HSD tests revealed consistent distinctions between Brocas aphasia and both Anomic and Wernickes aphasia, suggesting a shared activation profile between the latter two. Longitudinal tests revealed significant changes over time, especially in the final three layers (10-12). These findings suggest that transformer-based activation patterns reflect meaningful variation in aphasic discourse and could complement current diagnostic tools. Overall, GPT-2 provides a scalable tool to model representational dynamics in aphasia and enhance the clinical interpretability of deep language models.

Objective: To systematically evaluate the efficacy and safety of Deep Brain Stimulation (DBS) for the management of disabling tremor in patients with Multiple Sclerosis (MS) by synthesizing data from available clinical studies. Methods: This systematic review and meta analysis followed PRISMA 2020 guidelines and was registered with PROSPERO (CRD420261347426). A comprehensive search of PubMed, Scopus, Web of Science, and Embase was performed from database inception until December 2025 with no time or language limitation. A pre-post meta analysis design was used to estimate the pooled effect size using the Standardized Mean Change (SMC) between baseline and follow up tremor severity. Because most included studies were single arm cohorts and clinical heterogeneity was anticipated, a random effects model using the Restricted Maximum Likelihood (REML) estimator with the Hartung-Knapp adjustment was applied. Safety outcomes including hardware complications and postoperative infections were pooled using random effects meta analysis of proportions. Results: Thirteen studies including 131 patients met the eligibility criteria. Eight studies with adequate outcome data were included in the pooled efficacy analysis. DBS was associated with a significant reduction in tremor severity with an overall pooled SMC of 1.42 (95% CI 1.07 to 1.77). Statistical heterogeneity was minimal (I2 = 0.0%, p = 0.6300), although this finding should be interpreted cautiously given the limited number of studies and clinical variability in surgical targets, most commonly the ventral intermediate nucleus (VIM), and follow up duration ranging from months to more than 20 years. The pooled incidence of postoperative infection was approximately 7% with substantial heterogeneity across studies (I2 = 74.1%). The most frequently reported adverse events were stimulation related effects such as dysarthria and disequilibrium, which were generally reversible after adjustment of stimulation parameters. Overall methodological quality of included studies was predominantly moderate. Conclusion: Deep brain stimulation may provide meaningful tremor reduction in selected patients with disabling and medication refractory MS tremor, with a large pooled treatment effect (SMC = 1.42). Although complications such as postoperative infection (approximately 7%) and transient stimulation related adverse effects can occur, these events appear manageable in most cases. However, the current evidence base remains limited by small sample sizes, heterogeneous study designs, and variability in surgical targets and outcome reporting. Larger prospective studies with standardized tremor outcome measures and consistent reporting of safety outcomes are needed to better define the long term efficacy and optimal clinical role of DBS in patients with MS related tremor.

BackgroundFunctional neurological disorder (FND) is one of the most common, but least researched, conditions in neurology. Debate exists as to whether the clinical entity referred to as FND is truly a single disorder or is in fact multiple entities which have been erroneously amalgamated into the same condition. We sought to provide empirical evidence on this question by treating it as a problem of model comparison. MethodsWe formulated statistical models equivalent to: (1) FND being a single entity with variation in phenotype, represented by latent trait (binary factor/item response theory) models, and (2) FND being multiple discrete entities, represented by latent class analysis (LCA) models. We fitted these models to data on the symptoms experienced by 697 people with FND from the FND Research Connect database (fnd-research.org) and used Bayesian model comparison methods to compare them. ResultsAll but one of the latent trait models, representing FND as a single entity with heterogeneous phenotype, fit the data better than all the LCA models. Secondary analysis of the LCA models showed results compatible with the models capturing discretisation of continuous variation rather than true discrete categories. DiscussionOur results suggest that the symptom structure of FND is the result of a single pathophysiological process, either as a single entity, or a common pathway preceded by multiple causative processes where the common pathway is solely responsible for the phenotype of the condition.

Background and Objectives: Normal appearing white matter (NAWM) may already harbor subtle microstructural alterations not yet visible on conventional MRI. Quantitative Multi-Parametric Mapping (qMPM) such as Magnetization Transfer saturation (MTsat), longitudinal relaxation rate (R1), and Proton Density (PD) offer new possibilities for analyzing NAWM which are sensitive to demyelination, axonal loss, and edema. We aimed to characterize these alterations within white matter hyperintensities (WMH) and the perilesional NAWM (pNAWM), to gain insights into the underlying process of lesion progression. We also investigated their association with cerebrovascular risk factors (CVRF) and long-term cognitive performance. Methods: This investigation included the cerebral MRI data of 245 participants from the prospective Berlin Longterm Observation of Vascular Events (BeLOVE) study. Furthermore, 121 participants cognitive performance was evaluated at baseline and longitudinally at 2 years follow-up using Montreal Cognitive Assessment (MoCA). Regions of interest (ROIs) of WMH, pNAWM at 1, 2, 3 mm were assessed in comparison to the mirrored contralesional white matter (cWM). Linear mixed effects models were employed to demonstrate the pairwise comparisons between each region using estimated marginal means and the association of MPM metrics with CVRFs. Linear regression was used to assess the association with cognitive performance. Results: In 245 participants, (mean age 62 years, SD: 12 years; 29.8% females), MPM metrics demonstrated a clear spatial gradient of microstructural injury. MTsat and R1 values were lower in WMH compared to cWM (lower case Greek beta = -0.48 (-0.52 - -0.44) and lower case Greek beta = -0.07 (-0.08 - -0.06), p<0.001, respectively) and showed gradual recovery with increasing distance indicating a microstructural gradient in pNAWM. Conversely, PD values were higher in WMH and decreased peripherally (lower case Greek beta = 2.32 (2.05 - 2.61, p<0.001). No substantial associations were found between MPM parameters and CVRFs in our cohort. At baseline and 2-year follow-up, cognitive performance was associated with higher pNAWM R1 values, whereas MTsat were only moderately associated. Discussion: Quantitative MPM reliably detects microstructural alterations not only within WMH, but also in pNAWM, confirming the high sensitivity of qMPM to subtle tissue pathology and support its utility as a promising biomarker for longitudinal studies and monitoring therapeutic effects.

Introduction. There is consistent evidence of a disadvantage in bilinguals' speech production compared to monolinguals in healthy individuals, but studies investigating this phenomenon in clinical populations such as Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are scarce. Given that both clinical groups are characterized by wordfinding difficulties, understanding how bilingualism influences speech production in these populations is essential. Methods. Early and highly proficient Catalan-Spanish bilinguals (active bilinguals) were compared to Spanish-dominant speakers with low proficiency in Catalan (passive bilinguals) using a picture-naming task. The study included 58 older adults, 66 patients with AD, and 124 individuals with MCI. Reaction times, accuracy, and error types were collected in the naming task in each individual's dominant language. Results. First, active bilinguals demonstrated faster naming latencies than passive bilinguals, particularly for low-frequency words. Second, active bilinguals with MCI exhibited more naming errors than passive bilinguals with MCI, including a higher incidence of crosslanguage intrusions and anomia. Third, passive bilinguals with MCI and AD showed more semantic errors than active bilinguals. Discussion. These findings underscore the impact of second language use on naming performance in MCI and AD. Moreover, they provide insight into the potential mechanisms underlying lexical retrieval differences in bilinguals, including lexico-semantic processing and language control.

Spatialization in working memory refers to the spatial coding of non-spatial information along a mental horizontal line when encoding verbal material. This phenomenon is thought to support working memory by facilitating order encoding. Although it has been observed for both visually and auditorily presented stimuli, no direct comparison has yet examined whether these modalities rely on similar neural mechanisms. In this study, we investigated whether spatialization in visual and auditory modalities involves shared or distinct patterns of activity within the working-memory network. Forty-nine participants performed both a visual and an auditory working memory SPoARC task of the same verbal material, allowing to study the cortical patterns associated with distinct serial positions at both encoding and recognition across sensory modalities. Whole-brain analyses revealed similar frontoparietal networks across conditions. In addition, a representational similarity analysis (RSA) was conducted to assess the similarity of neural patterns between early and late serial positions in a sequence and across sensory modalities. This multivoxel pattern analysis revealed modality-dependent patterns distinguishing early and late positions in the inferior frontal gyrus. Additional modality-specific effects were observed in the anterior intraparietal sulcus in the visual modality and in the posterior hippocampus in the auditory modality. Drawing on the framework proposed by Bottini & Doeller (2020), we propose that order decoding in the IPS might reflect a low-dimensional spatial coding of order (e.g., along a horizontal axis), whereas order decoding in the hippocampus might reflect higher-dimensional spatial representations or temporal representations.

BackgroundDifferential expression analysis is a central tool for studying the biological processes altered in human diseases via transcriptomic signatures. However, transcriptomic datasets are systematically confounded by latent variables from two distinct sources: unmeasured technical and biological heterogeneity within the expression data, and expression differences driven by population stratification. Correction using expression-based surrogate variables (SVs) and genotype-based principal components (PCs) addresses these sources independently, yet no study has directly evaluated their combined use against either method alone within a differential expression framework. In this study we hypothesised that simultaneously including both correction layers would produce more biologically valid and reproducible results than either approach alone, and tested this in two independent RNA-seq datasets of amyotrophic lateral sclerosis (ALS) cases and controls with matching genotype data. ResultsFour nested differential expression models (corrected for PC-only, SV-only, both SV and PC, and neither PCs nor SVs) were evaluated across the KCLBB (96 cases and 52 controls) and ALS Consortium (272 cases and 35 controls) datasets. Models were evaluated on: cross-dataset effect size concordance, cross-dataset replicability quantified by the Jaccard Similarity Index, and biological recall against a curated reference set of 66 known ALS genes. The combined SV+PC framework consistently outperformed simpler models across all metrics. Replicability improved nearly ten-fold compared to the non-corrected model, (Jaccard index: 2.28% to 19.5%), and the combined framework exhibited a statistically significant 2.1% gain over the SV-only model. The biological recall ALS genes recovered doubled comparing to the SV correction alone. Crucially, effect size stability was preserved, with the combined model expanding the shared transcriptomic signal without sacrificing consistency. These findings remained generally robust to PC number in sensitivity analyses. ConclusionsThis study found that SVs and genotype PCs address non-redundant sources of confounding, and we recommend their combined use as standard practice in differential expression analysis where matched genotype data are available. Notably PCs capturing population structure can also be derived directly from RNA-seq data, extending the applicability of this framework to studies lacking matched genotype data. Although this analysis was restricted to ALS datasets, we expect these findings to generalise to other traits.

Transcendent thinking (TT) is an enduring affective and cognitive process characterized by abstract meaning-making, moral reflection, self-referential integration, and strong emotional engagement. Despite growing interest in its developmental and affective significance, the intrinsic neural dynamics that predict individual differences in disposition to TT remain poorly understood. Most prior work has relied on linear functional connectivity measures, which may be insufficient to capture the nonlinear and multiscale nature of brain dynamics underlying higher-order affective dispositions like TT. Here, we introduce a nonlinear functional brain network (FBN) framework based on multiscale entropy (MSE) to investigate whether intrinsic resting-state nonlinear brain dynamics predict disposition to TT in adolescents. Functional connectivity was defined as inter-regional similarity in MSE profiles derived from resting-state fMRI, yielding weighted networks that capture scale-dependent dynamical correspondence rather than linear synchrony. Graph-theoretical, spectral, and information-theoretic measures were computed and evaluated against signal-level and network-level null models. Predictive performance was assessed using machine-learning models and compared with conventional time series-based FBNs. Global intelligence (IQ) was examined as a control cognitive variable. MSE-based network features, particularly spectral energy and Shannon entropy, showed significant associations with TT and enabled reliable prediction of individual differences, whereas time series-based network measures failed to predict TT. No network measures reliably predicted IQ. Overall, these results indicate that intrinsic nonlinear brain dynamics carry predictive information about affective dispositions, rather than domainspecific or network-localized cognitive abilities such as IQ. This work demonstrates that nonlinear, multiscale network representations of resting-state brain activity provide a principled and predictive framework for modeling individual differences in enduring affective dispositions.

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

Cerebellar ataxias are a rare group of disorders manifesting with motor incoordination and cognitive-affective deficits of variable severity. Although neurogenetic has revealed multiple mutations, the study of ataxias still relies on clinical evaluation, while the underlying neural network changes remain unclear. It has been argued that the less severe symptoms in congenital (like Joubert syndrome, JS) than in slowly progressive (SP) ataxias reflect a different interplay of alteration and compensation but direct evidence is still lacking. Moreover, it is unclear why, in front of a wide heterogeneity of molecular alterations, SPs show common clinical symptoms. To address these questions, we created brain digital twins for each participant by combining volumetry, graph theory analysis of structural and functional connectivity, and dynamical simulations using the virtual brain. We studied 8 JS (3 females, 21{+/-}6years), 8 SP (3 females, 20{+/-}5years) and 11 healthy controls (HC; 5 females, 21{+/-}2years).Volumetry quantified atrophy, graph metrics (centrality, segregation and integration) characterized topology, and neural dynamical simulations estimated excitation/inhibition balance, providing anatomo-physiological parameters within the somatomotor (SMN) and ventral attention (VAN) networks. Anatomo-physiological parameters were correlated with clinical/neuropsychological scores, and unsupervised clustering was applied to assess whether network features can discriminate between JS and SP beyond clinical classification. MRI morphometry confirmed selective vermis reduction in JS and a widespread cerebellar atrophy in SP compared to HC. In both ataxia groups, SMN and VAN showed reduced volume and structural connectivity but with different patterns of topological and dynamical alterations. In the SMN of SP, reduced centrality and excitation/inhibition balance depressed information transfer through the network. In the VAN of JS, reduced centrality, segregation, and integration, were detrimental but coexisted with a higher number of functional core nodes and an increased large-scale excitatory coupling, supporting compensatory reorganisation in extracerebellar nodes. Clustering confirmed that SMN better differentiates SP, whereas VAN better clusters JS. Importantly, anatomo-physiological parameters of network volume, topology, and dynamics correlated with patients motor and cognitive performance. In conclusion, primary cerebellar damage secondarily impacts large-scale brain networks, altered in both ataxia groups but compensated only in JS. Similar clinical symptoms in SP reflects the similarity of network changes, while differential involvement of SMN and VAN in JS and SP reflects the connectivity pattern of the lesioned areas inside these large-scale brain circuits. Importantly, anatomo-physiological parameters are sufficient to explain individual motor and cognitive performance, offering a basis for improved patient profiling and personalized therapies.

Early childhood development is scaffolded by rapid maturation of brain white matter structure, believed to support the emergence of cognitive and socioemotional functions. Previous whole-tract studies have suggested patterns of white matter development occurring along posterior-anterior, deep-superficial and inferior-superior axes. However, little is known as to whether these patterns are evident within tracts. Using longitudinal diffusion imaging data from 133 children (4-8 years; 76 females), the present work characterizes along-tract patterns of white matter development across association, commissural and projection bundles using fixel-based analyses of microstructure and macrostructure. Within long range association bundles, faster age-related changes were observed for segments adjacent to the visual cortices relative to segments located near association regions, supporting a sensorimotor-association axis of brain development. An inferior-superior pattern was found for projection tracts, with faster age-effects observed for segments near the brainstem. Lastly, while several association and commissural bundles exhibited faster maturation within central segments; indicative of a deep-superficial axis, effects were mixed between micro- and macrostructure, underscoring the unique developmental timing of these different fiber properties. Our findings provide evidence that within-tract white matter maturation unfolds along key spatiotemporal axes and suggests that increased spatial precision can advance our understanding of early childhood brain development.

Lesion network mapping (LNM) is increasingly used to link focal brain lesions to distributed functional networks. Recent work has raised concerns that LNM results may be spatially biased by dominant features of the normative connectome. If this were the case, three testable predictions would follow: (i) a consistent spatial pattern of false positives across LNM studies, (ii) that this pattern can be consistently explained by intrinsic connectome organization, and (iii) that symptom-associated LNM findings preferentially occur in regions with high spatial bias. We tested these predictions across three independent LNM datasets (n = 49/101/200), evaluating each prediction in all cohorts. Spatial bias maps derived from 4,000,000 random permutations under the null hypothesis showed minimal correspondence across cohorts (R2 = 0.4-0.8%), indicating strong cohort specificity. Moreover, dominant connectome features--captured by the first 10 principal components of connectivity profiles from 1,000 atlas regions--did not systematically explain these bias maps. Finally, symptom-associated results showed no enrichment in high-bias regions. Together, these findings provide strong evidence that spatial bias in LNM is not driven by dominant connectome features. With appropriate inferential statistics and rigorous study design, LNM remains a valid approach for mapping symptom-related brain networks.

Developmental stuttering is a complex neurodevelopmental disorder characterized by disfluent speech. At the individual level, the behavioral manifestations of stuttering vary considerably, likely reflecting heterogeneity in underlying neural mechanisms. In this study, we examined individual-specific differences in the brains of children who stutter (CWS), by implementing normative modeling, a framework that quantifies how an individual deviates from an age- and sex-matched reference population. We applied this approach to identify individual-specific structural brain atypicalities using gray and white matter volumes. These volumes were derived from MRI scans from a large mixed-longitudinal dataset of 235 and 240 scans from CWS and fluent controls respectively, aged between 3 and 12 years. Individual deviation maps capturing these atypicalities were then used to cluster CWS into subtypes based on similarities in their neuroanatomical profiles. This analysis identified four neural subtypes with distinct neuroanatomical atypicalities relative to fluent controls. The key findings were a basal ganglia-thalamo-cerebellar subtype associated with higher stuttering severity and lower rates of recovery, and a white matter subtype characterized by mild severity and a higher likelihood of recovery. The remaining two subtypes showed cerebellar differences alongside alterations in brain regions involved in sensorimotor integration. Moreover, cerebellar volume atypicalities were present in all four subtypes, indicating that cerebellar alterations were present across otherwise distinct neural profiles and may represent a shared neuroanatomical feature of stuttering. These findings indicate that examining individual-specific neural differences and subtyping based on patterns of neural atypicalities provides valuable insight into the heterogeneity of developmental stuttering and represents a promising direction for improving our understanding of the disorder.

BackgroundTourette Syndrome (TS) is a neurodevelopmental movement disorder involving involuntary motor and vocal tics believed to be characterised by disordered neural inhibition. Cortical representations have previously been manipulated by disruptions in the inhibitory neurotransmitter {gamma}-aminobutyric acid (GABA). However, while facial tics are the most reported motor tic, it is unclear if facial sensorimotor representations differ in TS. MethodsSixteen individuals with Tourette Syndrome (TS) or chronic tic disorder and twenty typically developing (TD) control participants underwent 3-Tesla functional magnetic resonance imaging (fMRI). Blood-oxygenation level-dependent (BOLD) responses were measured during a block-design task comprising cued facial movements of common facial tics (blinking, grimacing and jaw clenching). Activations in bilateral pre- and post-central cortices and supplementary motor areas (SMA) were examined. Conjunction analyses identified voxels commonly and uniquely activated across movements within each group. ResultsBoth groups showed significant activations in the bilateral sensorimotor cortices and SMA in response to blink, grimace and jaw clench movements, with no significant between-group differences. Between-group similarities were lowest for unique blink maps. Common voxel maps also revealed low between-group similarity, with reduced sensorimotor activation and no shared SMA activation across movements in the TS group. ConclusionVoluntary facial sensorimotor representations do not differ between groups. However, low similarities between group unique blink maps may reflect greater prevalence of blinking tics in TS. Additionally, reduced overlap in sensorimotor activation and absent common SMA engagement across cued movements in the TS group may indicate altered motor integration or action initiation.

Both episodic memory and word retrieval have been linked to power decreases in the alpha and beta oscillatory bands, but these patterns have rarely been related to each other, partly due to a lack of methodological approaches available. In this explorative study, we investigate the similarities and dissimilarities in the oscillatory fingerprints of the retrieval of words and episodes by directly comparing the activity patterns across time, frequency, and space. We acquired electroencephalography (EEG) data of participants performing a language and an episodic memory task based on the same stimulus material. With a newly developed approach, we directly compared the source-reconstructed oscillatory activity using mutual information and a feature-impact analysis. While left temporal and frontal regions showed dissimilarities between the tasks, right-hemispheric parietal regions exhibited similarities. We speculate that this could indicate a homologous function of these regions, potentially sharing less-specific representations between the tasks. We further uncovered a dissociation of the alpha and beta bands regarding the similarity across tasks. While the beta band was dissimilar between word and episodic memory retrieval, the alpha band seemed to contribute to the similarity we observed in right parietal regions. Whether this points to a task-unspecific function of the alpha band or a functional role in the retrieval process of the presumed representations, remains to be determined. In summary, we present an approach to study similarity across tasks using the temporal, spectral, and spatial dimensions of EEG data, and present results of exploring the shared oscillatory fingerprints between episodic memory and word retrieval.