Neuroimage: Reports

Background: Adolescence is a critical period of neurodevelopment with the emergence of chronic medical conditions and increasing exposure to long-term medications. P-tau217 is a sensitive blood-based biomarker of neuropathology in older adults, yet its developmental behavior and susceptibility to common clinical factors in youth are unclear. Here we tested whether p-tau217 varies with age, comorbidity, or medication use during adolescence; and whether collection method (venous vs Tasso+ capillary) yields comparable concentrations. Methods: In an adolescent cohort, plasma p-tau217 was measured by Simoa-X. Paired venous and Tasso+ capillary samples were also analyzed from adult volunteers for methodological comparison Results: In adolescents (n=41; mean age 16{+/-}2.6 years), p-tau217 did not correlate with age or BMI z-score and did not differ by psychiatric, cardiometabolic, or gastrointestinal comorbidity, nor by corresponding medication use. In contrast, p-tau217 concentrations were >10-fold higher in Tasso+ capillary plasma than venous plasma, a discordance replicated in paired adult samples. Conclusion: Plasma p-tau217 appears physiologically stable across common clinical variables in adolescence, but highly sensitive to biospecimen collection method. Venous and Tasso+ capillary plasma should not be directly compared or pooled until methodological differences are resolved. These data provide a developmental baseline and critical methodological caution for pediatric neuroscience and decentralized biomarker studies.

Non-invasive brain stimulation (NiBS) studies frequently report exploratory correlations between individual-level changes in neurophysiological and behavioural measures. However, these analyses are typically underpowered and rely on ratio-based change scores with known statistical limitations. We addressed these limitations by pooling individual data from three independent studies (total N = 69), providing adequate power to detect small-to-medium effects. All studies applied 20 Hz transcranial alternating current stimulation (tACS) targeting the pre-supplementary motor area (preSMA) and right inferior frontal gyrus (rIFG), regions central to inhibitory control. Changes in preSMA-rIFG connectivity measured with EEG imaginary coherence (ImCoh) and response inhibition (stop-signal reaction time, SSRT) were quantified using reliable change indices (RCI), which were z-standardised within studies to enable pooled mixed-effects regression. No meaningful association was found between tACS-induced ImCoh change and SSRT change (r = .013, marginal R{superscript 2} = .004), with project-wise correlations that were small, non-significant, and inconsistent in direction. Sensitivity analysis using ratio-based change scores converged on the same null result (r = .014), though ratio scores showed severe distributional violations relative to the approximately normal RCI distributions, supporting the methodological case for RCI on statistical grounds. These results provide no support for a systematic individual-level brain-behaviour coupling between preSMA-rIFG connectivity and response inhibition following 20 Hz tACS, and suggest that any true effect is likely to be small. The present work offers a methodological benchmark for quantifying individual-level brain-behaviour coupling in NiBS research, and highlights the need for more sensitive neural markers and adequately powered design.

BackgroundAttention-Deficit/Hyperactivity Disorder (ADHD) affects approximately 7.6% of children globally and exhibits heterogeneous cognitive and behavioral manifestations. Conventional group-level MRI analyses often obscure individual variability in brain structure, limiting understanding of personalized neuroanatomical profiles. ObjectiveThis study quantified individualized gray matter volume (GMV) deviations in children with ADHD using age- and sex-matched normative structural MRI references. MethodsStructural MRI data from 31 children with ADHD (16 males, 15 females; ages 7-15) and 413 typically developing controls (TDC; ages 7-22) were analyzed. Voxel-based morphometry extracted regional GMV across prefrontal cortex, striatal nuclei, and cerebellar vermis. Individual deviations were calculated as z-scores relative to normative distributions and categorized as typical, mild, moderate, strong, and extreme. ResultsLateral and orbital prefrontal regions exhibited the highest deviations: for females, the Lateral Orbital Gyrus (LOrG) showed 33.3% mild-to-strong deviations and 13.3% extreme deviations, while the Opercular Inferior Frontal Gyrus (OpIFG) had 73.3% mild-to-strong deviations. In males, the LOrG showed 31.2% moderate, 6.2% strong, and 18.8% extreme deviations. Striatal nuclei exhibited mixed patterns: female caudate volumes were typical in 33.3% of participants, moderate-to-extreme deviations occurred in 46.7%; male putamen was typical in 31.2%, with 37.5% showing strong or extreme deviations. Cerebellar vermis values were mostly typical (50-60%) with occasional mild-to-strong deviations. Medial and superior frontal regions remained largely typical (40-73%). ConclusionChildren with ADHD display heterogeneous and region-specific GMV deviations, most pronounced in lateral and orbital prefrontal cortex and select striatal regions. Individualized z-score profiling captures variability obscured in group averages, supporting personalized neuroanatomical assessment for understanding ADHD and guiding targeted treatment.

Objective: While ADHD symptoms often decline from childhood into adulthood, the underlying neurobiological mechanisms, such as altered brain maturation or neural reorganization, remain incompletely understood. This study investigated how grey matter development relates to ADHD symptom trajectories into adulthood. Method: We analyzed data of individuals with ADHD and controls from the longitudinal Dutch NeuroIMAGE cohort, utilizing dimensional ADHD symptom scores (Conners Parent Rating Scale) from three waves and T1-weighted structural MRI scans from the final two waves. Using General Linear Models with permutation-based inference, we examined: 1) cross-sectional associations between ADHD symptoms and vertex-wise cortical thickness and surface area, and subcortical volumes at Wave 1 (n = 765, mean age = 16.95 years); and 2) longitudinal associations between symptom progression and brain morphometric changes (Wave 0 to 1: n = 644, mean age = 11.55-17.24 years; Wave 1 to 2: n = 149, mean age = 16.45-20.11 years). Results: Cross-sectionally, at Wave 1, more ADHD symptoms were related to widespread reductions in surface area, most prominently in the frontal cortex, and smaller volumes of the cerebellum, amygdala, and hippocampus. Longitudinally, symptom improvement from Wave 1 to Wave 2 was associated with stronger reductions in surface area, particularly in prefrontal and occipital regions, and with more pronounced cortical thinning across multiple brain regions. Conclusion: These findings suggest an association between symptom trajectories and structural brain changes, indicating that clinical improvement in ADHD behaviors might coincide with ongoing neural refinement during the transition to adulthood.

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

BackgroundAdolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain development. Neuroimaging-based biomarkers derived from resting-state functional magnetic resonance imaging (rs-fMRI) hold promise for objective diagnosis; however, pronounced inter-individual variability and limited sample sizes pose major challenges for robust model development. MethodsWe propose a memory-augmented Meta-Graph Convolutional Network (BrainMetaGCN) to classify AMDD using rs-fMRI functional connectivity. Individual functional connectivity matrices were constructed by parcellating rs-fMRI time series into cortical regions of interest and computing pairwise correlations. A meta-graph generator dynamically learned subject-specific graph structures, which were processed by lightweight graph convolutional layers. A memory neural network was incorporated to encode population-level prototypical connectivity patterns and generate individualized representations via attention-based retrieval. Model performance was evaluated across multiple independent datasets and compared with state-of-the-art deep learning approaches. Additionally, network interpretability was examined through cortical hierarchy analysis and functional enrichment of discriminative network components. ResultsThe proposed BrainMetaGCN consistently outperformed baseline models, including convolutional and transformer-based approaches, achieving higher accuracy, area under the receiver operating characteristic curve, sensitivity, and specificity. Memory-module-derived functional networks exhibited clear modular organization and showed a significant positive correlation with cortical functional hierarchy, supporting their neurobiological validity. Functional enrichment analyses implicated synaptic transmission, axon guidance, receptor tyrosine kinase signaling, and immune-related pathways, suggesting neurodevelopmental and neuroimmune mechanisms underlying AMDD. Ablation analyses confirmed that memory augmentation and dynamic meta-graph construction were critical for robust performance under small-sample conditions. ConclusionsThis study introduces a robust and interpretable memory-augmented graph learning framework for AMDD classification. By effectively balancing individual specificity and population-level generalization, BrainMetaGCN advances neuroimaging-based precision diagnosis and provides new insights into the neural and biological mechanisms of adolescent depression.

Working memory (WM) supports the temporary maintenance of goal-relevant information and is disrupted across many neuropsychiatric disorders. We examined whether scalp electroencephalography (EEG) data features beyond spectral power, including waveform shape, broadband spectral structure, and signal complexity, provide complementary information for predicting cognitive and clinical outcomes. EEG was recorded from 200 adults spanning a broad range of neuropsychiatric symptom severity while they completed three WM task paradigms: Sternberg spatial WM (SWM), delayed face recognition (DFR), and dot pattern expectancy (DPX). Separate machine learning models were trained on EEG features from the encoding, delay, and probe phase of each task to predict participants task accuracy, reaction time (RT) variability, WM capacity, and psychopathology scores (Brief Psychiatric Rating Scale). A split-half analytic framework was used, with cross-validated model development in an exploratory dataset (N=100) and evaluation of statistically significant models in a held-out validation dataset (N=100). In the exploratory dataset, SWM task data best predicted WM capacity, DPX task data predicted RT variability, and DFR task data predicted psychopathology, suggesting that these three WM paradigms engage distinct neural processes relevant to different outcomes. No models reliably predicted task accuracy. Models incorporating features beyond spectral power generally outperformed power-only models, and task-derived features outperformed resting-state-derived features. However, only those models predicting WM capacity and RT variability generalized to the validation dataset; models predicting psychopathology did not. These findings demonstrate functional heterogeneity across WM paradigms, show that complementary EEG features enhance predictive modeling, and highlight the importance of rigorous validation for identifying robust brain-behavior relationships.

Background: Major depressive disorder (MDD) is associated with altered brain structure and evidence of accelerated brain aging. However, previous studies have been limited by clinical samples with mixed medication status and multiple mood states, modest sample sizes, small percentage of MDD individuals older than 65 years of age, and/or reliance on summary-level data. Methods: Harmonized T1-weighted MRI from MDD (n = 645), all medication-free and in a current depressive episode, and matched healthy controls (n = 645), segmented into 145 regional volumes, from 11 sites in COORDINATE-MDD consortium. Brain age gap (BAG) was estimated using gradient boosting regression with nested cross-validation. Group differences in BAG (and age-corrected BAG [cBAG]) were examined across age strata. Regional contributions were evaluated using Shapley Additive exPlanations. Results: MDD was associated with significantly elevated cBAG compared with healthy controls (mean difference + 2.01 years). Age-stratified analyses showed no differences before mid-30s, with progressively larger gaps thereafter, reaching +6.85 years in MDD aged 55 and older. cBAG differed across neuroanatomical phenotypes associated with differential antidepressant response, cognitive impairment, increased adverse life events, increased self-harm and suicide attempts, and a pro-atherogenic metabolic profile. Key contributing regions included lateral and medial prefrontal regions, middle temporal gyrus, putamen, supplementary motor cortex, central operculum, and cerebellum. Conclusions: Accelerated structural brain aging in MDD is age-dependent and is most pronounced in a neuroanatomical phenotype associated with worse key clinical outcomes. The findings support neuroprogression models of MDD while demonstrating that cBAG is not a uniform feature of MDD and seem to be more strongly expressed in a specifically clinically vulnerable disease phenotype.

BackgroundThe relationship between neighborhood-level socioeconomic disadvantage and brain health is an emerging area of research with critical implications for public health and clinical practice, yet its influence on brain structure remains unclear. PurposeTo investigate the epidemiological association between neighborhood-level socioeconomic disadvantage [Area Deprivation Index (ADI)] and morphometric neuroimaging variables in a consecutive, non-disease enriched patient population. Materials and MethodsThis study, conducted at an academic medical center and associated community partners, used consecutive cross-sectional MRI neuroimaging data from 2,826 inpatient and outpatient individuals without radiological evidence of disease from January 2024 to June 2024. ADI, a geospatially determined index of neighborhood-level disadvantage, was calculated for each individual. Linear regressions tested the relationship between ADI and multiple morphometric variables: brain age gap (BAG; estimated - chronological BA), total brain tissue volume (TBV; total gray + white matter), five subcortical region volumes (hippocampus, thalamus, caudate, putamen, and nucleus accumbens) and four cortical region volumes [anterior cingulate cortex, posterior cingulate cortex, medial prefrontal cortex (MPFC), lateral PFC (LPFC)]. Volumetric measures were normalized to intracranial volume. Models controlled for age, sex, and total white matter hyperintensity volume (WMHV). Results2,826 individuals (mean age, 52.7 {+/-} 18.8 [standard deviation]; 1732 women) were evaluated. Residence in the 20% most disadvantaged neighborhoods was associated with a higher BAG ({beta}s > 2.12, Ps < .01) and decreased TBV ({beta}s < -5.12, Ps < .05). Additionally, increased WMHV was higher among those in the most disadvantaged neighborhoods (ts < - 2.50, Ps < .05) and associated with lower volume in most regions. Interaction models showed increased negative associations between WMHV and volumes of the caudate, nucleus accumbens, and lateral prefrontal cortex among those in the most disadvantaged neighborhoods. ConclusionsNeighborhood disadvantage is associated with adverse brain morphometry, including higher BAG, lower TBV, and amplified vascular-related regional volume loss. Key ResultsO_LIIn 2,826 adults (mean age, 53 years {+/-} 19; 1,732 women), residence in the most disadvantaged neighborhoods (national: 116/2,826, 4%; state: 129/2826, 5%) was associated with higher brain age gap at the national ({beta} = 2.12, 95% CI = 0.81 to 3.43, P = .001) and state levels ({beta} = 2.36; 95% CI = 1.10 to 3.61, P < .001). C_LIO_LITotal brain tissue volume was lower at the national ({beta} = -5.12, 95% CI = -10.13 to -0.11, P = .045) and state levels ({beta} = -6.13, 95% CI = -10.90 to -1.37, P = .011). C_LIO_LIWhite matter hyperintensity volume was higher in the most disadvantaged group (national: P = .013; state: P = .003) and demonstrated amplified associations with caudate, nucleus accumbens, and lateral prefrontal cortex volumes in the most disadvantaged group at the national and/or state levels (Ps < .05). C_LI

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

BackgroundDepression is associated with social dysfunction, but the mechanisms linking affective symptoms to disrupted close relationships remain poorly understood. One possibility is that depression alters how people experience rewards shared with close others and how they interpret partners actions. It remains unclear whether neural sensitivity to shared reward predicts social valuation during more complex interactions such as reciprocated trust. MethodsIn this preregistered fMRI study, participants completed a reward-sharing task and a Trust Game with a close friend, a stranger, and a computer. We measured striatal shared reward sensitivity (SRS; friend > computer) and tested whether it related to subsequent investment behavior and brain responses to trust reciprocation. Depressive symptoms and perceived closeness were assessed via self-report. ResultsIn a final sample of n = 123, participants reporting more depressive symptoms invested more in their friend than in the computer. Striatal SRS predicted temporoparietal junction responses to reciprocated trust, but this association depended jointly on social closeness and depression -- with depression reversing the expected pattern among individuals reporting closer relationships. Striatal SRS was also inversely associated with connectivity between the default mode network and cerebellum during reciprocity. ConclusionsThese findings suggest that closeness calibrates the striatal SRS link to regional activity and network-level responses during social exchange, while depression alters how striatal SRS relates to regional activity, potentially disrupting how individuals interpret and respond to close others.

An inflammatory subtype of major depressive disorder (MDD) is associated with treatment resistance pointing to an unmet need for adjunctive treatments. To evaluate treatment-related changes in brain inflammation, diffusion basis spectrum imaging (DBSI) is a promising non-radiation-based technique for longitudinal designs which has been verified with histopathology. However, its use as an endpoint in clinical trials is dependent on its individual-level reliability to robustly track changes. Here, we evaluated two DBSI runs acquired in 94 participants (including 43 participants with MDD) on the same day about 1.5 h apart to assess short-term test-retest reliability. Fiber fraction (reflecting axonal/dendrite density) and hindered fraction (reflecting edema) showed moderate to high test-retest reliability in both gray and white matter regions, whereas restricted fraction (reflecting cellularity) showed lower values in gray and white matter. Group-level reliability was similar in participants with MDD, except for lower reliability of hindered fraction in gray matter. Re-identification rates of individual brain maps were higher using voxel-level white matter signatures compared to gray matter regions of interest (ROIs) (p<.001). Crucially, participants with MDD showed reduced fiber fraction (tmax=4.68, k=38) and elevated hindered fraction (tmax=4.74, k=32) in the cingulate bundle, consistent with increased white matter inflammation, while gray matter ROI-based classification failed to identify cases. We conclude that DBSI is a promising technique to track inflammatory signatures in MDD, particularly in white matter tracts. Since several frontal and subcortical gray matter ROIs showed insufficient reliability, their assessment would require multiple DBSI runs to provide robust estimates.

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.

BackgroundContemporary research indicates that psychedelics induce notable neurophysiological changes, some lasting weeks to months after a single dose. However, most evidence derives from acute administration studies and limited post-acute follow-ups. Long-term naturalistic psychedelic users remain critically underexamined, yet may exhibit distinct neurobiological profiles informing our understanding of persistent alterations following repeated exposure. MethodsWe recorded resting-state EEG in 57 long-term psychedelic users (abstinent [&ge;]30 days) and 49 matched non-users across two independent sites under eyes-open and eyes-closed conditions. We analyzed oscillatory power, signal complexity, and source-localized effective connectivity, focusing on five canonical frequency bands and regions of the Default Mode, Salience, and Central Executive Networks. Analyses included linear mixed-effects modeling for power spectra and complexity results and a rank-based approach combining ordinary least squares regression with randomization inference for effective connectivity. ResultsWe observed predominantly null findings. No significant between-group differences emerged for oscillatory power. Complexity comparison yielded results contrary to our hypothesis: psychedelic users exhibited lower complexity values in the eyes-open condition. Effective connectivity revealed no within- or between-network differences that would survive statistical corrections. Additionally, we report a few small-magnitude effects uncovered by exploratory analyses. Conclusions Long-term naturalistic psychedelic users showed largely non-significant differences in oscillatory power, complexity, and network connectivity compared to non-users -- across several measures commonly reported as altered in acute administration studies. These findings raise the question of whether psychedelics neurophysiological signatures persist during abstinence despite repeated prior use, or whether they reflect homeostatic receptor adaptation, individual variability, or contextual factors. Null, incongruous, or subtle effects contribute to the existing evidence base, yet underscore the need for replication in larger, more ecologically valid populations to advance the emerging field of psychedelic neuroscience.

BackgroundLarge-scale T1-weighted MRI studies have established grey-matter abnormalities in bipolar disorder (BD), with our group contributing to consensus findings. However, structural connectivity, particularly within emotion- and reward-related circuits, remains poorly understood. Diffusion-weighted MRI (dMRI) enables investigation of white-matter pathways, yet prior work is constrained by small samples, methodological heterogeneity, and unclear medication effects. We conducted the largest dMRI network analysis in BD, relating symptom burden and polypharmacy to tractography-derived connectivity and graph-theoretic metrics. MethodsCross-sectional structural and diffusion MRI scans from 449 individuals with BD (35.7{+/-}12.6 years) and 510 controls (33.3{+/-}12.6 years), aged 18-65, were analyzed across 16 ENIGMA-BD sites. Standardized segmentation/parcellation and constrained spherical deconvolution tractography generated individual structural connectivity matrices. Graph-theoretic metrics of global and subnetwork organization were related to symptom severity and medications. ResultsBD showed widespread network alterations (lower density and efficiency, longer path length, and higher betweenness centrality), altered microstructural organization in a limbic-basal ganglia circuit, and abnormal streamline counts in a default-mode/salience/fronto-limbic-basal ganglia network. Longer illness duration, later onset, and psychosis history were associated with greater abnormalities in network architecture, whereas more manic episodes were associated with greater fronto-limbic connectivity. Antidepressant (particularly SSRI), anticonvulsant, and antipsychotic use related to poorer global and fronto-limbic connectivity; no clear lithium effects emerged. ConclusionsAs the largest structural connectivity study in BD, we reveal widespread disruption in reward and emotion-regulation networks influenced by illness severity and medication use. Results show that multisite harmonization is feasible and highlight ENIGMA-BD as a scalable framework for identifying reproducible neurobiological markers.

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.

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.

The hippocampal CA1 subregion supports learning, memory formation, and spatial navigation. Although its three-layered architecture has been described in ex-vivo investigations, the in-vivo microstructural profile of CA1 and its relation to individual variations in memory performance remain poorly characterized. In this study, we used ultra-high field structural MRI at 7 Tesla to investigate the depth-dependent myelination patterns (measured by quantitative T1) of CA1 in younger adults, their relation to the local arterial architecture, and their association with individual differences in cognitive functions, specifically memory performance. Results show that left and right CA1 present depth-dependent patterns of myelination, with the outer and inner compartments showing higher myelination than the middle compartment. No significant relationship between layer-specific myelination of CA1 and distance to the nearest artery was observed. Right CA1 was found to be more myelinated than left CA1. Pairwise correlations and regression models showed that higher left CA1 myelination is linked to higher accuracy in object localization. Together, our data demonstrates the feasibility of describing the three layered myelin architecture of CA1 in vivo, and provides information on how alterations in the architecture of CA1 may relate to alterations in cognitive performance in younger adults.

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.

BackgroundTranscranial Magnetic Stimulation (TMS) studies identify the Resting Motor Threshold (RMT) to calibrate stimulation intensity. However, this procedure is time-consuming and subject to variability. We developed an automated procedure to improve the efficiency and standardization of RMT determination. New methodWe developed an algorithm that measures MEP amplitudes and automatically adjusts stimulation intensity to determine the RMT. Experiment 1 compared this automated method with the manual procedure in terms of reliability and equivalence. Experiment 2 developed a "Fast" automated process, assessing it against both the manual and initial automated procedures. ResultsAcross both experiments the automated approach demonstrated excellent test-retest reliability and strong agreement with the manual method (Intraclass Correlation Coefficients [&ge;]0.95), giving estimates of RMT statistically equivalent to those of manual measurements within {+/-}3% MSO, with the majority of comparisons within {+/-}2% MSO. Experiment 2 optimized the procedure, allowing empirical determination of the RMT in an average of <3 minutes with only 33-34 pulses. Comparison with existing methods RMT-Finder provides a reliable and time-efficient alternative to manual approaches. To the best of our knowledge RMT-Finder presents the first closed-loop feedback approach to identify the RMT without manual intervention. This procedure can improve standardization and reproducibility in TMS studies. ConclusionsAutomating RMT assessment allows rapid and highly reproducible assessment of this standard TMS measurement, making it viable for inclusion in routine clinical applications that require standardized procedures.