Neuroimage: Reports

BackgroundNormal brain function requires a dynamic balance between inhibition and excitation. While magnetic resonance spectroscopy (MRS) quantifies the chief inhibitory and excitatory neurometabolites, GABA and glutamate-glutamine (Glx), the combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) provides a complementary measure of cortical inhibition-excitation dynamics via transcranial evoked potentials (TEPs). However, the relationship between neurometabolite concentrations and TEPs is unclear. ObjectiveTo characterize the relationship between neurometabolite concentrations and TEPs, as a function of TEP component, TMS pulse type, brain region, neurometabolite, and MRS brain state. MethodsTwenty-five young healthy adults completed a 4-day protocol. Sessions 1 and 2 involved screening, anatomical MRI, and functional MRI localization of DLPFC. In session 3, single- and paired-pulse TMS-EEG were applied over SM1 and DLPFC. Session 4 included resting-state and motor-task-related MRS of SM1 and DLPFC. ResultsIn SM1, task-related GABAergic tone strongly predicted early to mid-latency TEPs. In DLPFC, local early to mid-latency TEPs showed no relationship to neurometabolites, whereas late and global TEP outcomes revealed some links. The later N100 TEP was the only component consistently modulated by paired-pulse TMS. Task-related MRS measures consistently outperformed resting-state measures in predicting TEPs for SM1, while the opposite was true for DLPFC. ConclusionsSingle-pulse TEPs reliably index the local inhibitory tone in SM1, with limited added value from paired-pulse paradigms. These findings support the use of single-pulse TEPs as accessible markers of cortical inhibition, especially for SM1, and may inform biomarkers and strategies for individualized neuromodulation. Key point summaryO_LINormal brain function relies on a balance between inhibition and excitation, yet the relationship between local cortical neurometabolite levels and cortical excitability in humans remains poorly understood. C_LIO_LIWe measured inhibitory and excitatory neurometabolites using magnetic resonance spectroscopy and assessed cortical excitability responses to non-invasive stimulation using combined transcranial magnetic stimulation - electroencephalography (TMS-EEG). C_LIO_LIIn the primary sensorimotor cortex (SM1), local cortical responses to stimulation were best explained by inhibitory GABA levels during motor task performance. In the dorsolateral prefrontal cortex (DLPFC), global cortical responses were best predicted by resting-state neurometabolite levels. C_LIO_LIOverall, paired-pulse TMS paradigms offered little additional value over single-pulse paradigms for informing on neurometabolite levels. C_LIO_LIBy demonstrating region- and state-dependent links between neurometabolite levels and cortical excitability, our findings position TMS-EEG as an accessible biomarker of cortical inhibition, particularly in SM1. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/701225v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@132b3faorg.highwire.dtl.DTLVardef@1c5ebeeorg.highwire.dtl.DTLVardef@1048a75org.highwire.dtl.DTLVardef@11e1a4f_HPS_FORMAT_FIGEXP M_FIG C_FIG Abstract figure legendThe left panel provides a stepwise overview of the study protocol, including (i) participant screening, (ii) functional and anatomical magnetic resonance imaging (MRI) scanning for individualization purposes, (iii) single- and paired-pulse transcranial magnetic stimulation-electroencephalography (TMS-EEG), and (iv) magnetic resonance spectroscopy (MRS) acquired at rest and during a bimanual motor task. The middle and right panels show the grand-average EEG responses to single-pulse TMS over the sensorimotor cortex (SM1, red) and dorsolateral prefrontal cortex (DLPFC, blue), respectively. The topographical maps below each EEG response depict the averaged scalp distributions corresponding to each canonical transcranial evoked potential. Per region, the top left illustration shows the group-level MRS voxel placement, whereas the top right illustration shows the simulated TMS-induced electric fields. While early to mid-latency local cortical responses were best predicted by task-related GABAergic inhibition in SM1, global cortical responses following DLPFC stimulation were best predicted by resting-state neurometabolite levels.

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.

BackgroundTraumatic brain injury (TBI) has lasting effects on white matter, yet sex-specific factors such as menopause timing and hormone replacement therapy (HRT) may modulate these outcomes in females. ObjectivesTo investigate how TBI, menopause timing, HRT use, and reproductive history relate to corpus callosum white matter microstructure in female UK Biobank participants. DesignCross-sectional analysis of UK Biobank diffusion MRI data using propensity score matching to compare females with TBI to controls. MethodsWe analyzed diffusion MRI data from females with and without TBI. Corpus callosum fractional anisotropy (FA), mean diffusivity (MD), and isotropic volume fraction (ISOVF) were assessed. TBI effects were examined across pre- and post-menopausal groups, accounting for HRT use, duration, and reproductive factors. ResultsFemales with TBI (n=363) exhibited widespread corpus callosum alterations compared to propensity-matched controls (n=10,128), with reduced FA across all regions (genu: {beta}=-0.006, FDR p=0.027; body: {beta}=-0.006, FDR p=0.002; splenium: {beta}=-0.004, FDR p=0.009) and elevated MD in anterior regions (genu: FDR p=0.001; body: FDR p=0.002). TBI sustained before menopause was associated with significantly lower splenium FA ({beta}=-0.010, p=0.031) and higher body MD ({beta}=0.000019, p=0.021) compared with TBI sustained after menopause and controls. HRT use did not modify TBI-related alterations in primary analyses. However, among HRT users (n=3,108), a significant TBIxduration interaction emerged for genu MD ({beta}=2.00x10-6, p=0.0295), indicating that the effect of HRT duration on white matter microstructure differed between TBI cases and healthy females. Reproductive factors (parity, reproductive lifespan) independently predicted some white matter measures but did not confound TBI, menopause timing, or HRT associations. ConclusionsTBI-related white matter changes in females are influenced by menopause timing and hormonal exposure, with HRT effects dependent on duration and injury context. These findings highlight the importance of sex- and hormone-specific approaches in TBI research and the need for longitudinal studies to clarify mechanisms and potential interventions.

BackgroundMaternal mental health is an important contributor to child neurodevelopment. While there are multiple studies on prenatal exposure, early postnatal exposure has received little attention in neuroimaging research. Methods5-year-old children (n = 173) were recruited from the FinnBrain Birth Cohort study. Maternal distress was assessed using questionnaires on depressive and anxiety symptoms at 14, 24 and 34 gestational weeks and postnatally at 3, 6 and 24 months. T1-weighted structural images were processed using a voxel-based morphometry pipeline to map associations between maternal distress exposure and regional gray matter (GM) volumes, while accounting for potential confounders. ResultsWe found widespread associations between maternal distress symptoms and offspring brain morphology. Higher prenatal distress at 14 gestational weeks was positively associated with regional GM volume in the right superior parietal lobe and precuneus. In contrast, postnatal distress at 3 months was negatively associated with GM volumes in multiple motor regions, the left anterior insula, right superior frontal areas and supramarginal gyrus. Postnatal distress at 6 months demonstrated a positive relationship with GM volumes in the right calcarine and lingual gyri, while distress at 24 months was negatively associated with GM volumes in the left supramarginal and right superior frontal gyri. ConclusionsThis study provides support for hypotheses proposing that fetal and early life exposure to maternal distress can influence the structural development of the brain. Furthermore, it highlights the role of early postnatal period and calls for further research into this so far overlooked period and pathways that explain the associations.

Normative Modelling ( brain growth charting) is now a well-established method for computational psychiatry and involves charting centiles of variation across a population in terms of mappings between biology and behavior, providing statistical inferences at the level of the individual. These models have helped the field to move away from case-control analysis toward individual-level analysis. Correspondingly, normative modelling has now been applied to chart brain development and ageing in many populations and has been used to quantify individual deviations across various neurological and psychiatric conditions. This has been supported by large-scale models that are openly accessible for diverse brain imaging modalities. As normative modelling continues to grow, several recent methodological developments, such as non-Gaussian models, longitudinal models, and federated learning, have been implemented in different software tools, including the Predictive Clinical Neuroscience toolkit (PCNtoolkit). In this protocol update, we provide: (i) a revised overview of this methodological landscape; (ii) an update to our 2022 standardised analytical protocol for normative modelling of neuroimaging data, including options for federated and longitudinal normative models; (iii) practical guidance suited to both novice and experienced practitioners supported by open-source code examples implemented in the refactored version of PCNtoolkit; and (iv) updated models for cortical thickness, volumetric data, diffusion-weighted imaging and longitudinal data for use by the community.

ImportancePrenatal cannabis exposure is increasing in prevalence, yet its associations with early brain development--particularly how the timing and frequency of exposure across gestation relate to neonatal brain structure--remain insufficiently understood. Clarifying these associations is essential for informing early risk identification and guiding perinatal care. ObjectiveTo examine associations between patterns of maternal prenatal cannabis exposure, including exposure presence, gestational timing, and frequency of exposure, and neonatal brain structure and microstructure during the first month of life. Design, Setting, and ParticipantsThis cohort study included 1,782 mother-infant dyads (221 with PCE) from the HEALthy Brain and Child Development Study. Mother-reported prenatal cannabis exposure was assessed using the validated Timeline Follow-back method. Infants underwent natural-sleep magnetic resonance imaging, including T2-weighted structural imaging and diffusion imaging, within the first month of life. Main Outcomes and MeasuresAssociations between prenatal cannabis exposure and regional T2-weighted volumes and diffusion white matter microstructure metrics examined (1) exposure presence, (2) gestational timing of exposure, and (3) frequency of exposure within exposed infants. ResultsAny prenatal cannabis exposure was associated with brain volume differences in cerebellar and subcortical limbic regions, including smaller amygdala, thalamic, and cerebellar vermis volumes and larger caudate, hippocampal, and cerebellar cortex volumes. Timing-specific analyses revealed divergent patterns: first trimester exposure was associated with smaller volumes in select regions, whereas exposure that continued into the third trimester was associated with larger volumes in overlapping structures, with additional subcortical volumetric differences observed. White matter microstructure alterations were observed only among infants with exposure that continued into the third trimester. Within the exposed subgroup, higher frequency of cannabis exposure was associated with larger cerebral white matter volumes and white matter microstructural differences in white matter regions. Conclusions and RelevanceIn infants with maternal prenatal cannabis exposure, we observed timing- and frequency-dependent differences in brain development within the first month of life. These findings underscore the importance of considering not only the presence of exposure, but also when and how much cannabis is used during pregnancy to support targeted prenatal counseling and early developmental monitoring for exposed infants. Key PointsO_ST_ABSQuestionC_ST_ABSIs prenatal cannabis exposure associated with brain development in the first month of life? FindingsIn a cohort[ABS] of 1,782 mother-infant dyads, prenatal cannabis exposure was associated with region-specific differences in neonatal brain volumes. Brain volume and diffusion white matter microstructure associations differed between exposure limited to the first trimester versus exposure that continued into the third trimester. Greater frequency of exposure across gestation was also associated with volumetric and microstructural differences. MeaningThe timing and frequency of prenatal cannabis exposure is associated with alterations in neonatal brain development, underscoring the importance of addressing cannabis use in pregnancy.

Understanding the neurobiological basis of mental health disorders and their symptoms is a central goal of research in psychiatry. Yet, identifying robust brain-psychopathology associations with neuroimaging remains difficult, in part due to substantial heterogeneity within and comorbidity between diagnostic categories. Transdiagnostic latent factor models aim to address this structure by separating shared and unique symptom variance. This can potentially yield more reliable and neurobiologically-relevant dimensions of psychopathology. However, the extent to which latent factor models improve brain-psychopathology associations remains largely unclear. Using two large developmental cohorts, we compared transdiagnostic bifactor models, correlated factor models, and typical summary scores derived from the Child Behaviour Checklist (CBCL) in their reliability and multivariate associations with whole-brain structure (MRI) and function (resting-state fMRI). We found no consistent evidence that latent factors (bifactor or correlated factor models) strengthened reliability or brain-psychopathology associations, relative to summary scores. Whole-brain predictive models revealed broadly distributed neural signatures that were highly similar between corresponding factor and summary score constructs, with general psychopathology factors and total problem summary scores approaching numerical equivalence. Bifactor scores did, however, display more distinct neural signatures between general, internalising, and externalising dimensions than did summary or correlated factor scores. These results suggest that phenotypic modelling of psychopathology alone does not systematically strengthen the predictive utility of psychiatric neuroimaging, possibly reflecting fundamental limits on the amount of explainable symptom variance by brain features. While latent factor models may aid in distinguishing neural correlates between constructs, improving phenotypic assessment may be necessary for improvements to brain-psychopathology association strength.

Lesion network mapping (LNM) and related techniques have been used in over 200 studies, primarily to test whether anatomically distributed lesions that cause the same symptom fall within a common brain network. A recent article1 challenges the specificity and validity of this technique, suggesting that lesion network maps primarily reflect intrinsic properties of the normative connectome rather than lesion-symptom relationships. However, the data and procedures in van den Heuvel et al. do not reflect those used in most LNM studies. Further, the main conclusions were based on similarity between maps, but similarity does not imply the absence of meaningful differences. In contrast, LNM provides evidence for meaningful differences using specificity testing. Exemplary analyses of 1090 lesion locations from 34 prior LNM studies do not support van den Heuvels concerns and confirm the lesion-deficit specificity of LNM. While we encourage further methodological investigation, the analyses of van den Heuvel et al. do not invalidate prior LNM findings or future applications.

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.

IntroductionLactate plays dual roles in neuronal energy metabolism and signalling. The dorsal anterior cingulate cortex (dACC), a region with high baseline glycolytic activity implicated in psychiatric disorders, may exhibit dynamic lactate responses to graded cognitive-emotional demands. Because mitochondrial function declines with age, aging may model whether fMRS-derived lactate dynamics can detect latent neurometabolic vulnerabilities. MethodsUsing fMRS, we monitored dACC metabolite changes in 34 healthy participants (aged 21-69) during an emotional face-processing task with escalating cognitive-emotional workload. The paradigm comprised a 2-minute baseline, 10-minute task of increasing intensity, and 10-minute recovery. ResultsdACC lactate increased significantly, tracking task intensity and peaking 19.5% above baseline at maximum cognitive load (z = 2.66, p = 0.004). The response showed both linear task-related increases (z = 2.08, p = 0.02) and a quadratic inverted-U profile (z = 2.72, p = 0.004). Total creatine, total NAA and Glx (Glutamate+Glutamine) exhibited no task-dependent changes. Age influenced task-period lactate AUC (z = 2.19, p = 0.014). Participants over 40 exhibited greater peak responses (54% vs 28%), steeper upslopes (14% vs 7% per block), and larger AUC (155% vs 16%) than those under 40. Sex differences were also observed. Baseline lactate did not correlate with age. ConclusionsdACC lactate dynamics are sensitive to cognitive-emotional demand, with evidence of age-and sex-dependent modulation. The dissociation between static and dynamic measures establishes a metabolic stress-testing paradigm for detecting latent neuroenergetic vulnerabilities, supporting fMRS utility for probing mitochondrial function in health and psychiatric disorders.

IntroductionSignificant effort has been put towards mapping patterns of atrophy in the cerebral cortex that are related to pathological aging, including the characteristic patterns of neurodegeneration in Alzheimers disease (AD). In contrast, brain structural patterns that support preserved or even exceptional cognition throughout the adult age-span and especially in later life are much less known. It is possible that superior cognitive performance in late life is supported by a preservation of brain structures vulnerable to typical aging. Alternatively, elevated performance could be related to preservation of brain regions vulnerable to age-associated pathology. Examination of individuals that exhibit superior cognition throughout the adult lifespan may provide unique insights into neural mechanisms that support cognitive resilience in late life. MethodsWe examined cross-sectional associations between cortical brain structure and cognitive performance across three stages of adulthood: midlife (36-59), young-old (60-79), and older adults (80+) in typically aging individuals enrolled as part of the Human Connectome Project Lifespan-Aging (HCP-A)/Aging Adult Brain Connectome (AABC) studies. Participants were considered generally healthy and excluded for significant and/or atypical health conditions for their demographic category, including a clinical diagnosis of cognitive impairment or dementia. Domain-specific cognitive factor scores representing memory, fluid intelligence, and crystalized intelligence were sex-stratified and residualized relative to age, and participants were classified as high, middle, or low performers based on their unique performance relative to the study sample. ResultsIn the full sample, high performers demonstrated greater cortical thickness in regions of somatomotor, visual, and auditory cortices, as well as cortical areas in the frontal, parietal, and insular cortices (e.g., 5m, LIPv, MBelt). We also found associations between medial temporal and cingulate cortical thickness and cognitive performance, but only for select analyses. Group differences in cortical thickness were greatest when contrasting high and low performers for fluid intelligence compared to the other cognitive factors and were most prominent in the midlife participants compared to the other age strata. These group differences were primarily driven by reduced cortical thickness in the low performing individuals in the younger age bins relative to the typically performing sample. Effects were rather limited when contrasting high and low performers in the 80+ age group. The cortical areas of high statistical significance appeared to show a cross-sectional convergence effect, such that the differences in cortical thickness between high vs. low cognitive performance groups diminished with increasing age. Despite lower statistical power, effect sizes were greatest when contrasting individuals at the extremes of performance (e.g., top 10% vs. bottom 10% performers). These effects were robust to subsample replications using longitudinally defined cognitive classifications. DiscussionElevated cognitive performance was cross-sectionally associated with increased regional cortical thickness and effects were most prominent in mid-life compared to later ages. Notably, contrary to brain regions that may be expected to support such high-order cognitive performance, a significant portion of primary sensory, motor, and insular cortical areas exhibited group differences between the high- and low-performing groups in this relatively younger age group. Group differences were due to lower thickness in these regions in the low performing group relative to the typical performers. In contrast to the younger portion of the sample, regions typically considered vulnerable to Alzheimers disease (i.e., regions in the medial temporal lobe) were only infrequently implicated. These results suggest that specific patterns of cortical brain structural integrity including preserved thickness of primary motor and sensory cortical regions may be a necessary but not sufficient mechanism supporting superior cognitive abilities in earlier adulthood, while alternate neural mechanisms may support cognitive resilience later in life. These results must be interpreted with caution given the cross-sectional nature of this study. Cognitive capacity can only be estimated from a single timepoint, and several factors contribute to inter-individual variation that will not be accounted for in the models applied here. Longitudinal assessment of cognitive resilience in the HCP-A/AABC cohort will be performed in future work.

BackgroundTo understand the neurobiology underlying psychopathology, we need valid measurements of brain function. Group atlases for brain functional connectivity (FC) allow for efficient comparisons, but they fail to account for inter-individual variability in network topography, a problem that personalized methods address. We assess the validity and predictive utility of group and personalized approaches of quantifying FC by 1) comparing effect sizes of associations with clinical metrics; and 2) accounting for spatial features of brain networks when examining the association between FC and clinical metrics. Methods324 teens ages 13-16 participated. Personalized networks were estimated using a hierarchical Bayesian model. Effect size comparisons were done by comparing the correlations between FC and clinical metrics (depression, ruminative coping style, and sensitivity to punishment/reward) with Steiglers Z-test. We also conducted regressions, with clinical metrics as the dependent variables. Those models included FC and spatial features, together and alone. ResultsThe effect size comparisons did not survive FDR correction. However, exploratory permutation tests show that 1) the magnitude of the correlations with depression are larger on average for the intersection estimates of FC than the group estimates; and 2) the magnitude of the correlations with a ruminative coping style are larger on average for the intersection estimates of FC than the personalized estimate. The other comparisons conducted using permutation tests are not significant. Multiple regression analyses demonstrated that only spatial features of networks, not FC, are associated with sensitivity to reward. DiscussionThese results imply that the intersection estimates are more valid than the group estimates, and that the intersection estimates have greater predictive utility than personalized estimates. Further, spatial features of functions networks may be useful in and of themselves in certain contexts. Therefore, researchers in psychiatry should take into consideration functional network topography in order to gain a better understanding of the neurobiology underlying psychopathology.

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.

The impact of age and hearing loss on the brain has garnered significant attention, as both factors have been implicated in the development of cognitive impairment or dementia. In this study, we investigated the impact of hearing loss and tinnitus on gray matter in the brain, while accounting for age. We used a comprehensive secondary analysis of structural MRI data obtained from multiple research sites (256 unique individuals) using voxel-based and surface-based morphology. After harmonization of this multi-site brain data, our research replicated the previously reported finding of age-related decline in total cortical volume, but there was no significant effect of either hearing loss or tinnitus on total cortical volume. When a region of interest analysis was conducted, the hippocampus emerged as the only brain region that showed a direct impact of hearing loss, after accounting for variance associated with age. This effect on hippocampal volume was evident in our sample from age 52 years onwards; when adjusted for hearing loss, the decline began at age 56 years. For the presence of tinnitus, ventral posterior cingulate gyrus showed main effects with respect to cortical volume and surface area while medial occipito-temporal gyrus and operculum of the inferior frontal gyrus showed significant main effects only with surface area. Post-hoc analysis revealed that posterior cingulate gyrus showed significantly higher volume and larger surface area in individuals with tinnitus compared to those without tinnitus. Similarly medial occipito-temporal gyrus surface area was increased whereas surface area of the inferior frontal opercular gyrus was reduced in those with tinnitus when compared to those without tinnitus. Notably, while past studies have reported that the presence of tinnitus appeared to moderate some of these effects in certain participant groups, our results suggest a more complex relationship between sensory degradation, chronic tinnitus, and brain structure in individuals across the adult lifespan. HighlightsO_LIHearing loss and tinnitus can exacerbate regional brain atrophy in the adult lifespan. C_LIO_LIHigh-frequency hearing loss affects auditory cortex gray matter volume to a larger degree in older age. C_LIO_LIHearing loss may accelerate decline in hippocampal volume by about 4 years. C_LIO_LIChronic subjective tinnitus is associated with a larger volume of cingulate cortex, increased surface area in cingulate cortex and the lingual gyrus, and decreased surface area of frontal operculum compared to controls. C_LIO_LITinnitus-related effects on regional brain atrophy are not modified by the degree of hearing deficits. C_LI

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

BackgroundHIV and cocaine use (CU) each relate to cognitive deficits and brain abnormalities, yet their combined impact on brain aging remains unclear. This study examined how comorbid HIV and CU relate to brain aging and cognitive impairment. MethodsWe trained a morphometry-based brain-age model using harmonized Human Connectome Project-Aging data (HCP-A; n=725) with Gaussian Process Regression. The model was applied to an independent cohort with varying HIV/CU burden (HIV-/CU-, n=34; one disorder [HIV+/CU- or HIV-/CU+], n=72; HIV+/CU+, n=80). Brain age gap (BAG; predicted minus chronological age) was examined in relation to comorbidity burden and neurocognitive impairment (NCI; NIH Toolbox), adjusting for age, sex, education, depression, and image-quality indices. Analyses on SHapley Additive exPlanation (SHAP) values characterized network-wise feature-level contributions to brain age estimates. ResultsA dose-dependent effect of comorbidity burden on BAG was observed, with the HIV+/CU+ group showing the highest BAG. Greater BAG was associated with increased likelihood of NCI, and BAG partially mediated the relationship between comorbidity burden and NCI, with a stronger mediation effect in the two-disorder group than in the one-disorder group. Structural contributors to elevated BAG in the HIV/CU cohort included cortical thickness in the visual, ventral attention, and frontoparietal networks, and sulcal depth in the sensorimotor network. ConclusionComorbid HIV/CU is linked to accelerated structural brain aging. BAG may reflect brain-level alterations underlying the association between comorbid HIV/CU and cognitive impairment, and may help identify network-specific targets for intervention.

BackgroundExecutive functions are a key target of cognitive interventions for older adults due to their central role in daily functioning and maintaining a good quality of life. Piano training has been proposed as an ecologically valid method of improving cognition and brain structure in older adults. The primary aims of this study were to (i) evaluate the feasibility and acceptability of Piano Instruction for Adult Novices as an Online Cognitive Intervention (PIANO-Cog), a novel bespoke 8-week self-guided piano training programme for adults over 50 years of age, and (ii) assess the feasibility of conducting a fully-powered randomised controlled trial (RCT), including recruitment, retention, and adherence. Secondary aims explored effects of PIANO-Cog on executive functions and brain microstructure using advanced diffusion-weighted imaging (DWI). MethodThirty-three healthy music novices aged 51-80 years (M = 63.73, SD = 7.94) participated in a two-arm unblinded feasibility RCT. Participants were assigned by stratified allocation for age and sex to either (i) 8 weeks of PIANO-Cog, requiring 30 minutes of practice, 5 days per week, or (ii) a passive control group. Cognitive assessment and MRI scanning were conducted before and after the intervention using a strong-gradient (300mT/m) 3T Connectom scanner to acquire multi-shell DWI data with b-values ranging from 200 to 6,000 s/mm{superscript 2}. Grey and white matter microstructure were modelled with Soma And Neurite Density Imaging (SANDI) and Neurite Orientation Density and Dispersion Imaging (NODDI). ResultsAccording to predefined criteria, feasibility was established for recruitment (91.6%), retention (75%) and adherence (>100%) rates. Preliminary observations suggest that piano training compared with control was associated with improvements in verbal fluency and multiple changes in brain microstructure including increases in apparent soma size and radius and reductions in extracellular signal in frontal and temporal cortical regions, larger apparent neurite density in right inferior frontal gyrus and changes in neurite dispersion in left middle temporal and right precentral gyri. DiscussionThe results demonstrate that short-term remote piano training is a feasible cognitive intervention for healthy adults over 50. Preliminary evidence suggest that PIANO-Cog was associated cognitive improvements and changes in brain microstructure in executive, auditory and motor regions.

BackgroundThe population receptive field (pRF) in vision reflects the functional receptive field arising from millions of overlapping single receptive fields across visual areas and eccentricities. pRFs are typically estimated with fMRI to gain insight into visual processing. Alternative methods of pRF estimation, such as using optical illusions, have been explored only sparingly. In this study, we explore the rotating tilted lines illusion (RTLI), in which a circle formed by tilted lines appears to rotate as it expands or contracts in the visual field (e.g., from moving the head back and forth). New MethodWe propose a novel set of computer-generated animations of the RTLI that measure the visual and temporal characteristics of the illusory rotation, enabling quantitative estimation of the spatial extent and temporal dynamics of the pRF. ResultsWe derived pRF size estimates consistent with those estimated from fMRI and electrophysiological methods. We then projected changes in RTLI percept trends according to abnormalities in visual processing in autism spectrum disorder (ASD), schizophrenia (SZ), aging, and Alzheimers disease (AD). Comparison with existing methodsCompared to fMRI and electrophysiology, RTLI-based pRF estimation is accessible, low-cost, and feasible at home or during inpatient visits without specialized equipment. ConclusionsWe show that our novel method can approximate pRFs, which in turn can be potentially applied for early detection, probing the progress, and treatment screening in AD, SZ and ASD.

Human intelligence is essential to understand complex ideas, to engage in various forms of reasoning, to learn from experience, and to adapt to new situations by taking thought. The P300 event-related brain potential has been related to intelligence scores and thus represents as a promising biomarker of general cognitive ability. However, empirical results are characterized by enormous heterogeneity, and a quantitative assessment of this literature is lacking. This preregistered meta-analysis provides the first systematic overview of neuroscientific studies that have examined associations between general cognitive ability and the P300 in healthy adult participants. Out of 5641 articles screened, 49 studies with up to 381 effects were eligible for PRISMA-based meta-analytic comparison. Study quality was evaluated using a novel Study Design and Implementation Assessment Device, which we developed particularly for Individual Difference Research (DIAD-ID) and provide together with our analysis code as free online resources to support future meta-analyses. Confirming our hypotheses, a small but significant positive across-study association was observed for general cognitive ability and P300 amplitudes (r = .13; 95%-CI [.06, .19]), while a significant negative across-study association was revealed for P300 latencies (r = -.18; 95%-CI [-.24, -.13]). Study heterogeneity was substantial, and sub-analyses highlighted potential moderators such as type of the task during EEG recording. We discuss limitations, open questions, and provide concrete guidelines for future research on the neurobiological underpinnings of individual differences in cognitive ability.