Harri Merisaari

Maternal 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. Five‐year‐old children ( n = 161) 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. Whole‐brain voxel‐wise general linear models were used, accounting for potential confounders. Additionally, a latent class mixed model approach was used to model longitudinal trajectories of distress exposure. We found widespread associations between maternal postpartum distress symptoms and offspring brain morphology. Postnatal distress at 3 months was negatively associated with GM volumes in the left supplementary motor cortex, right cerebellum/fusiform gyrus, and left anterior insula/frontal operculum. Postnatal distress at 6 months was positively associated with GM volumes in the right cuneus and lingual gyrus, right insula/operculum, and right inferior occipital gyrus, while distress at 24 months was negatively associated with GM volumes in the left inferior/middle frontal gyrus. There were no structural brain differences between the latent classes. This study provides support for hypotheses proposing that early‐life exposure to maternal distress can influence the structural development of the brain. It highlights the role of the early postnatal period and calls for further research into this so far overlooked period and the developmental pathways that explain these associations.

The immune system has emerged as a critical factor influencing neurodevelopment in offspring. While this is increasingly well‐studied in the context of maternal immune activation (MIA), the relationship between postnatal immune activation and brain development in childhood is far less studied. In the current study, we performed targeted immune proteomics to analyse 356 inflammation‐associated proteins and examine whether peripheral immune physiology is associated with multimodal neuroimaging metrics, brain growth centiles, and cortical microstructure, in 5‐year‐old children. We included 101 typically developing children with immune proteomics and multimodal neuroimaging data covering structural and diffusion‐weighted magnetic resonance imaging (MRI) that was modelled with FSL's linked independent component analysis (FLICA) to yield 10 multimodal brain components. For a subset of the analyses utilising immune proteomics and structural brain growth centile data, the sample size was 126 children. We observed consistent negative associations between serum immune proteins and both grey matter volume growth and global cortical thickness; for the latter, associations were statistically significant after false discovery rate correction for CXCL10, IL17A, and LILRB4. Similarly, many immune proteins were associated with increased mean diffusivity across the cortex, most notably LAMP3, NCR1, SIGLEC1, FASLG, and CXCL10 in the right medial orbitofrontal cortex. Collectively, these findings provide the first characterisation of the relationship between immune physiology and brain structure in children, and demonstrate notable associations with global cerebrocortical development. This should be extended by future studies with larger samples and multiple sampling.

Most neuroimaging studies of speech disfluency have compared individuals who stutter with fluent controls. However, treating speech disfluency as a continuous, dimensional trait offers new insights into the neural basis of fluency during early childhood. We investigated whether naturally occurring variation in speech disfluency is associated with grey matter structure in a population‐based sample of 5‐year‐old children ( N = 120; 65 M, 55 F). Disfluency was derived from audio and video‐recorded conversational speech samples transcribed and coded using the Ambrose and Yairi classification into stuttering‐like disfluencies (SLDs) and other disfluencies (ODs). T1‐weighted magnetic resonance imaging data were analyzed using voxel‐based morphometry and complemented by surface‐based measures of cortical thickness and surface area (FreeSurfer). Whole brain voxel‐wise regression tested associations between grey matter and disfluency while adjusting for age, sex, handedness, and maternal education, with additional robustness checks (including intracranial volume adjustment and alternative SLD operationalizations). Higher SLDs were positively correlated with regional grey matter volume primarily in the frontal cortex, with clusters in the left middle frontal gyrus and right superior frontal gyrus showing the greatest cross‐model stability (uncorrected voxel‐level p < 0.001; FDR‐corrected cluster‐level p < 0.05); additional regions, including posterior cerebellum, were more sensitive to model specification. No significant voxel‐wise associations were observed for ODs, and surface‐based analyses yielded no significant associations with SLDs. Boys showed higher SLD rates than girls, but voxel‐wise sex × SLD interactions were not significant. These findings suggest that dimensionally measured SLDs across a population‐based cohort are most consistently related to frontal control systems implicated in planning and monitoring demands during speech. The overlap with frontal regions reported in diagnosis‐based stuttering studies suggests that frontal control circuitry is relevant to fluency variation in early childhood across both dimensional and categorical approaches. These findings support a complementary, dimensional approach to studying fluency variation in early childhood, while underscoring the need for longitudinal work to determine how naturally occurring disfluency relates to later developmental stuttering.

IMPORTANCE: Childhood obesity is a growing global health concern associated with adverse physical, psychiatric, and neurodevelopmental outcomes. Although previous neuroimaging studies have linked obesity to widespread alterations in brain structure and function, it remains unclear how well multimodal neuroimaging measures and genetic markers can predict future weight gain and inform early intervention strategies. OBJECTIVE: To evaluate the predictive utility of multimodal MRI measures and polygenic risk scores for obesity in estimating proportional body weight at baseline and predicting weight gain over one year in preadolescent children. DESIGN, SETTING, AND PARTICIPANTS: This study used data from the Adolescent Brain Cognitive Development (ABCD) Study, a large-scale, multisite longitudinal cohort of children aged 9 to 10 years (N = 11,880). Analyses included baseline data collected between 2016 and 2018, and one-year follow-up data collected between 2018 and 2020 across multiple imaging sites. MAIN OUTCOMES AND MEASURES: Elastic net regression models were applied to structural MRI (including diffusion tensor imaging) and resting-state functional MRI data to predict baseline triponderal mass index (TMI), a weight-for-height measure that more accurately reflects adiposity in children than body-mass index (BMI). Longitudinal classification models were developed to predict excess weight gain relative to normative developmental trajectories at one-year follow-up. Models were evaluated with and without the inclusion of polygenic risk scores and other non-imaging covariates. Generalizability was assessed using leave-one-site-out cross-validation. RESULTS: Structural MRI measures predicted baseline TMI with an R² of 0.21, whereas resting-state functional MRI measures predicted TMI with an R² of 0.08. Classification models predicted one-year weight gain with area under the receiver operating characteristic curve (AUC) values of 0.73 for structural MRI and 0.60 for resting-state functional MRI. Including polygenic risk scores and other covariates improved model performance (structural MRI: R² = 0.25, AUC = 0.75; resting-state functional MRI: R² = 0.15, AUC = 0.69). Leave-one-site-out cross-validation revealed reduced generalizability across imaging sites (structural MRI R² = 0.13–0.17; resting-state functional MRI R² = 0.02–0.09; structural MRI AUC = 0.73–0.74; resting-state functional MRI AUC = 0.60–0.67). CONCLUSIONS AND RELEVANCE: Multimodal MRI measures were associated with proportional body weight and demonstrated modest predictive utility for future weight gain in preadolescent children, explaining up to one fifth of the variance in weight-related outcomes. The addition of genetic and non-imaging variables improved prediction accuracy, underscoring the multifactorial nature of childhood obesity. However, the observed decline in performance under site-wise cross-validation highlights the need to address site-related variability to enhance reproducibility and generalizability in neuroimaging-based predictive models of pediatric obesity.

The aim of this study was to explore the associations between an aspect of self‐regulation (SR), effortful control (EC) and cortical brain structure in 5‐year‐old children. Efficient EC is a predictor of many attributes and important outcomes in life, such as social–emotional functioning, finance, psychiatric and somatic health. The early brain correlates of EC are not widely studied, and a better understanding of them would aid in understanding how self‐regulatory capacities emerge over development. Participants ( N = 155) were a part of the FinnBrain Birth Cohort Study in Finland. T1‐weighted brain magnetic resonance images were processed using FreeSurfer. The data were statistically analysed with a vertex‐wise general linear model. At the age of 5 years, EC was assessed via parental report using The Children's Behaviour Questionnaire. We found positive associations between EC and cortical volume in the left supramarginal region and in the right inferior temporal region. We also found positive associations between EC and surface area on the left hemisphere in the superior parietal region. We extended the previous literature by shedding light on early structural brain correlates of EC in a large sample of typically developing 5‐year‐olds. The main results differed significantly from previous findings in older children. The results were only present with questionnaire‐ and not task‐based evaluation of EC. Both questionnaire and task‐based evaluations are required to consider different aspects of EC and SR. In addition, longitudinal studies are needed to better understand the neural underpinnings of SR throughout development.

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 points: An 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. As 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.

The functional organization of the human brain is established early, yet the ontogeny of its large-scale functional gradients remains unclear. Using resting-state fMRI data from 714 neonates in the Developing Human Connectome Project, we mapped neonatal brain gradients via functional connectome harmonics (FCH). We identified adult-like sensory-to-multimodal and cognitive gradient patterns present at birth. Applying three FCH-derived metrics, entropy, power, and energy, we found that power and energy were higher in term-born compared to preterm neonates, while entropy was elevated in preterms. These metrics predicted up to ~30% of postmenstrual age, indicating their biological relevance. Our findings reveal that the neonatal brain possesses a robust gradient architecture underpinning early functional organization, offering novel biomarkers for assessing brain maturity and the impact of prematurity on neurodevelopment.

The maturation of large-scale functional brain networks during preadolescence underpins critical cognitive and behavioral development. However, the spatial and temporal organization of these networks in this age group remains incompletely characterized. Here we applied Functional Connectome Harmonics (FCH) and Leading Eigenvector Dynamics Analysis (LEiDA) to resting-state fMRI data from over 11,000 children aged 9-10 years in the ABCD Study. FCH revealed hierarchical spatial gradients spanning cortical and subcortical regions, while LEiDA identified recurrent dynamic brain states aligned with canonical intrinsic connectivity networks. Linking these spatial and temporal components, we established a low-dimensional harmonic scaffold constraining brain dynamics during this developmental window. These findings provide a large-scale spatiotemporal reference framework of preadolescent functional brain organization, offering a foundation for characterizing neurodevelopmental benchmarks and early neural markers relevant to adolescent mental health.

Perinatal period is a critical time for brain development and premature-born children have an elevated likelihood for neurodevelopmental conditions. While strong associations of structural magnetic resonance imaging with preterm birth and post-menstrual age (PMA) have been reported, results with functional MRI (fMRI) have been considerably weaker. Using the task-free neonatal fMRI dataset from the Developing Human Connectome Project (dHCP), we first studied the associations of the aperiodic component of the blood-oxygen-level-dependent (BOLD) signal power spectrum from pre- and postcentral gyri with preterm birth and mapped the associations with PMA, postnatal age, and sex, and found clear differences between preterm and full-term groups, as well as males and females. Second, we used machine learning regression to predict participants’ age from the aperiodic parameters of the BOLD signal from 90 cortical and subcortical regions of interest with relatively high accuracy (test R²’s 0.20–0.41).