An end-to-end infant brain parcellation pipeline.

Objective: Accurate infant brain parcellation is crucial for understanding early brain development; however, it is challenging due to the inherent low tissue contrast, high noise, and severe partial volume effects in infant magnetic resonance images (MRIs). The aim of this study was to develop an end-to-end pipeline that enabled accurate parcellation of infant brain MRIs. Methods: We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs. Specifically, in the global regions of interest (ROIs) localization stage, a combination of transformer and convolution operations was employed to capture both global spatial features and fine texture features, enabling an approximate localization of the ROIs across the whole brain. In the local ROIs refinement stage, leveraging the position priors from the first stage along with the raw MRIs, the boundaries o the ROIs are refined for a more accurate parcellation. Results: We utilized the Dice ratio to evaluate the accuracy of parcellation results. Results on 263 subjects from National Database for Autism Research (NDAR), Baby Connectome Project (BCP) and Cross-site datasets demonstrated the better accuracy and robustness of our method than other competing methods. Conclusion: Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.

A Novel Registration Framework for Aligning Longitudinal Infant Brain Tensor Images.

Registering longitudinal infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months and years of life. Diffusion tensor images (DTI) have relatively consistent tissue properties over the course of infancy compared to commonly used T1 or T2-weighted images, presenting great potential for infant brain registration. Moreover, groupwise registration has been widely used in infant neuroimaging studies to reduce bias introduced by predefined atlases that may not be well representative of samples under study. To date, however, no methods have been developed for groupwise registration of tensor-based images. Here, we propose a novel registration approach to groupwise align longitudinal infant DTI images to a sample-specific common space. Longitudinal infant DTI images are first clustered into more homogenous subgroups based on image similarity using Louvain clustering. DTI scans are then aligned within each subgroup using standard tensor-based registration. The resulting images from all subgroups are then further aligned onto a sample-specific common space. Results show that our approach significantly improved registration accuracy both globally and locally compared to standard tensor-based registration and standard fractional anisotropy-based registration. Additionally, clustering based on image similarity yielded significantly higher registration accuracy compared to no clustering, but comparable registration accuracy compared to clustering based on chronological age. By registering images groupwise to reduce registration bias and capitalizing on the consistency of features in tensor maps across early infancy, our groupwise registration framework facilitates more accurate alignment of longitudinal infant brain images.

Cortical Face-Selective Responses Emerge Early in Human Infancy.

In human adults, multiple cortical regions respond robustly to faces, including the occipital face area (OFA) and fusiform face area (FFA), implicated in face perception, and the superior temporal sulcus (STS) and medial prefrontal cortex (MPFC), implicated in higher-level social functions. When in development, does face selectivity arise in each of these regions? Here, we combined two awake infant functional magnetic resonance imaging (fMRI) datasets to create a sample size twice the size of previous reports (n = 65 infants; 2.6-9.6 months). Infants watched movies of faces, bodies, objects, and scenes, while fMRI data were collected. Despite variable amounts of data from each infant, individual subject whole-brain activation maps revealed responses to faces compared to nonface visual categories in the approximate location of OFA, FFA, STS, and MPFC. To determine the strength and nature of face selectivity in these regions, we used cross-validated functional region of interest analyses. Across this larger sample size, face responses in OFA, FFA, STS, and MPFC were significantly greater than responses to bodies, objects, and scenes. Even the youngest infants (2-5 months) showed significantly face-selective responses in FFA, STS, and MPFC, but not OFA. These results demonstrate that face selectivity is present in multiple cortical regions within months of birth, providing powerful constraints on theories of cortical development.

Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels.

Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.

Cortical specialization associated with native speech category acquisition in early infancy.

This study investigates neural processes in infant speech processing, with a focus on left frontal brain regions and hemispheric lateralization in Mandarin-speaking infants' acquisition of native tonal categories. We tested 2- to 6-month-old Mandarin learners to explore age-related improvements in tone discrimination, the role of inferior frontal regions in abstract speech category representation, and left hemisphere lateralization during tone processing. Using a block design, we presented four Mandarin tones via [ta] and measured oxygenated hemoglobin concentration with functional near-infrared spectroscopy. Results showed age-related improvements in tone discrimination, greater involvement of frontal regions in older infants indicating abstract tonal representation development and increased bilateral activation mirroring native adult Mandarin speakers. These findings contribute to our broader understanding of the relationship between native speech acquisition and infant brain development during the critical period of early language learning.

Assessing Awareness, Knowledge, and Attitude of Saudi Mothers Regarding Shaken Baby Syndrome in the Eastern Province of Saudi Arabia: A Cross-Sectional Prospective Study.

Shaken baby syndrome (SBS) is a form of traumatic brain injury. Shaking babies can cause the brain matter to bounce within the cranium causing bruising and bleeding, which can result in permanent brain injury. Understanding the attitudes and knowledge of mothers on SBS would help establish effective interventions to raise awareness and establish preventive measures and education programs to avoid debilitating sequelae from SBS in newborns and infants. This study aimed to explore the awareness and attitude regarding SBS. An observational, cross-sectional study was conducted from April 1st through July 31st, 2023. The study population is comprised of mothers who are residents of the Eastern Province of Saudi Arabia and excluded females with no children and those who refused to participate, in addition to mothers not in the Eastern Province. The final sample size included 403 participants. An online-based validated questionnaire was used in the Arabic language. The questionnaire included demographic information and questions to assess the knowledge and attitude of participants regarding SBS. The chi-square test was used to test for significant associations. The majority of the participants were married (72%), while 15.6% were divorced and 10.2% were widowed. Only 7.4% of the participants were illiterates, 30.5% had primary education only, and 15.9% had postgraduate studies. Of note, 37% of the participants said that they would shake their children to calm them if they started to cry. Only 33% of the participants said that shaking babies is harmful. The most commonly reported complications of shaking babies were intracranial bleeding (48.1%), behavioral changes (23.8%), and learning disability (23.5%). Regarding attitude toward SBS, more than two-thirds (72.5%) of the participants said that they want to know more about SBS. Only the educational level had statistically significant relationship between the awareness and the sociodemographic level of the participants. This study concludes that Saudi mothers' knowledge about SBS is inadequate despite the favorable attitude toward gaining information about it. The awareness level is significantly associated with educational status, which reflects the importance of education programs, especially during the pregnancy period, in raising awareness about SBS and its complications.

Approaches to Minimise the Neurodevelopmental Impact of Choroid Plexus Carcinoma and Its Treatment.

Choroid plexus carcinomas (CPC) are rare aggressive tumours that primarily affect very young children. Treatment for CPC typically involves a combination of surgery, chemotherapy, and radiation therapy. Whilst considered necessary for a cure, these therapies have significant neurocognitive consequences for patients, negatively impacting cognitive function including memory, attention, executive functioning, and full-scale intelligence quotients (FSIQ). These challenges significantly impact the quality of life and ultimately socioeconomic parameters such as the level of educational attainment, marital status, and socioeconomic status. This review looks at the tumour- and treatment-related causes of neurocognitive damage in CPC patients and the progress made in finding strategies to reduce these. Opportunities to mitigate the neurodevelopmental consequences of surgery, chemotherapy, and radiation therapy are explored in the context of CPC treatment. Evaluation of the pathological and biological mechanisms of injury has identified innovative approaches to neurocognitive protection and neurorehabilitation, which aim to limit the neurocognitive damage. This review aims to highlight multiple approaches physicians can use when treating young children with CPC, to focus on neurocognitive outcomes as a measure of success.

Effect of number of diffusion encoding directions in neonatal diffusion tensor imaging using Tract-Based Spatial Statistical analysis.

Diffusion tensor imaging (DTI) has been used to study the developing brain in early childhood, infants and in utero studies. In infants, number of used diffusion encoding directions has traditionally been smaller in earlier studies down to the minimum of 6 orthogonal directions. Whereas the more recent studies often involve more directions, number of used directions remain an issue when acquisition time is optimized without compromising on data quality and in retrospective studies. Variability in the number of used directions may introduce bias and uncertainties to the DTI scalar estimates that affect cross-sectional and longitudinal study of the brain. We analysed DTI images of 133 neonates, each data having 54 directions after quality control, to evaluate the effect of number of diffusion weighting directions from 6 to 54 with interval of 6 to the DTI scalars with Tract-Based Spatial Statistics (TBSS) analysis. The TBSS analysis was applied to DTI scalar maps, and the mean region of interest (ROI) values were extracted using JHU atlas. We found significant bias in ROI mean values when only 6 directions were used (positive in fractional anisotropy [FA] and negative in fractional anisotropy [MD], axial diffusivity [AD] and fractional anisotropy [RD]), while when using 24 directions and above, the difference to scalar values calculated from 54 direction DTI was negligible. In repeated measures voxel-wise analysis, notable differences to 54 direction DTI were observed with 6, 12 and 18 directions. DTI measurements from data with at least 24 directions may be used in comparisons with DTI measurements from data with higher numbers of directions.

Fine-grained functional parcellation maps of the infant cerebral cortex.

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.

Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network.

Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of image contents. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both tissue contrasts and anatomical details.

Negative versus withdrawn maternal behavior: Differential associations with infant gray and white matter during the first 2 years of life.

Distinct neural effects of threat versus deprivation emerge by childhood, but little data are available in infancy. Withdrawn versus negative parenting may represent dimensionalized indices of early deprivation versus early threat, but no studies have assessed neural correlates of withdrawn versus negative parenting in infancy. The objective of this study was to separately assess the links of maternal withdrawal and maternal negative/inappropriate interaction with infant gray matter volume (GMV), white matter volume (WMV), amygdala, and hippocampal volume. Participants included 57 mother-infant dyads. Withdrawn and negative/inappropriate aspects of maternal behavior were coded from the Still-Face Paradigm at four months infant age. Between 4 and 24 months (M age = 12.28 months, SD = 5.99), during natural sleep, infants completed an MRI using a 3.0 T Siemens scanner. GMV, WMV, amygdala, and hippocampal volumes were extracted via automated segmentation. Diffusion weighted imaging volumetric data were also generated for major white matter tracts. Maternal withdrawal was associated with lower infant GMV. Negative/inappropriate interaction was associated with lower overall WMV. Age did not moderate these effects. Maternal withdrawal was further associated with reduced right hippocampal volume at older ages. Exploratory analyses of white matter tracts found that negative/inappropriate maternal behavior was specifically associated with reduced volume in the ventral language network. Results suggest that quality of day-to-day parenting is related to infant brain volumes during the first two years of life, with distinct aspects of interaction associated with distinct neural effects.

Neural Network Functional Interactions Mediate or Suppress White Matter-Emotional Behavior Relationships in Infants.

BACKGROUND: Elucidating the neural basis of infant positive emotionality and negative emotionality can identify biomarkers of pathophysiological risk. Our goal was to determine how functional interactions among large-scale networks supporting emotional regulation influence white matter (WM) microstructural-emotional behavior relationships in 3-month-old infants. We hypothesized that microstructural-emotional behavior relationships would be differentially mediated or suppressed by underlying resting-state functional connectivity (rsFC), particularly between default mode network and central executive network structures. METHODS: The analytic sample comprised primary caregiver-infant dyads (52 infants [42% female, mean age at scan = 15.10 weeks]), with infant neuroimaging and emotional behavior assessments conducted at 3 months. Infant WM and rsFC were assessed by diffusion-weighted imaging/tractography and resting-state magnetic resonance imaging during natural, nonsedated sleep. The Infant Behavior Questionnaire-Revised provided measures of infant positive emotionality and negative emotionality. RESULTS: After significant WM-emotional behavior relationships were observed, multimodal analyses were performed using whole-brain voxelwise mediation. Results revealed that greater cingulum bundle volume was significantly associated with lower infant positive emotionality (ß = -0.263, p = .031); however, a pattern of lower rsFC between central executive network and default mode network structures suppressed this otherwise negative relationship. Greater uncinate fasciculus volume was significantly associated with lower infant negative emotionality (ß = -0.296, p = .022); however, lower orbitofrontal cortex-amygdala rsFC suppressed this otherwise negative relationship, while greater orbitofrontal cortex-central executive network rsFC mediated this relationship. CONCLUSIONS: Functional interactions among neural networks have an important influence on WM microstructural-emotional behavior relationships in infancy. These relationships can elucidate neural mechanisms that contribute to future behavioral and emotional problems in childhood.

Connections between spatially distant primary language regions strengthen with age during infancy, as revealed by resting-state fNIRS.

Objective.Hearing is an important sensory function that plays a key role in how children learn to speak and develop language skills. Although previous neuroimaging studies have established that much of brain network maturation happens in early childhood, our understanding of the developmental trajectory of language areas is still very limited. We hypothesized that typical development trajectory of language areas in early childhood could be established by analyzing the changes of functional connectivity in normal hearing infants at different ages using functional near-infrared spectroscopy.Approach.Resting-state data were recorded from two bilateral temporal and prefrontal regions associated with language processing by measuring the relative changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Connectivity was calculated using magnitude-squared coherence of channel pairs located in (a) inter-hemispheric homologous and (b) intra-hemispheric brain regions to assess connectivity between homologous regions across hemispheres and two regions of interest in the same hemisphere, respectively.Main results.A linear regression model fitted to the age vs coherence of inter-hemispheric homologous test group revealed a significant coefficient of determination for both HbO (R2= 0.216,p= 0.0169) and HbR (R2= 0.206,p= 0.0198). A significant coefficient of determination was also found for intra-hemispheric test group for HbO (R2= 0.237,p= 0.0117) but not for HbR (R2= 0.111,p= 0.0956).Significance.The findings from HbO data suggest that both inter-hemispheric homologous and intra-hemispheric connectivity between primary language regions significantly strengthen with age in the first year of life. Mapping out the developmental trajectory of primary language areas of normal hearing infants as measured by functional connectivity could potentially allow us to better understand the altered connectivity and its effects on language delays in infants with hearing impairments.

Infant brain regional cerebral blood flow increases supporting emergence of the default-mode network.

Human infancy is characterized by most rapid regional cerebral blood flow (rCBF) increases across lifespan and emergence of a fundamental brain system default-mode network (DMN). However, how infant rCBF changes spatiotemporally across the brain and how the rCBF increase supports emergence of functional networks such as DMN remains unknown. Here, by acquiring cutting-edge multi-modal MRI including pseudo-continuous arterial-spin-labeled perfusion MRI and resting-state functional MRI of 48 infants cross-sectionally, we elucidated unprecedented 4D spatiotemporal infant rCBF framework and region-specific physiology-function coupling across infancy. We found that faster rCBF increases in the DMN than visual and sensorimotor networks. We also found strongly coupled increases of rCBF and network strength specifically in the DMN, suggesting faster local blood flow increase to meet extraneuronal metabolic demands in the DMN maturation. These results offer insights into the physiological mechanism of brain functional network emergence and have important implications in altered network maturation in brain disorders.

3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images.

Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infants. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review.

Introduction: To improve comprehension of initial brain growth in wellness along with sickness, it is essential to precisely segment child brain magnetic resonance imaging (MRI) into white matter (WM) and gray matter (GM), along with cerebrospinal fluid (CSF). Nonetheless, in the isointense phase (6-8 months of age), the inborn myelination and development activities, WM along with GM display alike stages of intensity in both T1-weighted and T2-weighted MRI, making tissue segmentation extremely difficult. Methods: The comprehensive review of studies related to isointense brain MRI segmentation approaches is highlighted in this publication. The main aim and contribution of this study is to aid researchers by providing a thorough review to make their search for isointense brain MRI segmentation easier. The systematic literature review is performed from four points of reference: (1) review of studies concerning isointense brain MRI segmentation; (2) research contribution and future works and limitations; (3) frequently applied evaluation metrics and datasets; (4) findings of this studies. Results and discussion: The systemic review is performed on studies that were published in the period of 2012 to 2022. A total of 19 primary studies of isointense brain MRI segmentation were selected to report the research question stated in this review.

Studying hemispheric lateralization of 4-month-old infants from different language groups through near-infrared spectroscopy-based connectivity.

Introduction: Early monolingual versus bilingual experience affects linguistic and cognitive processes during the first months of life, as well as functional activation patterns. The previous study explored the influence of a bilingual environment in the first months of life on resting-state functional connectivity and reported no significant difference between language groups. Methods: To further explore the influence of a bilingual environment on brain development function, we used the resting-state functional near-infrared spectroscopy public dataset of the 4-month-old infant group in the sleep state (30 Spanish; 33 Basque; 36 bilingual). Wavelet Transform Coherence, graph theory, and Granger causality methods were performed on the functional connectivity of the frontal lobes. Results: The results showed that functional connectivity strength was significantly higher in the left hemisphere than that in the right hemisphere in both monolingual and bilingual groups. The graph theoretic analysis showed that the characteristic path length was significantly higher in the left hemisphere than in the right hemisphere for the bilingual infant group. Contrary to the monolingual infant group, the left-to-right direction of information flow was found in the frontal regions of the bilingual infant group in the effective connectivity analysis. Discussion: The results suggested that the left hemispheric lateralization of functional connectivity in frontal regions is more pronounced in the bilingual group compared to the monolingual group. Furthermore, effective connectivity analysis may be a useful method to investigate the resting-state brain networks of infants.

Hybrid Convolutional Neural Network (hCNN) for Image Reconstruction in Near-Infrared Optical Tomography.

Near-infrared optical tomography (NIROT), a promising imaging modality for early detection of oxygenation in the brain of preterm infants, requires data acquisition at the tissue surface and thus an image reconstruction adaptable to cephalometric variations and surface topologies. Widely used model-based reconstruction methods come with the drawback of huge computational cost. Neural networks move this computational load to an offline training phase, allowing much faster reconstruction. Our aim is a data-driven volumetric image reconstruction that generalises well to different surfaces, increases reconstruction speed, localisation accuracy and image quality. We propose a hybrid convolutional neural network (hCNN) based on the well-known V-net architecture to learn inclusion localisation and absorption coefficients of heterogenous arbitrary shapes via a joint cost function. We achieved an average reconstruction time of 30.45 s, a time reduction of 89% and inclusion detection with an average Dice score of 0.61. The CNN is flexible to surface topologies and compares well in quantitative metrics with the traditional model-based (MB) approach and state-of-the-art neuronal networks for NIROT. The proposed hCNN was successfully trained, validated and tested on in-silico data, excels MB methods in localisation accuracy and provides a remarkable increase in reconstruction speed.

Maternal Childhood Maltreatment Is Associated With Lower Infant Gray Matter Volume and Amygdala Volume During the First Two Years of Life.

Background: Childhood maltreatment affects approximately 25% of the world's population. Importantly, the children of mothers who have been maltreated are at increased risk of behavioral problems. Thus, one important priority is to identify child neurobiological processes associated with maternal childhood maltreatment (MCM) that might contribute to such intergenerational transmission. This study assessed the impact of MCM on infant gray and white matter volumes and infant amygdala and hippocampal volumes during the first 2 years of life. Methods: Fifty-seven mothers with 4-month-old infants were assessed for MCM, using both the brief Adverse Childhood Experiences screening questionnaire and the more detailed Maltreatment and Abuse Chronology of Exposure scale. A total of 58% had experienced childhood maltreatment. Between 4 and 24 months (age in months: mean = 12.28, SD = 5.99), under natural sleep, infants completed a magnetic resonance imaging scan using a 3T Siemens scanner. Total brain volume, gray matter volume, white matter volume, and amygdala and hippocampal volumes were extracted via automated segmentation. Results: MCM on the Adverse Childhood Experiences and Maltreatment and Abuse Chronology of Exposure scales were associated with lower infant total brain volume and gray matter volume, with no moderation by infant age. However, infant age moderated the association between MCM and right amygdala volume, such that MCM was associated with lower volume at older ages. Conclusions: MCM is associated with alterations in infant brain volumes, calling for further identification of the prenatal and postnatal mechanisms contributing to such intergenerational transmission. Furthermore, the brief Adverse Childhood Experiences questionnaire predicted these alterations, suggesting the potential utility of early screening for infant risk.

Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks.

Preterm birth is a worldwide problem that affects infants throughout their lives significantly. Therefore, differentiating brain disorders, and further identifying and characterizing the corresponding biomarkers are key issues to investigate the effects of preterm birth, which facilitates the interventions for neuroprotection and improves outcomes of prematurity. Until now, many efforts have been made to study the effects of preterm birth; however, most of the studies merely focus on either functional or structural perspective. In addition, an effective framework not only jointly studies the brain function and structure at a group-level, but also retains the individual differences among the subjects. In this study, a novel dense individualized and common connectivity-based cortical landmarks (DICCCOL)-based multi-modality graph neural networks (DM-GNN) framework is proposed to differentiate preterm and term infant brains and characterize the corresponding biomarkers. This framework adopts the DICCCOL system as the initialized graph node of GNN for each subject, utilizing both functional and structural profiles and effectively retaining the individual differences. To be specific, functional magnetic resonance imaging (fMRI) of the brain provides the features for the graph nodes, and brain fiber connectivity is utilized as the structural representation of the graph edges. Self-attention graph pooling (SAGPOOL)-based GNN is then applied to jointly study the function and structure of the brain and identify the biomarkers. Our results successfully demonstrate that the proposed framework can effectively differentiate the preterm and term infant brains. Furthermore, the self-attention-based mechanism can accurately calculate the attention score and recognize the most significant biomarkers. In this study, not only 87.6% classification accuracy is observed for the developing Human Connectome Project (dHCP) dataset, but also distinguishing features are explored and extracted. Our study provides a novel and uniform framework to differentiate brain disorders and characterize the corresponding biomarkers.