Machine-learning to characterise neonatal functional connectivity in the preterm brain

Cognitive shifts in pain perception under moral enhancement conditions: Evidence from an EEG study

In social life, empathy and morality are often viewed as inseparable and mutually reinforcing. Pain empathy is a key form of empathy, and understanding how social moral factors affect pain empathy is an important challenge. This study uses various EEG analysis methods to explore the cognitive and neural mechanisms by which moral enhancement affects pain empathy. Behavioral results showed significantly higher ratings for painful stimuli compared to non-painful ones. ERP analysis revealed that, under moral enhancement, pain stimuli elicited more negative N1 amplitudes and more positive P3 amplitudes. Time-frequency analysis indicated that moral enhancement inhibited theta band activity in response to painful stimuli. Functional connectivity analysis showed stronger connections in the frontal, right temporal, and occipital regions under moral enhancement and in the frontal, right temporal, and parietal regions when viewing painful stimuli. Additionally, machine learning results indicated that functional connections between the right temporal and parietal regions have significant negative predictive power for moral enhancement during painful stimuli. This study reveals the complex effects of moral enhancement on pain-related stimuli, demonstrating that it not only increases adaptability to pain but also enhances moral judgment, offering new insights into the interaction between moral cognition and emotional responses with significant theoretical and practical implications.

Early Development and Co-Evolution of Microstructural and Functional Brain Connectomes: A Multi-Modal MRI Study in Preterm and Full-Term Infants

Functional networks characterized by coherent neural activity across distributed brain regions have been observed to emerge early in neurodevelopment. Synchronized maturation across regions that relate to functional connectivity (FC) could be partially reflected in the developmental changes in underlying microstructure. Nevertheless, covariation of regional microstructural properties, termed "microstructural connectivity" (MC), and its relationship to the emergence of functional specialization during the early neurodevelopmental period remain poorly understood. We investigated the evolution of MC and FC postnatally across a set of cortical and subcortical regions, focusing on 45 preterm infants scanned longitudinally, and compared to 45 matched full-term neonates as part of the developing Human Connectome Project (dHCP) using direct comparisons of grey-matter connectivity strengths as well as network-based analyses. Our findings revealed a global strengthening of both MC and FC with age, with connection-specific variability influenced by the connection maturational stage. Prematurity at term-equivalent age was associated with significant connectivity disruptions, particularly in FC. During the preterm period, direct comparisons of MC and FC strength showed a positive linear relationship, which seemed to weaken with development. On the other hand, overlaps between MC- and FC-derived networks (estimated with Mutual Information) increased with age, suggesting a potential convergence towards a shared underlying network structure that may support the co-evolution of microstructural and functional systems. Our study offers novel insights into the dynamic interplay between microstructural and functional brain development and highlights the potential of MC as a complementary descriptor for characterizing brain network development and alterations due to perinatal insults such as premature birth.

Brain connectomes in youth at risk for serious mental illness: a longitudinal perspective

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one of 5 groups: healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9). Constrained spherical deconvolution was used to generate whole brain tractography maps, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional magnetic resonance imaging (fMRI) signal between pairs of brain regions. Linear mixed models revealed structural and functional abnormalities in global metrics of small world lambda, and resting state networks involving the fronto-parietal, default mode, and deep grey matter networks, along with the visual and dorsal attention networks. Machine learning analysis additionally identified changes in nodal metrics of betweenness centrality in the angular gyrus and bilateral temporal gyri as potential features which can discriminate between the groups. Our findings further support the view that abnormalities in large scale networks (particularly those involving fronto-parietal, temporal, default mode, and deep grey matter networks) may underlie transdiagnostic risk for SMIs.

Development of segregation and integration of functional connectomes during the first 1,000 days

The first 1,000 days of human life lay the foundation for brain development and later cognitive growth. However, the developmental rules of the functional connectome during this critical period remain unclear. Using high-resolution, longitudinal, task-free functional magnetic resonance imaging data from 930 scans of 665 infants aged 28 postmenstrual weeks to 3 years, we report the early maturational process of connectome segregation and integration. We show the dominant development of local connections alongside a few global connections, the shift of brain hubs from primary regions to high-order association cortices, the developmental divergence of network segregation and integration along the anterior-posterior axis, the prediction of neurocognitive outcomes, and their associations with gene expression signatures of microstructural development and neuronal metabolic pathways. These findings advance our understanding of the principles of connectome remodeling during early life and its neurobiological underpinnings and have implications for studying typical and atypical development.

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

Brain Structural Connectivity Guided Vision Transformers for Identification of Functional Connectivity Characteristics in Preterm Neonates

Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis.

Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment

Brain dynamic functional connectivity characterises transient connections between brain regions. Features of brain dynamics have been linked to emotion and cognition in adult individuals, and atypical patterns have been associated with neurodevelopmental conditions such as autism. Although reliable functional brain networks have been consistently identified in neonates, little is known about the early development of dynamic functional connectivity. In this study we characterise dynamic functional connectivity with functional magnetic resonance imaging (fMRI) in the first few weeks of postnatal life in term-born (n = 324) and preterm-born (n = 66) individuals. We show that a dynamic landscape of brain connectivity is already established by the time of birth in the human brain, characterised by six transient states of neonatal functional connectivity with changing dynamics through the neonatal period. The pattern of dynamic connectivity is atypical in preterm-born infants, and associated with atypical social, sensory, and repetitive behaviours measured by the Quantitative Checklist for Autism in Toddlers (Q-CHAT) scores at 18 months of age.

Multi-source multi-modal markers for Bayesian Networks: Application to the extremely preterm born brain

The preterm phenotype results from the interplay of multiple disorders affecting the brain and cognitive outcomes. Accurately characterising these interactions can reveal prematurity markers. Bayesian Networks (BNs) are powerful tools to disentangle these relationships, as they inherently measure associations between variables while mitigating confounding factors. We present Modified PC-HC (MPC-HC), a Bayesian Network (BN) structural learning algorithm. MPC-HC employs statistical testing and search-and-score techniques to explore equivalent classes. We employ MPC-HC to estimate BNs for extremely preterm (EP) young adults and full-term controls. Using MRI measurements and cognitive performance markers, we investigate predictive relationships and mutual influences through predictions and sensitivity analysis. We assess the confidence in the estimated BN structures using bootstrapping. Furthermore, MPC-HC's validation involves assessing its ability to recover benchmark BN structures. MPC-HC achieves an average prediction accuracy of 72.5% compared to 62.5% of PC, 64.5% of MMHC, and 71.5% of HC, while it outperforms PC, MMHC, and HC algorithms in reconstructing the true structure of benchmark BNs. The sensitivity analysis shows that MRI measurements mainly affect EP cognitive scores. Our work has two key contributions: first, the introduction and validation of a new BN structure learning method. Second, demonstrating the potential of BNs in modelling variable relationships, predicting variables of interest, modelling uncertainty, and evaluating how variables impact each other. Finally, we demonstrate this by characterising complex phenotypes, such as preterm birth, and discovering results consistent with literature findings.

Global trends in neonatal MRI brain neuroimaging research over the last decade: a bibliometric analysis

Background

Neuroimaging plays a central role in the evaluation, treatment, and prognosis of neonates. In recent years, the exploration of the developing brain has been a major focus of research for researchers and clinicians. In this study, we conducted bibliometric and visualization analyses of the related studies in the field of neonatal magnetic resonance imaging (MRI) brain neuroimaging from the past 10 years, and summarized its research status, hotspots, and frontier development trends.

Methods

The Web of Science core collection database was used as the literature source from which to retrieve the relevant papers and reviews in the field of neonatal MRI brain neuroimaging published in the Science Citation Index-Expanded from 2013 to 2022. VOSviewer and CiteSpace were used to conduct bibliometric and visualization analyses of the annual publication volume, countries, institutions, journals, authors, co-cited literature, and the overall distribution of keywords.

Results

We retrieved 3,568 papers and reviews published from 2013 to 2022. The number of publications increased during this period. The United States (US) and the United Kingdom were the largest contributors, with the US receiving the highest H-index and number of citations. The institutions that published the most were the University of London and Harvard University. The research mainly focused on cerebral cortex, brain tissue, brain structure network, artificial intelligence algorithm, automatic image segmentation, and premature infants.

Conclusions

This study reveals the research status and hotspots of magnetic resonance imaging in the field of neonatal brain neuroimaging in the past decade, which helps researchers to better understand the research status, hotspots, and frontier development trends.

Distinct Neurodevelopmental Trajectories in Groups of Very Preterm Children Screening Positively for Autism Spectrum Conditions

Very preterm (VPT; < 33 weeks' gestation) toddlers screening positively for autism spectrum conditions (ASC) may display heterogenous neurodevelopmental trajectories. Here we studied neonatal brain volumes and childhood ASC traits evaluated with the Social Responsiveness Scale (SRS-2) in VPT-born toddlers (N = 371; median age 20.17 months) sub-divided into three groups based on their Modified-Checklist for Autism in Toddlers scores. These were: those screening positively failing at least 2 critical items (critical-positive); failing any 3 items, but less than 2 critical items (non-critical-positive); and screening negatively. Critical-positive scorers had smaller neonatal cerebellar volumes compared to non-critical-positive and negative scorers. However, both positive screening groups exhibited higher childhood ASC traits compared to the negative screening group, suggesting distinct aetiological trajectories associated with ASC outcomes.

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.

Changes of Functional Brain Network in Neonates with Different Degrees of Hypoxic-Ischemic Encephalopathy

Background: Neonatal hypoxic-ischemic encephalopathy (HIE) is the main cause of neonatal death and disability worldwide. At present, there are few researches on the application of resting-state functional magnetic resonance imaging (rs-fMRI) to explore the brain development of HIE children. This study aimed to explore the changes of brain function in neonates with different degrees of HIE using rs-fMRI. Methods: From February 2018 to May 2020, 44 patients with HIE were recruited, including 21 mild patients and 23 moderate and severe patients. The recruited patients were scanned by conventional and functional magnetic resonance image, and the method of amplitude of low-frequency fluctuation and connecting edge analysis of brain network was used. Results: Compared with the mild group, the connections between the right supplementary motor area and the right precentral gyrus, the right lingual gyrus and the right hippocampus, the left calcarine cortex and the right amygdala, and the right pallidus and the right posterior cingulate cortex in the moderate and severe groups were reduced (t values were 4.04, 4.04, 4.04, 4.07, all p < 0.001, uncorrected). Conclusion: By analyzing the functional connection changes of brain network in infants with different degrees of HIE, the findings of the current study suggested that neonates with moderate to severe HIE lag behind those with mild HIE in emotional processing, sensory movement, cognitive function, and learning and memory. Chinese Clinical Trial Registry registration number: ChiCTR1800016409.

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.

Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks

Very preterm birth (VPT; ≤32 weeks' gestation) is associated with altered brain development and cognitive and behavioral difficulties across the lifespan. However, heterogeneity in outcomes among individuals born VPT makes it challenging to identify those most vulnerable to neurodevelopmental sequelae. Here, we aimed to stratify VPT children into distinct behavioral subgroups and explore between-subgroup differences in neonatal brain structure and function. 198 VPT children (98 females) previously enrolled in the Evaluation of Preterm Imaging Study (EudraCT 2009-011602-42) underwent Magnetic Resonance Imaging at term-equivalent age and neuropsychological assessments at 4-7 years. Using an integrative clustering approach, we combined neonatal socio-demographic, clinical factors and childhood socio-emotional and executive function outcomes, to identify distinct subgroups of children based on their similarity profiles in a multidimensional space. We characterized resultant subgroups using domain-specific outcomes (temperament, psychopathology, IQ and cognitively stimulating home environment) and explored between-subgroup differences in neonatal brain volumes (voxel-wise Tensor-Based-Morphometry), functional connectivity (voxel-wise degree centrality) and structural connectivity (Tract-Based-Spatial-Statistics). Results showed two- and three-cluster data-driven solutions. The two-cluster solution comprised a 'resilient' subgroup (lower psychopathology and higher IQ, executive function and socio-emotional scores) and an 'at-risk' subgroup (poorer behavioral and cognitive outcomes). No neuroimaging differences between the resilient and at-risk subgroups were found. The three-cluster solution showed an additional third 'intermediate' subgroup, displaying behavioral and cognitive outcomes intermediate between the resilient and at-risk subgroups. The resilient subgroup had the most cognitively stimulating home environment and the at-risk subgroup showed the highest neonatal clinical risk, while the intermediate subgroup showed the lowest clinical, but the highest socio-demographic risk. Compared to the intermediate subgroup, the resilient subgroup displayed larger neonatal insular and orbitofrontal volumes and stronger orbitofrontal functional connectivity, while the at-risk group showed widespread white matter microstructural alterations. These findings suggest that risk stratification following VPT birth is feasible and could be used translationally to guide personalized interventions aimed at promoting children's resilience.

Maturation of large-scale brain systems over the first month of life

The period immediately after birth is a critical developmental window, capturing rapid maturation of brain structure and a child's earliest experiences. Large-scale brain systems are present at delivery, but how these brain systems mature during this narrow window (i.e. first weeks of life) marked by heightened neuroplasticity remains uncharted. Using multivariate pattern classification techniques and functional connectivity magnetic resonance imaging, we detected robust differences in brain systems related to age in newborns (n = 262; R2 = 0.51). Development over the first month of life occurred brain-wide, but differed and was more pronounced in brain systems previously characterized as developing early (i.e. sensorimotor networks) than in those characterized as developing late (i.e. association networks). The cingulo-opercular network was the only exception to this organizing principle, illuminating its early role in brain development. This study represents a step towards a normative brain "growth curve" that could be used to identify atypical brain maturation in infancy.

Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review

BACKGROUND

Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In this study, we have carried out a systematic review to identify findings and challenges to date.

METHODS

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors.

RESULTS

The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural networks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing brain structure and function. Studies to date also sought to identify features predictive of outcomes; however, results varied greatly.

CONCLUSIONS

Initial studies in this field have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes.

IMPACT

This systematic review looks at the question of whether machine learning can be used to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, but many challenges and opportunities remain. Quality assessment of relevant articles is conducted using the Newcastle-Ottawa Scale. This work identifies challenges that remain and suggests several key directions for future research. To the best of the authors' knowledge, this is the first systematic review to explore this topic.

The confound of head position in within-session connectome fingerprinting in infants

Individuals differ in their functional connectome, which can be demonstrated using a "fingerprinting" analysis in which the connectome from an individual in one dataset is used to identify the same person from an independent dataset. Recently, the origin of these fingerprints has been studied by examining if they are present in infants. The results have varied considerably, with identification rates from 10 to 90%. When fingerprinting has been performed by splitting a single imaging session into two split-sessions (within session), identification rates were higher than when two full-sessions (between sessions) were compared. This study examined whether a methodological difference could account for this variation. It was hypothesized that the infant's exact head position in the head coil may affect the measured connectome, due to the gradual inhomogeneity of signal-to-noise in phased-array coils and the breadth of possible positions for a small infant head in a head coil. This study examined the impact of this using resting state functional MRI data from the Developing Human Connectome Project second release. Using functional timeseries, fingerprinting identification was high (84-91%) within a session while between sessions it was low (7%).Using N = 416 infants' head positions, a map of the average signal-to-noise across the physical volume of the head coil was calculated and was used (independent group of 44 infants with two scan sessions) to demonstrate a significant relationship between head position in the head coil and functional connectivity. Using only the head positions (signal-to-noise values extrapolated from the group average map) of the independent group of 44 infants, high identification success was achieved across split-sessions (within session) but not full-sessions (between sessions). Using a model examining factors influencing the stability of the functional connectome, head position was seen as the strongest of the explanatory variables. We conclude within-session fingerprinting is affected by head position and future infant functional fingerprint analyses must use a different strategy or account for this impact.

Large-scale brain network dynamics in very preterm children and relationship with socio-emotional outcomes: an exploratory study

BACKGROUND

Children born very preterm (VPT; <32 weeks' gestation) are at high risk of neurodevelopmental and behavioural difficulties associated with atypical brain maturation, including socio-emotional difficulties. The analysis of large-scale brain network dynamics during rest allows us to investigate brain functional connectivity and its association with behavioural outcomes.

METHODS

Dynamic functional connectivity was extracted by using the innovation-driven co-activation patterns framework in VPT and full-term children aged 6-9 to explore changes in spatial organisation, laterality and temporal dynamics of spontaneous large-scale brain activity (VPT, n = 28; full-term, n = 12). Multivariate analysis was used to explore potential biomarkers for socio-emotional difficulties in VPT children.

RESULTS

The spatial organisation of the 13 retrieved functional networks was comparable across groups. Dynamic features and lateralisation of network brain activity were also comparable for all brain networks. Multivariate analysis unveiled group differences in associations between dynamical functional connectivity parameters with socio-emotional abilities.

CONCLUSION

In this exploratory study, the group differences observed might reflect reduced degrees of maturation of functional architecture in the VPT group in regard to socio-emotional abilities. Dynamic features of functional connectivity could represent relevant neuroimaging markers and inform on potential mechanisms through which preterm birth leads to neurodevelopmental and behavioural disorders.

IMPACT

Spatial organisation of the retrieved resting-state networks was comparable between school-aged very preterm and full-term children. Dynamic features and lateralisation of network brain activity were also comparable across groups. Multivariate pattern analysis revealed different patterns of association between dynamical functional connectivity parameters and socio-emotional abilities in the very preterm and full-term groups. Findings suggest a reduced degree of maturation of the functional architecture in the very preterm group in association with socio-emotional abilities.

Adult outcome of preterm birth: Implications for neurodevelopmental theories of psychosis

Preterm birth is associated with an elevated risk of developmental and adult psychiatric disorders, including psychosis. In this review, we evaluate the implications of neurodevelopmental, cognitive, motor, and social sequelae of preterm birth for developing psychosis, with an emphasis on outcomes observed in adulthood. Abnormal brain development precipitated by early exposure to the extra-uterine environment, and exacerbated by neuroinflammation, neonatal brain injury, and genetic vulnerability, can result in alterations of brain structure and function persisting into adulthood. These alterations, including abnormal regional brain volumes and white matter macro- and micro-structure, can critically impair functional (e.g. frontoparietal and thalamocortical) network connectivity in a manner characteristic of psychotic illness. The resulting executive, social, and motor dysfunctions may constitute the basis for behavioural vulnerability ultimately giving rise to psychotic symptomatology. There are many pathways to psychosis, but elucidating more precisely the mechanisms whereby preterm birth increases risk may shed light on that route consequent upon early neurodevelopmental insult.

Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model

Objective

Analyzing neuroimages being useful method in the field of neuroscience and neurology and solving the incompatibilities across protocols and vendors have become a major problem. We referred to this incompatibility as "center effects," and in this study, we attempted to correct such center effects of cortical feature obtained from multicenter magnetic resonance images (MRIs).

Methods

For MRI of a total of 4,321 multicenter subjects, the harmonized w-score was calculated by correcting biological covariates such as age, sex, years of education, and intercranial volume (ICV) as fixed effects and center information as a random effect. Afterward, we performed classification tasks using principal component analysis (PCA) and linear discriminant analysis (LDA) to check whether the center effect was successfully corrected from the harmonized w-score.

Results

First, an experiment was conducted to predict the dataset origin of a random subject sampled from two different datasets, and it was confirmed that the prediction accuracy of linear mixed effect (LME) model-based w-score was significantly closer to the baseline than that of raw cortical thickness. As a second experiment, we classified the data of the normal and patient groups of each dataset, and LME model-based w-score, which is biological-feature-corrected values, showed higher classification accuracy than the raw cortical thickness data. Afterward, to verify the compatibility of the dataset used for LME model training and the dataset that is not, intraobject comparison and w-score RMSE calculation process were performed.

Conclusion

Through comparison between the LME model-based w-score and existing methods and several classification tasks, we showed that the LME model-based w-score sufficiently corrects the center effects while preserving the disease effects from the dataset. We also showed that the preserved disease effects have a match with well-known disease atrophy patterns such as Alzheimer's disease or Parkinson's disease. Finally, through intrasubject comparison, we found that the difference between centers decreases in the LME model-based w-score compared with the raw cortical thickness and thus showed that our model well-harmonizes the data that are not used for the model training.

Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis

BACKGROUND

Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed.

METHODS

We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split).

RESULTS

In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset.

LIMITATIONS

The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects.

CONCLUSIONS

This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects.

Thalamocortical functional connectivity in infants with prenatal opioid exposure correlates with severity of neonatal opioid withdrawal syndrome

PURPOSE

Prenatal opioid exposure (POE) is a growing public health concern due to its associated adverse outcomes including neonatal opioid withdrawal syndrome (NOWS). The aim of this study was to assess alterations in thalamic functional connectivity in neonates with POE using resting-state functional magnetic resonance imaging (rs-fMRI) and identify whether these altered connectivity measures were associated with NOWS severity.

METHODS

In this prospective, IRB-approved study, we performed rs-fMRI in 19 infants with POE and 20 healthy control infants without POE. Following standard pre-processing, we performed seed-based functional connectivity analysis with the right and left thalamus as the regions of interest. We performed post hoc analysis in the prenatal opioid exposure group to identify associations of altered thalamocortical connectivity with severity of NOWS. P value of < .05 was considered statistically significant.

RESULTS

There were several regions of significantly altered thalamic to cortical functional connectivity in infants with POE compared to the healthy infants. Distinct regions of thalamocortical functional connectivity correlated with maximum modified Finnegan score. Association between thalamocortical connectivity and severity of NOWS was nominally modified by maternal psychological conditions and polysubstance use.

CONCLUSION

Our findings reveal prenatal opioid exposure-related alterations in thalamic functional connectivity in the infant brain that are correlated with severity of NOWS. Future studies may benefit from evaluation of thalamocortical resting state functional connectivity in infants with POE to help  stratify risk of long term neurodevelopmental outcomes.

Advanced Brain Imaging in Preterm Infants: A Narrative Review of Microstructural and Connectomic Disruption

Preterm birth disrupts the in utero environment, preventing the brain from fully developing, thereby causing later cognitive and behavioral disorders. Such cerebral alteration occurs beneath an anatomical scale, and is therefore undetectable by conventional imagery. Prematurity impairs the microstructure and thus the histological process responsible for the maturation, including the myelination. Cerebral MRI diffusion tensor imaging sequences, based on water’s motion into the brain, allows a representation of this maturation process. Similarly, the brain’s connections become disorganized. The connectome gathers structural and anatomical white matter fibers, as well as functional networks referring to remote brain regions connected one over another. Structural and functional connectivity is illustrated by tractography and functional MRI, respectively. Their organizations consist of core nodes connected by edges. This basic distribution is already established in the fetal brain. It evolves greatly over time but is compromised by prematurity. Finally, cerebral plasticity is nurtured by a lifetime experience at microstructural and macrostructural scales. A preterm birth causes a negative and early disruption, though it can be partly mitigated by positive stimuli based on developmental neonatal care.

Early development of sleep and brain functional connectivity in term-born and preterm infants

The proper development of sleep and sleep-wake rhythms during early neonatal life is crucial to lifelong neurological well-being. Recent data suggests that infants who have poor quality sleep demonstrate a risk for impaired neurocognitive outcomes. Sleep ontogenesis is a complex process, whereby alternations between rudimentary brain states-active vs. wake and active sleep vs. quiet sleep-mature during the last trimester of pregnancy. If the infant is born preterm, much of this process occurs in the neonatal intensive care unit, where environmental conditions might interfere with sleep. Functional brain connectivity (FC), which reflects the brain's ability to process and integrate information, may become impaired, with ensuing risks of compromised neurodevelopment. However, the specific mechanisms linking sleep ontogenesis to the emergence of FC are poorly understood and have received little investigation, mainly due to the challenges of studying causal links between developmental phenomena and assessing FC in newborn infants. Recent advancements in infant neuromonitoring and neuroimaging strategies will allow for the design of interventions to improve infant sleep quality and quantity. This review discusses how sleep and FC develop in early life, the dynamic relationship between sleep, preterm birth, and FC, and the challenges associated with understanding these processes. IMPACT: Sleep in early life is essential for proper functional brain development, which is essential for the brain to integrate and process information. This process may be impaired in infants born preterm. The connection between preterm birth, early development of brain functional connectivity, and sleep is poorly understood. This review discusses how sleep and brain functional connectivity develop in early life, how these processes might become impaired, and the challenges associated with understanding these processes. Potential solutions to these challenges are presented to provide direction for future research.

Brain functional and effective connectivity based on electroencephalography recordings: A review

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

The role of artificial intelligence in paediatric neuroradiology

Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.

OUP accepted manuscript

Very preterm children are more likely to exhibit difficulties in socio-emotional processing than their term-born peers. Emerging socio-emotional problems may be partly due to alterations in limbic system development associated with infants’ early transition to extrauterine life. The amygdala is a key structure in this system and plays a critical role in various aspects of socio-emotional development, including emotion regulation. The current study tested the hypothesis that amygdala resting-state functional connectivity at term-equivalent age would be associated with socio-emotional outcomes in childhood. Participants were 129 very preterm infants (<33 weeks' gestation) who underwent resting-state functional MRI at term and received a neurodevelopmental assessment at 4–7 years (median = 4.64). Using the left and right amygdalae as seed regions, we investigated associations between whole-brain seed-based functional connectivity and three socio-emotional outcome factors which were derived using exploratory factor analysis (Emotion Moderation, Social Function and Empathy), controlling for sex, neonatal sickness, post-menstrual age at scan and social risk. Childhood Emotion Moderation scores were significantly associated with neonatal resting-state functional connectivity of the right amygdala with right parahippocampal gyrus and right middle occipital gyrus, as well as with functional connectivity of the left amygdala with the right thalamus. No significant associations were found between amygdalar resting-state functional connectivity and either Social Function or Empathy scores. The current findings show that amygdalar functional connectivity assessed at term is associated with later socio-emotional outcomes in very preterm children.

Structural and functional connectivity in premature neonates

Advances in neuroimaging have increasingly enabled researchers to investigate whether alterations in brain development commonly identified in preterm infants underlie their high risk of long-term neurodevelopmental impairment, including sensory, motor, cognitive, and psychiatric deficits. This review begins by examining the growing body of literature utilizing advanced magnetic resonance imaging (MRI) techniques to probe structural (via diffusion MRI) and functional (via resting state-functional MRI) connectivity development in the preterm brain during the neonatal period, both in the presence and absence of brain injury. It then details the recent work linking neonatal brain connectivity measures to neurodevelopmental and psychiatric outcomes in prematurely-born cohorts. Finally, building upon the recent substantive growth in the utilization of these neuroimaging modalities, it concludes by highlighting areas in which continued optimization of age-specific acquisition and analysis techniques for these data remains necessary, efforts fundamental to advancing the field toward establishing individual-level predictive capabilities in this high-risk population.

Structural and functional brain connectivity in moderate-late preterm infants with low-grade intraventricular hemorrhage

PURPOSE

Brain functional connectivity (FC) changes and microstructural abnormalities are reported in infants born moderate and late preterm (MLPT). We evaluated the effect of low-grade (grades I, II) intraventricular hemorrhage (IVH) in MLPT babies on brain structural connectivity (SC) and FC.

METHODS

Babies born MLPT between January 2014 and May 2017 underwent brain ultrasound (US) at 72 h and 7 days after birth, and MRI at around term equivalent. The MRI protocol comprised T1- and T2-weighted sequences, diffusion tensor imaging (DTI), and resting-state functional MRI (fMRI). SC and FC were assessed using graph analysis.

RESULTS

Of 350 MLPT neonates, 15 showed low-grade IVH on US at 72 h, for which brain MRI was available in 10. These 10 infants, with mean gestational age (GA) 34.0 ± 0.8 weeks, comprised the study group, and 10 MLPT infants of mean GA 33.9 ± 1.1 weeks, with no abnormalities on brain US and MRI, were control subjects. All study subjects presented modularity, small world topology, and rich club organization for both SC and FC. The patients with low-grade IVH had lower FC rich club coefficient and lower SC betweenness centrality in the left frontoparietal operculum, and lower SC rich club coefficient in the right superior orbitofrontal cortex than the control subjects.

CONCLUSIONS

Topological and functional properties of mature brain connectivity are present in MLPT infants. IVH in these infants was associated with structural and functional abnormalities in the left frontoparietal operculum and right orbitofrontal cortex, regions related to language and cognition.

Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit

Background: preterm and critically ill neonates often experience clinically suspected sepsis during their prolonged hospitalization in the neonatal intensive care unit (NICU), which can be the initial sign of final adverse outcomes. Therefore, we aimed to utilize machine learning approaches to predict neonatal in-hospital mortality through data-driven learning. Methods: a total of 1095 neonates who experienced clinically suspected sepsis in a tertiary-level NICU in Taiwan between August 2017 and July 2020 were enrolled. Clinically suspected sepsis was defined based on clinical features and laboratory criteria and the administration of empiric antibiotics by clinicians. The variables used for analysis included patient demographics, clinical features, laboratory data, and medications. The machine learning methods used included deep neural network (DNN), k-nearest neighbors, support vector machine, random forest, and extreme gradient boost. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: the final in-hospital mortality of this cohort was 8.2% (90 neonates died). A total of 765 (69.8%) and 330 (30.2%) patients were randomly assigned to the training and test sets, respectively. Regarding the efficacy of the single model that most accurately predicted the outcome, DNN exhibited the greatest AUC (0.923, 95% confidence interval [CI] 0.953–0.893) and the best accuracy (95.64%, 95% CI 96.76–94.52%), Cohen’s kappa coefficient value (0.74, 95% CI 0.79–0.69) and Matthews correlation coefficient value (0.75, 95% CI 0.80–0.70). The top three most influential variables in the DNN importance matrix plot were the requirement of ventilator support at the onset of suspected sepsis, the feeding conditions, and intravascular volume expansion. The model performance was indistinguishable between the training and test sets. Conclusions: the DNN model was successfully established to predict in-hospital mortality in neonates with clinically suspected sepsis, and the machine learning algorithm is applicable for clinicians to gain insights and have better communication with families in advance.

Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses

Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood‐oxygen‐level dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting‐state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low‐frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data‐driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.

White matter tracts related to memory and emotion in very preterm children

BACKGROUND

Very preterm (VP) children are at risk of memory and emotional impairments; however, the neural correlates remain incompletely defined. This study investigated the effect of VP birth on white matter tracts traditionally related to episodic memory and emotion.

METHODS

The cingulum, fornix, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation were reconstructed using tractography in 144 VP children and 33 full-term controls at age 7 years.

RESULTS

Compared with controls, VP children had higher axial, radial, and mean diffusivities and neurite orientation dispersion, and lower volume and neurite density in the fornix, along with higher neurite orientation dispersion in the medial forebrain bundle. Support vector classification models based on tract measures significantly classified VP children and controls. Higher fractional anisotropy and lower diffusivities in the cingulum, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation were associated with better episodic memory, independent of key perinatal risk factors. Support vector regression models using tract measures did not predict episodic memory and emotional outcomes.

CONCLUSIONS

Altered tract structure is related to adverse episodic memory outcomes in VP children, but further research is required to determine the ability of tract structure to predict outcomes of individual children.

IMPACT

We studied white matter fibre tracts thought to be involved in episodic memory and emotion in VP and full-term children using diffusion magnetic resonance imaging and machine learning. VP children have altered fornix and medial forebrain bundle structure compared with full-term children. Altered tract structure can be detected using machine learning, which accurately classified VP and full-term children using tract data. Altered cingulum, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation structure was associated with poorer episodic memory skills using linear regression. The ability of tract structure to predict episodic memory and emotional outcomes of individual children based on support vector regression was limited.

MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances

In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.

An Overview of ICA/BSS-Based Application to Alzheimer's Brain Signal Processing

Alzheimer's disease (AD) is by far the most common cause of dementia associated with aging. Early and accurate diagnosis of AD and ability to track progression of the disease is increasingly important as potential disease-modifying therapies move through clinical trials. With the advent of biomedical techniques, such as computerized tomography (CT), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI), large amounts of data from Alzheimer's patients have been acquired and processed from which AD-related information or "signals" can be assessed for AD diagnosis. It remains unknown how best to mine complex information from these brain signals to aid in early diagnosis of AD. An increasingly popular technique for processing brain signals is independent component analysis or blind source separation (ICA/BSS) that separates blindly observed signals into original signals that are as independent as possible. This overview focuses on ICA/BSS-based applications to AD brain signal processing.

The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity

The Developing Human Connectome Project is an Open Science project that provides the first large sample of neonatal functional MRI data with high temporal and spatial resolution. These data enable mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organization in humans. Here, we characterize in unprecedented detail the maturation and integrity of resting state networks (RSNs) at term-equivalent age in 337 infants (including 65 born preterm). First, we applied group independent component analysis to define 11 RSNs in term-born infants scanned at 43.5–44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37–43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder, which occur frequently in preterm children. Overall, we found a robust, modular, symmetrical functional brain organization at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity.

Functional thalamocortical connectivity at term equivalent age and outcome at 2 years in infants born preterm

Infants born preterm are at high risk of long-term motor and neurocognitive deficits. In the majority of these infants structural MRI at the time of normal birth does not predict motor or cognitive outcomes accurately, and many infants without apparent brain lesions later develop motor and cognitive deficits. Thalamocortical connections are known to be necessary for normal brain function; they develop during late fetal life and are vulnerable to perinatal adversity. This study addressed the hypothesis that abnormalities in the functional connectivity between cortex and thalamus underlie neurocognitive impairments seen after preterm birth. Using resting state functional connectivity magnetic resonance imaging (fMRI) in a group of 102 very preterm infants without major focal brain lesions, we used partial correlations between thalamus and functionally-derived cortical areas to determine significant connectivity between cortical areas and thalamus, and correlated the parameter estimates of these connections with standardised neurocognitive assessments in each infant at 20 months of age. Pre-motor association cortex connectivity to thalamus correlates with motor function, while connectivity between primary sensory-motor cortex and thalamus correlates with cognitive scores. These results demonstrate the importance and vulnerability of functional thalamocortical connectivity development in the perinatal period for later neurocognitive functioning.

Microglia-Mediated Neurodegeneration in Perinatal Brain Injuries

Perinatal brain injuries, including encephalopathy related to fetal growth restriction, encephalopathy of prematurity, neonatal encephalopathy of the term neonate, and neonatal stroke, are a major cause of neurodevelopmental disorders. They trigger cellular and molecular cascades that lead in many cases to permanent motor, cognitive, and/or behavioral deficits. Damage includes neuronal degeneration, selective loss of subclasses of interneurons, blocked maturation of oligodendrocyte progenitor cells leading to dysmyelination, axonopathy and very likely synaptopathy, leading to impaired connectivity. The nature and severity of changes vary according to the type and severity of insult and maturation stage of the brain. Microglial activation has been demonstrated almost ubiquitously in perinatal brain injuries and these responses are key cell orchestrators of brain pathology but also attempts at repair. These divergent roles are facilitated by a diverse suite of transcriptional profiles and through a complex dialogue with other brain cell types. Adding to the complexity of understanding microglia and how to modulate them to protect the brain is that these cells have their own developmental stages, enabling them to be key participants in brain building. Of note, not only do microglia help build the brain and respond to brain injury, but they are a key cell in the transduction of systemic inflammation into neuroinflammation. Systemic inflammatory exposure is a key risk factor for poor neurodevelopmental outcomes in preterm born infants. Based on these observations, microglia appear as a key cell target for neuroprotection in perinatal brain injuries. Numerous strategies have been developed experimentally to modulate microglia and attenuate brain injury based on these strong supporting data and we will summarize these.

Functional Connectivity Network Disruption Underlies Domain-Specific Impairments in Attention for Children Born Very Preterm

Attention problems are common in school-age children born very preterm (VPT; < 32 weeks gestational age), but the contribution of aberrant functional brain connectivity to these problems is not known. As part of a prospective longitudinal study, brain functional connectivity (fc) was assessed alongside behavioral measures of selective, sustained, and executive attention in 58 VPT and 65 full-term (FT) born children at corrected-age 12 years. VPT children had poorer sustained, shifting, and divided attention than FT children. Within the VPT group, poorer attention scores were associated with between-network connectivity in ventral attention, visual, and subcortical networks, whereas between-network connectivity in the frontoparietal, cingulo-opercular, dorsal attention, salience and motor networks was associated with attention functioning in FT children. Network-level differences were also evident between VPT and FT children in specific attention domains. Findings contribute to our understanding of fc networks that potentially underlie typical attention development and suggest an alternative network architecture may help support attention in VPT children.

Back to basics: the neuronal substrates and mechanisms that underlie the electroencephalogram in premature neonates

Electroencephalography is the only clinically available technique that can address the premature neonate normal and pathological functional development week after week. The changes in the electroencephalogram (EEG) result from gradual structural and functional modifications that arise during the last trimester of pregnancy. Here, we review the structural changes over time that underlie the establishment of functional immature neural networks, the impact of certain anatomical specificities (fontanelles, connectivity, etc.) on the EEG, limitations in EEG interpretation, and the utility of high-resolution EEG (HR-EEG) in premature newborns (a promising technique with a high degree of spatiotemporal resolution). In particular, we classify EEG features according to whether they are manifestations of endogenous generators (i.e. theta activities that coalesce with a slow wave or delta brushes) or come from a broader network. Furthermore, we review publications on EEG in premature animals because the data provide a better understanding of what is happening in premature newborns. We then discuss the results and limitations of functional connectivity analyses in premature newborns. Lastly, we report on the magnetoelectroencephalographic studies of brain activity in the fetus. A better understanding of complex interactions at various structural and functional levels during normal neurodevelopment (as assessed using electroencephalography as a benchmark method) might lead to better clinical care and monitoring for premature neonates.

Cortical Gray Matter Injury in Encephalopathy of Prematurity: Link to Neurodevelopmental Disorders

Preterm-born infants frequently suffer from an array of neurological damage, collectively termed encephalopathy of prematurity (EoP). They also have an increased risk of presenting with a neurodevelopmental disorder (e.g., autism spectrum disorder; attention deficit hyperactivity disorder) later in life. It is hypothesized that it is the gray matter injury to the cortex, in addition to white matter injury, in EoP that is responsible for the altered behavior and cognition in these individuals. However, although it is established that gray matter injury occurs in infants following preterm birth, the exact nature of these changes is not fully elucidated. Here we will review the current state of knowledge in this field, amalgamating data from both clinical and preclinical studies. This will be placed in the context of normal processes of developmental biology and the known pathophysiology of neurodevelopmental disorders. Novel diagnostic and therapeutic tactics required integration of this information so that in the future we can combine mechanism-based approaches with patient stratification to ensure the most efficacious and cost-effective clinical practice.

Peak Width of Skeletonized Water Diffusion MRI in the Neonatal Brain

Preterm birth is closely associated with cognitive impairment and generalized dysconnectivity of neural networks inferred from water diffusion MRI (dMRI) metrics. Peak width of skeletonized mean diffusivity (PSMD) is a metric derived from histogram analysis of mean diffusivity across the white matter skeleton, and it is a useful biomarker of generalized dysconnectivity and cognition in adulthood. We calculated PSMD and five other histogram based metrics derived from diffusion tensor imaging (DTI) and neurite orientation and dispersion imaging (NODDI) in the newborn, and evaluated their accuracy as biomarkers of microstructural brain white matter alterations associated with preterm birth. One hundred and thirty five neonates (76 preterm, 59 term) underwent 3T MRI at term equivalent age. There were group differences in peak width of skeletonized mean, axial, and radial diffusivities (PSMD, PSAD, PSRD), orientation dispersion index (PSODI) and neurite dispersion index (PSNDI), all p < 10-4. PSFA did not differ between groups. PSNDI was the best classifier of gestational age at birth with an accuracy of 81±10%, followed by PSMD, which had 77±9% accuracy. Models built on both NODDI metrics, and on all dMRI metrics combined, did not outperform the model based on PSNDI alone. We conclude that histogram based analyses of DTI and NODDI parameters are promising new image markers for investigating diffuse changes in brain connectivity in early life.

Correlates of Normal and Abnormal General Movements in Infancy and Long-Term Neurodevelopment of Preterm Infants: Insights from Functional Connectivity Studies at Term Equivalence

Preterm infants born before 32 weeks gestation have increased risks for neurodevelopmental impairment at two years of age. How brain function differs between preterm infants with normal or impaired development is unknown. However, abnormal spontaneous motor behavior at 12-15 weeks post-term age is associated with neurodevelopmental impairment. We imaged brain blood oxygen level-dependent signals at term-equivalent age in 62 infants born at <32 weeks gestation and explored whether resting state functional connectivity (rsFC) differed with performances on the General Movement Assessment (GMA) at 12-15 weeks, and Bayley III scores at two years of corrected age. Infants with aberrant general movements exhibited decreased rsFC between the basal ganglia and regions in parietal and frontotemporal lobes. Infants with normal Bayley III cognitive scores exhibited increased rsFC between the basal ganglia and association cortices in parietal and occipital lobes compared with cognitively impaired children. Infants with normal motor scores exhibited increased rsFC between the basal ganglia and visual cortices, compared with children with motor impairment. Thus, the presence of abnormal general movements is associated with region-specific differences in rsFC at term. The association of abnormal long-term neurodevelopmental outcomes with decreased rsFC between basal ganglia and sub-score specific cortical regions may provide biomarkers of neurodevelopmental trajectory and outcome.

Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth

Multi-contrast MRI captures information about brain macro- and micro-structure which can be combined in an integrated model to obtain a detailed "fingerprint" of the anatomical properties of an individual's brain. Inter-regional similarities between features derived from structural and diffusion MRI, including regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging measures, can be modelled as morphometric similarity networks (MSNs). Here, individual MSNs were derived from 105 neonates (59 preterm and 46 term) who were scanned between 38 and 45 weeks postmenstrual age (PMA). Inter-regional similarities were used as predictors in a regression model of age at the time of scanning and in a classification model to discriminate between preterm and term infant brains. When tested on unseen data, the regression model predicted PMA at scan with a mean absolute error of 0.70  ±  0.56 weeks, and the classification model achieved 92% accuracy. We conclude that MSNs predict chronological brain age accurately; and they provide a data-driven approach to identify networks that characterise typical maturation and those that contribute most to neuroanatomic variation associated with preterm birth.

Machine learning in resting-state fMRI analysis

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.

Altered Functional Brain Network Integration, Segregation, and Modularity in Infants Born Very Preterm at Term-Equivalent Age

OBJECTIVES

To determine the functional network organization of the brain in infants born very preterm at term-equivalent age and to relate network alterations to known clinical risk factors for poor neurologic outcomes in prematurity.

STUDY DESIGN

Resting-state functional magnetic resonance imaging data from 66 infants born very preterm (gestational age <32 weeks and birth weight <1500 g) and 66 healthy neonates born at full term, acquired as part of a prospective, cross-sectional study, were compared at term age using graph theory. Features of resting-state networks, including integration, segregation, and modularity, were derived from correlated hemodynamic activity arising from 93 cortical and subcortical regions of interest and compared between groups.

RESULTS

Despite preserved small-world topology and modular organization, resting-state networks of infants born very preterm at term-equivalent age were less segregated and less integrated than those of infants born full term. Chronic respiratory illness (ie, bronchopulmonary dysplasia and the length of oxygen support) was associated with decreased global efficiency and increased path lengths (P < .05). In both cohorts, 4 functional modules with similar composition were observed (parietal/temporal, frontal, subcortical/limbic, and occipital). The density of connections in 3 of the 4 modules was decreased in the very preterm network (P < .01); however, in the occipital/visual cortex module, connectivity was increased in infants born very preterm relative to control infants (P < .0001).

CONCLUSIONS

Early exposure to the ex utero environment is associated with altered resting-state network functional organization in infants born very preterm at term-equivalent age, likely reflecting disrupted brain maturational processes.

Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age

Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.

Early Pain Exposure Influences Functional Brain Connectivity in Very Preterm Neonates

Background

Early exposure to nociceptive events may cause brain structural alterations in preterm neonates, with long-lasting consequences on neurodevelopmental outcome. Little is known on the extent to which early pain may affect brain connectivity. We aim to evaluate brain functional connectivity changes in preterm neonate that underwent multiple invasive procedures during the postnatal period, and to correlate them with the neurodevelopmental outcome at 24 months.

Methods

In this prospective case-control study, we collected information about exposure to painful events during the early postnatal period and resting-state BOLD-fMRI data at term equivalent age from two groups of preterm neonate: 33 subjected to painful procedures during the neonatal intensive care (mean gestational age 27.9 ± 1.8 weeks) and 13 who did not require invasive procedures (average gestational age 31.2 ± 2.1 weeks). A data-driven principal-component-based multivariate pattern analysis (MVPA) was used to investigate the effect of early pain exposure on brain functional connectivity, and the relationship between connectivity changes and neurodevelopmental outcome at 24 months, assessed with Griffiths, Developmental Scale-Revised: 0-2.

Results

Early pain was associated with decreased functional connectivity between thalami and bilateral somatosensory cortex, and between the right insular cortex and ipsilateral amygdala and hippocampal regions, with a more evident effect in preterm neonate undergoing more invasive procedures. Functional connectivity of the right thalamocortical pathway was related to neuromotor outcome at 24 months (P = 0.003).

Conclusion

Early exposure to pain is associated with abnormal functional connectivity of developing networks involved in the modulation of noxious stimuli in preterm neonate, contributing to the neurodevelopmental consequence of preterm birth.

Heart rate variability as a biomarker in health and affective disorders: A perspective on neuroimaging studies

The dynamic embodiment of psychological processes is evident in the association of health outcomes, behavioural traits and psychological functioning with Heart Rate Variability (HRV). The dominant high-frequency component of HRV is an index of the central neural control of heart rhythm, mediated via the parasympathetic vagus nerve. HRV provides a potential objective measure of action policies for the adaptive and predictive allostatic regulation of homeostasis within the cardiovascular system. In its support, a network of brain regions (referred to as the 'central autonomic network') maps internal state, and controls autonomic responses. This network includes regions of prefrontal cortex, anterior cingulate cortex, insula, amygdala, periaqueductal grey, pons and medulla. Human neuroimaging studies of neural activation and functional connectivity broadly endorse this architecture, and its link with cardiac regulation at rest and dysregulation in clinical states that include affective disorders. In this review, we appraise neuroimaging research and related evidence for HRV as an informative marker of autonomic integration with affect and cognition, taking a perspective on function and organisation. We consider evidence for the utility of HRV as a metric to inform targeted interventions to improve autonomic and affective dysregulation, and suggest research questions for further investigation.