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Background Despite advances in antenatal and neonatal care, preterm birth remains a leading cause of neurological disabilities in children. Infants born prematurely, particularly those delivered at the earliest gestational ages, commonly demonstrate increased rates of impairment across multiple neurodevelopmental domains. Indeed, the current literature establishes that preterm birth is a leading risk factor for cerebral palsy, is associated with executive function deficits, increases risk for impaired receptive and expressive language skills, and is linked with higher rates of co-occurring attention deficit hyperactivity disorder, anxiety, and autism spectrum disorders. These same infants also demonstrate elevated rates of aberrant cerebral structural and functional connectivity, with persistent changes evident across advanced magnetic resonance imaging modalities as early as the neonatal period. Emerging findings from cross-sectional and longitudinal investigations increasingly suggest that aberrant connectivity within key functional networks and white matter tracts may underlie the neurodevelopmental impairments common in this population. Main body This review begins by highlighting the elevated rates of neurodevelopmental disorders across domains in this clinical population, describes the patterns of aberrant structural and functional connectivity common in prematurely-born infants and children, and then reviews the increasingly established body of literature delineating the relationship between these brain abnormalities and adverse neurodevelopmental outcomes. We also detail important, typically understudied, clinical, and social variables that may influence these relationships among preterm children, including heritability and psychosocial risks. Conclusion Future work in this domain should continue to leverage longitudinal evaluations of preterm infants which include both neuroimaging and detailed serial neurodevelopmental assessments to further characterize relationships between imaging measures and impairment, information necessary for advancing our understanding of modifiable risk factors underlying these disorders and best practices for improving neurodevelopmental trajectories in this high-risk clinical population.

•We assessed HD-DOT for mapping brain regions supporting overt verb production.•We found HD-DOT is resilient to movement associated with overt speaking.•Key areas were found that activate and deactivate during language production.•HD-DOT is sensitive to spatially distributed differential activity to task types. Gold standard neuroimaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and more recently electrocorticography (ECoG) have provided profound insights regarding the neural mechanisms underlying the processing of language, but they are limited in applications involving naturalistic language production especially in developing brains, during face-to-face dialogues, or as a brain-computer interface. High-density diffuse optical tomography (HD-DOT) provides high-fidelity mapping of human brain function with comparable spatial resolution to that of fMRI but in a silent and open scanning environment similar to real-life social scenarios. Therefore, HD-DOT has potential to be used in naturalistic settings where other neuroimaging modalities are limited. While HD-DOT has been previously validated against fMRI for mapping the neural correlates underlying language comprehension and covert (i.e., “silent”) language production, HD-DOT has not yet been established for mapping the cortical responses to overt (i.e., “out loud”) language production. In this study, we assessed the brain regions supporting a simple hierarchy of language tasks: silent reading of single words, covert production of verbs, and overt production of verbs in normal hearing right-handed native English speakers (n = 33). First, we found that HD-DOT brain mapping is resilient to movement associated with overt speaking. Second, we observed that HD-DOT is sensitive to key activations and deactivations in brain function underlying the perception and naturalistic production of language. Specifically, statistically significant results were observed that show recruitment of regions in occipital, temporal, motor, and prefrontal cortices across all three tasks after performing stringent cluster-extent based thresholding. Our findings lay the foundation for future HD-DOT studies of imaging naturalistic language comprehension and production during real-life social interactions and for broader applications such as presurgical language assessment and brain-machine interfaces. [Display omitted]

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., “scrubbing”) and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8–24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

Contemporary theory suggests that prefrontal cortex (PFC) function is associated with individual variability in the psychobiology of the stress response. Advancing our understanding of this complex biobehavioral pathway has potential to provide insight into processes that determine individual differences in stress susceptibility. The present study used functional magnetic resonance imaging to examine brain activity during a variation of the Montreal Imaging Stress Task (MIST) in 53 young adults. Salivary cortisol was assessed as an index of the stress response, trait anxiety was assessed as an index of an individual's disposition toward negative affectivity, and self-reported stress was assessed as an index of an individual's subjective psychological experience. Heart rate and skin conductance responses were also assessed as additional measures of physiological reactivity. Dorsomedial PFC, dorsolateral PFC, and inferior parietal lobule demonstrated differential activity during the MIST. Further, differences in salivary cortisol reactivity to the MIST were associated with ventromedial PFC and posterior cingulate activity, while trait anxiety and self-reported stress were associated with dorsomedial and ventromedial PFC activity, respectively. These findings underscore that PFC activity regulates behavioral and psychobiological components of the stress response.

Excessive stress exposure often leads to emotional dysfunction, characterized by disruptions in healthy emotional learning, expression, and regulation processes. A prefrontal cortex (PFC)-amygdala circuit appears to underlie these important emotional processes. However, limited human neuroimaging research has investigated whether these brain regions underlie the altered emotional function that develops with stress. Therefore, the present study used functional magnetic resonance imaging (fMRI) to investigate stress-induced changes in PFC-amygdala function during Pavlovian fear conditioning. Participants completed a variant of the Montreal Imaging Stress Task (MIST) followed (25 min later) by a Pavlovian fear conditioning task during fMRI. Self-reported stress to the MIST was used to identify three stress-reactivity groups (Low, Medium, and High). Psychophysiological, behavioral, and fMRI signal responses were compared between the three stress-reactivity groups during fear conditioning. Fear learning, indexed via participant expectation of the unconditioned stimulus during conditioning, increased with stress reactivity. Further, the High stress-reactivity group demonstrated greater autonomic arousal (i.e., skin conductance response, SCR) to both conditioned and unconditioned stimuli compared to the Low and Medium stress-reactivity groups. Finally, the High stress group did not regulate the emotional response to threat. More specifically, the High stress-reactivity group did not show a negative relationship between conditioned and unconditioned SCRs. Stress-induced changes in these emotional processes paralleled changes in dorsolateral, dorsomedial, and ventromedial PFC function. These findings demonstrate that acute stress facilitates fear learning, enhances autonomic arousal, and impairs emotion regulation, and suggests these stress-induced changes in emotional function are mediated by the PFC. •Acute stress leads to abrupt shifts in emotional learning and regulation processes.•Altered PFC-amygdala function may underlie disruption of these emotion processes.•Psychosocial stress was induced prior to a Pavlovian fear conditioning task.•Behavior and fMRI signal during conditioning were compared with stress-reactivity.•Stress appears to disrupt anticipatory PFC functions that mediate emotional processes.

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

Background Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits. Methods We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models. Results We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits. Limitations Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism. Conclusions This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

Brain injury sustained during the neonatal period may disrupt development of critical structural and functional connectivity networks leading to subsequent neurodevelopmental impairment in affected children. These networks can be characterized using structural (via diffusion MRI) and functional (via resting state-functional MRI) neuroimaging techniques. Advances in neuroimaging have led to expanded application of these approaches to study term- and prematurely-born infants, providing improved understanding of cerebral development and the deleterious effects of early brain injury. Across both modalities, neuroimaging data are conducive to analyses ranging from characterization of individual white matter tracts and/or resting state networks through advanced ‘connectome-style’ approaches capable of identifying highly connected network hubs and investigating metrics of network topology such as modularity and small-worldness. We begin this review by summarizing the literature detailing structural and functional connectivity findings in healthy term and preterm infants without brain injury during the postnatal period, including discussion of early connectome development. We then detail common forms of brain injury in term- and prematurely-born infants. In this context, we next review the emerging body of literature detailing studies employing diffusion MRI, resting state-functional MRI and other complementary neuroimaging modalities to characterize structural and functional connectivity development in infants with brain injury. We conclude by reviewing technical challenges associated with neonatal neuroimaging, highlighting those most relevant to studying infants with brain injury and emphasizing the need for further targeted study in this high-risk population. •Emerging structural and functional connectivity architecture can be identified in infants.•Brain injury in infants alters structural and functional connectivity development.•The effects of injury depend upon injury type, spatial pattern, location and severity.•Connectivity analysis methods must account for the heterogeneous effects of injury.•Few ‘connectome-style’ analyses have included injured infants to date.

Lead represents a highly prevalent metal toxicant with potential to alter human biology in lasting ways. A population segment that is particularly vulnerable to the negative consequences of lead exposure is the human fetus, as exposure events occurring before birth are linked to varied and long-ranging negative health and behavioral outcomes. An area that has yet to be addressed is the potential that lead exposure during pregnancy alters brain development even before an individual is born. Here, we combine prenatal lead exposure information extracted from newborn bloodspots with the human fetal brain functional MRI data to assess whether neural network connectivity differs between lead-exposed and lead-naïve fetuses. We found that neural connectivity patterns differed in lead-exposed and comparison groups such that fetuses that were not exposed demonstrated stronger age-related increases in cross-hemispheric connectivity, while the lead-exposed group demonstrated stronger age-related increases in posterior cingulate cortex (PCC) to lateral prefrontal cortex (PFC) connectivity. These are the first results to demonstrate metal toxicant-related alterations in human fetal neural connectivity. Remarkably, the findings point to alterations in systems that support higher-order cognitive and regulatory functions. Objectives for future work are to replicate these results in larger samples and to test the possibility that these alterations may account for significant variation in future child cognitive and behavioral outcomes. •In humans, prenatal exposure to lead relates to connectivity of large-scale fetal brain systems.•Age-related strengthening of insular/temporal cross-hemispheric functional connectivity was stronger in fetuses that did not appear to have been exposed to lead.•Fetuses exposed to lead showed age-related strengthening of lateral prefrontal to posterior cingulate functional connectivity.

Threat-related emotional function is supported by a neural circuit that includes the prefrontal cortex (PFC), hippocampus, and amygdala. The function of this neural circuit is altered by negative life experiences, which can potentially affect threat-related emotional processes. Notably, Black-American individuals disproportionately endure negative life experiences compared to White-American individuals. However, the relationships among negative life experiences, race, and the neural substrates that support threat-related emotional function remains unclear. Therefore, the current study investigated whether the brain function that supports threat-related emotional processes varies with racial differences in negative life experiences. In the present study, adolescent violence exposure, family income, and neighborhood disadvantage were measured prospectively (i.e., at 11–19 years of age) for Black-American and White-American volunteers. Participants then, as young adults (i.e., 18–23 years of age), completed a Pavlovian fear conditioning task during functional magnetic resonance imaging (fMRI). Cued and non-cued threats were presented during the conditioning task and behavioral (threat expectancy) and psychophysiological responses (skin conductance response; SCR) were recorded simultaneously with fMRI. Racial differences were observed in neural (fMRI activity), behavioral (threat expectancy), and psychophysiological (SCR) responses to threat. These threat-elicited responses also varied with negative life experiences (violence exposure, family income, and neighborhood disadvantage). Notably, racial differences in brain activity to threat were smaller after accounting for negative life experiences. The present findings suggest that racial differences in the neural and behavioral response to threat are due, in part, to exposure to negative life experiences and may provide new insight into the mechanisms underlying racial disparities in mental health. •Studied racial differences in emotional function linked to developmental environment.•Childhood exposure to negative life events (e.g., violence, poverty) varied by race.•Threat-elicited brain and emotional responses varied by race.•Racial disparities in negative life events accounted for differences in brain function.