Under typical conditions, medial prefrontal cortex (mPFC) connections with the amygdala are immature during childhood and become adult-like during adolescence. Rodent models show that maternal deprivation accelerates this development, prompting examination of human amygdala-mPFC phenotypes following maternal deprivation. Previously institutionalized youths, who experienced early maternal deprivation, exhibited atypical amygdalamPFC connectivity. Specifically, unlike the immature connectivity (positive amygdala-mPFC coupling) of comparison children, children with a history of early adversity evidenced mature connectivity (negative amygdala-mPFC coupling) and thus, resembled the adolescent phenotype. This connectivity pattern was mediated by the hormone cortisol, suggesting that stress-induced modifications of the hypothalamic-pituitary-adrenal axis shape amygdala-mPFC circuitry. Despite being age-atypical, negative amygdala-mPFC coupling conferred some degree of reduced anxiety, although anxiety was still significantly higher in the previously institutionalized group. These findings suggest that accelerated amygdala-mPFC development is an ontogenetic adaptation in response to early adversity.fMRI | emotion regulation | stress | neurodevelopment

Functional connections (FC) between the amygdala and cortical and subcortical regions underlie a range of affective and cognitive processes. Despite the central role amygdala networks have in these functions, the normative developmental emergence of FC between the amygdala and the rest of the brain is still largely undefined. This study employed amygdala subregion maps and resting-state functional magnetic resonance imaging to characterize the typical development of human amygdala FC from age 4 to 23 years old (n = 58). Amygdala FC with subcortical and limbic regions was largely stable across this developmental period. However, three cortical regions exhibited age-dependent changes in FC: amygdala FC with the medial prefrontal cortex (mPFC) increased with age, while amygdala FC with a region including the insula and superior temporal sulcus decreased with age, and amygdala FC with a region encompassing the parahippocampal gyrus and posterior cingulate also decreased with age. The transition from childhood to adolescence (around age 10 years) marked an important change-point in the nature of amygdala-cortical FC. We distinguished unique developmental patterns of coupling for three amygdala subregions and found particularly robust convergence of FC for all subregions with the mPFC. These findings suggest that there are extensive changes in amygdala-cortical functional connectivity that emerge between childhood and adolescence.

Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.

Mature amygdala-prefrontal circuitry regulates affect in adulthood, but shows protracted development. In (semi-)altricial species, caregivers provide potent affect regulation when mature neurocircuitry is absent. This investigation examined a potential mechanism through which caregivers provide regulatory influences in childhood. Children, but not adolescents, showed evidence of maternal buffering, such that maternal stimuli suppressed amygdala reactivity. In the absence of maternal stimuli, children exhibited immature amygdala-prefrontal connectivity. However, in the presence of maternal stimuli, children displayed mature-like connectivity that resembled adolescents’ connectivity. Children showed improved affect-related regulation in the presence of their mother. Individual differences emerged, with maternal influence on amygdalaprefrontal circuitry associated with stronger mother-child relationships and maternal modulation of behavioral regulation. These findings suggest a neural mechanism through which caregivers modulate children's regulatory behavior by inducing mature-like connectivity and buffering against heightened reactivity. Maternal buffering in childhood, but not adolescence, suggests that childhood may be a sensitive period for amygdala-prefrontal development.

No abstract

Current research suggests that autism spectrum disorder (ASD) is characterized by asynchronous neural oscillations. However, it is unclear whether changes in neural oscillations represent an index of the disorder or are shared more broadly among both affected and unaffected family members. Additionally, it remains unclear how early these differences emerge in development and whether they remain constant or change over time. In this study we examined developmental trajectories in spectral power in infants at high- or low-risk for ASD. Spectral power was extracted from resting EEG recorded over frontal regions of the scalp when infants were 6, 9, 12, 18 and 24 months of age. We used multilevel modeling to assess change over time between risk groups in the delta, theta, low alpha, high alpha, beta, and gamma frequency bands. The results indicated that across all bands, spectral power was lower in high-risk infants as compared to low-risk infants at 6-months of age. Furthermore high-risk infants showed different trajectories of change in spectral power in the subsequent developmental window indicating that not only are the patterns of change different, but that group differences are dynamic within the first two years of life. These findings remained the same after removing data from a subset of participants who displayed ASD related behaviors at 24 or 36 months. These differences in the nature of the trajectories of EEG power represent important endophenotypes of ASD.

Depression is a common outcome for those having experienced early life stress (ELS). For those individuals, depression typically increases during adolescence and appears to endure into adulthood, suggesting alterations in the development of brain systems involved in depression. Developmentally, the nucleus accumbens (NAcc), a limbic structure associated with reward learning and motivation, typically undergoes dramatic functional change during adolescence; therefore, age-related changes in NAcc function may underlie increases in depression in adolescence following ELS. The current study examined the effects of ELS in 38 previously institutionalized children and adolescents in comparison to a group of 31 youth without a history of ELS. Consistent with previous research, the findings showed that depression was higher in adolescents than children with a history of ELS. Additionally, fMRI results showed atypical NAcc development, where the ELS group did not show a typical increase in NAcc reactivity during adolescence. Consequently, the ELS group showed NAcc hypoactivation during adolescence, and lower NAcc reactivity was correlated with higher depression scores. The results have important implications for understanding how ELS may influence increases in depression via neural development during the transition to adolescence and highlight the importance of identifying at-risk individuals in childhood, a potential critical period for depression-targeted intervention.

An aim of autism spectrum disorder (ASD) research is to identify early biomarkers that inform ASD pathophysiology and expedite detection. Brain oscillations captured in electroencephalography (EEG) are thought to be disrupted as core ASD pathophysiology. We leverage longitudinal EEG power measurements from 3 to 36 months of age in infants at low- and high-risk for ASD to test how and when power distinguishes ASD risk and diagnosis by age 3-years. Power trajectories across the first year, second year, or first three years postnatally were submitted to data-driven modeling to differentiate ASD outcomes. Power dynamics during the first postnatal year best differentiate ASD diagnoses. Delta and gamma frequency power trajectories consistently distinguish infants with ASD diagnoses from others. There is also a developmental shift across timescales towards including higher-frequency power to differentiate outcomes. These findings reveal the importance of developmental timing and trajectory in understanding pathophysiology and classifying ASD outcomes.