Experience-Dependent Development of Amygdala-Prefrontal Cortex Circuitry and Function

Dramatic changes occur across childhood and adolescence in the activity and connectivity of an amygdala-medial prefrontal cortex circuit critical for emotional learning and regulation. However, little is currently known about how neuroplasticity within the circuit changes during development in the human. Experiences that occur during developmental sensitive periods of increased neuroplasticity have the capacity to sculpt neural function with lifelong consequences for cognition and behavior, though. This dissertation will therefore investigate when and how experience may shape amygdala-medial prefrontal cortex functional circuitry (Aim 1) and what the implications of experience-dependent circuitry development are for emotion regulation behaviors (Aim 2) across childhood, adolescence, and adulthood in three studies. Study 1 (previously published as Gabard-Durnam, Gee et al., 2016) posits and tests the long-term phasic molding hypothesis that tonic amygdala-prefrontal cortex functional connectivity, the functional architecture of the brain, is shaped during development by recurring stimulus-elicited connectivity in the circuitry using prospective examination of these connectivities’ development across childhood and adolescence. Study 1 also tests whether the ability of amygdala-prefrontal cortex stimulus-elicited connectivity to shape the amygdala-prefrontal cortex resting-state functional architecture changes across development, reflecting changing plasticity of the circuitry. Study 2 examines how the timing and duration of an early adverse experience, parental deprivation, interacts with genetically-driven differences in neuroplasticity levels indexed by the Brain-Derived Neurotrophic Factor val66met polymorphism to influence the developmental trajectory of amygdala-prefrontal cortex functional architecture using a population of previously-institutionalized children and adolescents and a never-institutionalized comparison sample. Study 2 further examines how the experience- and plasticity-related changes to the functional architecture influence both concurrent and future internalizing symptomatology across childhood and adolescence. Study 3 builds on the first two developmental studies by explicitly testing whether childhood is a sensitive period for medial prefrontal cortex-mediated regulatory signal learning through a retrospective design in adults. Study 3 additionally assesses the effects of developmental experience on adult emotion regulation behavior and physiology. My findings at the levels of brain circuitry, behavior, physiology, and genetics together delineate a period of increased sensitivity to the environment within prefrontal cortex-amygdala functional circuitry from infancy through childhood, modifiable by genetically-conferred variation in plasticity and the nature of the early environment. Moreover, experiences occurring during the sensitive period have consequences for future emotion regulation behavior both during development and lasting into young adulthood. Together, these findings demonstrate how experience-dependent development has enduring effects on amygdala-prefrontal cortex circuitry function and affective behavior

Longitudinal EEG power in the first postnatal year differentiates autism outcomes

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.R01 DC010290 - NIDCD NIH HHS; T32 MH112510 - NIMH NIH HHS; U54 HD090255 - NICHD NIH HHSPublished versio

Neural correlates of face processing associated with development of social communication in 12-month infants with familial risk of autism spectrum disorder

BACKGROUND: Differences in face processing in individuals with ASD is hypothesized to impact the development of social communication skills. This study aimed to characterize the neural correlates of face processing in 12-month-old infants at familial risk of developing ASD by (1) comparing face-sensitive event-related potentials (ERP) (Nc, N290, P400) between high-familial-risk infants who develop ASD (HR-ASD), high-familial-risk infants without ASD (HR-NoASD), and low-familial-risk infants (LR), and (2) evaluating how face-sensitive ERP components are associated with development of social communication skills. METHODS: 12-month-old infants participated in a study in which they were presented with alternating images of their mother's face and the face of a stranger (LR = 45, HR-NoASD = 41, HR-ASD = 24) as EEG data were collected. Parent-reported and laboratory-observed social communication measures were obtained at 12 and 18 months. Group differences in ERP responses were evaluated using ANOVA, and multiple linear regressions were conducted with maternal education and outcome groups as covariates to assess relationships between ERP and behavioral measures. RESULTS: For each of the ERP components (Nc [negative-central], N290, and P400), the amplitude difference between mother and stranger (Mother-Stranger) trials was not statistically different between the three outcome groups (Nc p = 0.72, N290 p = 0.88, P400 p = 0.91). Marginal effects analyses found that within the LR group, a greater Nc Mother-Stranger response was associated with better expressive language skills on the Mullen Scales of Early Learning, controlling for maternal education and outcome group effects (marginal effects dy/dx = 1.15; p < 0.01). No significant associations were observed between the Nc and language or social measures in HR-NoASD or HR-ASD groups. In contrast, specific to the HR-ASD group, amplitude difference between the Mother versus Stranger P400 response was positively associated with expressive (dy/dx = 2.1, p < 0.001) and receptive language skills at 12 months (dy/dx = 1.68, p < 0.005), and negatively associated with social affect scores on the Autism Diagnostic Observation Schedule (dy/dx = - 1.22, p < 0.001) at 18 months. CONCLUSIONS: In 12-month-old infant siblings with subsequent ASD, increased P400 response to Mother over Stranger faces is positively associated with concurrent language and future social skills.K23 DC017983 - NIDCD NIH HHS; P50 HD105351 - NICHD NIH HHS; R21 DC08637 - NIDCD NIH HHSPublished versio

Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

BACKGROUND: Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. METHODS: Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). RESULTS: Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. CONCLUSIONS: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.K23 DC017983 - NIDCD NIH HHS; P50 HD105351 - NICHD NIH HHS; R01 DC010290 - NIDCD NIH HHS; R21 DC008637 - NIDCD NIH HHSPublished versio

EEG phase-amplitude coupling strength and phase preference: association with age over the first three years after birth

Phase-amplitude coupling (PAC), the coupling of the phase of slower electrophysiological oscillations with the amplitude of faster oscillations, is thought to facilitate dynamic integration of neural activity in the brain. Although the brain undergoes dramatic change and development during the first few years of life, how PAC changes through this developmental period has not been extensively studied. Here, we examined PAC through electroencephalography (EEG) data collected during an awake, eyes-open EEG collection paradigm in 98 children between the ages of three months and three years. We employed non-parametric clustering methods to identify areas of significant PAC across a range of frequency pairs and electrode locations, and examined how PAC strength and phase preference develops in these areas. We found that PAC, primarily between the α-β and γ frequencies, was positively correlated with age from early infancy to early childhood (p = 2.035 × 10-6). Additionally, we found γ over anterior electrodes coupled with the rising phase of the α-β waveform, while γ over posterior electrodes coupled with the falling phase of the α-β waveform; this regionalized phase preference became more prominent with age. This opposing trend may reflect each region's specialization toward feedback or feedforward processing, respectively, suggesting opportunities for back translation in future studies.P50 HD105351 - NICHD NIH HHS; R21 DC008637 - NIDCD NIH HHSPublished versio

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data

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

Image1.PDF

<p>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.</p

Data_Sheet_1_BEAPP: The Batch Electroencephalography Automated Processing Platform.PDF

<p>Electroencephalography (EEG) offers information about brain function relevant to a variety of neurologic and neuropsychiatric disorders. EEG contains complex, high-temporal-resolution information, and computational assessment maximizes our potential to glean insight from this information. Here we present the Batch EEG Automated Processing Platform (BEAPP), an automated, flexible EEG processing platform incorporating freely available software tools for batch processing of multiple EEG files across multiple processing steps. BEAPP does not prescribe a specified EEG processing pipeline; instead, it allows users to choose from a menu of options for EEG processing, including steps to manage EEG files collected across multiple acquisition setups (e.g., for multisite studies), minimize artifact, segment continuous and/or event-related EEG, and perform basic analyses. Overall, BEAPP aims to streamline batch EEG processing, improve accessibility to computational EEG assessment, and increase reproducibility of results.</p

The development of human amygdala functional connectivity at rest from 4 to 23 years: a cross-sectional study

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 23years 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, 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 10years) 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