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Molloy, M. F.

Publications and source records attributed to Molloy, M. F..

3 recordsLinked to original sources

Cognitive and affective neurodevelopment in youth exposed to deprivation and threat

BackgroundEarly life adversity (ELA) is associated with notable negative consequences across development. Experiences of deprivation may affect neurocognitive development, while experiences of threat may alter emotion processing. Deprivation and threat may also differentially influence reward processing. However, unique consequences of deprivation and threat beyond low family resources are debated. MethodsWe employed an exposure vs. control data analytic approach to isolate deprivation and threat influences from socioeconomic resources. Adolescent Brain Cognitive Development (ABCD(R)) Study youth exposed to neither deprivation nor threat (N=2408-2962) were matched to youth exposed to deprivation-only (N=638-721), threat-only (N=198-232), or threat non-exclusively (threat+: N=382-464) based on family income, parental education, race/ethnicity, sex, and age. Multivariate analyses were used to distinguish each ELA group from their respective control groups in the neurocognitive domain (resting-state connectomic maturation, cognitive task performance, and cortical grey matter thickness at two timepoints) and in the neuroaffective domain (nucleus accumbens and caudate activation to reward anticipation and amygdala and insula activation to fearful faces). ResultsIn the neurocognitive domain, similar latent variables (LVs) differentiated the deprivation and threat+ groups from their respective matched control groups. This LV corresponded to neurocognitive maturation, loading positively on cortical functional maturation and task performance, and negatively on cortical grey matter thickness. This LV was weaker in the deprivation and threat+ groups compared to controls. In the neuroaffective domain, no significant LVs were found. ConclusionBoth threat and deprivation exposure during childhood may delay neurocognitive development in early adolescence beyond their co-occurrence with low socioeconomic resources.

neuroscience↗

Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapes

Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individuals resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youths connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6,489) and 2-year-follow-up (Y2: 11-12 years; n = 5,089). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.

neuroscience↗

Innate Organization of the Human Brain

The adult brain is organized into distinct functional networks, forming the basis of information processing and determining individual differences in behavior. Is this network organization genetically determined and present at birth? And what is the individual variability in this organization in neonates? Here, we use unsupervised learning to uncover intrinsic functional brain organization using resting-state connectivity from a large cohort of neonates (Developing Human Connectome Project). We identified a set of symmetric, hierarchical, and replicable networks: sensorimotor, visual, default mode, ventral attention, and high-level vision. We quantified individual variability across neonates, and found the most individual variability in the ventral attention networks. Crucially, the variability of these networks were not driven by SNR differences or differences from adult networks (Yeo et al., 2011). Finally, differential gene expression provided a potential explanation for the emergence of these distinct networks and identified potential genes of interest for future developmental and individual variability research. Overall, we found neonatal connectomes (even at the voxel-level) can reveal broad individual- specific information processing units. The presence of individual differences in neonates and the framework for personalized parcellations demonstrated here has the potential to improve prediction of behavior and future outcomes from neonatal and infant brain data.

neuroscience↗