{"id":{"repo_id":"colostate","oai_identifier":"oai:mountainscholar.org:10217/244873"},"canonical_url":"https://search.dev.ndltd.org/etd/colostate/oai:mountainscholar.org:10217/244873","repository":{"repo_id":"colostate","name":"Colorado State University","base_url":"https://api.mountainscholar.org/server/oai/request"},"display":{"title":"Early neighborhood disadvantage and risk for internalizing problems: underlying neurobiological mechanisms","abstract":"Early neighborhood-level socioeconomic disadvantage and air pollutant exposure predict an increased risk for mental health difficulties across the lifespan. However, the neurobiological mechanisms underlying these associations remain unclear. In this study, I investigated the associations of early neighborhood-level disadvantage and air pollutant exposure with neural network topology and stress-related outcomes in emerging adulthood. Participants were typically developing 18- to 19-year-olds (N = 77) from socioeconomically diverse backgrounds. They reported their home address history and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Geocoding of participants' residential locations was used to compute neighborhood disadvantage and fine particulate matter 2.5 (PM2.5) exposure. Global efficiency and clustering in the central executive (CEN), default mode (DMN), and salience networks (SN) were computed. Hair cortisol concentration (HCC) and perceived stress were measured for the full sample. For a subsample (N = 40), resting heart rate variability (HRV) was measured in a separate session after the MRI scan. Results showed that greater early neighborhood disadvantage was significantly associated with lower global efficiency in the DMN as well as higher clustering coefficient in the DMN and greater resting respiratory sinus arrhythmia (RSA) at a trend level. Greater early exposure to average PM2.5 levels was associated with reduced global efficiency in the DMN. Greater number of days with exposure to high PM2.5 levels was associated with reduced clustering coefficient and higher global efficiency in the CEN. Early PM2.5 exposure was not associated with stress physiology or perceived stress. These findings align with the notion that early-life neighborhood conditions may influence the organization of large-scale neural networks known to be associated with mental health. Findings from this study can be used to inform future prevention efforts and policies aimed at reducing health disparities and promoting resilience.","abstract_html":"Early neighborhood-level socioeconomic disadvantage and air pollutant exposure predict an increased risk for mental health difficulties across the lifespan. However, the neurobiological mechanisms underlying these associations remain unclear. In this study, I investigated the associations of early neighborhood-level disadvantage and air pollutant exposure with neural network topology and stress-related outcomes in emerging adulthood. Participants were typically developing 18- to 19-year-olds (N = 77) from socioeconomically diverse backgrounds. They reported their home address history and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Geocoding of participants' residential locations was used to compute neighborhood disadvantage and fine particulate matter 2.5 (PM2.5) exposure. Global efficiency and clustering in the central executive (CEN), default mode (DMN), and salience networks (SN) were computed. Hair cortisol concentration (HCC) and perceived stress were measured for the full sample. For a subsample (N = 40), resting heart rate variability (HRV) was measured in a separate session after the MRI scan. Results showed that greater early neighborhood disadvantage was significantly associated with lower global efficiency in the DMN as well as higher clustering coefficient in the DMN and greater resting respiratory sinus arrhythmia (RSA) at a trend level. Greater early exposure to average PM2.5 levels was associated with reduced global efficiency in the DMN. Greater number of days with exposure to high PM2.5 levels was associated with reduced clustering coefficient and higher global efficiency in the CEN. Early PM2.5 exposure was not associated with stress physiology or perceived stress. These findings align with the notion that early-life neighborhood conditions may influence the organization of large-scale neural networks known to be associated with mental health. Findings from this study can be used to inform future prevention efforts and policies aimed at reducing health disparities and promoting resilience.","abstract_has_math":false,"creators":["Hansen, Melissa, author","Merz, Emily, advisor","Henry, Kimberly, committee member","Thomas, Michael, committee member","Montrose, Luke, committee member"],"institution":"Colorado State University. Libraries","degree_name":"Doctor of Philosophy (Ph.D.)","degree_level":"Doctoral","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-27T19:13:06Z","subjects":[],"languages":["eng","English"],"rights":["Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. 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Participants were typically developing 18- to 19-year-olds (N = 77) from socioeconomically diverse backgrounds. They reported their home address history and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Geocoding of participants' residential locations was used to compute neighborhood disadvantage and fine particulate matter 2.5 (PM2.5) exposure. Global efficiency and clustering in the central executive (CEN), default mode (DMN), and salience networks (SN) were computed. Hair cortisol concentration (HCC) and perceived stress were measured for the full sample. For a subsample (N = 40), resting heart rate variability (HRV) was measured in a separate session after the MRI scan. Results showed that greater early neighborhood disadvantage was significantly associated with lower global efficiency in the DMN as well as higher clustering coefficient in the DMN and greater resting respiratory sinus arrhythmia (RSA) at a trend level. Greater early exposure to average PM2.5 levels was associated with reduced global efficiency in the DMN. Greater number of days with exposure to high PM2.5 levels was associated with reduced clustering coefficient and higher global efficiency in the CEN. Early PM2.5 exposure was not associated with stress physiology or perceived stress. These findings align with the notion that early-life neighborhood conditions may influence the organization of large-scale neural networks known to be associated with mental health. Findings from this study can be used to inform future prevention efforts and policies aimed at reducing health disparities and promoting resilience."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["born digital","doctoral dissertations"]},{"key":"dc:title","label":"Title","values":["Early neighborhood disadvantage and risk for internalizing problems: underlying neurobiological mechanisms"]}]}],"canonical_facts":{"dc:creator":["Hansen, Melissa, author","Merz, Emily, advisor","Henry, Kimberly, committee member","Thomas, Michael, committee member","Montrose, Luke, committee member"],"dc:date.accessioned":["2026-06-08T10:33:05Z"],"dc:date.issued":["2026"],"dc:description.abstract":["Early neighborhood-level socioeconomic disadvantage and air pollutant exposure predict an increased risk for mental health difficulties across the lifespan. However, the neurobiological mechanisms underlying these associations remain unclear. In this study, I investigated the associations of early neighborhood-level disadvantage and air pollutant exposure with neural network topology and stress-related outcomes in emerging adulthood. Participants were typically developing 18- to 19-year-olds (N = 77) from socioeconomically diverse backgrounds. They reported their home address history and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Geocoding of participants' residential locations was used to compute neighborhood disadvantage and fine particulate matter 2.5 (PM2.5) exposure. Global efficiency and clustering in the central executive (CEN), default mode (DMN), and salience networks (SN) were computed. Hair cortisol concentration (HCC) and perceived stress were measured for the full sample. For a subsample (N = 40), resting heart rate variability (HRV) was measured in a separate session after the MRI scan. Results showed that greater early neighborhood disadvantage was significantly associated with lower global efficiency in the DMN as well as higher clustering coefficient in the DMN and greater resting respiratory sinus arrhythmia (RSA) at a trend level. Greater early exposure to average PM2.5 levels was associated with reduced global efficiency in the DMN. Greater number of days with exposure to high PM2.5 levels was associated with reduced clustering coefficient and higher global efficiency in the CEN. Early PM2.5 exposure was not associated with stress physiology or perceived stress. These findings align with the notion that early-life neighborhood conditions may influence the organization of large-scale neural networks known to be associated with mental health. 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