Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes
AbstractAssociations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qua...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The MIT Press
2023-01-01
|
| Series: | Network Neuroscience |
| Online Access: | https://direct.mit.edu/netn/article/7/2/787/114499/Characterizing-heterogeneity-in-early-adolescent |
| _version_ | 1797796673925152768 |
|---|---|
| author | Matthew Mattoni David V. Smith Thomas M. Olino |
| author_facet | Matthew Mattoni David V. Smith Thomas M. Olino |
| author_sort | Matthew Mattoni |
| collection | DOAJ |
| description |
AbstractAssociations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual. |
| first_indexed | 2024-03-13T03:36:36Z |
| format | Article |
| id | doaj.art-97900212f7d7457bac390e5eced61ad6 |
| institution | Directory Open Access Journal |
| issn | 2472-1751 |
| language | English |
| last_indexed | 2024-03-13T03:36:36Z |
| publishDate | 2023-01-01 |
| publisher | The MIT Press |
| record_format | Article |
| series | Network Neuroscience |
| spelling | doaj.art-97900212f7d7457bac390e5eced61ad62023-06-23T18:42:22ZengThe MIT PressNetwork Neuroscience2472-17512023-01-017278781010.1162/netn_a_00306Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomesMatthew Mattoni0http://orcid.org/0000-0001-8931-0707David V. Smith1Thomas M. Olino2Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USADepartment of Psychology and Neuroscience, Temple University, Philadelphia, PA, USADepartment of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA AbstractAssociations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual.https://direct.mit.edu/netn/article/7/2/787/114499/Characterizing-heterogeneity-in-early-adolescent |
| spellingShingle | Matthew Mattoni David V. Smith Thomas M. Olino Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes Network Neuroscience |
| title | Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes |
| title_full | Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes |
| title_fullStr | Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes |
| title_full_unstemmed | Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes |
| title_short | Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes |
| title_sort | characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes |
| url | https://direct.mit.edu/netn/article/7/2/787/114499/Characterizing-heterogeneity-in-early-adolescent |
| work_keys_str_mv | AT matthewmattoni characterizingheterogeneityinearlyadolescentrewardnetworksandindividualizedassociationswithbehavioralandclinicaloutcomes AT davidvsmith characterizingheterogeneityinearlyadolescentrewardnetworksandindividualizedassociationswithbehavioralandclinicaloutcomes AT thomasmolino characterizingheterogeneityinearlyadolescentrewardnetworksandindividualizedassociationswithbehavioralandclinicaloutcomes |