1 Social Brain Network Connectivity Relates to Social and Adaptive Outcomes Following Pediatric Traumatic Brain Injury
Notice bibliographique
Résumé
Objective: Traumatic brain injury (TBI) is a prevalent cause of long-term morbidity in children and adolescents and can lead to persistent difficulties with social and behavioral function. TBI may impact brain structures that support social cognition, social perception, and day-to-day social interactions—termed the social brain network (SBN). We examined differences in links among the SBN and regions of interest from other neural networks thought to support social outcomes, i.e., the default mode network (DMN) and salience network (SN). Furthermore, we examined how differences in co-activation among the SBN and these other key networks were associated with ratings of social and day-to-day adaptive outcomes. Participants and Methods: Participants included children and adolescents with moderate to severe TBI (msTBI; n=11, Mage=11.78, 6 male), complicated-mild TBI (cmTBI; n=12, Mage=12.59, 9 male), and orthopedic injury (OI; n=22, Mage=11.69, 15 male). Participants underwent resting-state functional MRI on a 3Tesla Siemens Prisma scanner. Parents rated their child’s social and adaptive function on the Child Behavior Checklist (CBCL) and Adaptive Behavior Assessment System-Third Edition (ABAS-3). Resting-state connectivity was assessed using the CONN Toolbox, including preprocessing, denoising, and alignment to the participants’ processed T1 MPRAGE sequence followed by seed-to-voxel analysis using a SBN mask and targeted regions of interest within the DMN and SN. Individual-level r-to-z correlations were extracted from resulting clusters of co-activation with the SBN mask and exported into SPSSv28.0 for integration with behavioral data. Results: One-way ANOVAs used to examine group differences in social and adaptive outcome revealed significant group differences in CBCL Social Competence (F=4.49, p=.019) and all composite scores on the ABAS-3 (Fs=3.78 to 5.17, ps=.031 to .010). In each domain, children with msTBI were rated as having elevated difficulties relative to cmTBI or OI, whereas cmTBI and OI groups did not differ. Connectivity also differed significantly between groups, with children with OI demonstrating greater connectivity between the SBN and the anterior cingulate cortex of the SN (t=5.19, p(FDR)<.0001) and posterior cingulate cortex of the DMN (f=4.30, p(FDR)<.001) than children with msTBI. Children with cmTBI also showed greater connectivity between the SBN and left temporal pole of the DMN (t=7.45, p(FDR)<.000001) than children with msTBI. Degree of connectivity between the SBN and posterior cingulate was significantly positively correlated across all domains of adaptive function (rs=.451 to .504, ps=.010 to .003), whereas degree of connectivity between the SBN and left temporal pole was strongly positively related to Social Competence (a=.633, p=.006) and conceptual adaptive skills on the ABAS (A=.437, p=.037). Conclusions: Our findings provide insights into the neural substrates of social and adaptive morbidity after pediatric TBI, particularly msTBI, by linking alterations in connectivity among the SBN, DMN, and SN with measures of social and adaptive outcome. While the posterior cingulate was broadly associated with adaptive outcome, the temporal pole was particularly strongly associated with social competence. This may reflect the diverse functions and high degree of interconnectivity of the posterior cingulate, which contributes to various cognitive and attentional processes, relative to the strong amygdala/limbic connections of the temporal pole.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».