Early Infant Prefrontal Cortical Microstructure Predicts Present and Future Emotionality.

brain development cortical microstructure diffusion MRI (dMRI) infant emotionality infant neuroimaging neurite orientation dispersion and density imaging (NODDI) prefrontal cortex

Journal

Biological psychiatry
ISSN: 1873-2402
Titre abrégé: Biol Psychiatry
Pays: United States
ID NLM: 0213264

Informations de publication

Date de publication:
09 Apr 2024
Historique:
received: 25 10 2023
revised: 05 03 2024
accepted: 01 04 2024
medline: 12 4 2024
pubmed: 12 4 2024
entrez: 11 4 2024
Statut: aheadofprint

Résumé

High levels of infant negative emotionality (NE) and low positive emotionality (PE) predict future emotional and behavioral problems. The prefrontal cortex (PFC) supports emotional regulation, with each PFC subregion specializing in specific emotional processes. Neurite Orientation Dispersion and Density Imaging (NODDI) estimates microstructural integrity and myelination via the neurite density index (NDI) and dispersion via the orientation dispersion index (ODI), with potential to more accurately evaluate microstructural alterations in the developing brain. Yet, no study has used these indices to examine associations between PFC microstructure and concurrent or developing infant emotionality. We modeled PFC subregional NDI and ODI at 3 months with caregiver-reported infant NE and PE at 3 months (n=61) and at 9 months (n=50), using multivariable and subsequent bivariate regression models. The most robust statistically-significant findings were positive associations among 3-month rACC ODI and cACC NDI and concurrent NE, and 3-month lOFC ODI and prospective NE; and a negative association between 3-month dlPFC ODI and concurrent PE. Multivariate models also revealed that other PFC subregional microstructure measures, and infant and caregiver sociodemographic and clinical factors, predicted infant 3- and 9-month NE and PE. Greater NDI and ODI, reflecting greater microstructural complexity, in PFC regions supporting salience perception (rACC), decision-making (lOFC), action selection (cACC), and attentional processes (dlPFC) might result in greater integration of these subregions with other neural networks, greater attention to salient negative external cues, thus higher NE and/or lower PE. These findings provide potential infant cortical markers of future psychopathology risk.

Sections du résumé

BACKGROUND BACKGROUND
High levels of infant negative emotionality (NE) and low positive emotionality (PE) predict future emotional and behavioral problems. The prefrontal cortex (PFC) supports emotional regulation, with each PFC subregion specializing in specific emotional processes. Neurite Orientation Dispersion and Density Imaging (NODDI) estimates microstructural integrity and myelination via the neurite density index (NDI) and dispersion via the orientation dispersion index (ODI), with potential to more accurately evaluate microstructural alterations in the developing brain. Yet, no study has used these indices to examine associations between PFC microstructure and concurrent or developing infant emotionality.
METHODS METHODS
We modeled PFC subregional NDI and ODI at 3 months with caregiver-reported infant NE and PE at 3 months (n=61) and at 9 months (n=50), using multivariable and subsequent bivariate regression models.
RESULTS RESULTS
The most robust statistically-significant findings were positive associations among 3-month rACC ODI and cACC NDI and concurrent NE, and 3-month lOFC ODI and prospective NE; and a negative association between 3-month dlPFC ODI and concurrent PE. Multivariate models also revealed that other PFC subregional microstructure measures, and infant and caregiver sociodemographic and clinical factors, predicted infant 3- and 9-month NE and PE.
CONCLUSIONS CONCLUSIONS
Greater NDI and ODI, reflecting greater microstructural complexity, in PFC regions supporting salience perception (rACC), decision-making (lOFC), action selection (cACC), and attentional processes (dlPFC) might result in greater integration of these subregions with other neural networks, greater attention to salient negative external cues, thus higher NE and/or lower PE. These findings provide potential infant cortical markers of future psychopathology risk.

Identifiants

pubmed: 38604525
pii: S0006-3223(24)01220-4
doi: 10.1016/j.biopsych.2024.04.001
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Yicheng Zhang (Y)

University of Pittsburgh Swanson School of Engineering, Department of Bioengineering, Pittsburgh, PA. Electronic address: yiz170@pitt.edu.

Layla Banihashemi (L)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Amelia Versace (A)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Alyssa Samolyk (A)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Megan Taylor (M)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Gabrielle English (G)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Vanessa J Schmithorst (VJ)

UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA.

Vincent K Lee (VK)

University of Pittsburgh Swanson School of Engineering, Department of Bioengineering, Pittsburgh, PA; UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA.

Richelle Stiffler (R)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Haris Aslam (H)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Ashok Panigrahy (A)

UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA.

Alison E Hipwell (AE)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Mary L Phillips (ML)

University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.

Classifications MeSH