Test–Retest Reliability of Deep Learning Analysis of Brain Volumes in Adolescent Brain

Magnetic resonance imaging (MRI) is essential for studying brain development and psychiatric disorders in adolescents. However, the imaging consistency remains challenging, highlighting the need for advanced methodologies to improve the diagnostic and research reliability in this unique developmental period. Adolescence is marked by significant neuroanatomical changes, distinguishing adolescent brains from those of adults and making age-specific imaging research crucial for understanding the neuropsychiatric conditions in youth. This study examines the test–retest reliability of anatomical brain MRI scans in adolescents diagnosed with depressive disorders, emphasizing a developmental perspective on neuropsychiatric disorders. Using a sample of 42 adolescents, we assessed the consistency of structural imaging metrics across 95 brain regions with deep learning-based neuroimaging analysis pipelines. The results demonstrated moderate to excellent reliability, with the intraclass correlation coefficients (ICC) ranging from 0.57 to 0.99 across regions. Notably, regions such as the pallidum, amygdala, entorhinal cortex, and white matter hypointensities showed moderate reliability, likely reflecting the challenges in the segmentation or inherent anatomical variability unique to this age group. This study highlights the necessity of integrating advanced imaging technologies to enhance the accuracy and reliability of the neuroimaging data specific to adolescents. Addressing the regional variability and strengthening the methodological rigor are essential for advancing the understanding of brain development and psychiatric disorders in this distinct developmental stage. Future research should focus on larger, more diverse samples, multi-site studies, and emerging imaging techniques to further validate the neuroimaging biomarkers. Such advancements could improve the clinical outcomes and deepen our understanding of the neuropsychiatric conditions unique to adolescence.

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Published
2024-11-25
DOI
https://doi.org/10.3390/info15120748
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1
Primary Topic
Functional Brain Connectivity Studies
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article
Field-Weighted Citation Impact
0.34
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Test–Retest Reliability of Deep Learning Analysis of Brain Volumes in Adolescent Brain

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Test–Retest Reliability of Deep Learning Analysis of Brain Volumes in Adolescent Brain

Eckhard Schönaü, Anna‐Maria Kasparbauer, Friederike Körber, Heidrun Lioba Wunram, Stephan Bender, İbrahim Duran, Fabian Abuhsin
article en
1 citations

Abstract

Magnetic resonance imaging (MRI) is essential for studying brain development and psychiatric disorders in adolescents. However, the imaging consistency remains challenging, highlighting the need for advanced methodologies to improve the diagnostic and research reliability in this unique developmental period. Adolescence is marked by significant neuroanatomical changes, distinguishing adolescent brains from those of adults and making age-specific imaging research crucial for understanding the neuropsychiatric conditions in youth. This study examines the test–retest reliability of anatomical brain MRI scans in adolescents diagnosed with depressive disorders, emphasizing a developmental perspective on neuropsychiatric disorders. Using a sample of 42 adolescents, we assessed the consistency of structural imaging metrics across 95 brain regions with deep learning-based neuroimaging analysis pipelines. The results demonstrated moderate to excellent reliability, with the intraclass correlation coefficients (ICC) ranging from 0.57 to 0.99 across regions. Notably, regions such as the pallidum, amygdala, entorhinal cortex, and white matter hypointensities showed moderate reliability, likely reflecting the challenges in the segmentation or inherent anatomical variability unique to this age group. This study highlights the necessity of integrating advanced imaging technologies to enhance the accuracy and reliability of the neuroimaging data specific to adolescents. Addressing the regional variability and strengthening the methodological rigor are essential for advancing the understanding of brain development and psychiatric disorders in this distinct developmental stage. Future research should focus on larger, more diverse samples, multi-site studies, and emerging imaging techniques to further validate the neuroimaging biomarkers. Such advancements could improve the clinical outcomes and deepen our understanding of the neuropsychiatric conditions unique to adolescence.

InformationVol. 15(12)
University of Cologne (DE), Düsseldorf University Hospital (DE), University Hospital Cologne (DE), Heinrich Heine University Düsseldorf (DE)
Openalex Percentile: Top 39%
Functional Brain Connectivity Studies
0.34
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