Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds

Kardan, Omid; Kaplan, Sydney; Wheelock, Muriah D.; Feczko, Eric; Day, Trevor K. M.; Miranda-Domínguez, Óscar; Meyer, Dominique; Eggebrecht, Adam T.; Moore, Lucille A; Sung, Sooyeon; Chamberlain, Taylor A; Earl, Eric; Snider, Kathy; Graham, Alice M.; Berman, Marc G.; Uğurbil, Kâmil; Yacoub, Essa; Elison, Jed T.; Smyser, Christopher D.; Fair, Damien A.; Rosenberg, Monica D.

doi:10.1016/j.dcn.2022.101123

Cited by 23 publications

(16 citation statements)

References 65 publications

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“…The volumes of segmented regions are important biomarkers on their own 8 , but accurate segmentation of cortical gray matter is also necessary to produce more advanced morphological metrics such as cortical thickness, surface area, and gyrification 9 . Finally, other modalities such as fMRI and dMRI rely on accurate segmentations to produce more computationally sophisticated metrics like functional or structural connectivity [10][11][12][13][14] .…”

Section: Infant Brain Imaging Relies On Quality Brain Segmentationsmentioning

confidence: 99%

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

Hendrickson

Reiners

Moore

et al. 2023

Preprint

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Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design: Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations: Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions: BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

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Section: Infant Brain Imaging Relies On Quality Brain Segmentationsmentioning

confidence: 99%

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

Hendrickson

Reiners

Moore

et al. 2023

Preprint

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“…While research using brain age predictions and BAGs is robust in adolescents and adults 12,13 , they are not well-studied with connectome data from the perinatal period. Four studies 9,[14][15][16] have used functional connectomes, and two 17,18 have used structural connectomes to predict age in the first months of life. However, none of these studies have compared structural and functional brain ages.…”

Section: Introductionmentioning

confidence: 99%

Brain age prediction and deviations from normative trajectories in the neonatal connectome

Sun,

Mehta,

Khaitova

et al. 2024

Preprint

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Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age, or brain age gap (BAG),represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks, even putatively later developing ones, generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

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“…Yet, biological interpretation of ML results remains a challenge for myriad reasons. While studies have interpreted the beta weights of features from ML models (Plitt et al 2015; Finn et al 2015; Dhamala et al 2020; Jiang et al 2020; Greene et al 2020; Bellantuono et al 2021; Kardan et al 2022), it has been shown that such interpretation could be seriously misleading because some features that are not related to the target label can still have significant weights in prediction (Chen et al, 2022; Haufe et al, 2014). For example, features with the strongest ML beta weights may reflect non-neuronal or nuisance signal such as head motion or machine noise (Chen et al, 2022; Haufe et al, 2014; Siegel et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…These canonical atlases can be used to describe within-network and between-network connectivity - herein referred to as “network block” connectivity. One commonly employed approach is an “individual network block” method in which only functional connections within a single network block (i.e., either within or between network rsFC) are used as features within the model (Kardan et al, 2022; Millar et al, 2020, 2022; Nielsen et al, 2019; Rudolph et al, 2018). Given the fact that increasing feature count is commonly associated with increasing prediction accuracy, limiting ML models to only contain features within specific networks has resulted in limited accuracy (Bellantuono et al, 2021; Cui & Gong, 2018; Nielsen et al, 2019) and reliability (Mellema et al, 2021; Tian & Zalesky, 2021).…”

Section: Introductionmentioning

confidence: 99%

Network level enrichment provides a framework for biological interpretation of machine learning results

Li,

Segel,

Feng

et al. 2023

Preprint

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Machine learning algorithms are increasingly used to identify brain connectivity biomarkers linked to behavior and clinical outcomes. However, non-standard methodological choices in neuroimaging datasets, especially those with families or twins, have prevented robust machine learning applications. Additionally, prioritizing prediction accuracy over biological interpretability has made it challenging to understand the biological processes behind psychopathology. In this study, we employed a linear support vector regression model to study the relationship between resting-state functional connectivity networks and chronological age using data from the Human Connectome Project. We examined the effect of shared variance from twins and siblings by using cross-validation, either randomly assigning or keeping family members together. We also compared models with and without a Pearson feature filter and utilized a network enrichment approach to identify predictive brain networks. Results indicated that not accounting for shared family variance inflated prediction performance, and the Pearson filter reduced accuracy and reliability. Enhancing biological interpretability was achieved by inverting the machine learning model and applying network-level enrichment on the connectome, while directly using regression coefficients as feature weights led to misleading interpretations. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for comprehensible biological interpretation.

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Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds

Cited by 23 publications

References 65 publications

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

Brain age prediction and deviations from normative trajectories in the neonatal connectome

Network level enrichment provides a framework for biological interpretation of machine learning results

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