Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
Pediatric Radiology
Published in: Pediatric Radiology 11/2022

22-09-2022 | Original Article

A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data

Authors: Redha Ali, Hailong Li, Jonathan R. Dillman, Mekibib Altaye, Hui Wang, Nehal A. Parikh, Lili He

Published in: Pediatric Radiology | Issue 11/2022

Login to get access

Abstract

Background

Deep learning has been employed using brain functional connectome data for evaluating the risk of cognitive deficits in very preterm infants. Although promising, training these deep learning models typically requires a large amount of labeled data, and labeled medical data are often very difficult and expensive to obtain.

Objective

This study aimed to develop a self-training deep neural network (DNN) model for early prediction of cognitive deficits at 2 years of corrected age in very preterm infants (gestational age ≤32 weeks) using both labeled and unlabeled brain functional connectome data.

Materials and methods

We collected brain functional connectome data from 343 very preterm infants at a mean (standard deviation) postmenstrual age of 42.7 (2.5) weeks, among whom 103 children had a cognitive assessment at 2 years (i.e. labeled data), and the remaining 240 children had not received 2-year assessments at the time this study was conducted (i.e. unlabeled data). To develop a self-training DNN model, we built an initial student model using labeled brain functional connectome data. Then, we applied the trained model as a teacher model to generate pseudo-labels for unlabeled brain functional connectome data. Next, we combined labeled and pseudo-labeled data to train a new student model. We iterated this procedure to obtain the best student model for the early prediction task in very preterm infants.

Results

In our cross-validation experiments, the proposed self-training DNN model achieved an accuracy of 71.0%, a specificity of 71.5%, a sensitivity of 70.4% and an area under the curve of 0.75, significantly outperforming transfer learning models through pre-training approaches.

Conclusion

We report the first self-training prognostic study in very preterm infants, efficiently utilizing a small amount of labeled data with a larger share of unlabeled data to aid the model training. The proposed technique is expected to facilitate deep learning with insufficient training data.
Appendix
Available only for authorised users
Literature
1.
Ancel P-Y, Goffinet F, Kuhn P et al (2015) Survival and morbidity of preterm children born at 22 through 34 weeks’ gestation in France in 2011: results of the EPIPAGE-2 cohort study. JAMA Pediatr 169:230–238PubMedCrossRef
2.
Vassar R, Schadl K, Cahill-Rowley K et al (2020) Neonatal brain microstructure and machine-learning-based prediction of early language development in children born very preterm. Pediatr Neurol 108:86–92PubMedCrossRef
3.
He L, Parikh NA (2015) Aberrant executive and frontoparietal functional connectivity in very preterm infants with diffuse white matter abnormalities. Pediatr Neurol 53:330–337PubMedCrossRef
4.
Jarjour IT (2015) Neurodevelopmental outcome after extreme prematurity: a review of the literature. Pediatr Neurol 52:143–152PubMedCrossRef
5.
Erdei C, Austin NC, Cherkerzian S et al (2020) Predicting school-aged cognitive impairment in children born very preterm. Pediatrics 145:4CrossRef
6.
Hack M, Taylor HG, Drotar D et al (2005) Poor predictive validity of the Bayley scales of infant development for cognitive function of extremely low birth weight children at school age. Pediatrics 116:333–341PubMedCrossRef
7.
Ment LR, Vohr B, Allan W et al (2003) Change in cognitive function over time in very low-birth-weight infants. JAMA 289:705–711PubMedCrossRef
8.
Spencer-Smith MM, Spittle AJ, Lee KJ et al (2015) Bayley-III cognitive and language scales in preterm children. Pediatrics 135:1258–1265CrossRef
9.
10.
11.
Kim J, Calhoun VD, Shim E et al (2016) Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage 124:127–146PubMedCrossRef
12.
Kuang D, Guo X, An X et al (2014) Discrimination of ADHD based on fMRI data with deep belief network. In: Huang DS, Han K, Gromiha M (eds) Intelligent computing in bioinformatics. ICIC 2014. Lecture notes in computer science, vol 8590. Springer, Cham, pp 225–232
13.
dos Santos Siqueira A, Biazoli CE Jr, Comfort WE et al (2014) Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data. Biomed Res Int 2014:380531
14.
Heinsfeld AS, Franco AR, Craddock RC et al (2018) Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin 17:16–23PubMedCrossRef
15.
He L, Li H, Chen M et al (2021) Deep multimodal learning from MRI and clinical data for early prediction of neurodevelopmental deficits in very preterm infants. Front Neurosci 15:753033PubMedPubMedCentralCrossRef
16.
He L, Li H, Wang J et al (2020) A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants. Sci Rep 10:1–13CrossRef
17.
18.
Hjelm RD, Calhoun VD, Salakhutdinov R et al (2014) Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks. Neuroimage 96:245–260PubMedCrossRef
19.
20.
Chen M, Li H, Wang J et al (2019) A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection. Radiol Artif Intell 2:e190012PubMedPubMedCentralCrossRef
21.
Li H, Parikh NA, He L (2018) A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front Neurosci 12:491PubMedPubMedCentralCrossRef
22.
He L, Li H, Holland SK et al (2018) Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework. Neuroimage Clin 18:290–297PubMedPubMedCentralCrossRef
23.
Xie Q, Luong M-T, Hovy E, Le QV (2020) Self-training with noisy student improves ImageNet classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, New York, pp 10687–10698
24.
Pham H, Dai Z, Xie Q et al (2021) Meta pseudo labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, New York, pp 11557–11568
25.
Zhai X, Kolesnikov A, Houlsby N et al (2022) Scaling vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, New York, pp 12104–12113
26.
He K, Girshick R, Dollár P (2019) Rethinking ImageNet pre-training. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, New York, pp 4918–4927
27.
Zoph B, Ghiasi G, Lin T-Y et al (2020) Rethinking pre-training and self-training. In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pp 3833–3845
28.
Deng J, Dong W, Socher R et al (2009) ImageNet: a large-scale hierarchical image database. In: 2009 Conference on Computer Vision and Pattern Recognition. IEEE, New York, pp 248–255
29.
Vohr BR, Stephens BE, Higgins RD et al (2012) Are outcomes of extremely preterm infants improving? Impact of Bayley assessment on outcomes. J Pediatr 161:222–228PubMedPubMedCentralCrossRef
30.
Aylward GP (2013) Continuing issues with the Bayley-III: where to go from here. J Dev Behav Pediatr 34:697–701PubMedCrossRef
31.
Reuner G, Fields AC, Wittke A et al (2013) Comparison of the developmental tests Bayley-III and Bayley-II in 7-month-old infants born preterm. Eur J Pediatr 172:393–400PubMedCrossRef
32.
Power JD, Barnes KA, Snyder AZ et al (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142–2154PubMedCrossRef
33.
Behzadi Y, Restom K, Liau J, Liu TT (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37:90–101PubMedCrossRef
34.
35.
Marrelec G, Krainik A, Duffau H et al (2006) Partial correlation for functional brain interactivity investigation in functional MRI. Neuroimage 32:228–237PubMedCrossRef
36.
Whitfield-Gabrieli S, Nieto-Castanon A (2012) Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2:125–141PubMedCrossRef
37.
Zhao H, Liu F, Li L, Luo C (2018) A novel softplus linear unit for deep convolutional neural networks. Appl Intell 48:1707–1720CrossRef
38.
39.
Di Martino A, Yan C-G, Li Q et al (2014) The Autism Brain Imaging Data Exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19:659–667PubMedCrossRef
40.
Seiffert C, Khoshgoftaar TM, Van Hulse J et al (2009) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern A Syst Hum 40:185–197CrossRef
41.
Ho TK (1998) Nearest neighbors in random subspaces. In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp 640–648
42.
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comp Syst Sci 55:119–139CrossRef
43.
44.
45.
46.
Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML'10). ACM, New York, pp 807–814
47.
Yang Y, Xu Z (2020) Rethinking the value of labels for improving class-imbalanced learning. Adv Neural Inf Process Syst 33:19290–19301
48.
Lafer-Sousa R, Conway BR (2013) Parallel, multi-stage processing of colors, faces and shapes in macaque inferior temporal cortex. Nat Neurosci 16:1870–1878PubMedPubMedCentralCrossRef
49.
Hadland KA, Rushworth MFS, Gaffan D, Passingham RE (2003) The effect of cingulate lesions on social behaviour and emotion. Neuropsychologia 41:919–931PubMedCrossRef
50.
Kozlovskiy S, Vartanov A, Pyasik M et al (2013) Anatomical characteristics of cingulate cortex and neuropsychological memory tests performance. Proc Soc Behav Sci 86:128–133CrossRef
51.
Kozlovskiy SA, Nikonova EY, Pyasik MM et al (2012) The cingulate cortex and human memory processes. Psychol Russia 5:231–243
52.
53.
Metadata
Title
A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data
Authors
Redha Ali
Hailong Li
Jonathan R. Dillman
Mekibib Altaye
Hui Wang
Nehal A. Parikh
Lili He
Publication date
22-09-2022
Publisher
Springer Berlin Heidelberg
Published in
Pediatric Radiology / Issue 11/2022
Print ISSN: 0301-0449
Electronic ISSN: 1432-1998
DOI
https://doi.org/10.1007/s00247-022-05510-8

Other articles of this Issue 11/2022

Pediatric Radiology 11/2022 Go to the issue

Original Article

Development and evaluation of deep-learning measurement of leg length discrepancy: bilateral iliac crest height difference measurement

Artificial intelligence in pediatric radiology

Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know

Artificial intelligence in pediatric radiology

The augmented radiologist: artificial intelligence in the practice of radiology

Artificial intelligence in pediatric radiology

Current and emerging artificial intelligence applications for pediatric interventional radiology

Artificial intelligence in pediatric radiology

Current and emerging artificial intelligence applications for pediatric abdominal imaging

Artificial intelligence in pediatric radiology

The current and future roles of artificial intelligence in pediatric radiology