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
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 12/2020

Open Access 01-12-2020 | Neuro

Feed-forward neural networks using cerebral MR spectroscopy and DTI might predict neurodevelopmental outcome in preterm neonates

Authors: T. Janjic, S. Pereverzyev Jr, M. Hammerl, V. Neubauer, H. Lerchner, V. Wallner, R. Steiger, U. Kiechl-Kohlendorfer, M. Zimmermann, A. Buchheim, A. E. Grams, E. R. Gizewski

Published in: European Radiology | Issue 12/2020

Login to get access

Abstract

Objectives

We aimed to evaluate the ability of feed-forward neural networks (fNNs) to predict the neurodevelopmental outcome (NDO) of very preterm neonates (VPIs) at 12 months corrected age by using biomarkers of cerebral MR proton spectroscopy (1H-MRS) and diffusion tensor imaging (DTI) at term-equivalent age (TEA).

Methods

In this prospective study, 300 VPIs born before 32 gestational weeks received an MRI scan at TEA between September 2013 and December 2017. Due to missing or poor-quality spectroscopy data and missing neurodevelopmental tests, 173 VPIs were excluded. Data sets consisting of 103 and 115 VPIs were considered for prediction of motor and cognitive developmental delay, respectively. Five metabolite ratios and two DTI characteristics in six different areas of the brain were evaluated. A feature selection algorithm was developed for receiving a subset of characteristics prevalent for the VPIs with a developmental delay. Finally, the predictors were constructed employing multiple fNNs and fourfold cross-validation.

Results

By employing the constructed fNN predictors, we were able to predict cognitive delays of VPIs with 85.7% sensitivity, 100% specificity, 100% positive predictive value (PPV) and 99.1% negative predictive value (NPV). For the prediction of motor delay, we achieved a sensitivity of 76.9%, a specificity of 98.9%, a PPV of 90.9% and an NPV of 96.7%.

Conclusion

FNNs might be able to predict motor and cognitive development of VPIs at 12 months corrected age when employing biomarkers of cerebral 1H-MRS and DTI quantified at TEA.

Key Points

• A feed-forward neuronal network is a promising tool for outcome prediction in premature infants.
• Cerebral proton magnetic resonance spectroscopy and diffusion tensor imaging can be used for the construction of early prognostic biomarkers.
• Premature infants that would most benefit from early intervention services can be spotted at the time of optimal neuroplasticity.
Appendix
Available only for authorised users
Literature
1.
Gopagondanahalli KR, Li J, Fahey MC et al (2016) Preterm hypoxic-ischemic encephalopathy. Front Pediatr 4:114
2.
Lee YA (2017) White matter injury of prematurity: its mechanisms and clinical features. J Pathol Transl Med 51(5):449–455
3.
Volpe JJ (2001) Neurobiology of periventricular leukomalacia in the premature infant. Pediatr Res 50(5):553–562
4.
Kidokoro H, Anderson PJ, Doyle LW, Woodward LJ, Neil JJ, Inder TE (2014) Brain injury and altered brain growth in preterm infants: predictors and prognosis. Pediatrics 134(2):e444–ee53
5.
Miller SP, Ferriero DM, Leonard C et al (2005) Early brain injury in premature newborns detected with magnetic resonance imaging is associated with adverse early neurodevelopmental outcome. J Pediatr 147(5):609–616
6.
Woodward LJ, Anderson PJ, Austin NC, Howard K, Inder TE (2006) Neonatal MRI to predict neurodevelopmental outcomes in preterm infants. N Engl J Med 355(7):685–694
7.
Hüppi PS, Murphy B, Maier SE et al (2001) Microstructural brain development after perinatal cerebral white matter injury assessed by diffusion tensor magnetic resonance imaging. Pediatrics 107(3):455–460
8.
Inder T, Huppi PS, Zientara GP et al (1999) Early detection of periventricular leukomalacia by diffusion-weighted magnetic resonance imaging techniques. J Pediatr 134(5):631–634
9.
Ludeman N, Berman J, Wu Y et al (2008) Diffusion tensor imaging of the pyramidal tracts in infants with motor dysfunction. Neurology 71(21):1676–1682
10.
Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G (1996) Diffusion tensor MR imaging of the human brain. Radiology 201(3):637–648
11.
Mori S, Van Zijl PC (1995) Diffusion weighting by the trace of the diffusion tensor within a single scan. Magn Reson Med 33(1):41–52
12.
Mukherjee P, Miller JH, Shimony JS et al (2002) Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation. AJNR Am J Neuroradiol 23(9):1445–1456
13.
Boujraf S, Luypaert R, Shabana W, De Meirleir L, Sourbron S, Osteaux M (2002) Study of pediatric brain development using magnetic resonance imaging of anisotropic diffusion. Magn Reson Imaging 20(4):327–336
14.
van der Knaap MS, van der Grond J, van Rijen PC, Faber J, Valk J, Willemse K (1990) Age-dependent changes in localized proton and phosphorus MR spectroscopy of the brain. Radiology 176(2):509–515
15.
Neil J, Miller J, Mukherjee P, Hüppi PS (2002) Diffusion tensor imaging of normal and injured developing human brain-a technical review. NMR Biomed 15(7–8):543–552
16.
Bapat R, Narayana PA, Zhou Y, Parikh NA (2014) Magnetic resonance spectroscopy at term-equivalent age in extremely preterm infants: association with cognitive and language development. Pediatr Neurol 51(1):53–59
17.
Rose J, Butler EE, Lamont LE, Barnes PD, Atlas SW, Stevenson DK (2009) Neonatal brain structure on MRI and diffusion tensor imaging, sex, and neurodevelopment in very-low-birthweight preterm children. Dev Med Child Neurol 51(7):526–535
18.
Arzoumanian Y, Mirmiran M, Barnes P et al (2003) Diffusion tensor brain imaging findings at term-equivalent age may predict neurologic abnormalities in low birth weight preterm infants. AJNR Am J Neuroradiol 24(8):1646–1653
19.
Bayley N (2006) Bayley scales of infant and toddler development: PsychCorp. Pearson
20.
Stergiou C, Siganos D (1996) Neural networks. Imperial College of Science Technology and Medicine, Engineering DoCaDoEaE, London
21.
Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346(8983):1135–1138
22.
Guliyev NJ, Ismailov VE (2016) A single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function. Neural Comput 28(7):1289–1304
23.
Fuiko R, Oberleitner-Leeb C, Klebermass-Schrehof K, Berger A, Brandstetter S, Giordano V (2019) The impact of norms on the outcome of children born very-preterm when using the Bayley-III: differences between US and German norms. Neonatology 116(1):29–36
24.
Neubauer V, Griesmaier E, Baumgartner K, Mallouhi A, Keller M, Kiechl-Kohlendorfer U (2011) Feasibility of cerebral MRI in non-sedated preterm-born infants at term-equivalent age: report of a single centre. Acta Paediatr 100(12):1544–1547
25.
Kidokoro H, Neil JJ, Inder TE (2013) New MR imaging assessment tool to define brain abnormalities in very preterm infants at term. AJNR Am J Neuroradiol 34(11):2208–2214
26.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366
27.
Hart AR, Smith MF, Whitby EH et al (2014) Diffusion-weighted imaging and magnetic resonance proton spectroscopy following preterm birth. Clin Radiol 69(8):870–879
28.
Kendall GS, Melbourne A, Johnson S et al (2014) White matter NAA/Cho and Cho/Cr ratios at MR spectroscopy are predictive of motor outcome in preterm infants. Radiology 271(1):230–238
29.
Vigneron DB, Barkovich AJ, Noworolski SM et al (2001) Three-dimensional proton MR spectroscopic imaging of premature and term neonates. AJNR Am J Neuroradiol 22(7):1424–1433
30.
Parikh NA (2016) Advanced neuroimaging and its role in predicting neurodevelopmental outcomes in very preterm infants. Semin Perinatol 40(8):530–541
31.
Chau V, Synnes A, Grunau RE, Poskitt KJ, Brant R, Miller SP (2013) Abnormal brain maturation in preterm neonates associated with adverse developmental outcomes. Neurology 81(24):2082–2089
32.
Kawahara J, Brown CJ, Miller SP et al (2017) BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. Neuroimage 146:1038–1049
33.
Beaulieu C (2002) The basis of anisotropic water diffusion in the nervous system–a technical review. NMR Biomed 15(7–8):435–455
34.
Alexander AL, Lee JE, Lazar M, Field AS (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4(3):316–329
35.
De Bruïne FT, Van Wezel-Meijler G, Leijser LM et al (2013) Tractography of white-matter tracts in very preterm infants: a 2-year follow-up study. Dev Med Child Neurol 55(5):427–433
36.
Rose J, Vassar R, Cahill-Rowley K, Guzman XS, Stevenson DK, Barnea-Goraly N (2014) Brain microstructural development at near-term age in very-low-birth-weight preterm infants: an atlas-based diffusion imaging study. Neuroimage 86:244–256
37.
Kreis R, Ernst T, Ross BD (1993) Development of the human brain: in vivo quantification of metabolite and water content with proton magnetic resonance spectroscopy. Magn Reson Med 30(4):424–437
38.
Kreis R, Hofmann L, Kuhlmann B, Boesch C, Bossi E, Huppi PS (2002) Brain metabolite composition during early human brain development as measured by quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 48(6):949–958
39.
Kimura H, Fujii Y, Itoh S et al (1995) Metabolic alterations in the neonate and infant brain during development: evaluation with proton MR spectroscopy. Radiology 194(2):483–489
40.
Bartha A, Yap K, Miller SP et al (2007) The normal neonatal brain: MR imaging, diffusion tensor imaging, and 3D MR spectroscopy in healthy term neonates. AJNR Am J Neuroradiol 28(6):1015–1021
41.
Hüppi PS, Posse S, Lazeyras F, Burri R, Bossi E, Herschkowitz N (1991) Magnetic resonance in preterm and term newborns: 1 H-spectroscopy in developing human brain. Pediatr Res 30(6):574–578
42.
Moffett JR, Ross B, Arun P, Madhavarao CN, Namboodiri AM (2007) N-Acetylaspartate in the CNS: from neurodiagnostics to neurobiology. Prog Neurobiol 81(2):89–131
43.
Brighina E, Bresolin N, Pardi G, Rango M (2009) Human fetal brain chemistry as detected by proton magnetic resonance spectroscopy. Pediatr Neurol 40(5):327–342
44.
Buser JR, Maire J, Riddle A et al (2012) Arrested preoligodendrocyte maturation contributes to myelination failure in premature infants. Ann Neurol 71(1):93–109
45.
Back SA (2017) White matter injury in the preterm infant: pathology and mechanisms. Acta Neuropathol 134(3):331–349
46.
Riddle A, Luo NL, Manese M et al (2006) Spatial heterogeneity in oligodendrocyte lineage maturation and not cerebral blood flow predicts fetal ovine periventricular white matter injury. J Neurosci 26(11):3045–3055
47.
Kinney HC, Back SA editors (1998) Human oligodendroglial development: relationship to periventricular leukomalacia. Semin Pediatr Neurol 5(3):180–189
48.
Back SA, Luo NL, Borenstein NS, Levine JM, Volpe JJ, Kinney HC (2001) Late oligodendrocyte progenitors coincide with the developmental window of vulnerability for human perinatal white matter injury. J Neurosci 21(4):1302–1312
49.
Miller E, Wallis J (2009) Executive function and higher-order cognition: definition and neural substrates. Encycl Neurosci 4(99–104)
50.
Guo T, Duerden EG, Adams E et al (2017) Quantitative assessment of white matter injury in preterm neonates: association with outcomes. Neurology 88(7):614–622
51.
Feldman HM, Yeatman JD, Lee ES, Barde LH, Gaman-Bean S (2010) Diffusion tensor imaging: a review for pediatric researchers and clinicians. J Dev Behav Pediatr 31(4):346
52.
Hintz SR, Kendrick DE, Stoll BJ et al (2005) Neurodevelopmental and growth outcomes of extremely low birth weight infants after necrotizing enterocolitis. Pediatrics 115(3):696–703
53.
Pannek K, Scheck SM, Colditz PB, Boyd RN, Rose SE (2014) Magnetic resonance diffusion tractography of the preterm infant brain: a systematic review. Dev Med Child Neurol 56(2):113–124
54.
Bassi L, Chew A, Merchant N et al (2011) Diffusion tensor imaging in preterm infants with punctate white matter lesions. Pediatr Res 69(6):561–566
55.
Tortora D, Martinetti C, Severino M et al (2018) The effects of mild germinal matrix-intraventricular haemorrhage on the developmental white matter microstructure of preterm neonates: a DTI study. Eur Radiol 28(3):1157–1166
56.
Mukerji A, Shah V, Shah PS (2015) Periventricular/intraventricular hemorrhage and neurodevelopmental outcomes: a meta-analysis. Pediatrics 136(6):1132–1143
57.
Nguyen AL, Ding Y, Suffren S, Londono I, Luck D, Lodygensky GA (2019) The brain’s kryptonite: overview of punctate white matter lesions in neonates. Int J Dev Neurosci 77:77–88
58.
Back SA (2006) Perinatal white matter injury: the changing spectrum of pathology and emerging insights into pathogenetic mechanisms. Ment Retard Dev Disabil Res Rev 12(2):129–140
59.
Hamrick SE, Miller SP, Leonard C et al (2004) Trends in severe brain injury and neurodevelopmental outcome in premature newborn infants: the role of cystic periventricular leukomalacia. J Pediatr 145(5):593–599
60.
Nuyten DS, Hastie T, Chi JT, Chang HY, van de Vijver MJ (2008) Combining biological gene expression signatures in predicting outcome in breast cancer: an alternative to supervised classification. Eur J Cancer 44(15):2319–2329
61.
Mangesius S, Hussl A, Krismer F et al (2018) MR planimetry in neurodegenerative parkinsonism yields high diagnostic accuracy for PSP. Parkinsonism Relat Disord 46:47–55
62.
Breiman L, Friedman J, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth & Brooks/Cole Advanced Books & Software, Monterey
63.
Hack M, Taylor HG, Drotar D et al (2005) Chronic conditions, functional limitations, and special health care needs of school-aged children born with extremely low-birth-weight in the 1990s. JAMA 294(3):318–325
64.
Barnett A, Mercuri E, Rutherford M et al (2002) Neurological and perceptual-motor outcome at 5–6 years of age in children with neonatal encephalopathy: relationship with neonatal brain MRI. Neuropediatrics 33(5):242–248
65.
Sun Y, Wong AK, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(04):687–719
66.
Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions. Progress Artif Intell 5(4):221–232
67.
Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185
Metadata
Title
Feed-forward neural networks using cerebral MR spectroscopy and DTI might predict neurodevelopmental outcome in preterm neonates
Authors
T. Janjic
S. Pereverzyev Jr
M. Hammerl
V. Neubauer
H. Lerchner
V. Wallner
R. Steiger
U. Kiechl-Kohlendorfer
M. Zimmermann
A. Buchheim
A. E. Grams
E. R. Gizewski
Publication date
01-12-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 12/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-07053-8

Other articles of this Issue 12/2020

European Radiology 12/2020 Go to the issue

Forensic Medicine

Forensic age estimation in living adolescents with CT imaging of the clavicula—impact of low-dose scanning on readers’ confidence

Imaging Informatics and Artificial Intelligence

Machine learning–based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation

Editorial Comment

Dental MRI: a road beyond CBCT

Neuro

Perfusion recovery on TTP maps after endovascular stroke treatment might predict favorable neurological outcomes

Correction

Correction to: No evidence of improved efficacy of covered stents over uncovered stents in percutaneous palliation of malignant hilar biliary obstruction: results of a prospective randomized trial

Musculoskeletal

Diffusion tensor imaging of the anterior cruciate ligament graft following reconstruction: a longitudinal study