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- An ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field.Developmental Cognitive Neuroscience. 2022; 54101083
- Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.Perspect Psychol Sci. 2017; 12: 1100-1122
- When optimism hurts: inflated predictions in psychiatric neuroimaging.Biol Psychiatry. 2014; 75: 746-748
- Machine learning: Trends, perspectives, and prospects.Science. 2015; 349: 255-260
- Ten simple rules for predictive modeling of individual differences in neuroimaging.Neuroimage. 2019; 193: 35-45
- How Machine Learning Will Transform Biomedicine.Cell. 2020; 181: 92-101
Biermann AW (1986): Fundamental mechanisms in machine learning and inductive inference. In: Bibel W, Jorrand P, editors. Fundamentals of Artificial Intelligence: An Advanced Course. Berlin, Heidelberg: Springer Berlin Heidelberg, pp 133–169.
- Machine learning in neuroimaging: Progress and challenges.Neuroimage. 2019; 197: 652-656
- Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.Biol Psychiatry Cogn Neurosci Neuroimaging. 2018; 3: 798-808
- Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry.Biol Psychiatry Cogn Neurosci Neuroimaging. 2020; 5: 791-798
- Toward a unified framework for interpreting machine-learning models in neuroimaging.Nat Protoc. 2020; 15: 1399-1435
- Linking interindividual variability in brain structure to behaviour.Nature Reviews Neuroscience. 2022; 23: 307-318
- Establishment of Best Practices for Evidence for Prediction: A Review.JAMA Psychiatry. 2020; 77: 534-540
- A dedicated neonatal brain imaging system.Magn Reson Med. 2017; 78: 794-804
- MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances.Journal of Magnetic Resonance Imaging. 2021; 53: 1318-1343
- Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison.Front Neurosci. 2019; 13: 729
- Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias.PLoS Biol. 2019; 17e3000042
Spisak T (2021): Statistical quantification of confounding bias in predictive modelling. arXiv. Retrieved from https://arxiv.org/abs/2111.00814
- Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study. Machine Learning and Knowledge Discovery in Databases.Applied Data Science and Demo Track. 2021; : 3-18
- Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging.IEEE Signal Processing Magazine. 2022; 39: 107-118
- On the interpretation of weight vectors of linear models in multivariate neuroimaging.Neuroimage. 2014; 87: 96-110
Kamkar I, Gupta SK, Phung D, Venkatesh S (2015): Exploiting feature relationships towards stable feature selection. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). https://doi.org/10.1109/dsaa.2015.7344859
- Brain–phenotype models fail for individuals who defy sample stereotypes.Nature. 2022; 609: 109-118
- Combining multiple connectomes improves predictive modeling of phenotypic measures.NeuroImage. 2019; 201116038
- Development of BOLD signal hemodynamic responses in the human brain.Neuroimage. 2012; 63: 663-673
- Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations.Neuroimage. 2022; 247118838
- Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies.Dev Cogn Neurosci. 2022; 53101055
- The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.Neuroimage. 2020; 223117303
- Un)common space in infant neuroimaging studies: A systematic review of infant templates.Hum Brain Mapp. 2022; 43: 3007-3016
- A method for handling intensity inhomogenieties in fMRI sequences of moving anatomy of the early developing brain.Med Image Anal. 2014; 18: 285-300
- Optimization of the image contrast for the developing fetal brain using 3D radial VIBE sequence in 3 T magnetic resonance imaging.BMC Med Imaging. 2022; 22: 11
D’Andrea CB, Kenley JK, Montez DF, Mirro AE, Miller RL, Earl EA, et al. (2022): Real-time motion monitoring improves functional MRI data quality in infants. https://doi.org/10.1101/2021.11.10.468084
Rutherford S, Sturmfels P, Angstadt M, Hect J, Wiens J, van den Heuvel MI, et al. (2021): Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics. https://doi.org/10.1007/s12021-021-09528-5
- Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0–2 years.NeuroImage. 2020; 218116946
- A survey of protocols from 54 infant and toddler neuroimaging research labs.Dev Cogn Neurosci. 2022; 54101060
- A neuromarker of sustained attention from whole-brain functional connectivity.Nat Neurosci. 2016; 19: 165-171
- Detecting Neuroimaging Biomarkers for Psychiatric Disorders: Sample Size Matters.Front Psychiatry. 2016; 7: 50
- Cross-validation failure: Small sample sizes lead to large error bars.Neuroimage. 2018; 180: 68-77
- Excitatory actions of gaba during development: the nature of the nurture.Nat Rev Neurosci. 2002; 3: 728-739
- Interneurons set the tune of developing networks.Trends Neurosci. 2004; 27: 422-427
- Measurement of Neurovascular Coupling in Neonates.Front Physiol. 2019; 10: 65
- Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness.PLoS One. 2017; 12e0188122
- Rayyan—a web and mobile app for systematic reviews.Systematic Reviews. 2016; 5https://doi.org/10.1186/s13643-016-0384-4
Gamer, Lemon, Gamer, Robinson, Kendall’s (2019): Package “irr.” Various coefficients of interrater reliability and agreement.
Raurale SA, Nalband S, Boylan GB, Lightbody G, O’Toole JM (2019): Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG. Conf Proc IEEE Eng Med Biol Soc 2019: 4125–4128.
- Neonatal encephalopathy prediction of poor outcome with diffusion-weighted imaging connectome and fixel-based analysis.Pediatr Res. 2022; 91: 1505-1515
- Risk for infantile spasms after acute symptomatic neonatal seizures.Epilepsia. 2020; 61: 2774-2784
- Discrimination of secondary hypsarrhythmias to Zika virus congenital syndrome and west syndrome based on joint moments and entropy measurements.Sci Rep. 2022; 12: 7389
- Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture.Neural Netw. 2020; 123: 12-25
- Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor.Clin Neurophysiol. 2016; 127: 3014-3024
- EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach.Sci Rep. 2018; 8: 6828
- Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months.J Neurodev Disord. 2021; 13: 57
- Novel machine learning methods for ERP analysis: a validation from research on infants at risk for autism.Dev Neuropsychol. 2012; 37: 274-298
- Neonatal neural networks predict children behavioral profiles later in life.Hum Brain Mapp. 2017; 38: 1362-1373
- Longitudinal study of neonatal brain tissue volumes in preterm infants and their ability to predict neurodevelopmental outcome.Neuroimage. 2019; 185: 728-741
Vareilles H de, de Vareilles H, Rivière D, Sun Z, Fischer C, Leroy F, et al. (2022): Shape variability of the central sulcus in the developing brain: a longitudinal descriptive and predictive study in preterm infants. https://doi.org/10.1101/2021.12.15.472770
- Machine-learning to characterise neonatal functional connectivity in the preterm brain.Neuroimage. 2016; 124: 267-275
- Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises.Biol Psychiatry. 2020; 88: 818-828
- Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.NeuroImage: Clinical. 2018; 18: 290-297
- Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data.Neuroimage. 2019; 185: 783-792
- A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.Brain Behav. 2015; 5e00391
Bayley N (2006): Bayley Scales of Infant and Toddler Development: Administration Manual.
- Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal.Computers in Biology and Medicine. 2013; 43: 2110-2117
Fenchel D, Dimitrova R, Robinson EC, Batalle D, Chew A, Falconer S, et al. (n.d.): Neonatal multi-modal cortical profiles predict 18-month developmental outcomes. https://doi.org/10.1101/2021.09.23.461464
- Prediction of Neurodevelopment in Infants With Tuberous Sclerosis Complex Using Early EEG Characteristics.Front Neurol. 2020; 11582891
- Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data.Neuroimage. 2017; 145: 218-229
- Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity.Neuroimage. 2022; 252119040
- A Survey on Bias and Fairness in Machine Learning.ACM Computing Surveys. 2021; 54: 1-35
- Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity.Sci Adv. 2022; 8eabj1812
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.Frontiers in Big Data. 2019; 2https://doi.org/10.3389/fdata.2019.00013
- How do fairness definitions fare? Testing public attitudes towards three algorithmic definitions of fairness in loan allocations.Artificial Intelligence. 2020; 283103238
- Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age.Sci Transl Med. 2017; 9https://doi.org/10.1126/scitranslmed.aag2882
- Functional connectivity in the developing language network in 4-year-old children predicts future reading ability.Dev Sci. 2021; 24e13041
- Neonatal Brain Microstructure and Machine-Learning-Based Prediction of Early Language Development in Children Born Very Preterm.Pediatric Neurology. 2020; 108: 86-92
- August 1): Using neuroimaging to predict brain age: insights into typical and atypical development and risk for psychopathology.Journal of Neurophysiology. 2020; 124: 400-403
- Benefits and burdens of newborn screening: public understanding and decision-making.Personalized Medicine. 2014; 11: 593-607
- Screening of Newborns for Disorders with High Benefit-Risk Ratios Should Be Mandatory.J Law Med Ethics. 2016; 44: 231-240
Esquerda M, Palau F, Lorenzo D, Cambra FJ, Bofarull M, Cusi V, Bioetica GI en (2021): Ethical questions concerning newborn genetic screening. Clinical Genetics, vol. 99. pp 93–98.
- The Ethics of Predicting Autism Spectrum Disorder in Infancy.Journal of the American Academy of Child & Adolescent Psychiatry. 2021; 60: 942-945
Eugenics and Scientific Racism (n.d.): National Human Genome Research Institute. Retrieved from https://www.genome.gov/about-genomics/fact-sheets/Eugenics-and-Scientific-Racism
- Diagnostic stability of psychiatric disorders in clinical practice.British Journal of Psychiatry. 2007; 190: 210-216
Horien C, Noble S, Greene A, Lee K, Barron D, Gao S, et al. (2021): A Hitchhiker’s Guide to Working with Large, Open-Source Neuroimaging Datasets. https://doi.org/10.20944/preprints202007.0153.v1
- An Opportunity to Increase Collaborative Science in Fetal, Infant, and Toddler Neuroimaging.Biological Psychiatry. 2022; https://doi.org/10.1016/j.biopsych.2022.07.005
Wachinger C, Rieckmann A, Pölsterl S, Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (2021): Detect and correct bias in multi-site neuroimaging datasets. Med Image Anal 67: 101879.
- Linear regression analysis: part 14 of a series on evaluation of scientific publications.Dtsch Arztebl Int. 2010; 107: 776-782
James G, Witten D, Hastie T, Tibshirani R (2013): An Introduction to Statistical Learning: With Applications in R. Springer Science & Business Media.
Vaher K, Galdi P, Cabez MB, Sullivan G, Stoye DQ, Quigley AJ, et al. (n.d.): General factors of white matter microstructure from DTI and NODDI in the developing brain. https://doi.org/10.1101/2021.11.29.470344
Pisner DA, Schnyer DM (2020): Support vector machine. Machine Learning. pp 101–121.
Zhang F, O’Donnell LJ (2020): Support vector regression. Machine Learning. pp 123–140.
- EEG-based neonatal seizure detection with Support Vector Machines.Clinical Neurophysiology. 2011; 122: 464-473
Pedregosa, Varoquaux, Gramfort (n.d.): Scikit-learn: Machine learning in Python. of machine Learning …. Retrieved from https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf?ref=https://githubhelp.com
Ramirez GNA, Avecilla Ramirez GN, Ruiz-Correa S, Marroquin JL, Harmony T, Alba A, Mendoza- Montoya O (2011): Electrophysiological auditory responses and language development in infants with periventricular leukomalacia. PsycEXTRA Dataset. https://doi.org/10.1037/e512592013-571
Hastie T, Tibshirani R, Friedman J (2013): The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
- Brain MRI radiomics analysis may predict poor psychomotor outcome in preterm neonates.European Radiology. 2021; https://doi.org/10.1007/s00330-021-07836-7
Neal (n.d.): Bayesian methods for machine learning. NIPS tutorial. Retrieved from http://media.nips.cc/Conferences/2004/Tutorials/slides/radfordSlides.pdf
Sadeghi N, Thomas Fletcher P, Prastawa M, Gilmore JH, Gerig G (2014): Subject-Specific Prediction Using Nonlinear Population Modeling: Application to Early Brain Maturation from DTI. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. pp 33–40.
- Deep learning.Nature. 2015; 521: 436-444
- Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model.Neuroimage. 2020; 215116807
Page MJ, McKenzie J, Bossuyt P, Boutron I, Hoffmann T, Mulrow C d., et al. (2021): The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. https://doi.org/10.31222/osf.io/v7gm2