Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

Tong He; Lijun An; Pansheng Chen; Jianzhong Chen; Jiashi Feng; Danilo Bzdok; Avram J. Holmes; Simon B. Eickhoff; B. T. Thomas Yeo

doi:10.1038/s41593-022-01059-9

Loading next page...

References (159)

(2019)
Surrogate Optimization Toolbox
Blinding Blinding is not relevant to this study as no data collection was involved. The persons performing analyses were unaware of the sample identity
(FinnES Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat. Neurosci 18, 1664–1671 (2015).26457551)
FinnES Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat. Neurosci 18, 1664–1671 (2015).26457551
FinnES Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat. Neurosci 18, 1664–1671 (2015).26457551, FinnES Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat. Neurosci 18, 1664–1671 (2015).26457551
Stephen Smith, C. Beckmann, J. Andersson, E. Auerbach, J. Bijsterbosch, G. Douaud, E. Duff, D. Feinberg, L. Griffanti, M. Harms, M. Kelly, Timothy Laumann, K. Miller, S. Moeller, S. Petersen, Jonathan Power, G. Khorshidi, A. Snyder, A. Vu, M. Woolrich, Junqian Xu, E. Yacoub, K. Uğurbil, D. Essen, M. Glasser (2013)
Resting-state fMRI in the Human Connectome Project
NeuroImage, 80
(EickhoffSB, YeoBTT & GenonS Imaging-based parcellations of the human brain. Nat. Rev. Neurosci 19, 672–686 (2018).30305712)
EickhoffSB, YeoBTT & GenonS Imaging-based parcellations of the human brain. Nat. Rev. Neurosci 19, 672–686 (2018).30305712
EickhoffSB, YeoBTT & GenonS Imaging-based parcellations of the human brain. Nat. Rev. Neurosci 19, 672–686 (2018).30305712, EickhoffSB, YeoBTT & GenonS Imaging-based parcellations of the human brain. Nat. Rev. Neurosci 19, 672–686 (2018).30305712
M. Glasser, S. Sotiropoulos, J. Wilson, Timothy Coalson, B. Fischl, J. Andersson, Junqian Xu, S. Jbabdi, Matthew Webster, J. Polimeni, D. Essen, M. Jenkinson (2013)
The minimal preprocessing pipelines for the Human Connectome Project
NeuroImage, 80
Kevin Murphy (2012)
Machine learning - a probabilistic perspective
V. Kebets, V. Kebets, A. Holmes, C. Orban, C. Orban, Siyi Tang, Siyi Tang, Jingwei Li, Nanbo Sun, Ru Kong, R. Poldrack, B. Yeo (2019)
Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology
Biological Psychiatry, 86
A. Schaefer, Ru Kong, Evan Gordon, Timothy Laumann, X. Zuo, A. Holmes, S. Eickhoff, B. Yeo (2017)
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI
bioRxiv
Sebastian Ruder (2017)
An Overview of Multi-Task Learning in Deep Neural Networks
ArXiv, abs/1706.05098
B. Yeo, Fenna Krienen, Jorge Sepulcre, Jorge Sepulcre, M. Sabuncu, M. Sabuncu, D. Lashkari, Marisa Hollinshead, Marisa Hollinshead, J. Roffman, Jordan Smoller, Lilla Zöllei, J. Polimeni, B. Fischl, B. Fischl, Hesheng Liu, Randy Buckner (2011)
The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
Journal of neurophysiology, 106 3
(DosenbachNUF Prediction of individual brain maturity using fMRI. Science (80-. ) 329, 1358–1361 (2010).)
DosenbachNUF Prediction of individual brain maturity using fMRI. Science (80-. ) 329, 1358–1361 (2010).
DosenbachNUF Prediction of individual brain maturity using fMRI. Science (80-. ) 329, 1358–1361 (2010)., DosenbachNUF Prediction of individual brain maturity using fMRI. Science (80-. ) 329, 1358–1361 (2010).
(RuderS An Overview of Multi-Task Learning in Deep Neural Networks (2017).)
RuderS An Overview of Multi-Task Learning in Deep Neural Networks (2017).
RuderS An Overview of Multi-Task Learning in Deep Neural Networks (2017)., RuderS An Overview of Multi-Task Learning in Deep Neural Networks (2017).
(ChenJ Shared and unique brain network features predict multiple behavioral domains in the ABCD study. Nat. Commun Accpeted i, (2022).)
ChenJ Shared and unique brain network features predict multiple behavioral domains in the ABCD study. Nat. Commun Accpeted i, (2022).
ChenJ Shared and unique brain network features predict multiple behavioral domains in the ABCD study. Nat. Commun Accpeted i, (2022)., ChenJ Shared and unique brain network features predict multiple behavioral domains in the ABCD study. Nat. Commun Accpeted i, (2022).
Andrei Rusu, Dushyant Rao, Jakub Sygnowski, O. Vinyals, Razvan Pascanu, Simon Osindero, R. Hadsell (2018)
Meta-Learning with Latent Embedding Optimization
ArXiv, abs/1807.05960
Choong-Wan Woo, Luke Chang, M. Lindquist, T. Wager (2017)
Building better biomarkers: brain models in translational neuroimaging
Nature Neuroscience, 20
N. Dosenbach, Binyam Nardos, A. Cohen, D. Fair, Jonathan Power, J. Church, S. Nelson, G. Wig, Alecia Vogel, C. Lessov-Schlaggar, K. Barnes, Joseph Dubis, E. Feczko, R. Coalson, J. Pruett, D. Barch, S. Petersen, B. Schlaggar (2010)
Prediction of Individual Brain Maturity Using fMRI
Science, 329
S. Ravi, H. Larochelle (2016)
Optimization as a Model for Few-Shot Learning
G. Varoquaux, R. Poldrack (2019)
Predictive models avoid excessive reductionism in cognitive neuroimaging
Current Opinion in Neurobiology, 55
M. Glasser, Timothy Coalson, E. Robinson, C. Hacker, John Harwell, E. Yacoub, K. Uğurbil, J. Andersson, C. Beckmann, M. Jenkinson, Stephen Smith, D. Essen (2016)
A multi-modal parcellation of human cerebral cortex
Nature, 536
Chelsea Finn, P. Abbeel, S. Levine (2017)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Marcin Andrychowicz, Misha Denil, Sergio Colmenarejo, Matthew Hoffman, David Pfau, T. Schaul, Nando Freitas (2016)
Learning to learn by gradient descent by gradient descent
(FinnC, AbbeelP & LevineS Model-agnostic meta-learning for fast adaptation of deep networks. 34th Int. Conf. Mach. Learn. ICML 2017 3, 1856–1868 (2017).)
FinnC, AbbeelP & LevineS Model-agnostic meta-learning for fast adaptation of deep networks. 34th Int. Conf. Mach. Learn. ICML 2017 3, 1856–1868 (2017).
FinnC, AbbeelP & LevineS Model-agnostic meta-learning for fast adaptation of deep networks. 34th Int. Conf. Mach. Learn. ICML 2017 3, 1856–1868 (2017)., FinnC, AbbeelP & LevineS Model-agnostic meta-learning for fast adaptation of deep networks. 34th Int. Conf. Mach. Learn. ICML 2017 3, 1856–1868 (2017).
(WooCW, ChangLJ, LindquistMA & WagerTD Building better biomarkers: Brain models in translational neuroimaging. Nat. Neurosci 20, 365–377 (2017).28230847)
WooCW, ChangLJ, LindquistMA & WagerTD Building better biomarkers: Brain models in translational neuroimaging. Nat. Neurosci 20, 365–377 (2017).28230847
WooCW, ChangLJ, LindquistMA & WagerTD Building better biomarkers: Brain models in translational neuroimaging. Nat. Neurosci 20, 365–377 (2017).28230847, WooCW, ChangLJ, LindquistMA & WagerTD Building better biomarkers: Brain models in translational neuroimaging. Nat. Neurosci 20, 365–377 (2017).28230847
Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015)
Deep Learning
Nature, 521
(SeaboldS & PerktoldJ Statsmodels: Econometric and Statistical Modeling with Python. PROC. 9th PYTHON Sci. CONF 57 (2010).)
SeaboldS & PerktoldJ Statsmodels: Econometric and Statistical Modeling with Python. PROC. 9th PYTHON Sci. CONF 57 (2010).
SeaboldS & PerktoldJ Statsmodels: Econometric and Statistical Modeling with Python. PROC. 9th PYTHON Sci. CONF 57 (2010)., SeaboldS & PerktoldJ Statsmodels: Econometric and Statistical Modeling with Python. PROC. 9th PYTHON Sci. CONF 57 (2010).
(ErikssonD, BindelD & ShoemakerCA pysot: Surrogate Optimization Toolbox. GitHub https://github.com/dme65/pySOT (2019).)
ErikssonD, BindelD & ShoemakerCA pysot: Surrogate Optimization Toolbox. GitHub https://github.com/dme65/pySOT (2019).
ErikssonD, BindelD & ShoemakerCA pysot: Surrogate Optimization Toolbox. GitHub https://github.com/dme65/pySOT (2019)., ErikssonD, BindelD & ShoemakerCA pysot: Surrogate Optimization Toolbox. GitHub https://github.com/dme65/pySOT (2019).
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, E. Yang, Zach DeVito, Zeming Lin, Alban Desmaison, L. Antiga, Adam Lerer (2017)
Automatic differentiation in PyTorch
D. Essen, Stephen Smith, D. Barch, Timothy Behrens, E. Yacoub, K. Uğurbil (2013)
The WU-Minn Human Connectome Project: An overview
NeuroImage, 80
Jianzhong Chen, Angela Tam, V. Kebets, C. Orban, L.Q.R. Ooi, Christopher Asplund, Scott Marek, N. Dosenbach, S. Eickhoff, D. Bzdok, A. Holmes, B. Yeo (2022)
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
Nature Communications, 13
Ilija Ilievski, Taimoor Akhtar, Jiashi Feng, C. Shoemaker (2016)
Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates
X. Shen, F. Tokoglu, X. Papademetris, R. Constable (2013)
Groupwise whole-brain parcellation from resting-state fMRI data for network node identification
NeuroImage, 82
REFERENCES (METHODS)
R. Buckner, Fenna Krienen, Thomas Yeo (2013)
Opportunities and limitations of intrinsic functional connectivity MRI
Nature Neuroscience, 16
Yaoyi Zhu, Linda Zhang, Qi Chen, Wenting Xiao (2021)
Opportunities and Challenges
In Vitro Diagnostic Industry in China
(TanC A survey on deep transfer learning. Int. Conf. Artif. neural networks 11141 LNCS, 270–279 (2018).)
TanC A survey on deep transfer learning. Int. Conf. Artif. neural networks 11141 LNCS, 270–279 (2018).
TanC A survey on deep transfer learning. Int. Conf. Artif. neural networks 11141 LNCS, 270–279 (2018)., TanC A survey on deep transfer learning. Int. Conf. Artif. neural networks 11141 LNCS, 270–279 (2018).
(VaroquauxG Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage 145, 166–179 (2017).27989847)
VaroquauxG Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage 145, 166–179 (2017).27989847
VaroquauxG Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage 145, 166–179 (2017).27989847, VaroquauxG Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage 145, 166–179 (2017).27989847
(ScheinostD Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage 193, 35–45 (2019).30831310)
ScheinostD Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage 193, 35–45 (2019).30831310
ScheinostD Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage 193, 35–45 (2019).30831310, ScheinostD Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage 193, 35–45 (2019).30831310
(2001)
An Overview of the
D. Essen, K. Uğurbil, E. Auerbach, D. Barch, Timothy Behrens, R. Bucholz, A. Chang, Liyong Chen, M. Corbetta, Sandra Curtiss, S. Penna, D. Feinberg, M. Glasser, N. Harel, A. Heath, L. Larson-Prior, D. Marcus, G. Michalareas, S. Moeller, R. Oostenveld, S. Petersen, F. Prior, B. Schlaggar, Stephen Smith, A. Snyder, Junqian Xu, E. Yacoub (2012)
The Human Connectome Project: A data acquisition perspective
NeuroImage, 62
D. Steinwachs, P. Brill (1997)
Promises and pitfalls of the
P. Elliott, T. Peakman (2008)
The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine.
International journal of epidemiology, 37 2
S. Eickhoff, B. Yeo, S. Genon (2018)
Imaging-based parcellations of the human brain
Nature Reviews Neuroscience, 19
X. Li, Lequan Yu, Chi-Wing Fu, P. Heng (2019)
Difficulty-aware Meta-Learning for Rare Disease Diagnosis
ArXiv, abs/1907.00354
K. Miller, F. Alfaro-Almagro, N. Bangerter, D. Thomas, E. Yacoub, Junqian Xu, A. Bartsch, S. Jbabdi, S. Sotiropoulos, J. Andersson, L. Griffanti, G. Douaud, T. Okell, P. Weale, I. Dragonu, Steve Garratt, Sarah Hudson, R. Collins, M. Jenkinson, P. Matthews, Stephen Smith (2016)
Multimodal population brain imaging in the UK Biobank prospective epidemiological study
Nature neuroscience, 19
Yang Wei-we (2008)
A Review on
Reporting for specific materials, systems and methods
(ShenX, TokogluF, PapademetrisX & ConstableRT Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).23747961)
ShenX, TokogluF, PapademetrisX & ConstableRT Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).23747961
ShenX, TokogluF, PapademetrisX & ConstableRT Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).23747961, ShenX, TokogluF, PapademetrisX & ConstableRT Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).23747961
(ElliottP & PeakmanTC The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int. J. Epidemiol 37, 234–244 (2008).18381398)
ElliottP & PeakmanTC The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int. J. Epidemiol 37, 234–244 (2008).18381398
ElliottP & PeakmanTC The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int. J. Epidemiol 37, 234–244 (2008).18381398, ElliottP & PeakmanTC The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int. J. Epidemiol 37, 234–244 (2008).18381398
Marc-Andre Schulz, B. Yeo, J. Vogelstein, Janaina Mourao-Miranada, Jakob Kather, Konrad Kording, B. Richards, D. Bzdok (2020)
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Nature Communications, 11
(WeisS Sex Classification by Resting State Brain Connectivity. Cereb. cortex 30, 824–835 (2020).31251328)
WeisS Sex Classification by Resting State Brain Connectivity. Cereb. cortex 30, 824–835 (2020).31251328
WeisS Sex Classification by Resting State Brain Connectivity. Cereb. cortex 30, 824–835 (2020).31251328, WeisS Sex Classification by Resting State Brain Connectivity. Cereb. cortex 30, 824–835 (2020).31251328
(DuttRK Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates. Hum. Brain Mapp 1–17 (2021) doi:10.1002/hbm.25690.)
DuttRK Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates. Hum. Brain Mapp 1–17 (2021) doi:10.1002/hbm.25690.
DuttRK Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates. Hum. Brain Mapp 1–17 (2021) doi:10.1002/hbm.25690., DuttRK Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates. Hum. Brain Mapp 1–17 (2021) doi:10.1002/hbm.25690.
(HeT Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. Neuroimage 206, 116276 (2020).31610298)
HeT Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. Neuroimage 206, 116276 (2020).31610298
HeT Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. Neuroimage 206, 116276 (2020).31610298, HeT Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. Neuroimage 206, 116276 (2020).31610298
(MasoulehSK, EickhoffSB, HoffstaedterF & GenonS Empirical examination of the replicability of associations between brain structure and psychological variables. Elife 8, 1–25 (2019).)
MasoulehSK, EickhoffSB, HoffstaedterF & GenonS Empirical examination of the replicability of associations between brain structure and psychological variables. Elife 8, 1–25 (2019).
MasoulehSK, EickhoffSB, HoffstaedterF & GenonS Empirical examination of the replicability of associations between brain structure and psychological variables. Elife 8, 1–25 (2019)., MasoulehSK, EickhoffSB, HoffstaedterF & GenonS Empirical examination of the replicability of associations between brain structure and psychological variables. Elife 8, 1–25 (2019).
(ChuC, HsuAL, ChouKH, BandettiniP & LinCP Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60, 59–70 (2012).22166797)
ChuC, HsuAL, ChouKH, BandettiniP & LinCP Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60, 59–70 (2012).22166797
ChuC, HsuAL, ChouKH, BandettiniP & LinCP Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60, 59–70 (2012).22166797, ChuC, HsuAL, ChouKH, BandettiniP & LinCP Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60, 59–70 (2012).22166797
(WolpertD Stacked Generalization. Neural Networks 5, 241–259 (1992).)
WolpertD Stacked Generalization. Neural Networks 5, 241–259 (1992).
WolpertD Stacked Generalization. Neural Networks 5, 241–259 (1992)., WolpertD Stacked Generalization. Neural Networks 5, 241–259 (1992).
(ChenZ & LiuB Lifelong Machine Learning. Synth. Lect. Artif. Intell. Mach. Learn 10, 1–145 (2016).)
ChenZ & LiuB Lifelong Machine Learning. Synth. Lect. Artif. Intell. Mach. Learn 10, 1–145 (2016).
ChenZ & LiuB Lifelong Machine Learning. Synth. Lect. Artif. Intell. Mach. Learn 10, 1–145 (2016)., ChenZ & LiuB Lifelong Machine Learning. Synth. Lect. Artif. Intell. Mach. Learn 10, 1–145 (2016).
(FornitoA, ZaleskyA & BreakspearM The connectomics of brain disorders. Nat. Rev. Neurosci 16, 159–172 (2015).25697159)
FornitoA, ZaleskyA & BreakspearM The connectomics of brain disorders. Nat. Rev. Neurosci 16, 159–172 (2015).25697159
FornitoA, ZaleskyA & BreakspearM The connectomics of brain disorders. Nat. Rev. Neurosci 16, 159–172 (2015).25697159, FornitoA, ZaleskyA & BreakspearM The connectomics of brain disorders. Nat. Rev. Neurosci 16, 159–172 (2015).25697159
C. Xia, Zongming Ma, R. Ciric, Shi Gu, Richard Betzel, A. Kaczkurkin, M. Calkins, P. Cook, Ángel Garza, S. Vandekar, Zaixu Cui, T. Moore, D. Roalf, K. Ruparel, D. Wolf, C. Davatzikos, R. Gur, R. Gur, R. Shinohara, D. Bassett, T. Satterthwaite (2017)
Linked dimensions of psychopathology and connectivity in functional brain networks
Nature Communications, 9
R. Regis, C. Shoemaker (2013)
Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization
Engineering Optimization, 45
G. Varoquaux, Pradeep Raamana, D. Engemann, Andrés Idrobo, Y. Schwartz, B. Thirion (2016)
Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
NeuroImage, 145
M. Kuhn, Kjell Johnson (2013)
Applied Predictive Modeling
(2019)
road to utopia or Orwell? PLoS Biol
Zhiyuan Chen, B. Liu (2017)
Lifelong machine learning: a paradigm for continuous learning
Frontiers of Computer Science, 11
Stephen Smith, Thomas Nichols, D. Vidaurre, A. Winkler, T. Behrens, M. Glasser, K. Uğurbil, D. Barch, D. Essen, K. Miller (2015)
A positive-negative mode of population covariation links brain connectivity, demographics and behavior
Nature neuroscience, 18
Tong He, Ru Kong, A. Holmes, Minh-Quan Nguyen, M. Sabuncu, S. Eickhoff, D. Bzdok, Jiashi Feng, B. Yeo (2020)
Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics
NeuroImage, 206
D. Wolpert (1992)
Original Contribution: Stacked generalization
Neural Networks, 5
(FischlB Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).11832223)
FischlB Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).11832223
FischlB Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).11832223, FischlB Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).11832223
(NicholA, AchiamJ & SchulmanJ On First-Order Meta-Learning Algorithms 1–15 (2018).)
NicholA, AchiamJ & SchulmanJ On First-Order Meta-Learning Algorithms 1–15 (2018).
NicholA, AchiamJ & SchulmanJ On First-Order Meta-Learning Algorithms 1–15 (2018)., NicholA, AchiamJ & SchulmanJ On First-Order Meta-Learning Algorithms 1–15 (2018).
(IlievskiI, AkhtarT, FengJ & ShoemakerCA Efficient hyperparameter optimization of deep learning algorithms using deterministic RBF surrogates in 31st AAAI Conference on Artificial Intelligence, AAAI 2017 822–829 (2017).)
IlievskiI, AkhtarT, FengJ & ShoemakerCA Efficient hyperparameter optimization of deep learning algorithms using deterministic RBF surrogates in 31st AAAI Conference on Artificial Intelligence, AAAI 2017 822–829 (2017).
IlievskiI, AkhtarT, FengJ & ShoemakerCA Efficient hyperparameter optimization of deep learning algorithms using deterministic RBF surrogates in 31st AAAI Conference on Artificial Intelligence, AAAI 2017 822–829 (2017)., IlievskiI, AkhtarT, FengJ & ShoemakerCA Efficient hyperparameter optimization of deep learning algorithms using deterministic RBF surrogates in 31st AAAI Conference on Artificial Intelligence, AAAI 2017 822–829 (2017).
C. Chu, Ai-Ling Hsu, K. Chou, P. Bandettini, Ching-Po Lin (2012)
Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images
NeuroImage, 60
Stephen Smith, K. Miller, G. Khorshidi, Matthew Webster, C. Beckmann, Thomas Nichols, J. Ramsey, M. Woolrich (2011)
Network modelling methods for FMRI
NeuroImage, 54
M. Rosenberg, B. Casey, A. Holmes (2018)
Prediction complements explanation in understanding the developing brain
Nature Communications, 9
S. Haufe, F. Meinecke, Kai Görgen, Sven Dähne, J. Haynes, B. Blankertz, F. Biessmann (2014)
On the interpretation of weight vectors of linear models in multivariate neuroimaging
NeuroImage, 87
Jingwei Li, Ru Kong, Raphaël Liégeois, C. Orban, Yanrui Tan, Nanbo Sun, A. Holmes, M. Sabuncu, T. Ge, B. Yeo (2019)
Global signal regression strengthens association between resting-state functional connectivity and behavior
NeuroImage, 196
D. Barch, G. Burgess, M. Harms, S. Petersen, B. Schlaggar, M. Corbetta, M. Glasser, Sandra Curtiss, S. Dixit, Cindy Feldt, D. Nolan, Edward Bryant, T. Hartley, Owen Footer, J. Bjork, R. Poldrack, Steve Smith, H. Johansen-Berg, A. Snyder, D. Essen (2013)
Function in the human connectome: Task-fMRI and individual differences in behavior
NeuroImage, 80
Shahrzad Masouleh, S. Eickhoff, F. Hoffstaedter, S. Genon (2018)
Empirical examination of the replicability of associations between brain structure and psychological variables
eLife, 8
Mohammad Arbabshirani, S. Plis, J. Sui, V. Calhoun (2017)
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
NeuroImage, 145
Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
(BarchDM Function in the human connectome: Task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).23684877)
BarchDM Function in the human connectome: Task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).23684877
BarchDM Function in the human connectome: Task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).23684877, BarchDM Function in the human connectome: Task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).23684877
(KuhnM & JohnsonK Applied predictive modeling. Applied Predictive Modeling (2013). doi:10.1007/978-1-4614-6849-3.)
KuhnM & JohnsonK Applied predictive modeling. Applied Predictive Modeling (2013). doi:10.1007/978-1-4614-6849-3.
KuhnM & JohnsonK Applied predictive modeling. Applied Predictive Modeling (2013). doi:10.1007/978-1-4614-6849-3., KuhnM & JohnsonK Applied predictive modeling. Applied Predictive Modeling (2013). doi:10.1007/978-1-4614-6849-3.
(MurphyKP Machine Learning: A Probabilistic Perspective MIT Press (2012).)
MurphyKP Machine Learning: A Probabilistic Perspective MIT Press (2012).
MurphyKP Machine Learning: A Probabilistic Perspective MIT Press (2012)., MurphyKP Machine Learning: A Probabilistic Perspective MIT Press (2012).
B. Yeo, Fenna Krienen, J. Sepulcre, M. Sabuncu, D. Lashkari, Marisa Hollinshead, J. Roffman, J. Smoller, Lilla Zöllei, J. Polimeni, B. Fischl, Hesheng Liu, R. Buckner (2011)
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
(PoldrackRA, HuckinsG & VaroquauxG Establishment of Best Practices for Evidence for Prediction: A Review. JAMA Psychiatry 77, 534–540 (2020).31774490)
PoldrackRA, HuckinsG & VaroquauxG Establishment of Best Practices for Evidence for Prediction: A Review. JAMA Psychiatry 77, 534–540 (2020).31774490
PoldrackRA, HuckinsG & VaroquauxG Establishment of Best Practices for Evidence for Prediction: A Review. JAMA Psychiatry 77, 534–540 (2020).31774490, PoldrackRA, HuckinsG & VaroquauxG Establishment of Best Practices for Evidence for Prediction: A Review. JAMA Psychiatry 77, 534–540 (2020).31774490
C. Beckmann, Stephen Smith (2004)
Probabilistic independent component analysis for functional magnetic resonance imaging
IEEE Transactions on Medical Imaging, 23
(MillerKL Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci 19, 1523–1536 (2016).27643430)
MillerKL Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci 19, 1523–1536 (2016).27643430
MillerKL Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci 19, 1523–1536 (2016).27643430, MillerKL Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci 19, 1523–1536 (2016).27643430
A. Fornito, A. Zalesky, M. Breakspear (2015)
The connectomics of brain disorders
Nature Reviews Neuroscience, 16
S. Eickhoff, R. Langner (2019)
Neuroimaging-based prediction of mental traits: Road to utopia or Orwell?
PLoS Biology, 17
(BzdokD & Meyer-LindenbergA Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 223–230 (2018).29486863)
BzdokD & Meyer-LindenbergA Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 223–230 (2018).29486863
BzdokD & Meyer-LindenbergA Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 223–230 (2018).29486863, BzdokD & Meyer-LindenbergA Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 223–230 (2018).29486863
(SmithSM Network modelling methods for FMRI. Neuroimage 54, 875–891 (2011).20817103)
SmithSM Network modelling methods for FMRI. Neuroimage 54, 875–891 (2011).20817103
SmithSM Network modelling methods for FMRI. Neuroimage 54, 875–891 (2011).20817103, SmithSM Network modelling methods for FMRI. Neuroimage 54, 875–891 (2011).20817103
Jenna Reinen, Oliver Chén, R. Hutchison, B. Yeo, Kevin Anderson, M. Sabuncu, D. Öngür, J. Roffman, Jordan Smoller, J. Baker, A. Holmes (2018)
The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis
Nature Communications, 9
(RosenbergMD A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci 19, 165–171 (2016).26595653)
RosenbergMD A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci 19, 165–171 (2016).26595653
RosenbergMD A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci 19, 165–171 (2016).26595653, RosenbergMD A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci 19, 165–171 (2016).26595653
C. Biazoli, G. Salum, P. Pan, A. Zugman, E. Amaro, L. Rohde, E. Miguel, A. Jackowski, R. Bressan, J. Sato (2017)
Commentary: Functional connectome fingerprint: identifying individuals using patterns of brain connectivity
Frontiers in Human Neuroscience, 11
(BeckmannCF & SmithSM Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004).14964560)
BeckmannCF & SmithSM Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004).14964560
BeckmannCF & SmithSM Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004).14964560, BeckmannCF & SmithSM Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004).14964560
(BucknerRL, KrienenFM & YeoBTT Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci 16, 832–837 (2013).23799476)
BucknerRL, KrienenFM & YeoBTT Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci 16, 832–837 (2013).23799476
BucknerRL, KrienenFM & YeoBTT Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci 16, 832–837 (2013).23799476, BucknerRL, KrienenFM & YeoBTT Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci 16, 832–837 (2013).23799476
(GordonEM Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cereb. Cortex 26, 288–303 (2016).25316338)
GordonEM Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cereb. Cortex 26, 288–303 (2016).25316338
GordonEM Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cereb. Cortex 26, 288–303 (2016).25316338, GordonEM Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cereb. Cortex 26, 288–303 (2016).25316338
G. Koppe, A. Meyer-Lindenberg, D. Durstewitz (2020)
Deep learning for small and big data in psychiatry
Neuropsychopharmacology, 46
Anibal Heinsfeld, A. Franco, R. Craddock, A. Buchweitz, Felipe Meneguzzi (2017)
Identification of autism spectrum disorder using deep learning and the ABIDE dataset
NeuroImage : Clinical, 17
(BiswalB, FZY, VMH & JSH Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34, 537–541 (1995).8524021)
BiswalB, FZY, VMH & JSH Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34, 537–541 (1995).8524021
BiswalB, FZY, VMH & JSH Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34, 537–541 (1995).8524021, BiswalB, FZY, VMH & JSH Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34, 537–541 (1995).8524021
Skipper Seabold, Josef Perktold (2010)
Statsmodels: Econometric and Statistical Modeling with Python
Tao Blockindeng
See Blockindiscussions, Blockinstats, Blockinand Blockinauthor Blockinprofiles Blockinfor Blockinthis Blockinpublication
(BreimanL Stacked regressions. Mach. Learn 24, 49–64 (1996).)
BreimanL Stacked regressions. Mach. Learn 24, 49–64 (1996).
BreimanL Stacked regressions. Mach. Learn 24, 49–64 (1996)., BreimanL Stacked regressions. Mach. Learn 24, 49–64 (1996).
(VaroquauxG & PoldrackRA Predictive models avoid excessive reductionism in cognitive neuroimaging. Curr. Opin. Neurobiol 55, 1–6 (2019).30513462)
VaroquauxG & PoldrackRA Predictive models avoid excessive reductionism in cognitive neuroimaging. Curr. Opin. Neurobiol 55, 1–6 (2019).30513462
VaroquauxG & PoldrackRA Predictive models avoid excessive reductionism in cognitive neuroimaging. Curr. Opin. Neurobiol 55, 1–6 (2019).30513462, VaroquauxG & PoldrackRA Predictive models avoid excessive reductionism in cognitive neuroimaging. Curr. Opin. Neurobiol 55, 1–6 (2019).30513462
(VanschorenJ Meta-learning. Automated Machine Learning vol. 498 (Springer, Cham, 2019).)
VanschorenJ Meta-learning. Automated Machine Learning vol. 498 (Springer, Cham, 2019).
VanschorenJ Meta-learning. Automated Machine Learning vol. 498 (Springer, Cham, 2019)., VanschorenJ Meta-learning. Automated Machine Learning vol. 498 (Springer, Cham, 2019).
C. Sudlow, J. Gallacher, N. Allen, V. Beral, P. Burton, J. Danesh, P. Downey, P. Elliott, J. Green, M. Landray, Bette Liu, P. Matthews, G. Ong, J. Pell, A. Silman, A. Young, Tim Sprosen, T. Peakman, R. Collins (2015)
UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age
PLoS Medicine, 12
(RegisRG & ShoemakerCA Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Eng. Optim 45, 529–555 (2013).)
RegisRG & ShoemakerCA Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Eng. Optim 45, 529–555 (2013).
RegisRG & ShoemakerCA Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Eng. Optim 45, 529–555 (2013)., RegisRG & ShoemakerCA Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Eng. Optim 45, 529–555 (2013).
(2019)
Meta-learning. In: Automated Machine Learning (Springer, 2019)
(RosenbergMD, CaseyBJ & HolmesAJ Prediction complements explanation in understanding the developing brain. Nat. Commun 9, 1–13 (2018).29317637)
RosenbergMD, CaseyBJ & HolmesAJ Prediction complements explanation in understanding the developing brain. Nat. Commun 9, 1–13 (2018).29317637
RosenbergMD, CaseyBJ & HolmesAJ Prediction complements explanation in understanding the developing brain. Nat. Commun 9, 1–13 (2018).29317637, RosenbergMD, CaseyBJ & HolmesAJ Prediction complements explanation in understanding the developing brain. Nat. Commun 9, 1–13 (2018).29317637
Rosie Dutt, K. Hannon, T. Easley, Joseph Griffis, W. Zhang, J. Bijsterbosch (2021)
Mental health in the UK Biobank: A roadmap to self‐report measures and neuroimaging correlates
Human Brain Mapping, 43
Green color indicates that meta-matching methods were statistically better than classical (KRR)
Stephen Smith, P. Fox, K. Miller, D. Glahn, P. Fox, C. Mackay, N. Filippini, K. Watkins, R. Toro, A. Laird, C. Beckmann, C. Beckmann (2009)
Correspondence of the brain's functional architecture during activation and rest
Proceedings of the National Academy of Sciences, 106
(KoppeG, Meyer-LindenbergA & DurstewitzD Deep learning for small and big data in psychiatry. Neuropsychopharmacology 1–15 (2020) doi:10.1038/s41386-020-0767-z.)
KoppeG, Meyer-LindenbergA & DurstewitzD Deep learning for small and big data in psychiatry. Neuropsychopharmacology 1–15 (2020) doi:10.1038/s41386-020-0767-z.
KoppeG, Meyer-LindenbergA & DurstewitzD Deep learning for small and big data in psychiatry. Neuropsychopharmacology 1–15 (2020) doi:10.1038/s41386-020-0767-z., KoppeG, Meyer-LindenbergA & DurstewitzD Deep learning for small and big data in psychiatry. Neuropsychopharmacology 1–15 (2020) doi:10.1038/s41386-020-0767-z.
E. Finn, X. Shen, D. Scheinost, M. Rosenberg, Jessica Huang, M. Chun, X. Papademetris, R. Constable (2015)
Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity
Nature neuroscience, 18
(KongR Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cereb. Cortex 29, 2533–2551 (2019).29878084)
KongR Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cereb. Cortex 29, 2533–2551 (2019).29878084
KongR Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cereb. Cortex 29, 2533–2551 (2019).29878084, KongR Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cereb. Cortex 29, 2533–2551 (2019).29878084
R Kong (2019)
Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion
Cereb. Cortex, 29
Alex Nichol, Joshua Achiam, J. Schulman (2018)
On First-Order Meta-Learning Algorithms
ArXiv, abs/1803.02999
(CuiZ & GongG The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage 178, 622–637 (2018).29870817)
CuiZ & GongG The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage 178, 622–637 (2018).29870817
CuiZ & GongG The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage 178, 622–637 (2018).29870817, CuiZ & GongG The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage 178, 622–637 (2018).29870817
(MahajanK, SharmaM & VigL Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning. CVPR Work (2020).)
MahajanK, SharmaM & VigL Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning. CVPR Work (2020).
MahajanK, SharmaM & VigL Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning. CVPR Work (2020)., MahajanK, SharmaM & VigL Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning. CVPR Work (2020).
J. Gabrieli, Satrajit Ghosh, S. Whitfield-Gabrieli (2015)
Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience
Neuron, 85
R. Poldrack, Grace Huckins, G. Varoquaux (2019)
Establishment of Best Practices for Evidence for Prediction: A Review.
JAMA psychiatry
(KebetsV Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biol. Psychiatry 86, 779–791 (2019).31515054)
KebetsV Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biol. Psychiatry 86, 779–791 (2019).31515054
KebetsV Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biol. Psychiatry 86, 779–791 (2019).31515054, KebetsV Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biol. Psychiatry 86, 779–791 (2019).31515054
S. Weis, K. Patil, F. Hoffstaedter, Alessandra, Nostro, B. Yeo, S. Eickhoff (2019)
Sex Classification by Resting State Brain Connectivity
Cerebral Cortex (New York, NY), 30
M. Fox, M. Raichle (2007)
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Nature Reviews Neuroscience, 8
F. Alfaro-Almagro, M. Jenkinson, N. Bangerter, J. Andersson, L. Griffanti, G. Douaud, S. Sotiropoulos, S. Jbabdi, Moisés Hernández-Fernández, Emmanuel Vallée, D. Vidaurre, Matthew Webster, Paul McCarthy, C. Rorden, Alessandro Daducci, D. Alexander, Hui Zhang, I. Dragonu, P. Matthews, K. Miller, Stephen Smith (2017)
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank
Neuroimage, 166
B Biswal, Y FZ, H VM, H JS (1995)
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med., 34
D. Bzdok, A. Meyer-Lindenberg (2017)
Machine Learning for Precision Psychiatry: Opportunities and Challenges.
Biological psychiatry. Cognitive neuroscience and neuroimaging, 3 3
(AndrychowiczM Learning to learn by gradient descent by gradient descent. Adv. Neural Inf. Process. Syst 3988–3996 (2016).)
AndrychowiczM Learning to learn by gradient descent by gradient descent. Adv. Neural Inf. Process. Syst 3988–3996 (2016).
AndrychowiczM Learning to learn by gradient descent by gradient descent. Adv. Neural Inf. Process. Syst 3988–3996 (2016)., AndrychowiczM Learning to learn by gradient descent by gradient descent. Adv. Neural Inf. Process. Syst 3988–3996 (2016).
(FoxMD & RaichleME Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci 8, 700–711 (2007).17704812)
FoxMD & RaichleME Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci 8, 700–711 (2007).17704812
FoxMD & RaichleME Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci 8, 700–711 (2007).17704812, FoxMD & RaichleME Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci 8, 700–711 (2007).17704812
(SchaeferA Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex 3095–3114 (2018) doi:10.1093/cercor/bhx179.28981612)
SchaeferA Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex 3095–3114 (2018) doi:10.1093/cercor/bhx179.28981612
SchaeferA Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex 3095–3114 (2018) doi:10.1093/cercor/bhx179.28981612, SchaeferA Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex 3095–3114 (2018) doi:10.1093/cercor/bhx179.28981612
(RaviS & LarochelleH Optimization as a model for few-shot learning. 5th Int. Conf. Learn. Represent. ICLR 2017 - Conf. Track Proc 1–11 (2017).)
RaviS & LarochelleH Optimization as a model for few-shot learning. 5th Int. Conf. Learn. Represent. ICLR 2017 - Conf. Track Proc 1–11 (2017).
RaviS & LarochelleH Optimization as a model for few-shot learning. 5th Int. Conf. Learn. Represent. ICLR 2017 - Conf. Track Proc 1–11 (2017)., RaviS & LarochelleH Optimization as a model for few-shot learning. 5th Int. Conf. Learn. Represent. ICLR 2017 - Conf. Track Proc 1–11 (2017).
(YeoBTT The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol 106, 1125–1165 (2011).21653723)
YeoBTT The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol 106, 1125–1165 (2011).21653723
YeoBTT The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol 106, 1125–1165 (2011).21653723, YeoBTT The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol 106, 1125–1165 (2011).21653723
(HeinsfeldAS, FrancoAR, CraddockRC, BuchweitzA & MeneguzziF Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage Clin 17, 16–23 (2018).29034163)
HeinsfeldAS, FrancoAR, CraddockRC, BuchweitzA & MeneguzziF Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage Clin 17, 16–23 (2018).29034163
HeinsfeldAS, FrancoAR, CraddockRC, BuchweitzA & MeneguzziF Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage Clin 17, 16–23 (2018).29034163, HeinsfeldAS, FrancoAR, CraddockRC, BuchweitzA & MeneguzziF Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage Clin 17, 16–23 (2018).29034163
(XiaCH Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun 9, 1–14 (2018).29317637)
XiaCH Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun 9, 1–14 (2018).29317637
XiaCH Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun 9, 1–14 (2018).29317637, XiaCH Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun 9, 1–14 (2018).29317637
(Alfaro-AlmagroF Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).29079522)
Alfaro-AlmagroF Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).29079522
Alfaro-AlmagroF Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).29079522, Alfaro-AlmagroF Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).29079522
B. Fischl, D. Salat, Evelina Busa, M. Albert, M. Dieterich, C. Haselgrove, A. Kouwe, R. Killiany, D. Kennedy, Shuna Klaveness, A. Montillo, N. Makris, B. Rosen, A. Dale (2002)
Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain
Neuron, 33
(LiX, YuL, FuC-W & HengP-A Difficulty-aware Meta-Learning for Rare Disease Diagnosis (2019).)
LiX, YuL, FuC-W & HengP-A Difficulty-aware Meta-Learning for Rare Disease Diagnosis (2019).
LiX, YuL, FuC-W & HengP-A Difficulty-aware Meta-Learning for Rare Disease Diagnosis (2019)., LiX, YuL, FuC-W & HengP-A Difficulty-aware Meta-Learning for Rare Disease Diagnosis (2019).
Evan Gordon, Timothy Laumann, B. Adeyemo, Jeremy Huckins, W. Kelley, S. Petersen (2016)
Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations.
Cerebral cortex, 26 1
D. Scheinost, S. Noble, C. Horien, A. Greene, E. Lake, Mehraveh Salehi, Siyuan Gao, X. Shen, David O’Connor, D. Barron, S. Yip, M. Rosenberg, R. Constable (2019)
Ten simple rules for predictive modeling of individual differences in neuroimaging
NeuroImage, 193
(GabrieliJDE, GhoshSS & Whitfield-GabrieliS Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).25569345)
GabrieliJDE, GhoshSS & Whitfield-GabrieliS Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).25569345
GabrieliJDE, GhoshSS & Whitfield-GabrieliS Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).25569345, GabrieliJDE, GhoshSS & Whitfield-GabrieliS Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).25569345
(GlasserMF The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).23668970)
GlasserMF The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).23668970
GlasserMF The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).23668970, GlasserMF The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).23668970
(LiJ Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage 196, 126–141 (2019).30974241)
LiJ Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage 196, 126–141 (2019).30974241
LiJ Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage 196, 126–141 (2019).30974241, LiJ Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage 196, 126–141 (2019).30974241
Kushagra Mahajan, Monika Sharma, L. Vig (2020)
Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
(LecunY, BengioY & HintonG Deep learning. Nature 521, 436–444 (2015).26017442)
LecunY, BengioY & HintonG Deep learning. Nature 521, 436–444 (2015).26017442
LecunY, BengioY & HintonG Deep learning. Nature 521, 436–444 (2015).26017442, LecunY, BengioY & HintonG Deep learning. Nature 521, 436–444 (2015).26017442
M. Rosenberg, E. Finn, D. Scheinost, X. Papademetris, X. Shen, R. Constable, M. Chun (2015)
A neuromarker of sustained attention from whole-brain functional connectivity
Nature neuroscience, 19
L. Breiman (2004)
Stacked regressions
Machine Learning, 24
(HaufeS On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).24239590)
HaufeS On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).24239590
HaufeS On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).24239590, HaufeS On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).24239590
(Van EssenDC The Human Connectome Project: A data acquisition perspective. Neuroimage 62, 2222–2231 (2012).22366334)
Van EssenDC The Human Connectome Project: A data acquisition perspective. Neuroimage 62, 2222–2231 (2012).22366334
Van EssenDC The Human Connectome Project: A data acquisition perspective. Neuroimage 62, 2222–2231 (2012).22366334, Van EssenDC The Human Connectome Project: A data acquisition perspective. Neuroimage 62, 2222–2231 (2012).22366334
(ReinenJM The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis. Nat. Commun 9, 1–15 (2018).29317637)
ReinenJM The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis. Nat. Commun 9, 1–15 (2018).29317637
ReinenJM The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis. Nat. Commun 9, 1–15 (2018).29317637, ReinenJM The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis. Nat. Commun 9, 1–15 (2018).29317637
Ru Kong, Jingwei Li, C. Orban, M. Sabuncu, Hesheng Liu, A. Schaefer, Nanbo Sun, X. Zuo, A. Holmes, S. Eickhoff, B. Yeo (2017)
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion
bioRxiv
(ArbabshiraniMR, PlisS, SuiJ & CalhounVD Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145, 137–165 (2017).27012503)
ArbabshiraniMR, PlisS, SuiJ & CalhounVD Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145, 137–165 (2017).27012503
ArbabshiraniMR, PlisS, SuiJ & CalhounVD Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145, 137–165 (2017).27012503, ArbabshiraniMR, PlisS, SuiJ & CalhounVD Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145, 137–165 (2017).27012503
(SchulzMA Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat. Commun 11, (2020).)
SchulzMA Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat. Commun 11, (2020).
SchulzMA Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat. Commun 11, (2020)., SchulzMA Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat. Commun 11, (2020).
(AlnæsD, KaufmannT, MarquandAF, SmithSM & WestlyeLT Patterns of sociocognitive stratification and perinatal risk in the child brain. Proc. Natl. Acad. Sci. U. S. A 117, (2020).)
AlnæsD, KaufmannT, MarquandAF, SmithSM & WestlyeLT Patterns of sociocognitive stratification and perinatal risk in the child brain. Proc. Natl. Acad. Sci. U. S. A 117, (2020).
AlnæsD, KaufmannT, MarquandAF, SmithSM & WestlyeLT Patterns of sociocognitive stratification and perinatal risk in the child brain. Proc. Natl. Acad. Sci. U. S. A 117, (2020)., AlnæsD, KaufmannT, MarquandAF, SmithSM & WestlyeLT Patterns of sociocognitive stratification and perinatal risk in the child brain. Proc. Natl. Acad. Sci. U. S. A 117, (2020).
Chuanqi Tan, F. Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu (2018)
A Survey on Deep Transfer Learning
D. Alnæs, T. Kaufmann, A. Marquand, Stephen Smith, L. Westlye (2019)
Patterns of sociocognitive stratification and perinatal risk in the child brain
Proceedings of the National Academy of Sciences, 117
(RusuAA Meta-learning with latent embedding optimization. 7th Int. Conf. Learn. Represent. ICLR 2019 1–17 (2019).)
RusuAA Meta-learning with latent embedding optimization. 7th Int. Conf. Learn. Represent. ICLR 2019 1–17 (2019).
RusuAA Meta-learning with latent embedding optimization. 7th Int. Conf. Learn. Represent. ICLR 2019 1–17 (2019)., RusuAA Meta-learning with latent embedding optimization. 7th Int. Conf. Learn. Represent. ICLR 2019 1–17 (2019).
(PaszkeA Automatic differentiation in PyTorch. Adv. Neural Inf. Process. Syst 30 1–4 (2017).)
PaszkeA Automatic differentiation in PyTorch. Adv. Neural Inf. Process. Syst 30 1–4 (2017).
PaszkeA Automatic differentiation in PyTorch. Adv. Neural Inf. Process. Syst 30 1–4 (2017)., PaszkeA Automatic differentiation in PyTorch. Adv. Neural Inf. Process. Syst 30 1–4 (2017).
(SudlowC UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med 12, 1–10 (2015).)
SudlowC UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med 12, 1–10 (2015).
SudlowC UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med 12, 1–10 (2015)., SudlowC UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med 12, 1–10 (2015).
(EickhoffSB & LangnerR Neuroimaging-based prediction of mental traits: Road to Utopia or Orwell? PLoS Biol 17, 1–6 (2019).)
EickhoffSB & LangnerR Neuroimaging-based prediction of mental traits: Road to Utopia or Orwell? PLoS Biol 17, 1–6 (2019).
EickhoffSB & LangnerR Neuroimaging-based prediction of mental traits: Road to Utopia or Orwell? PLoS Biol 17, 1–6 (2019)., EickhoffSB & LangnerR Neuroimaging-based prediction of mental traits: Road to Utopia or Orwell? PLoS Biol 17, 1–6 (2019).
Zaixu Cui, G. Gong (2018)
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features
NeuroImage, 178

Publisher: Springer Journals
Copyright: Copyright © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022
ISSN: 1097-6256
eISSN: 1546-1726
DOI: 10.1038/s41593-022-01059-9
Publisher site: See Article on Publisher Site

Abstract

We propose a simple framework—meta-matching—to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = −0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching.

Journal

Nature Neuroscience – Springer Journals

Published: Jun 1, 2022

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

References (159)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies