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Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning

https://doi.org/10.1101/2023.11.03.565485
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Establishment of Best Practices for Evidence for Prediction
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Removing the Reliability Bottleneck in Functional Magnetic Resonance Imaging Research to Achieve Clinical Utility
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SIMPLS: An alternative approach to partial least squares regression
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On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting
inferredcites →https://doi.org/10.1016/j.jneumeth.2015.07.013openalex-snapshot
AICHA: An atlas of intrinsic connectivity of homotopic areas
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Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review
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Clinical applications of the functional connectome
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Ten simple rules for predictive modeling of individual differences in neuroimaging
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Benchmarking functional connectome-based predictive models for resting-state fMRI
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Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics
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A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum
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Optimising network modelling methods for fMRI
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Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery
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Recommendations for machine learning benchmarks in neuroimaging
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Brain Networks and Cognitive Architectures
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Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets
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PLS-regression: a basic tool of chemometrics
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The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
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A multi-modal parcellation of human cerebral cortex
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A positive-negative mode of population covariation links brain connectivity, demographics and behavior
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Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity
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The Human Connectome Project's neuroimaging approach
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Using connectome-based predictive modeling to predict individual behavior from brain connectivity
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Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
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On the nature and use of models in network neuroscience
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Reproducible brain-wide association studies require thousands of individuals
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Multivariate BWAS can be replicable with moderate sample sizes
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Reply to: Multivariate BWAS can be replicable with moderate sample sizes
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Meta-matching as a simple framework to translate phenotypic predictive models from big to small data
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The normative modeling framework for computational psychiatry
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Standardizing human brain parcellations
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The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses
inferredcites →https://doi.org/10.1073/pnas.1713532115openalex-snapshot
Robust prediction of individual creative ability from brain functional connectivity
inferredcites →https://doi.org/10.1093/cercor/bhx179openalex-snapshot
Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI
inferredcites →https://doi.org/10.1098/rstb.2017.0284openalex-snapshot
A distributed brain network predicts general intelligence from resting-state human neuroimaging data
inferredcites →https://doi.org/10.1101/2020.08.21.257758openalex-snapshot
Towards Reproducible Brain-Wide Association Studies
inferredcites →https://doi.org/10.1101/2021.03.18.435935openalex-snapshot
Predicting intelligence from fMRI data of the human brain in a few minutes of scan time
inferredcites →https://doi.org/10.1101/2023.02.18.529076openalex-snapshot
Is resting state fMRI better than individual characteristics at predicting cognition?
inferredcites →https://doi.org/10.1109/isbi.2016.7493501openalex-snapshot
Compressed online dictionary learning for fast resting-state fMRI decomposition
inferredcites →https://doi.org/10.1109/tmi.2009.2037756openalex-snapshot
Random Subspace Ensembles for fMRI Classification
inferredcites →https://doi.org/10.1117/12.2512063openalex-snapshot
Predicting cognitive scores from resting fMRI data and geometric features of the brain
inferredcites →https://doi.org/10.1126/science.1194144openalex-snapshot
Prediction of Individual Brain Maturity Using fMRI
inferredcites →https://doi.org/10.1145/355744.355749openalex-snapshot
An Efficient Method for Generating Discrete Random Variables with General Distributions
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Simulation physiologischer Regelkreise mit der objektorientierten Modellbibliothek “HumanLib”
inferredcites →https://doi.org/10.3389/conf.fninf.2013.09.00041openalex-snapshot
The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives
inferredcites →https://doi.org/10.3389/fninf.2014.00014openalex-snapshot
Machine learning for neuroimaging with scikit-learn
inferredcites →https://doi.org/10.3389/fnsys.2012.00069openalex-snapshot
ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements
inferredcites →https://doi.org/10.52294/001c.85074openalex-snapshot
Improving the Interpretability of fMRI Decoding Using Deep Neural Networks and Adversarial Robustness
inferredcites →https://doi.org/10.52294/2f9c5854-d10b-44ab-93fa-d485ef5b24f1openalex-snapshot
Putting behaviour back into brain–behaviour correlation analyses
inferredcites →https://doi.org/10.52294/51f2e656-d4da-457e-851e-139131a68f14openalex-snapshot
On the statistics of brain/behavior associations
inferredcites →https://doi.org/10.52294/5ba14033-72bb-4915-81a3-fa221302818aopenalex-snapshot
Brain–behavior associations depend heavily on user-defined criteria
inferredcites →https://doi.org/10.52294/725139d7-0b8a-49dc-a81d-ba2ca64ff6d9openalex-snapshot
Lost in transformation: fMRI power is diminished by unknown variability in methods and people
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Behavioral phenotypes, stochastic processes, entropy, evolution, and individual variability: Toward a unified field theory for neurodevelopment and psychopathology
inferredcites →https://doi.org/10.52294/de841a29-d684-4707-9042-5bbd3d764f84openalex-snapshot
Commentary on ‘Reproducible brain-wide association studies require thousands of individuals’
inferredcites →https://doi.org/10.52294/fb60878a-5ee2-44cf-a6a9-9cdcc2f1c397openalex-snapshot
Controversies on brain-wide association studies: commentaries from the field
inferredcites →https://doi.org/10.7554/elife.44443openalex-snapshot
Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1093/braincomms/fcag127openalex-snapshot
Individualized phenotyping of functional amyotrophic lateral sclerosis pathology in sensorimotor cortex
inferred← citeshttps://doi.org/10.1101/2025.01.13.632667openalex-snapshot
Individualized Phenotyping of Functional ALS Pathology in Sensorimotor Cortex
inferred← citeshttps://doi.org/10.1111/acps.13742openalex-snapshot
Diagnostic value of structural, functional and effective connectivity in bipolar disorder
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