ArticlePatterns (New York, N.Y.)2023
Connectome-based machine learning models are vulnerable to subtle data manipulations.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 16 citations in OpenAlex.
- Connectome-based substrates of emotional N-back performance in the adolescent brain cognitive development study.Developmental cognitive neuroscience · 2026Article
- Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.Nature human behaviour · 2026Article
- Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers.bioRxiv : the preprint server for biology · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Cost Efficiency of fMRI Studies Using Resting-State Vs. Task-Based Functional Connectivity.Human brain mapping · 2025Article
- Embracing Model Heterogeneity for Better Brain-Behavior Associations.Biological psychiatry global open science · 2025Article
- Power and reproducibility in the external validation of brain-phenotype predictions.Nature human behaviour · 2024Article
- Using a deep generation network reveals neuroanatomical specificity in hemispheres.Patterns (New York, N.Y.) · 2024Article
- Embracing digital innovation in neuroscience: 2023 in review at NEUROCCINO.Brain structure & function · 2024Review
- Stable multivariate lesion symptom mapping.Aperture neuro · 2024Article
- Power and reproducibility in the external validation of brain-phenotype predictions.bioRxiv : the preprint server for biology · 2023Article
- Article
- Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.ArXiv · 2023Article
- Connectome-based machine learning models are vulnerable to subtle data manipulations.Patterns (New York, N.Y.) · 2023Article
- Sex differences in default mode network connectivity in healthy aging adults.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.