Brain–phenotype models fail for individuals who defy sample stereotypes

Nature, Год журнала: 2022, Номер 609(7925), С. 109 - 118

Опубликована: Авг. 24, 2022

Abstract Individual differences in brain functional organization track a range of traits, symptoms and behaviours 1–12 . So far, work modelling linear brain–phenotype relationships has assumed that single such relationship generalizes across all individuals, but models do not equally well participants 13,14 A better understanding whom fail why is crucial to revealing robust, useful unbiased relationships. To this end, here we related activity phenotype using predictive models—trained tested on independent data ensure generalizability 15 —and examined model failure. We applied data-driven approach neurocognitive measures new, clinically demographically heterogeneous dataset, with the results replicated two independent, publicly available datasets 16,17 Across three datasets, find reflect unitary cognitive constructs, rather scores intertwined sociodemographic clinical covariates; is, stereotypical profiles, when individuals who defy them. Model failure reliable, specific generalizable datasets. Together, these highlight pitfalls one-size-fits-all effect biased phenotypic 18–20 interpretation utility resulting models. present framework address issues so may reveal neural circuits underlie phenotypes ultimately identify individualized targets for intervention.

Язык: Английский

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Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data

Nature Neuroscience, Год журнала: 2022, Номер 26(1), С. 4 - 11

Опубликована: Дек. 23, 2022

Язык: Английский

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Multivariate BWAS can be replicable with moderate sample sizes

Nature, Год журнала: 2023, Номер 615(7951), С. E4 - E7

Опубликована: Март 8, 2023

Язык: Английский

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Evidence for embracing normative modeling

eLife, Год журнала: 2023, Номер 12

Опубликована: Март 13, 2023

In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 Smith-10), an updated online platform for transferring these new data sources. We showcase value with a head-to-head comparison between features output by modeling raw several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification regression (predicting general cognitive ability). Across all benchmarks, show advantage features, strongest statistically significant results demonstrated tasks. intend accessible resources facilitate wider adoption across neuroimaging community.

Язык: Английский

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Functional connectomics in depression: insights into therapies

Trends in Cognitive Sciences, Год журнала: 2023, Номер 27(9), С. 814 - 832

Опубликована: Июнь 5, 2023

Язык: Английский

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Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

NeuroImage, Год журнала: 2023, Номер 273, С. 120010 - 120010

Опубликована: Март 12, 2023

Resting-state fMRI is commonly used to derive brain parcellations, which are widely for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local global approaches estimating areal-level cortical parcellations. The resulting local-global parcellations often referred as the Schaefer However, lack of homotopic correspondence between left right parcels has limited their use lateralization Here, we extend our previous Using resting-fMRI task-fMRI across diverse scanners, acquisition protocols, preprocessing demographics, show homogeneous while being more than five publicly available Furthermore, weaker correlations associated with greater in resting network organization, well language motor task activation. Finally, agree boundaries number areas estimated from histology visuotopic fMRI, capturing sub-areal (e.g., somatotopic visuotopic) features. Overall, these results suggest represent neurobiologically meaningful subdivisions cerebral cortex will be useful resource future Multi-resolution 1479 participants (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).

Язык: Английский

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Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies

Proceedings of the National Academy of Sciences, Год журнала: 2023, Номер 120(6)

Опубликована: Янв. 30, 2023

Despite the great promise that machine learning has offered in many fields of medicine, it also raised concerns about potential biases and poor generalization across genders, age distributions, races ethnicities, hospitals, data acquisition equipment protocols. In current study, context three brain diseases, we provide evidence which suggests when properly trained, models can generalize well diverse conditions do not necessarily suffer from bias. Specifically, by using multi-study magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, autism spectrum disorder, find well-trained have a high area-under-the-curve (AUC) on subjects different subgroups pertaining to attributes such as gender, age, racial groups, clinical studies are unbiased under multiple fairness metrics demographic parity difference, equalized odds equal opportunity difference etc. We incorporate multi-source demographic, clinical, genetic factors cognitive scores unbiased. These better predictive AUC than those trained only with features but there situations these additional help.

Язык: Английский

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Data leakage inflates prediction performance in connectome-based machine learning models

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Фев. 28, 2024

Abstract Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability unseen data. However, data leakage undermines the validity of predictive models by breaching separation between training Leakage always an incorrect practice but still pervasive machine learning. Understanding its effects on can inform how affects existing literature. Here, we investigate five forms leakage–involving feature selection, covariate correction, dependence subjects–on functional structural connectome-based learning across four datasets three phenotypes. via selection repeated subjects drastically inflates prediction performance, whereas other have minor effects. Furthermore, small exacerbate leakage. Overall, our results illustrate variable underscore importance avoiding improve reproducibility modeling.

Язык: Английский

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In vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

Proceedings of the National Academy of Sciences, Год журнала: 2024, Номер 121(23)

Опубликована: Май 30, 2024

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I remains unknown. Here, we noninvasively estimate a putative marker whole-cortex by fitting large-scale biophysically plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our generates realistic dynamics in the Human Connectome Project. Next, show estimated sensitive gamma-aminobutyric acid (GABA) agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced changes are spatially consistent with positron emission tomography measurement receptor density. then investigate relationship between and neurodevelopment. find declines heterogeneously across cerebral cortex youth, greatest reduction occurring sensorimotor systems relative association systems. Importantly, among children same chronological age, lower (especially cortex) linked better cognitive performance. This result replicated North American (8.2 23.0 y old) Asian (7.2 7.9 cohorts, suggesting more mature indexes improved cognition normative development. Overall, findings open door studying how disrupted trajectories may lead dysfunction psychopathology emerges youth.

Язык: Английский

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One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry

Biological Psychiatry, Год журнала: 2022, Номер 93(8), С. 717 - 728

Опубликована: Сен. 29, 2022

Язык: Английский

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