The cost of untracked diversity in brain-imaging prediction
Brain-imaging research enjoys increasing adoption of supervised machine learning for
singlesubject disease classification. Yet, the success of these algorithms likely depends on …
singlesubject disease classification. Yet, the success of these algorithms likely depends on …
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Brain imaging research enjoys increasing adoption of supervised machine learning for
single-participant disease classification. Yet, the success of these algorithms likely depends …
single-participant disease classification. Yet, the success of these algorithms likely depends …
[HTML][HTML] Brain-phenotype predictions can survive across diverse real-world data
Recent work suggests that machine learning models predicting psychiatric treatment
outcomes based on clinical data may fail when applied to unharmonized samples …
outcomes based on clinical data may fail when applied to unharmonized samples …
[HTML][HTML] Power and reproducibility in the external validation of brain-phenotype predictions
Identifying reproducible and generalizable brain-phenotype associations is a central goal of
neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype …
neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype …
Detect, quantify, and incorporate dataset bias: A neuroimaging analysis on 12,207 individuals
Neuroimaging datasets keep growing in size to address increasingly complex medical
questions. However, even the largest datasets today alone are too small for training complex …
questions. However, even the largest datasets today alone are too small for training complex …
[HTML][HTML] Performance reserves in brain-imaging-based phenotype prediction
This study examines the impact of sample size on predicting cognitive and mental health
phenotypes from brain imaging via machine learning. Our analysis shows a 3-to 9-fold …
phenotypes from brain imaging via machine learning. Our analysis shows a 3-to 9-fold …
The burden of reliability: How measurement noise limits brain-behaviour predictions
Current major efforts in human neuroimaging research aim to understand individual
differences and identify biomarkers for clinical applications. One particularly promising …
differences and identify biomarkers for clinical applications. One particularly promising …
Robust, generalizable, and interpretable artificial intelligence–derived brain fingerprints of autism and social communication symptom severity
Background Autism spectrum disorder (ASD) is among the most pervasive
neurodevelopmental disorders, yet the neurobiology of ASD is still poorly understood …
neurodevelopmental disorders, yet the neurobiology of ASD is still poorly understood …
Icam-reg: Interpretable classification and regression with feature attribution for mapping neurological phenotypes in individual scans
An important goal of medical imaging is to be able to precisely detect patterns of disease
specific to individual scans; however, this is challenged in brain imaging by the degree of …
specific to individual scans; however, this is challenged in brain imaging by the degree of …
[HTML][HTML] Population modeling with machine learning can enhance measures of mental health-Open-data replication
Efforts to predict trait phenotypes based on functional MRI data from large cohorts have
been hampered by low prediction accuracy and/or small effect sizes. Although these …
been hampered by low prediction accuracy and/or small effect sizes. Although these …