[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 …
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 …
[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 …
[HTML][HTML] 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 …
[引用][C] 212. Mapping the Neurobiological Markers of Psychopathology
R Jirsaraie, M Gatavins, A Pines… - Biological …, 2024 - biologicalpsychiatryjournal.com
Background Neuroimaging research has uncovered a multitude of neural abnormalities
associated with psychopathology, but few prediction-based studies have been conducted …
associated with psychopathology, but few prediction-based studies have been conducted …
[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 …
[HTML][HTML] One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
[HTML][HTML] Objective markers for psychiatric decision-making: How to move imaging into clinical practice
CE Wilcox, ME Brett, VD Calhoun - NeuroImage: Clinical, 2020 - ncbi.nlm.nih.gov
Psychiatric disorders result from abnormal brain functioning. However, despite significant
advances in our understanding of their neural underpinnings with the help of neuroimaging …
advances in our understanding of their neural underpinnings with the help of neuroimaging …
[HTML][HTML] Harnessing networks and machine learning in neuropsychiatric care
Highlights•Large cross-sectional and longitudinal data sets contribute uniquely to
neuropsychiatry.•Population-level and patient-level data complement each other in …
neuropsychiatry.•Population-level and patient-level data complement each other in …
Reliable and generalizable brain-based predictions of cognitive functioning across common psychiatric illness
A primary aim of precision psychiatry is the establishment of predictive models linking
individual differences in brain functioning with clinical symptoms. In particular, cognitive …
individual differences in brain functioning with clinical symptoms. In particular, cognitive …