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Evidence for embracing normative modeling
by
Ruhe, Henricus G
, Sripada, Chandra
, Beckmann, Christian F
, Tso, Ivy F
, Barkema, Pieter
, Rutherford, Saige
, Marquand, Andre F
in
Brain - diagnostic imaging
/ brain charts
/ computational psychiatry
/ functional neuroimaging
/ heterogeneity
/ Humans
/ individual prediction
/ machine learning
/ Magnetic Resonance Imaging - methods
/ Neuroimaging
/ Neuroscience
/ Research Advance
/ Schizophrenia
2023
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Evidence for embracing normative modeling
by
Ruhe, Henricus G
, Sripada, Chandra
, Beckmann, Christian F
, Tso, Ivy F
, Barkema, Pieter
, Rutherford, Saige
, Marquand, Andre F
in
Brain - diagnostic imaging
/ brain charts
/ computational psychiatry
/ functional neuroimaging
/ heterogeneity
/ Humans
/ individual prediction
/ machine learning
/ Magnetic Resonance Imaging - methods
/ Neuroimaging
/ Neuroscience
/ Research Advance
/ Schizophrenia
2023
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Do you wish to request the book?
Evidence for embracing normative modeling
by
Ruhe, Henricus G
, Sripada, Chandra
, Beckmann, Christian F
, Tso, Ivy F
, Barkema, Pieter
, Rutherford, Saige
, Marquand, Andre F
in
Brain - diagnostic imaging
/ brain charts
/ computational psychiatry
/ functional neuroimaging
/ heterogeneity
/ Humans
/ individual prediction
/ machine learning
/ Magnetic Resonance Imaging - methods
/ Neuroimaging
/ Neuroscience
/ Research Advance
/ Schizophrenia
2023
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Journal Article
Evidence for embracing normative modeling
2023
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Overview
In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.
Publisher
eLife Sciences Publications, Ltd,eLife Sciences Publications Ltd
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