Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.
Hum Brain Mapp
; 42(14): 4658-4670, 2021 10 01.
Article
in En
| MEDLINE
| ID: mdl-34322947
ABSTRACT
Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Schizophrenia
/
Diffusion Tensor Imaging
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White Matter
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Machine Learning
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Adult
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Female
/
Humans
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Male
/
Middle aged
Language:
En
Year:
2021
Type:
Article