Using normative models pre-trained on cross-sectional data to evaluate intra-individual longitudinal changes in neuroimaging data
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new method to analyze longitudinal neuroimaging data, revealing brain changes over time. The novel z-diff score effectively tracks individual brain development and disease progression, offering new insights into conditions like schizophrenia.
Area Of Science
- Neuroimaging
- Brain Development
- Disease Progression
Background
- Longitudinal neuroimaging is crucial for understanding brain changes over time.
- Current methods often focus on population variation, limiting analysis of individual dynamics.
- A need exists for methodologies that integrate population standards with individual longitudinal changes.
Purpose Of The Study
- To extend the normative modelling framework for analyzing longitudinal neuroimaging data.
- To introduce a quantitative metric (z-diff score) for assessing individual temporal changes against population standards.
- To apply this framework to schizophrenia patients to identify disease-related brain changes.
Main Methods
- Extended the normative modelling framework to assess longitudinal change relative to population dynamics.
- Developed a 'z-diff' score to quantify individual temporal changes.
- Applied the framework to a longitudinal MRI dataset of 98 early-stage schizophrenia patients.
Main Results
- The z-diff score revealed a significant normalization of frontal lobe grey matter thickness over one year in schizophrenia patients.
- This normalization was not detected by traditional cross-sectional or longitudinal analyses.
- Cross-sectional analysis showed global grey matter thinning at the initial visit.
Conclusions
- The proposed framework offers a flexible and effective method for analyzing longitudinal neuroimaging data.
- It provides novel insights into disease progression, particularly for conditions like schizophrenia.
- This approach enhances the understanding of individual brain dynamics in health and disease.
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