Leveraging Cross-Sectional Normative Models to Evaluate Longitudinal Changes in Neuroimaging Data

Concetti Chiave

A flexible framework for using pre-trained cross-sectional normative models to evaluate longitudinal changes in neuroimaging data, providing insights into disease progression that would otherwise be missed by traditional approaches.

Sintesi

The article presents a framework for leveraging pre-trained cross-sectional normative models to evaluate longitudinal changes in neuroimaging data. The key insights are:

The authors build on the existing normative modeling framework, which enables the evaluation of an individual's position compared to a population standard. They extend this framework to evaluate an individual's change compared to standard dynamics.

The authors introduce a quantitative metric termed "z-diff" score, which serves as an indicator of change of an individual compared to a population standard. This approach accounts for measurement noise and reliability, allowing for a more nuanced understanding of longitudinal changes.

The authors apply their framework to a longitudinal dataset of 98 patients diagnosed with early-stage schizophrenia. Compared to cross-sectional analyses, which showed global thinning of grey matter at the first visit, the z-diff score revealed a significant normalization of grey matter thickness in the frontal lobe over time. This result was not observed when using more traditional methods of longitudinal analysis, highlighting the increased sensitivity of the proposed approach.

The authors also link the z-diff scores to changes in clinical scales, finding that the first principal component of the z-diff scores, reflecting global changes in grey matter thickness, was negatively correlated with improvements in the Global Assessment of Functioning (GAF) scale. In contrast, the second principal component, capturing more localized changes, was positively correlated with improvements in the Positive and Negative Syndrome Scale (PANSS).

Overall, the framework presents a flexible and effective methodology for analyzing longitudinal neuroimaging data, providing insights into the progression of a disease that would otherwise be missed when using more traditional approaches.

Statistiche

"Patients suffering from psychotic mood disorders were excluded from the study."
"The study was carried out in accordance with the latest version of the Declaration of Helsinki. The study design was reviewed and approved by the Research Ethics Board. Each participant received a complete description of the study and provided written informed consent."

Citazioni

"Longitudinal neuroimaging studies offer valuable insight into intricate dynamics of brain development, ageing, and disease progression over time."
"To fully harness the potential of longitudinal neuroimaging data, we have to develop and refine methodologies that are adapted to longitudinal designs, considering the complex interplay between population variation and individual dynamics."
"Notably, our framework offers advantages such as flexibility in dataset size and ease of implementation."

Approfondimenti chiave tratti da

Using normative models pre-trained on cross-sectional data to evaluate longitudinal changes in neuroimaging data

by Buckova,B. R... alle www.biorxiv.org 06-14-2023

https://www.biorxiv.org/content/10.1101/2023.06.09.544217v3

Domande più approfondite

How can the proposed framework be extended to incorporate more complex longitudinal dynamics, such as non-linear trajectories or interactions between individual and population-level changes?

The proposed framework can be extended to incorporate more complex longitudinal dynamics by introducing additional components to the model that account for non-linear trajectories and interactions between individual and population-level changes. One way to achieve this is by incorporating hierarchical Bayesian models that allow for the estimation of individual-level trajectories within the context of population-level norms. By including random effects or latent variables in the model, it becomes possible to capture the variability in individual trajectories that deviate from the population norm.
Furthermore, the framework can be enhanced by integrating time-varying covariates that capture changes in individual characteristics over time. This would enable the model to adapt to the evolving nature of the data and provide more accurate assessments of longitudinal changes. Additionally, incorporating interaction terms between individual characteristics and population norms can help elucidate how individual dynamics interact with broader population trends.
Overall, by incorporating these elements into the framework, it can better capture the complexity of longitudinal dynamics, including non-linear trajectories and interactions between individual and population-level changes, leading to more nuanced and accurate analyses of longitudinal neuroimaging data.

What are the potential limitations of relying on cross-sectional normative models for longitudinal analysis, and how can these be addressed in future research?

Relying solely on cross-sectional normative models for longitudinal analysis poses several limitations. One key limitation is that cross-sectional models may not fully capture the individual variability and changes that occur over time. Since these models are based on population averages at specific time points, they may not accurately represent the longitudinal trajectories of individual subjects. This can lead to biased estimates of longitudinal changes and hinder the detection of subtle but significant alterations over time.
To address these limitations, future research can focus on developing longitudinal normative models that explicitly account for individual trajectories and changes. By incorporating longitudinal data into the model training process, it becomes possible to capture the full spectrum of individual variability and track changes over time more accurately. Additionally, advanced statistical techniques, such as mixed-effects models or growth curve modeling, can be employed to model non-linear trajectories and interactions between individual and population-level changes.
Moreover, efforts should be made to collect larger and more diverse longitudinal datasets to improve the generalizability and robustness of the models. By including data from different populations, time points, and imaging modalities, researchers can develop more comprehensive and reliable longitudinal normative models for neuroimaging analysis.

Could the z-diff score be used to predict future clinical outcomes or guide personalized treatment strategies for patients with neurological or psychiatric disorders?

The z-diff score, which quantifies the degree of change between visits beyond what is expected in the healthy population, holds potential for predicting future clinical outcomes and guiding personalized treatment strategies for patients with neurological or psychiatric disorders. By capturing deviations from expected changes based on normative models, the z-diff score can serve as a sensitive indicator of disease progression or treatment response.
In a clinical setting, the z-diff score could be used to monitor disease progression over time and identify patients who are experiencing significant changes in neuroimaging measures. This information can help clinicians tailor treatment plans and interventions based on individual responses and trajectories. For example, patients with larger deviations in the z-diff score may require closer monitoring or adjustments to their treatment regimen to address emerging issues.
Furthermore, the z-diff score can be integrated into predictive models that forecast future clinical outcomes based on neuroimaging data. By incorporating the z-diff score as a predictive feature, these models can enhance their accuracy in forecasting disease trajectories and identifying patients at higher risk of adverse outcomes.
Overall, the z-diff score has the potential to play a valuable role in predicting future clinical outcomes and guiding personalized treatment strategies for patients with neurological or psychiatric disorders, offering a data-driven approach to improving patient care and outcomes.