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Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling.

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Summary

This study introduces a new Bayesian modeling approach to detect early signs of neurodegeneration in healthy individuals. The method identifies accelerated brain volume loss, predicting disease onset and spread in dementia risk cases.

Keywords:
Alzheimer’s DiseaseBayesian linear mixed-effectBayesian predictionFrontotemporal Lobar Degeneration

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Area of Science:

  • Neuroscience
  • Radiology
  • Biostatistics

Background:

  • Early detection of neurodegeneration is crucial for timely intervention with disease-modifying treatments.
  • Neurodegenerative diseases are heterogeneous, with variable early manifestations across individuals.
  • Existing methods require prior assumptions about affected brain regions, limiting detection of novel patterns.

Purpose of the Study:

  • To develop and validate a novel Bayesian linear mixed-effects (BLME) model extension for detecting emerging neurodegeneration in cognitively healthy individuals at risk for dementia.
  • To quantify individualized rates of cerebral cortical volume loss and predict future brain changes.
  • To identify accelerated volume loss indicative of neurodegeneration without prior assumptions about affected regions.

Main Methods:

  • Utilized an extension of Bayesian linear mixed-effects (BLME) modeling to analyze longitudinal MRI data.
  • Quantified individualized rates of cerebral cortical volume loss from initial MRIs.
  • Predicted future brain volumes and identified voxels with accelerated volume loss compared to expected values.
  • Applied the model to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and frontotemporal lobar degeneration (FTLD) cases.

Main Results:

  • The BLME model successfully identified regions of accelerated volume loss in cognitively normal individuals who later developed dementia or FTLD.
  • Detected neurodegeneration in disease-specific regions (e.g., medial temporal for AD, insular/frontal for FTLD) prior to or concurrent with early symptoms.
  • The rate of spread of accelerated volume loss significantly predicted time to dementia conversion in ADNI participants.
  • Identified early neurodegenerative changes across the brain over time.

Conclusions:

  • The developed BLME model extension offers a promising, assumption-free method for detecting early neurodegeneration across various brain regions.
  • This approach can identify emerging neurodegeneration in individuals at risk for dementia, facilitating earlier diagnosis and treatment.
  • The model's ability to predict disease progression highlights its potential for clinical application in neurodegenerative disorders.