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Rafael Dolezal1, Zdenek Linha1, Matej Seifert1

  • 12nd Medical School, Charles University, Prague, Czech Republic, Czech Republic.

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Summary

This study developed a computational tool to predict early signs of neurodegeneration in cognitively normal individuals using brain imaging data. The model accurately identifies potential Mild Behavioural Impairment (MBI) by analyzing metabolic and structural brain changes.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Computational Biology

Background:

  • Neuroimaging faces challenges in detecting early neurodegeneration due to ambiguous morpho-functional indices.
  • Mild Behavioural Impairment (MBI) is investigated as a preclinical stage of neurodegeneration.
  • This study explores metabolic and structural brain changes in cognitively healthy individuals to predict MBI signs.

Purpose of the Study:

  • To develop a predictive model for Mild Behavioural Impairment (MBI) signs in cognitively normal individuals.
  • To investigate the relationship between brain morphometry, metabolic activity, and neuropsychiatric scores.
  • To establish a computational diagnostic tool for early detection of neurodegeneration.

Main Methods:

  • Utilized the ADNI database, including T1 scans, PET-FDG scans, and neuropsychiatric scores (NPI-Q) from 41 cognitively normal subjects.
  • Employed region-based and surface-based brain morphometry techniques, generating 597 functional and geometrical brain descriptors.
  • Applied partial-least-square regression (PLS) with backward iterative elimination of uninformative variables (BIEUV) for data analysis.

Main Results:

  • A statistically significant PLS model predicted NPI-Q scores across four pathopsychological domains (unrest, agitation, disbalance, psychosis) with high accuracy (global R² = 0.91, Q²LOO = 0.56).
  • The model identified 25 stable predictors after removing 97% of initial variables.
  • Agitation and psychosis domains showed the highest and lowest predictability, respectively, with some brain regions exhibiting domain-specific effects.

Conclusions:

  • A computational diagnostic tool was developed to predict NPI-Q scores in four pathopsychological domains using neuroimaging data.
  • The study successfully reproduced pathopsychological scores using a PLS model with BIEUV-based reduction of PET-FDG and T1 data.
  • This tool aids in the early detection of potential neurodegeneration in cognitively normal individuals.