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VIANNA

Interactive Visual Analytics for Longitudinal Neuropsychological Data

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Abstract

Neuropsychological assessments are widely used to monitor cognitive change and investigate progression from mild cognitive impairment (MCI) to dementia. However, exploratory analysis of longitudinal test batteries is often hindered by fragmented workflows relying on spreadsheets and ad hoc scripts.

VIANNA (VIsual ANalytics for Neuropsychological Assessments) is an interactive, domain-driven visual analytics framework designed to support exploratory analysis of heterogeneous, multi-visit neuropsychological datasets. It integrates hierarchical feature construction, cohort definition, distributional comparison, longitudinal trajectory visualization, and multivariate correlation analysis within a unified, codeless environment.
VIANNA architecture overview

Modular architecture of VIANNA. The system follows a coordinated multiple-view design integrating hierarchical feature construction, overview-based cohort definition, and task-specific analytical components.

Key Features

Hierarchical Attribute Management
Transparent and expert-guided feature derivation and dimensionality reduction.
Cohort-Based Comparison
Effect-size–driven ranking of group differences beyond p-values.
Longitudinal Trajectory Analysis
Integrated visualization and inference for repeated measures.
Correlation & Multivariate Exploration
Coordinated matrix, scatterplot, and PCA-based views.

Resources

Funding

This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 964220. This paper reflects only the authors' view and the Commission is not responsible for any use that may be made by the information it contains.

Citation

@article{vianna2026,
  title={VIANNA: An Interactive Visual Analytics Framework for Exploratory
Analysis of Longitudinal Neuropsychological Assessment Data},
  author={Jorge Acosta, Pablo Toharia},
  journal={Computers in Biology and Medicine (Under Review)},
  year={2026}
}