# NeuroFANN: identification of neuropathological subtypes in dementia with plasma proteins by using functionally annotated neural network

## TL;DR

NeuroFANN is a new method that uses plasma proteins and their interactions to classify dementia subtypes more accurately than existing methods.

## Contribution

NeuroFANN integrates protein–protein interactions and functional annotations to improve dementia subtype classification.

## Key findings

- NeuroFANN outperformed existing methods in classifying dementia subtypes using plasma proteins.
- 54 plasma proteins were identified and grouped into 16 functional clusters for dementia subtypes.
- NeuroFANN's risk scores correlated strongly with longitudinal cognitive decline.

## Abstract

Dementia diagnosis relies on identifying neuropathological features, such as beta-amyloid (Aβ) deposition, medial temporal lobe atrophy (MTA), and white matter hyperintensity (WMH). Recently, plasma protein biomarkers have emerged as a cost-effective and less invasive tool for identifying neuropathological features, enhanced by machine learning (ML) for precise diagnosis. However, most ML studies fail to account for protein–protein interactions (PPIs) and synergetic effects between proteins, overlooking their collective contributions to disease mechanisms. Additionally, the lack of consideration for functional properties may result in the redundant and imbalanced representation of proteins and their functions, potentially limiting the effectiveness of dementia diagnosis. In this study, we propose NeuroFANN, a method designed to classify three neuropathological subtypes in dementia—positivity for Aβ, MTA, and WMH—using plasma protein biomarkers. A key feature of NeuroFANN is the combination of the PPI network-based synergetic effects with the functional annotation-based protein biomarker clustering. NeuroFANN extracts synergetic effects by propagating independent effects of proteins across the PPI network, which are then aggregated in functional protein clusters, thereby enabling global PPI awareness and capturing the biological properties of protein biomarkers. From a South Korean cohort, 54 proteins were identified as plasma protein biomarkers for dementia subtypes and grouped into 16 clusters. NeuroFANN outperformed comparison methods in classifying dementia subtypes, with its core components validated as key contributors to superior performance. Additionally, the risk scores predicted by NeuroFANN showed a strong association with longitudinal cognitive decline, demonstrating its potential as a valuable diagnostic tool in clinical settings.