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Toward Continuous Neurocognitive Monitoring: Integrating Speech AI with Relational Graph Transformers for Rare Neurological Diseases

Raquel Norel, Michele Merler, Pavitra Modi

TL;DR

Cognitive symptoms in rare neurological diseases are often invisible to standard tests and escape episodic monitoring. The paper proposes a continuous neurocognitive monitoring framework that combines smartphone-based speech biomarkers with Relational Graph Transformers (RELGT) to fuse speech, labs, treatments, and symptoms into real-time predictive alerts. A PKU proof-of-concept shows speech-derived Proficiency in Verbal Discourse correlating with phenylalanine levels (ρ = -0.50, p < 0.005) and diverging from standard neuropsychological assessments, supporting ecological validity. If validated across diseases, this approach could transform neurology from episodic care to continuous, personalized monitoring for millions, while addressing data silos, workflow integration, and health equity challenges.

Abstract

Patients with rare neurological diseases report cognitive symptoms -"brain fog"- invisible to traditional tests. We propose continuous neurocognitive monitoring via smartphone speech analysis integrated with Relational Graph Transformer (RELGT) architectures. Proof-of-concept in phenylketonuria (PKU) shows speech-derived "Proficiency in Verbal Discourse" correlates with blood phenylalanine (p = -0.50, p < 0.005) but not standard cognitive tests (all |r| < 0.35). RELGT could overcome information bottlenecks in heterogeneous medical data (speech, labs, assessments), enabling predictive alerts weeks before decompensation. Key challenges: multi-disease validation, clinical workflow integration, equitable multilingual deployment. Success would transform episodic neurology into continuous personalized monitoring for millions globally.

Toward Continuous Neurocognitive Monitoring: Integrating Speech AI with Relational Graph Transformers for Rare Neurological Diseases

TL;DR

Cognitive symptoms in rare neurological diseases are often invisible to standard tests and escape episodic monitoring. The paper proposes a continuous neurocognitive monitoring framework that combines smartphone-based speech biomarkers with Relational Graph Transformers (RELGT) to fuse speech, labs, treatments, and symptoms into real-time predictive alerts. A PKU proof-of-concept shows speech-derived Proficiency in Verbal Discourse correlating with phenylalanine levels (ρ = -0.50, p < 0.005) and diverging from standard neuropsychological assessments, supporting ecological validity. If validated across diseases, this approach could transform neurology from episodic care to continuous, personalized monitoring for millions, while addressing data silos, workflow integration, and health equity challenges.

Abstract

Patients with rare neurological diseases report cognitive symptoms -"brain fog"- invisible to traditional tests. We propose continuous neurocognitive monitoring via smartphone speech analysis integrated with Relational Graph Transformer (RELGT) architectures. Proof-of-concept in phenylketonuria (PKU) shows speech-derived "Proficiency in Verbal Discourse" correlates with blood phenylalanine (p = -0.50, p < 0.005) but not standard cognitive tests (all |r| < 0.35). RELGT could overcome information bottlenecks in heterogeneous medical data (speech, labs, assessments), enabling predictive alerts weeks before decompensation. Key challenges: multi-disease validation, clinical workflow integration, equitable multilingual deployment. Success would transform episodic neurology into continuous personalized monitoring for millions globally.

Paper Structure

This paper contains 8 sections, 1 figure.

Table of Contents

  1. The Monitoring Gap
  2. Vision: Integrated Framework
  3. Key Research Challenges
  4. Challenge 1: Multi-Disease Validation
  5. Challenge 2: Scalable Integration
  6. Challenge 3: Clinical Workflow
  7. Challenge 4: Health Equity
  8. Towards Precision Neurology

Figures (1)

  • Figure 1: Contrasting Traditional and Digital Approaches to Neurocognitive Monitoring. Panel A illustrates limitations of traditional episodic assessment, clinic-based testing, and siloed data sources that result in reactive care. Panel B presents an envisioned solution using smartphone-based speech analysis integrated with medical databases via RELGT, enabling continuous monitoring and proactive precision neurology through real-time data capture in remote, natural environments.