Neural Coherence Fingerprinting System (NCFS): A Theoretical Framework for Predicting Cognitive Decline Through Linguistic Entropy Dynamics and Cross-Modal Integration Patterns

Cognitive decline affects over 55 million people globally, with Sub-Saharan Africa projected to experience the fastest growth in dementia prevalence by 2050. Current detection methods rely on expensive neuroimaging, invasive biomarkers, or specialist assessments; resources unavailable to 90% of Africans. This manuscript presents the Neural Coherence Fingerprinting System (NCFS), a novel theoretical framework proposing that early cognitive decline can be detected through computational analysis of linguistic entropy dynamics and cross-modal integration patterns in everyday speech. NCFS introduces three theoretical innovations: (1) Semantic Coherence Decay Signatures (SCDS); a mathematical formalism characterizing how semantic network connectivity deteriorates during prodromal impairment, manifesting as increased entropy in word associations; (2) Cross-Modal Temporal Binding Indices (CTBI); quantification of how language-gesture-prosody synchronization degrades as multimodal integration networks fail; (3) Linguistic Complexity Homeostasis (LCH); the hypothesis that healthy cognition maintains stable syntactic complexity through compensatory mechanisms, while early decline shows characteristic variability. Unlike conventional biomarkers measuring single pathological processes, NCFS captures systems-level functional disruption through language; cognition's most integrative output. The proposed framework requires only smartphone-recorded conversational speech, operates through computational linguistic analysis, and needs no specialized equipment or medical expertise. If validated, NCFS could enable early cognitive screening across Africa's 77 million adults over age 60 at costs under $2 per assessment; a 500-fold cost reduction versus neuroimaging while potentially detecting decline years before clinical symptoms. This work establishes a theoretical foundation and methodological blueprint for investigating linguistic entropy as a scalable, culturally-agnostic biomarker, with transformative implications for global cognitive health equity. Keywords: Cognitive decline prediction, linguistic entropy, semantic coherence, cross-modal integration, computational neurolinguistics, theoretical framework, digital biomarkers

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-10
DOI
https://doi.org/10.5281/zenodo.17568229
Primary Topic
Neurobiology of Language and Bilingualism
Type
article
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article

Neural Coherence Fingerprinting System (NCFS): A Theoretical Framework for Predicting Cognitive Decline Through Linguistic Entropy Dynamics and Cross-Modal Integration Patterns

Zenodo (CERN European Organization for Nuclear Research)
Neurobiology of Language and Bilingualism
article

Neural Coherence Fingerprinting System (NCFS): A Theoretical Framework for Predicting Cognitive Decline Through Linguistic Entropy Dynamics and Cross-Modal Integration Patterns

article en

Abstract

Cognitive decline affects over 55 million people globally, with Sub-Saharan Africa projected to experience the fastest growth in dementia prevalence by 2050. Current detection methods rely on expensive neuroimaging, invasive biomarkers, or specialist assessments; resources unavailable to 90% of Africans. This manuscript presents the Neural Coherence Fingerprinting System (NCFS), a novel theoretical framework proposing that early cognitive decline can be detected through computational analysis of linguistic entropy dynamics and cross-modal integration patterns in everyday speech. NCFS introduces three theoretical innovations: (1) Semantic Coherence Decay Signatures (SCDS); a mathematical formalism characterizing how semantic network connectivity deteriorates during prodromal impairment, manifesting as increased entropy in word associations; (2) Cross-Modal Temporal Binding Indices (CTBI); quantification of how language-gesture-prosody synchronization degrades as multimodal integration networks fail; (3) Linguistic Complexity Homeostasis (LCH); the hypothesis that healthy cognition maintains stable syntactic complexity through compensatory mechanisms, while early decline shows characteristic variability. Unlike conventional biomarkers measuring single pathological processes, NCFS captures systems-level functional disruption through language; cognition's most integrative output. The proposed framework requires only smartphone-recorded conversational speech, operates through computational linguistic analysis, and needs no specialized equipment or medical expertise. If validated, NCFS could enable early cognitive screening across Africa's 77 million adults over age 60 at costs under $2 per assessment; a 500-fold cost reduction versus neuroimaging while potentially detecting decline years before clinical symptoms. This work establishes a theoretical foundation and methodological blueprint for investigating linguistic entropy as a scalable, culturally-agnostic biomarker, with transformative implications for global cognitive health equity. Keywords: Cognitive decline prediction, linguistic entropy, semantic coherence, cross-modal integration, computational neurolinguistics, theoretical framework, digital biomarkers

Zenodo (CERN European Organization for Nuclear Research)
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Neurobiology of Language and Bilingualism
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Neural Coherence Fingerprinting System (NCFS): A Theoretical Framework for Predicting Cognitive Decline Through Linguistic Entropy Dynamics and Cross-Modal Integration Patterns · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS