From group-level neurophysiological biomarkers to individual-level phenotyping to identify mechanisms of neurodegenerative disease
- 1University of Cambridge
Abstract
Precision medicine approaches require physiological effects to be informative when translated to the individual. Reliable group differences may have limited individual value when distributions overlap, mechanisms are heterogeneous, or patients differ in phenotype. A feature can differ between group means and still overlap heavily across individuals Moreover, combinations of physiological abnormalities may reveal subject-level structure that is not captured by any single marker at the group level. We evaluate the translation between group- and individual-level features derived from resting-state magnetoencephalography (MEG) using behavioural-variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy (PSP) as demonstrator conditions. These illnesses have distinct molecular pathology but share the pressing need for therapeutic development and have partially overlapping clinical and physiological features. We combine clinical phenotype, power spectral density (PSD), and latent network mechanisms inferred by dynamic causal modelling (DCM) to compare groups and to access their individuals mapping translations. We used classification as an exploratory probe of how group-defined physiological features from 74 healthy controls, 45 participants with bvFTD and 55 participants with PSP support individual assignment. Clinical scores showed strong individual-level translation, consistent with clinical diagnostic definitions (e.g, controls-vs-PSP accuracy 0.94; bvFTD-vs-PSP accuracy 0.83). MEG spectra feature classified individuals weakly (accuracy ~0.6), despite group level re-organization (e.g., reduced beta-band and increased fronto-insular alpha-phase/beta-amplitude coupling in bvFTD). Inferred biophysical parameters gave intermediate performance (e.g. PSP-vs-control accuracy 0.70; bvFTD-vs-PSP accuracy 0.74) while retaining mechanistic interpretability. Using this approach, experimental medicine studies of neurological disease can formally trade between mechanistic insight and classification performance, in using biomarkers that can account for individual heterogeneity within and between diagnostic groups.
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BibTeX
@misc{jafarian2026from,
title = {{From group-level neurophysiological biomarkers to individual-level phenotyping to identify mechanisms of neurodegenerative disease}},
author = {A. Jafarian and L. E. Hughes and M. Naessens and M. Rouse and T. E. Cope and N. Holland and A. G. Murley and S. Tan Yong Wei and T. Rittman and D. J. Whiteside and J. Rowe},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.26.761866},
url = {https://www.biorxiv.org/content/10.64898/2026.10.26.761866v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - From group-level neurophysiological biomarkers to individual-level phenotyping to identify mechanisms of neurodegenerative disease
AU - A. Jafarian
AU - L. E. Hughes
AU - M. Naessens
AU - M. Rouse
AU - T. E. Cope
AU - N. Holland
AU - A. G. Murley
AU - S. Tan Yong Wei
AU - T. Rittman
AU - D. J. Whiteside
AU - J. Rowe
PY - 2026
DA - 2026/10/26
PB - bioRxiv
DO - 10.64898/2026.10.26.761866
UR - https://www.biorxiv.org/content/10.64898/2026.10.26.761866v1
AB - Precision medicine approaches require physiological effects to be informative when translated to the individual. Reliable group differences may have limited individual value when distributions overlap, mechanisms are heterogeneous, or patients differ in phenotype. A feature can differ between group means and still overlap heavily across individuals Moreover, combinations of physiological abnormalities may reveal subject-level structure that is not captured by any single marker at the group level. We evaluate the translation between group- and individual-level features derived from resting-state magnetoencephalography (MEG) using behavioural-variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy (PSP) as demonstrator conditions. These illnesses have distinct molecular pathology but share the pressing need for therapeutic development and have partially overlapping clinical and physiological features. We combine clinical phenotype, power spectral density (PSD), and latent network mechanisms inferred by dynamic causal modelling (DCM) to compare groups and to access their individuals mapping translations. We used classification as an exploratory probe of how group-defined physiological features from 74 healthy controls, 45 participants with bvFTD and 55 participants with PSP support individual assignment. Clinical scores showed strong individual-level translation, consistent with clinical diagnostic definitions (e.g, controls-vs-PSP accuracy 0.94; bvFTD-vs-PSP accuracy 0.83). MEG spectra feature classified individuals weakly (accuracy ~0.6), despite group level re-organization (e.g., reduced beta-band and increased fronto-insular alpha-phase/beta-amplitude coupling in bvFTD). Inferred biophysical parameters gave intermediate performance (e.g. PSP-vs-control accuracy 0.70; bvFTD-vs-PSP accuracy 0.74) while retaining mechanistic interpretability. Using this approach, experimental medicine studies of neurological disease can formally trade between mechanistic insight and classification performance, in using biomarkers that can account for individual heterogeneity within and between diagnostic groups.
ER -