Dementia Severity Is Associated with Resting-State fMRI Network Responses to Nuisance Regression
- 1PowerMechaniX LLC, Albuquerque, New Mexico, USA
Abstract
Apparent disease effects in resting-state fMRI may be confounded by the effects of preprocessing, making it essential to understand whether nuisance removal affects patients differently across levels of clinical severity. Yet few studies directly examine whether sensitivity to preprocessing is itself clinically informative. We investigated this in 201 ADNI participants spanning normal cognition, mild cognitive impairment, and dementia. We quantified the coordinated temporal structure of the seven Yeo resting-state networks using a delay-based concentration statistic, comparing estimates after regression of 24 motion parameters and linear trends with estimates obtained after additionally removing five anatomical CompCor components. Adding these components generally reduced the measured temporal concentration, but this reduction was smaller in participants with greater dementia severity (+0.472 percentage points per CDR-SB point). In contrast, the same network measures showed little association with severity before adding CompCor. To determine whether this finding depended on how temporal trends were removed, we repeated the analysis with identical discrete cosine regressors added to both preprocessing designs. The clinical association remained similar (+0.464). Controlled simulations showed that temporal concentration increased with the strength of delayed coupling between networks, even when their individual statistical properties and instantaneous covariance remained unchanged. However, changes in observation amplitude and contamination of nuisance regressors also altered the response to preprocessing without changing the underlying neural signal. These findings suggest that disease-related differences in fMRI may partly reflect differential sensitivity to preprocessing. Systematic sensitivity analyses, including multiverse comparisons across preprocessing strategies, may therefore be essential to distinguish genuine disease effects from preprocessing-dependent associations.
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BibTeX
@misc{gabitov2026dementia,
title = {{Dementia Severity Is Associated with Resting-State fMRI Network Responses to Nuisance Regression}},
author = {I. Gabitov and A. Volkov and T. Chattopadhyay and P. M. Thompson and Y. Maximov},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.04.764368},
url = {https://www.biorxiv.org/content/10.64898/2026.11.04.764368v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Dementia Severity Is Associated with Resting-State fMRI Network Responses to Nuisance Regression
AU - I. Gabitov
AU - A. Volkov
AU - T. Chattopadhyay
AU - P. M. Thompson
AU - Y. Maximov
PY - 2026
DA - 2026/11/04
PB - bioRxiv
DO - 10.64898/2026.11.04.764368
UR - https://www.biorxiv.org/content/10.64898/2026.11.04.764368v1
AB - Apparent disease effects in resting-state fMRI may be confounded by the effects of preprocessing, making it essential to understand whether nuisance removal affects patients differently across levels of clinical severity. Yet few studies directly examine whether sensitivity to preprocessing is itself clinically informative. We investigated this in 201 ADNI participants spanning normal cognition, mild cognitive impairment, and dementia. We quantified the coordinated temporal structure of the seven Yeo resting-state networks using a delay-based concentration statistic, comparing estimates after regression of 24 motion parameters and linear trends with estimates obtained after additionally removing five anatomical CompCor components. Adding these components generally reduced the measured temporal concentration, but this reduction was smaller in participants with greater dementia severity (+0.472 percentage points per CDR-SB point). In contrast, the same network measures showed little association with severity before adding CompCor. To determine whether this finding depended on how temporal trends were removed, we repeated the analysis with identical discrete cosine regressors added to both preprocessing designs. The clinical association remained similar (+0.464). Controlled simulations showed that temporal concentration increased with the strength of delayed coupling between networks, even when their individual statistical properties and instantaneous covariance remained unchanged. However, changes in observation amplitude and contamination of nuisance regressors also altered the response to preprocessing without changing the underlying neural signal. These findings suggest that disease-related differences in fMRI may partly reflect differential sensitivity to preprocessing. Systematic sensitivity analyses, including multiverse comparisons across preprocessing strategies, may therefore be essential to distinguish genuine disease effects from preprocessing-dependent associations.
ER -