Temporal Variability in Brain Connectivity for Estimating Two-Year Clinical Severity in Mild Cognitive Impairment
- 1PowerMechaniX LLC, Albuquerque, New Mexico, USA
Abstract
Resting-state functional MRI measures coordinated activity across brain networks and may reveal early changes relevant to cognitive decline. Conventional static functional connectivity analyses often compute time-series correlations across an entire scan, missing informative fluctuations over shorter timescales. We investigated whether these fluctuations, in fMRI scans collected at baseline, improve estimation of clinical severity two years later in mild cognitive impairment (MCI). In 48 ADNI participants with MCI, we summarized temporal variability using the standard deviation of windowed correlations between 21 pairs of brain networks. Using repeated nested cross-validation, the mean absolute error in predicting follow-up Clinical Dementia Rating Sum of Boxes (CDR-SB) was 1.62 points with clinical covariates alone, 1.66 after adding static connectivity, and 1.51 after adding connectivity variability. With additional motion covariates, the corresponding errors were 1.70, 1.73, and 1.59. Connectivity variability outperformed both conventional models in all three cross-validation repetitions, while randomized signals preserving the complete cross-spectrum did not reproduce the improvement. Perhaps surprisingly though, most of the gain occurred in participants without subsequent clinical worsening, and results were sensitive to the choice of temporal windows. These preliminary findings suggest that compact measures of connectivity variability may capture clinically relevant information beyond conventional measures of static functional connectivity, motivating larger studies to evaluate their value for predicting future cognitive impairment.
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BibTeX
@misc{maximov2026temporal,
title = {{Temporal Variability in Brain Connectivity for Estimating Two-Year Clinical Severity in Mild Cognitive Impairment}},
author = {Y. Maximov and A. Volkov and T. Chattopadhyay and P. M. Thompson and I. Gabitov},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.04.764369},
url = {https://www.biorxiv.org/content/10.64898/2026.11.04.764369v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Temporal Variability in Brain Connectivity for Estimating Two-Year Clinical Severity in Mild Cognitive Impairment
AU - Y. Maximov
AU - A. Volkov
AU - T. Chattopadhyay
AU - P. M. Thompson
AU - I. Gabitov
PY - 2026
DA - 2026/11/04
PB - bioRxiv
DO - 10.64898/2026.11.04.764369
UR - https://www.biorxiv.org/content/10.64898/2026.11.04.764369v1
AB - Resting-state functional MRI measures coordinated activity across brain networks and may reveal early changes relevant to cognitive decline. Conventional static functional connectivity analyses often compute time-series correlations across an entire scan, missing informative fluctuations over shorter timescales. We investigated whether these fluctuations, in fMRI scans collected at baseline, improve estimation of clinical severity two years later in mild cognitive impairment (MCI). In 48 ADNI participants with MCI, we summarized temporal variability using the standard deviation of windowed correlations between 21 pairs of brain networks. Using repeated nested cross-validation, the mean absolute error in predicting follow-up Clinical Dementia Rating Sum of Boxes (CDR-SB) was 1.62 points with clinical covariates alone, 1.66 after adding static connectivity, and 1.51 after adding connectivity variability. With additional motion covariates, the corresponding errors were 1.70, 1.73, and 1.59. Connectivity variability outperformed both conventional models in all three cross-validation repetitions, while randomized signals preserving the complete cross-spectrum did not reproduce the improvement. Perhaps surprisingly though, most of the gain occurred in participants without subsequent clinical worsening, and results were sensitive to the choice of temporal windows. These preliminary findings suggest that compact measures of connectivity variability may capture clinically relevant information beyond conventional measures of static functional connectivity, motivating larger studies to evaluate their value for predicting future cognitive impairment.
ER -