Biology subjects

Gabitov, I.

Publications and source records attributed to Gabitov, I..

5 recordsLinked to original sources

bioRxiv2026

Personalized Prediction of Differences in fMRI Connectivity Estimates Across Preprocessing Pipelines

Longitudinal functional MRI of the human brain is valuable for tracking changes in functional connectivity (FC) associated with disease progression or treatment response. Even so, measured FC values depend on preprocessing, and nuisance regression and temporal filtering affect individuals differently. We examined whether we could use earlier scans to predict how preprocessing would affect a later scan. In 69 ADNI (Alzheimers Disease Neuroimaging Initiative) participants scanned three times, we measured how nuisance regression and filtering, alone and in sequence, changed the measured FC. A ridge regression model combined these responses with baseline FC from the third scan to predict the effect of reversing the processing order. Just four coefficients were shared across 79,800 connections. In leave-one-participant-out testing, mean squared error was 0.012, compared with 0.017 for predicting the same average response for everyone. It outperformed the best of seven alternative predictions chosen for each participant (0.013; normalized gain 0.069, 95% bootstrap interval 0.006-0.134), with lower error in 44 of 69 participants. About half the benefit of using each persons earlier responses, rather than the average across participants, came from which connections changed. This advantage persisted after accounting for acquisition variables. The ADNI-trained model reduced mean error in ten Midnight Scan Club participants without refitting. These results support using personal preprocessing history to predict changes in connectivity estimates on newly acquired scans.

neuroscience↗

bioRxiv2026

Temporal Structure in Resting-State fMRI under Marginal and Cross-Spectral Surrogate Controls

Resting-state functional MRI is commonly analyzed using correlations between regional signals and their frequency spectra, which characterize average connectivity and spectral properties but may miss other aspects of temporal organization. Measures of temporal complexity offer complementary information about the recurrence of signal patterns, how fluctuations vary across timescales, and signal roughness. These properties may reveal disease-related alterations in brain activity, offering opportunities for biomarker discovery and insights into mechanisms of neurological and psychiatric disorders. Here we examined sample entropy (SampEn), multifractal de-trended fluctuation analysis (MFDFA), and Higuchi curve shapes in 732 scans from 270 ADNI participants, including cognitively normal individuals, people with mild cognitive impairment, and people with dementia. Using the seven Yeo resting-state networks (Yeo-7), we compared recordings with randomized signals preserving either each networks amplitude distribution and approximate spectrum (IAAFT), or the complete cross-spectrum across networks. If these properties fully explained temporal complexity, real recordings should resemble their randomized references. We therefore counted how often each real recording was the strongest outlier among itself and 20 references. This occurred in 21.7%, 12.7%, and 44.2% of 5,124 network-visits for SampEn, MFDFA, and Higuchi, versus 5.5%, 5.1%, and 6.3% in surrogate-only controls. Cross-spectrum-preserving references yielded 39.8%, 17.5%, and 49.8%. Simulations showed that changes in sampling or signal processing, including abrupt changes in signal amplitude, can produce similar differences without changing the underlying dynamics. The measures showed repeatability across visits but did not improve retrospective prediction of changes in Clinical Dementia Rating Sum of Boxes (CDR-SB) scores. These findings identify temporal features not reproduced by the tested spectral controls and provide a basis for evaluating their potential as disease biomarkers.

neuroscience↗

bioRxiv2026

Evaluating State-Transition Models in Short Resting-State fMRI with Continuous-Dynamics Controls

Hidden Markov models (HMMs) are widely used to describe changes in resting-state functional MRI as transitions between a finite set of inferred brain states. However, continuously varying brain activity can produce similar apparent transitions, raising questions about their biological validity. We tested whether discrete and continuous brain dynamics could be distinguished by predicting subsequent fMRI measurements, and if temporal filtering affects this distinction. We compared hidden Markov models (HMMs) with vector autoregressive (VAR) models, which predict signals from preceding measurements without assuming discrete states. Each time point contained signals from the seven Yeo functional networks. Both models used the same preceding observations to predict the next, with accuracy measured by the log probability density assigned to the observed signals. Across 864 simulated conditions, HMMs performed better in 139/144 unfiltered discrete-state cases, whereas VAR performed better in 67/72 continuous cases. Changing only the filter during testing increased the number of continuous cases favoring HMMs from 2/72 with fixed filtering to 68/72, despite unchanged underlying dynamics. We also analyzed resting-state fMRI from 270 ADNI participants (90 cognitively normal, 90 with mild cognitive impairment, and 90 with dementia). VAR outperformed HMM in 160/184 datasets with at least 180 time points and 60/86 shorter recordings. In simulations, HMMs inferred directional state transitions even from smoothly evolving activity without discrete switching. Thus, apparent fMRI state transitions do not necessarily indicate discrete underlying dynamics. Their interpretation requires comparison with continuous models and attention to temporal filtering.

neuroscience↗

bioRxiv2026

Dementia Severity Is Associated with Resting-State fMRI Network Responses to Nuisance Regression

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.

neuroscience↗

bioRxiv2026

Temporal Variability in Brain Connectivity for Estimating Two-Year Clinical Severity in Mild Cognitive Impairment

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.

neuroscience↗