Abstract Denoising is routinely applied in resting-state functional connectivity (RSFC) analyses of fMRI data. However, in task-modulated functional connectivity (TMFC) studies, which assess dynamic changes in FC between task conditions, advanced denoising strategies are often overlooked. In a systematic review of psychophysiological interaction (PPI) and beta-series correlation (BSC) studies, nearly half relied only on standard head motion regression or did not report a denoising strategy. Here, we introduce TMFC_denoise, an Statistical Parametric Mapping (SPM)-based toolbox with a graphical interface for applying denoising procedures to task-based activation and TMFC analyses. The toolbox updates first-level general linear models with nuisance regressors, including head motion expansions, spike regressors, physiological and global signals, anatomical component-based noise correction (aCompCor), and weighted constant terms for robust weighted least-squares regression (rWLS). It also provides quality control (QC) measures, including framewise displacement (FD), derivative of root mean square variance over voxels (DVARS), FD-DVARS correlations, and task-FD/task-DVARS correlations. Using an event-related motor task, we benchmarked 11 denoising pipelines across generalized PPI, BSC, and BSC with beta scrubbing. The results show that more aggressive denoising does not necessarily improve TMFC estimates; effective denoising requires balancing artifact reduction with preservation of TMFC effects. TMFC_denoise facilitates transparent implementation, evaluation, and reporting of denoising strategies in TMFC research.
Abstract Previous studies on chronic smokers have shown altered structural and functional connectivity in large-scale networks; however, both domains have largely been studied in isolation. Here, we estimated structural–functional (SC-FC) coupling in chronic tobacco smokers (47) versus nonsmokers (46) using multimodal magnetic resonance imaging. Structural and functional connectomes were derived, with structural connectivity estimated using SIFT2-weighted tractography and inverse node-volume normalization. SC-FC coupling was computed as the Spearman correlation between SC and FC edge weights for the whole brain and seven Schaefer–Yeo canonical networks. Estimates accounted for interregional distance and were evaluated across densities of 10%–30%. Smokers showed higher coupling in the frontoparietal control network (FPCN) across network densities and Schaefter 246–446 parcellations, but this effect did not survive multiple-comparison correction. Dorsal attention and subcortical effects were also observed at some resolutions, but they were less consistent across parcellations. The FPCN pattern remained directionally consistent in sensitivity analyses including network-specific thresholding and adjustment for motion and global signal, although it did not survive omnibus correction across all tested networks. Within smokers, FPCN coupling was not associated with smoking exposure, nicotine dependence, or trait impulsivity. These results suggest a networkspecific alteration in structure–function alignment that warrants further study.
Abstract The brain’s intrinsic organization into resting-state networks has long been suggested to be fundamental for the offline support of mental processes. Extensive task-based evidence support the relevance of the crosstalk between network segregation, supporting systems specialization, and network integration, allowing to flexibly implement complex behavior. However, only scarce evidence focusing on few behavioral measures directly link changes in these network properties at rest with interindividual differences in behavior. In this work, using a comprehensive set of behavioral measures together with resting-state functional magnetic resonance imaging from the human connectome project, we assessed whether connectivity patterns predictive of behavior reflected segregation or integration based on Groupe d’Imagerie Fonctionnelle Network Atlas, a 33 resting-state-networks atlas with cognitive characterization. We found that connectivity relevant for behavior organizes into three main latent dimensions, summarizing Cognition, Positive Affect and Negative Affect. Crucially, we found that connectivity predictive of Cognition, but not Affect, was associated with global network segregation and reduced network integration. We further reveal differential resting-state-network involvements, with Cognition associated with the segregation of higher-level resting-state networks, and the integration of lower-level, visual networks. All in all, the present results suggest that cognition may rest upon a segregated, modular intrinsic brain architecture.
Abstract I investigate a graph neural field model implemented on high-resolution multimodal individual human connectomes. The model, based on Wilson-Cowan and wave-diffusion equations, captures the harmonic power spectrum of functional magnetic resonance imaging and the temporal power spectrum of magnetoencephalography over a wide range of scales. Additionally, the model displays properties of neuronal activity thought to be relevant for healthy brain function, such as proximity to instability and long-range temporal correlations (LRTCs), without being explicitly designed or optimized to achieve them. Finally, I find that model LRTCs originate in specific temporal frequency bands, display distinct patterns of spatial localization on the cortical surface, and are nontrivially linked to structural connectivity, with particular contributions of long-range white-matter fibers. Together, these findings extend the scope of graph neural fields as an effective framework for model-based investigations of multimodal neuroimaging data.
Abstract Dynamic functional connectivity (FC) analyses of resting-state functional magnetic resonance imaging (fMRI) commonly apply k-means clustering to time-resolved whole-brain connectivity matrices to identify recurring “brain states” presumed to reflect shared neural dynamics. However, whether this approach reliably identifies group-level states in relatively small and heterogeneous clinical cohorts remains unclear. Here, we systematically evaluated k-means clustering in temporal lobe epilepsy (TLE) subgroups (bilateral, left, and right TLE) and healthy controls by incrementally increasing the number of clusters (k) from two to the number of participants in each group. Across all groups, clustering solutions converged toward participant-specific rather than shared group-level FC patterns. Identified FC states increasingly resembled participants’ static FC matrices, with higher k values approaching near one-to-one correspondence between states and participants. Elbow and silhouette analyses failed to identify a consistent optimal k and generally favored larger clustering solutions, consistent with increasing participant-specific structure. State occupancy analyses further showed prolonged residence within a single dominant state, providing limited evidence for shared dynamic transitions. These findings persisted under a leave-same-participant-out condition designed to reduce participant-specific influence. Together, these findings suggest that whole-brain k-means clustering in small clinical cohorts may primarily capture interindividual FC differences rather than reproducible group-level dynamic brain states.
