Dynamic functional connectivity (dFC), the time-varying reconfiguration of brain network interactions, has become a widely adopted method for studying how neural dynamics reflect ongoing cognition. Yet a fundamental question remains unresolved: can dFC reliably track when a person is cognitively engaged, and if not, why does it fail? Here, we address this question through a large-scale benchmark of seven widely used dFC methods, evaluating how well each predicts task presence across 16 fMRI datasets encompassing over 1,500 participants and 28 distinct experimental settings, complemented by realistic simulated data. Across experimental data, dFC-based tracking of cognitive engagement was unreliable in many cases: most method-experiment combinations performed near chance, and no single method succeeded across all contexts. This failure, however, was not uniform. Both experimental and simulated data showed that decoding performance varied systematically with three interacting factors, experimental design, data quality, and the choice of dFC method, rather than depending on dFC features alone. Critically, we identify specific experimental design conditions associated with more reliable tracking: paradigms with longer, more regular task blocks and fewer task-rest transitions were substantially more decodable, while data quality independently influenced performance across methods. These findings offer actionable principles for when dFC can, and cannot, be expected to serve as a reliable marker of underlying cognitive states.

Both periodic and aperiodic components in the electroencephalography (EEG) signal are known to play a role in motor control. In particular, periodic beta oscillations and their associated transient bursts (beta bursts) have been linked to motor inhibition. While the occurrence of these bursts is well-documented during simple motor tasks, their spatiotemporal distribution during more complex movements remains largely unexplored. This gap in our understanding extends to the relationship between transient EEG events and Blood Oxygenation Level Dependent (BOLD) activity, typically measured with functional magnetic resonance imaging (fMRI). To better understand these and their hemodynamic and functional correlates, simultaneous EEG and fMRI recordings were obtained at rest and during hand movements in 11 healthy adults. The spatiotemporal distribution for both aperiodic components and beta bursts was mapped during different phases of a handgrip task (low-level, ramp, and high-level grip force conditions). Additionally, the modulation of hemodynamic responses by beta bursts was investigated during both conditions. To this end, the detected beta bursts were used to estimate a hemodynamic response function (HRF) and predict the corresponding BOLD fMRI activity. During movement transition phases, a significant increase in the exponent and offset of the aperiodic components, as well as an increase in beta burst amplitude and rate were observed, as compared to sustained contractions. Furthermore, beta bursts in the contralateral/dominant motor regions of the moving hand elicited positive hemodynamic responses during movement but negative responses during rest, although the HRF features did not differ significantly between the two conditions. Other brain regions showed consistent negative hemodynamic responses across both motor tasks and resting state. These findings reveal a directional dissociation in hemodynamic responses to beta bursts between movement and rest states in motor regions, though future studies with larger sample sizes are needed to further characterize the state-dependence of this relationship. This work advances our understanding of the relationship between transient neural events and hemodynamic responses during movement-related processes in healthy individuals.

Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation technique with potential applications in motor rehabilitation, yet its concurrent effects on brain activity remain poorly understood due to the large stimulation artifacts in electroencephalography (EEG) recordings. In this study, we implemented a novel artifact removal algorithm, combining empirical wavelet transform and blind source separation, to remove the artifacts and investigate the effects of 20Hz and 70Hz tACS on cortical oscillations and motor task performance. To this end, fifteen healthy young adults received tACS at 20Hz, 70Hz, or sham as control over the contralateral motor cortex region while performing a handgrip motor task. We assessed pre-movement Mu/Beta spectral power, event-related desynchronization (ERD), and behavioural metrics (reaction time and grip force rise time) across both stimulation and post-stimulation periods. Results showed that 20Hz tACS increased pre-movement Mu/Beta power, enhanced ERD amplitude, and prolonged grip force rise time, whereas 70Hz tACS decreased Mu/Beta power and ERD amplitude, facilitating faster force generation. Both effects remained statistically significant up to 15 minutes post-stimulation. To our knowledge, this is the first study to characterize tACS-induced modulation of movement-related ERD using artifact-suppressed EEG. These findings demonstrate that frequency-specific entrainment of cortical oscillations influences motor output and support the development of individualized, closed-loop neurorehabilitation systems in the future.

Abstract Beta activity (13–30 Hz) is a prominent feature of motor control, widely distributed across the cerebral cortex. However, how beta bursts (transient high-amplitude events) propagate along cortical organizational axes, such as the posterior-anterior gradient that coordinates cortical structure and function from sensory to association regions, remains poorly understood. Using magnetoencephalography (MEG) data from the Cambridge Centre for Ageing and Neuroscience (CamCAN) dataset, beta burst propagation was characterized using burst onset timing and optical flow analysis during motor tasks and rest in 573 participants (ages 18-88). Beta bursts propagated systematically along the posterior-anterior cortical axis during motor tasks, with propagation direction reversing between movement phases and exhibiting hemispheric asymmetry. In contrast, the resting state exhibited no consistent spatial organization of beta burst propagation. Propagation patterns in motor tasks significantly correlated with cortical distributions of GABA A , cholinergic, and mu-opioid receptors in a hemisphere-specific and phase-dependent manner. Propagation energy was highest in sensorimotor regions and decreased towards more peripheral areas of the cortex. Older adults exhibited significant temporal expansion of beta activity (earlier pre-movement, later post-movement), suggesting a mediation effect of age on reaction time. These results suggest that the propagation of beta bursts is influenced by cortical architecture and may provide a mechanistic explanation for the age-related slowing of motor function.

Obstructive sleep apnea is an independent risk factor for stroke, potentially due to intermittent hypoxia (IH)-induced impairment of cerebral autoregulation. Human cerebral autoregulation during sleep is poorly characterized, and whether IH exposure during sleep alters cerebral autoregulation during sleep is unknown. In a secondary, exploratory analysis of previously collected cerebral blood flow (transcranial Doppler ultrasound measurement of peak blood velocity through the middle cerebral artery; [Formula: see text]), mean arterial pressure (MAP; finger photoplethysmography), and end-tidal partial pressure of CO2 ([Formula: see text]) data, dynamic cerebral autoregulation (dCA) was quantified using transfer function analysis gain, phase, and coherence in healthy males (n = 10; age: 26 ± 6 yr; body mass index: 24.5 ± 1.7 kg/m2; MAP: 87.5 ± 8.0 mmHg) during wakefulness and during nonrapid eye movement (NREM) sleep in normoxia or accompanied by IH. Compared with wakefulness, [Formula: see text] variability was lower during both sleep in normoxia and sleep accompanied by IH in the very low frequency (0.02-0.07 Hz) and low frequency (LF: 0.07-0.2 Hz) ranges (both comparisons, P ≤ 0.02) with MAP variability being lower in the LF range (P = 0.045); gain, phase, and coherence were similar between wakefulness and sleep (all comparisons, P ≥ 0.062). dCA measures during normoxic and IH-sleep were similar (all comparisons, P ≥ 0.09). Moreover, dCA gain and phase, and multiple and partial coherences during IH-sleep were not different between acute (<1 h) and prolonged (∼2 h) exposure (all comparisons, P ≥ 0.055) even though [Formula: see text] was lower following prolonged IH exposure (P = 0.002). These findings indicate dCA is effective during stage 2/3 NREM sleep and is not impacted by ∼2 h of sleep accompanied by IH.NEW & NOTEWORTHY This study found that dynamic cerebral autoregulation (dCA) is not different between wakefulness and stages 2/3 nonrapid eye movement (NREM) sleep in young healthy males and that ∼2 h of intermittent hypoxia exposure during sleep mimicking that experienced by patients with moderate-to-severe obstructive sleep apnea does not alter NREM stage 2/3 sleep dCA. This maintained dCA during sleep may result from the reduced cerebral blood flow and blood pressure variability observed during sleep.

Systemic low-frequency oscillations (sLFOs) can significantly influence blood oxygen level-dependent (BOLD) signals in functional magnetic resonance imaging (fMRI), confounding neural activity assessments. This study evaluates the impact of sLFO correction on functional connectivity-based identifiability using data from the Human Connectome Project. We compare global signal regression (GSR) and partial global signal regression (pGSR), which consists in removing the portion of the global signal explained by peripheral recordings (cardiac and breathing), in their ability to isolate sLFOs while preserving neural low-frequency oscillations (nLFOs). Our results show that GSR achieves significantly higher identifiability than the null model and pGSR suggesting improved sLFO removal based on a previous report that identifiability is primarily driven by the neural component as opposed to the measurable noise components. Our findings support the use of GSR as an effective sLFOs denoising strategy in fMRI fingerprinting and connectivity studies. In situations where less aggressive denoising is required, we propose pGSR as an alternative to GSR whenever the partial global signal can be estimated accurately from the peripheral recordings.

Accurately predicting post-stroke motor impairment remains a challenge due to the complexity of functional recovery and its association with neuroimaging biomarkers. This study presents a deep learning (DL) framework that integrates Magnetic Resonance Imaging (MRI)-based measures such as Diffusion Tensor Imaging (DTI) metrics-fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)-along with white matter (WM) and gray matter (GM) intensities to classify upper limb motor function. Unlike previous approaches, the proposed model directly extracts wholebrain volumetric features without predefined region-of-interest constraints. Feature representation is enhanced using residual connections, attention mechanisms, and Global Average Pooling (GAP), improving classification performance while maintaining computational efficiency. The ensemble framework combines six independently trained models to optimize multi-modality integration. The results demonstrate that the WM + FA combination achieved the highest accuracy (0.97), outperforming the full ensemble model (0.96). These findings exceed the performance reported in prior studies, emphasizing the effectiveness of microstructural and structural biomarkers in motor recovery prediction. This optimized DL framework has the potential to improve post-stroke motor impairment classification, supporting early rehabilitation planning, and personalized treatment strategies.

Changes in physiological state corresponding to fluctuations in heart rate and respiration drive non-neuronal contributions to the BOLD fMRI signal, complicating investigation of regions of the brain which participate in and process autonomic regulation: the central autonomic network (CAN). The estimation of physiological response functions (PRFs) provides a tool to interrogate and minimize the effects of these noise processes on fMRI connectivity. In this study, we explore the reproducibility of cardiac and respiratory response functions used to denoise resting and task data acquired with 3T MRI and their effect on the test-retest reliability of connectivity within the CAN. We characterize group-level PRFs during rest, fast-paced breathing and breath-holds, and a cold-pressor task and show that cardiac response dynamics vary significantly across scan conditions and subjects. Comparing physiological nuisance signals with indices of sympathetic and parasympathetic activity used to map the CAN, we further demonstrate that PRFs may provide an opportunity to disentangle neuronal and non-neuronal correlates of cardiac activity in fMRI data. Finally, we evaluate the effect of denoising on the test-retest reliability of connectivity between regions associated with the CAN, shedding light on the uses and limitations of PRFs for fMRI studies of brain-body interactions.

IntroductionFunctional brain connectivity measures extracted from resting-state functional magnetic resonance imaging (fMRI) scans have generated wide interest as potential noninvasive biomarkers. In this context, performing global signal regression (GSR) as a preprocessing step remains controversial. Specifically, while it has been shown that a considerable fraction of global signal variations is associated with physiological and motion sources, GSR may also result in removing neural activity.MethodsHere, we address this question by examining the fundamental sources of resting global signal fluctuations using simultaneous electroencephalography (EEG)-fMRI data combined with cardiac and breathing recordings.ResultsOur results suggest that systemic physiological fluctuations account for a significantly larger fraction of global signal variability compared to electrophysiological fluctuations. Furthermore, we show that GSR reduces artifactual connectivity due to heart rate and breathing fluctuations, but preserves connectivity patterns associated with electrophysiological activity within the alpha and beta frequency ranges.DiscussionOverall, these results provide evidence that the neural component of resting-state fMRI-based connectivity is preserved after the global signal is regressed out.

Functional magnetic resonance imaging (fMRI) is a valuable neuroimaging tool for studying brain function and connectivity. However, the blood oxygen level dependent (BOLD) signal used in fMRI is affected by various physiological factors, such as cardiac and respiratory activity, which can influence functional connectivity patterns. As such, physiological noise correction is a crucial preprocessing step in fMRI data analysis. When concurrent physiological recordings are available, researchers often generate nuisance regressors to account for the effect of heart rate and respiratory variations by convolving physiological response functions (PRF) with the corresponding physiological signals. However, it has been suggested that the PRF characteristics may vary across subjects and different regions of the brain, as well as across scans of the same subject. To explore this variability, we examine the performance of several different PRF models, in terms of BOLD variance explained, using resting-state fMRI data from the Human Connectome Project (N = 100). We examined both one-input (heart rate or respiration) and two-input (heart rate and respiration) PRF models and show that allowing PRFs to vary across subjects and brain regions generally improves PRF model performance. For one-input models, the improvement in model performance gained by allowing spatial variability was most prominent for respiration, particularly for a subset of the subjects (about a third) examined. Subject-specific or regional variability in cardiac response only enhanced performance when using two-input models. Overall, our results highlight the importance of considering spatial and subject-specific variability in PRFs when analyzing fMRI data, particularly regarding respiratory-related fluctuations.

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.

Cell sedimentation in 3D hydrogel cultures refers to the vertical migration of cells towards the bottom of the space. Understanding this poorly examined phenomenon may allow us to design better protocols to prevent it, as well as provide insights into the mechanobiology of cancer development. We conducted a multiscale experimental and mathematical examination of 3D cancer growth in triple negative breast cancer cells. Migration was examined in the presence and absence of Paclitaxel, in high and low adhesion environments and in the presence of fibroblasts. The observed behaviour was modeled by hypothesizing active migration due to self-generated chemotactic gradients. Our results did not reject this hypothesis, whereby migration was likely to be regulated by the MAPK and TGF-β pathways. The mathematical model enabled us to describe the experimental data in absence (normalized error<40%) and presence of Paclitaxel (normalized error<10%), suggesting inhibition of random motion and advection in the latter case. Inhibition of sedimentation in low adhesion and co-culture experiments further supported the conclusion that cells actively migrated downwards due to the presence of signals produced by cells already attached to the adhesive glass surface.

Objective. Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations. Approach. Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics. Main results. The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features. Significance. These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Functional connectivity is commonly used for studying functional interactions among brain regions. However, its results are affected by noise and/or physiological artifacts, especially when computed using blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals. In this study, we assessed the effect of these artifacts by simulating physiological and BOLD fMRI signals during resting and task conditions and quantifying the resulting functional connectivity results patterns by well established methods (full and partial correlation). Our results reveal that the regions with similar physiological response functions were adversely affected by physiological artifacts. Notably, functional connectivity values computed during task execution exhibited lower errors compared to those computed during the rest period. Furthermore, the results computed using the partial correlation method consistently yielded lower errors compared to those computed using full correlation. Overall, our findings quantitatively characterize the impact of physiological artifacts on functional connectivity patterns and emphasize the importance of method choice in mitigating the impact of artifacts.

BACKGROUND AND OBJECTIVE:The validation of mathematical models of tumour growth is frequently hampered by the lack of sufficient experimental data, resulting in qualitative rather than quantitative studies. Recent approaches to this problem have attempted to extract information about tumour growth by integrating multiscale experimental measurements, such as longitudinal cell counts and gene expression data. In the present study, we investigated the performance of several mathematical models of tumour growth, including classical logistic, fractional and novel multiscale models, in terms of quantifying in-vitro tumour growth in the presence and absence of therapy. We further examined the effect of genes associated with changes in chemosensitivity in cell death rates.METHODS:The multiscale expansion of logistic growth models was performed by coupling gene expression profiles to the cell death rates. State-of-the-art Bayesian inference, likelihood maximisation and uncertainty quantification techniques allowed a thorough evaluation of model performance.RESULTS:The results suggest that the classical single-cell population model (SCPM) was the best fit for the untreated and low-dose treatment conditions, while the multiscale model with a cell death rate symmetric with the expression profile of OCT4 (Sym-SCPM) yielded the best fit for the high-dose treatment data. Further identifiability analysis showed that the multiscale model was both structurally and practically identifiable under the condition of known OCT4 expression profiles.CONCLUSIONS:Overall, the present study demonstrates that model performance can be improved by incorporating multiscale measurements of tumour growth when high-dose treatment is involved.

Understanding the brain's functional network through functional connectivity (FC) is crucial for gaining deeper insights into brain functional mechanism and identifying a potential biomarker for diagnosing neurological disorders. Despite the development of various FC measures, their reliability under different conditions remains under-explored. Moreover, physiological noise can obscure true neural activity, and accordingly, introduce errors into FC patterns. This issue necessitates further investigation. In this study, we evaluate and compare the performance of various methods using Local Field Potential and Blood-Oxygen-Level-Dependent signals across different conditions. We also examine the impact of physiological artifacts on BOLD-FC results. Our comprehensive assessment covers multiple modalities of brain signals, diverse task paradigms, and varying noise levels. Our findings reveal that while Granger Causality-based methods exhibit significant limitations, particularly with BOLD data, multivariate techniques (e.g. partial correlation) demonstrate greater robustness in distinguishing between different types of connections within the network. Notably, our results indicate that physiological artifacts substantially affect FC values, leading to erroneous connectivity estimates, especially with bivariate methods. This research offers a foundational analysis of the effects of physiological artifacts on FC results and provides valuable insights for future studies. ### Competing Interest Statement The authors have declared no competing interest.

The brainstem is the site of key exchanges between the autonomic and central nervous systems but has historically presented a challenging target for study with BOLD fMRI. A potentially powerful although under-characterized approach to identifying nucleic activation within the brainstem is masked independent component analysis (mICA), which restricts signal decomposition to the brainstem itself, thus aiming to reduce the strong effect of physiological noise in nearby regions such as ventricles and large arteries. In this study, we systematically investigate the use of mICA to uncover signatures of autonomic activation in the brainstem at rest. We apply mICA on 40 subjects in a high-resolution resting state 7T dataset following different strategies for dimensionality selection, denoising, and component classification. We show that among the noise mitigation techniques investigated, cerebrospinal fluid denoising makes the largest impact in terms of mICA outcomes. We further demonstrate that across preprocessing pipelines and previously reported results the majority of components are spatially reproducible, but temporal outcomes differ widely depending on denoising strategy. Evaluating both hand-labelling and whole-brain specificity criteria, we develop an intuitive framework for mICA classifications. Finally, we make a comparison between mICA and atlas-based segmentations of brainstem nuclei, finding little consistency between these two approaches. Based on our evaluation of the effects of methodology on mICA and its relationship to other signals of interest in the brainstem, we provide recommendations for future uses of mICA to identify autonomically-relevant BOLD fluctuations in subcortical structures. ### Competing Interest Statement The authors have declared no competing interest.

Functional brain connectivity measures extracted from resting-state functional magnetic resonance imaging (fMRI) scans have generated wide interest as potential noninvasive biomarkers. In this context, performing global signal regression (GSR) as a preprocessing step remains controversial. Specifically, while it has been shown that a considerable fraction of global signal variations is associated with physiological and motion sources, GSR may also result in removing neural activity. Here, we address this question by examining the fundamental sources of resting global signal fluctuations using simultaneous electroencephalography (EEG)-fMRI data combined with cardiac and breathing recordings. Our results suggest that systemic physiological fluctuations account for a significantly larger fraction of global signal variability compared to electrophysiological fluctuations. Furthermore, we show that GSR reduces artifactual connectivity due to heart rate and breathing fluctuations, but preserves connectivity patterns associated with electrophysiological activity within the alpha and beta frequency ranges. Overall, these results provide evidence that the neural component of resting-state fMRI-based connectivity is preserved after the global signal is regressed out. ### Competing Interest Statement The authors have declared no competing interest.

Background Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods.Methods We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity.Results Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages.Conclusions Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.