Biology subjects

Keilholz, S.

Publications and source records attributed to Keilholz, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Altered Attention Network Dynamics in Resting-State fMRI of Individuals with High Smartphone Addiction Scores

Altered attention network activity has been consistently reported in recent neuroimaging literature of smartphone addiction and problematic smartphone use. However, many functional imaging studies have relied on traditional static functional connectivity analyses, which cannot capture time-varying or recurrent dynamics of attention networks. Given the importance of temporal and spatial dynamics for attentional processing, we applied two complementary dynamic functional connectivity approaches: functional connectivity-based attractor networks (fcANN), which characterize transitions between discrete attractor states related to internal-external context and action-perception dynamics, and cPCA-based quasi-periodic pattern (QPP) analysis, which captures phase relationships between cortical networks including the default mode network, dorsal attention network, and frontoparietal control network. In a resting-state fMRI dataset comparing participants with High and Low SAS-M scores (SAS-M: Smartphone addiction scale-Malay version questionnaire), we observed reduced DAN participation within subject-specific cPCA-derived QPP-like components, along with context-dependent differences in DMN-attention-network relationships across attractor states. The most consistent effects emerged during transitions between internal and external attractor states, where subjects with high smartphone addiction (SPA) scores showed greater DMN dominance relative to attention-related networks. Overall, these findings provide preliminary evidence that smartphone addiction may be associated with altered large-scale attentional dynamics at rest

neuroscience↗

bioRxiv2026

Behavior Differentially Shapes Spontaneous Cortical Network Dynamics Across Frequencies

The rapid and coordinated propagation of neural activity across a network of distributed brain regions underpins complex behavior and cognition. Yet how multiple distributed processes are facilitated in parallel across timescales and behavioral states remains unclear. Using simultaneous fast wide-field voltage and hemodynamic imaging in awake mice, we show that behavior differentially shapes cortical network dynamics across frequencies and signal types. Although static functional connectivity preserved canonical networks across frequencies, the underlying dynamics diverged. Low-frequency and hemodynamic activity were dominated by behavior-dependent persistence of network states, whereas higher-frequency activity exhibited stable network structure with modulation through changes in state expression. Despite these differences, shared network organization was preserved across timescales and signal types. This indicates that similar functional connectivity can arise from distinct temporal dynamics. These findings reveal that cortical networks are governed by frequency-dependent principles through which behavior shapes the persistence and expression of large-scale brain states.

neuroscience↗

bioRxiv2026

Harmonizing brain rhythms: cortex-wide neuronal dynamics underpin quasi-periodic patterns in resting-state fMRI

Functional magnetic resonance imaging (fMRI) captures whole-brain activity fluctuations non-invasively in humans and animals. Beyond task/stimuli-locked responses, fMRI measures large-scale patterned activity during rest. Among these, quasi-periodic patterns (QPPs) represent recurring waves of activity that unfold over seconds and exhibit consistent spatiotemporal characteristics. Notably, certain fMRI-QPPs are well-preserved across species and altered in various neuropsychiatric and neurodegenerative diseases. Yet, our collective understanding of their neural underpinnings is limited given the indirect nature of blood-oxygen-level dependent (BOLD) fMRI signals. Simultaneous measures of local field potentials have provided some affirmation that fMRI-QPPs have neural origins, but these point-measurements are limited to a handful of sites. Here, we use a unique multimodal implementation of simultaneous wide-field calcium (WF-Ca2+) imaging and fMRI to investigate the neural origins of fMRI-based QPPs. We uncover a robust time-locked correlation between QPPs detected by cortex-wide fluorescent WF-Ca2+ imaging of neural activity and QPPs of BOLD-fMRI. These data validate the hypothesis that BOLD-fMRI QPPs derive from preceding slow waves of neural activity with regional and temporal precision.

neuroscience↗

bioRxiv2026

Connectomes across temporal scales with simultaneous wide-field optical imaging and resting-state functional MRI

Resting-state functional MRI (rs-fMRI) is a cornerstone of human brain research, yet its interpretation is complicated by its sensitivity to the slow hemodynamic response that obscures the organization of neural activity across faster time scales. Here we use simultaneous wide-field optical imaging (WOI) and rs-fMRI to directly examine the relationship between neural and hemodynamic functional connectomes across time scales. We show that much of the large-scale spatial structure is preserved across modalities, across time scales, and across frequencies. Although rs-fMRI robustly captures time-averaged neural activity, time-resolved rs-fMRI estimates of functional connectivity exhibit significantly greater variability, which partially reflects sensitivity limitations. Hemodynamic WOI signals maintain greater similarity to neural activity than rs-fMRI, although their fidelity is reduced at high frequencies. Together, our findings demonstrate that the time-averaged spatial structure of neural activity is faithfully represented in hemodynamics and rs-fMRI; provide insight into the reliability of time-resolved rs-fMRI across temporal scales; and establish a multimodal framework for validating features of dynamic brain activity. TeaserSpatial patterns of neural activity present across time scales are largely preserved in hemodynamics measured with optical imaging and rs-fMRI.

neuroscience↗

bioRxiv2026

An integrated platform for simultaneous wide-field voltage/calcium imaging and fMRI (EPI & ZTE) reveals neuronal infraslow dynamics underlying functional connectivity

Wide-field optical imaging acquired simultaneously with functional MRI (fMRI) has the ability to provide unprecedented insight into the neural origins of time-varying whole-brain activity. Simultaneously linking cellular-scale activity to whole-brain fMRI remains challenging due to optical access, RF coil placement, and transmission constraints in the MRI environment. We present an integrated platform that combines a long-distance tube-lens optical path (>98% transmission), a chronically stable optically-fused cranial window, and a subject-conformal RF surface coil compatible with both echo planar imaging (EPI) and zero-echo-time (ZTE) fMRI. The system supports concurrent wide-field imaging of genetically encoded voltage or calcium indicators concurrently with intrinsic hemoglobin signals. In individual mice, wide-field optical and fMRI measures yield concordant functional connectivity, and cross-modal timing analyses demonstrate that neuronal infraslow dynamics (<0.1 Hz) underlie the majority of fMRI connectivity, after removing hemodynamic crosstalk. The platform's sensitivity, chronic stability, and sequence flexibility broaden access to cellular-to-whole-brain investigations across basic and translational neuroimaging.

neuroscience↗

bioRxiv2026

Fidelity of Spatiotemporal Patterns of Brain Activity Across Sampling Rate, Scan Duration, and Frequency Content

Intrinsic brain activity is characterized by large-scale spatiotemporal patterns that underpin functional connectivity and cognition. Quasi-periodic patterns (QPPs) and complex principal component analysis (cPCA) have emerged as reproducible methods for capturing spatiotem-poral network interactions in resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods remain sensitive to methodological factors such as scan duration, repetition time (TR), and frequency band selection. This study systematically evaluates how these parameters influence the stability and reliability of QPP- and cPCA-derived functional connectivity patterns across multiple datasets. Using five independent rs-fMRI datasets, we evaluate the impact of scan length on pattern reliability, explore the effects of TR on spatiotemporal patterns, and compare the sensitivity of different frequency bands (Slow-5, Slow-4, infraslow) in capturing network dynamics. Our findings reveal that while both QPPs and cPCA detect intrinsic network activity, their reliability varies with acquisition parameters. QPPs exhibit greater stability in shorter scans, making them suitable for individual-level analyses, whereas cPCA provides a broader representation of phase-coherent fluctuations but shows greater between-subject variability and benefits more from longer, group-level acquisi-tions. Additionally, frequency band selection significantly influences the temporal structure of extracted patterns: in our analyses, Slow-5 (0.01-0.027 Hz) tended to emphasize more recurrent, synchronized network configurations, whereas Slow-4 (0.027-0.073 Hz) more often revealed transitions between connectivity states. These results provide critical insights into optimizing methodological choices for dynamic functional connectivity analysis, enhancing the interpretability of spatiotemporal patterns in both basic and clinical neuroimaging research.

neuroscience↗

bioRxiv2025

Spatiotemporal Network Dynamics Reveal Alzheimer's Disease Progression

Alzheimers disease (AD) is characterized by progressive disruptions in large-scale brain networks that precede cognitive decline, yet conventional functional connectivity analyses often fail to detect disruptions in coordination among large-scale brain networks that may be critical for early detection. This study leverages quasi periodic patterns (QPPs) and complex principal component analysis (cPCA) to characterize spatiotemporal network alterations across longitudinally stable (normal cognitive, mild cognitive impairment, dementia of Alzheimers type) and transitioning (normal cognitive to mild cognitive impairment, mild cognitive impairment to dementia of Alzheimers type) cohorts from the Alzheimers Disease Neuroimaging Initiative using resting state fMRI. QPPs were used to derive recurrent spatiotemporal templates and network integrity measures at the intrinsic connectivity network level, while cPCA decomposed Hilbert transformed time series into complex valued patterns that capture amplitude and phase relationships. Nonparametric group comparisons revealed a structured trajectory in which limbic, subcortical, and higher cognition networks, including triple network components, are affected early, followed by progressive disruption in visual, cerebellar, sensorimotor, and additional triple network systems. Transitioning cohorts showed many of these alterations before formal diagnostic conversion, indicating that spatiotemporal signatures carry preclinical information. QPP based metrics were particularly sensitive to limbic and subcortical degradation, whereas cPCA emphasized changes in higher order, visual, and cerebellar patterns, revealing complementary aspects of the same underlying pathology. These findings extend prior QPP only work and highlight the utility of combining QPP and cPCA based measures as a dynamic, network-level biomarker framework for AD progression. with potential applications in early detection, characterizing disease trajectories, and treatment monitoring.

neuroscience↗

bioRxiv2024

Altered functional connectivity and spatiotemporal dynamics in individuals with sleep disorders

Idiopathic hypersomnia (IH) is a sleep disorder characterized by highly disruptive symptoms. Like narcolepsy type 1, a well-characterized sleep disorder, individuals with IH suffer from excessive daytime sleepiness, though there is little overlap in metabolic or neural biomarkers across these two disorders. This lack of common pathophysiology, combined with the clear overlap in symptoms presents an ideal paradigm for better understanding the impact of IH on an individuals functional activity and organization, and potentially, the underlying pathophysiology. This study examines the observed functional connectivity in patients with IH, and patients with narcolepsy type 1 (NT1) against healthy control individuals. Static functional connectivity is compared, as are quasi-periodic patterns, acquired from the BOLD timecourse, for all groups. In addition to baseline data comparison, the study also included a post-nap condition, where the individuals included in this analysis napped for at least 10 minutes prior to the scanning session, to explore why individuals with IH do not feel "refreshed" after a nap like individuals with NT1 do. Assessing the groups spatiotemporal patterns revealed key differences across both disorders and conditions: static connectivity revealed at baseline higher subcortical connectivity in the NT1 group. There was also observably less connectivity in the IH group both at baseline and post-nap, though none of these static analyses survived multiple comparisons correction to reach significance. The QPP results however found significant differences in the IH group in key networks, particularly the DAN/FPCN correlation is significantly different at baseline vs. post-nap, a trend not observed in either the control or NT1 groups. The DAN and FPCN are drastically altered both at baseline and post-nap when compared to the other groups, and may likely be a disorder-specific result. This study demonstrates that key networks for arousal are more heavily disrupted in IH patients, who are less affected by a nap, confirmed through both subject reporting and functional evidence through spatiotemporal patterns.

pathology↗

bioRxiv2024

The Effects of Locus Coeruleus Optogenetic Stimulation on Global Spatiotemporal Patterns in Rats

Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. These results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting.

neuroscience↗

bioRxiv2024

Variation in the Distribution of Large-scale Spatiotemporal Patterns of Activity Across Brain States

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

neuroscience↗

bioRxiv2024

Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention. Significance StatementThis study provides significant insight into how cortical dynamics shift along a spectrum of external vs internal attention. Association and sensory networks show shifts in their relationship with the default mode network (DMN) along this spectrum. Notably, the constituent networks of the task positive network (TPN), such as the DAN, frontoparietal network (FPN), and ventral attention network (VAN) show divergent responses along this spectrum, indicating that definitions of a single "task positive" network are misleading. This is one of the first studies of large-scale spatiotemporal dynamics across such a breadth of tasks. These results provide a neurotypical reference spanning an axis of attention to interpret the growing functional dynamics literature and to help characterize functional dynamics as a potential biomarker.

neuroscience↗

bioRxiv2024

Creative tempo: Spatiotemporal dynamics of the default mode network in improvisational musicians

The intrinsic dynamics of human brain activity display a recurring pattern of anti-correlated activity between the default mode network (DMN), associated with internal processing and mentation, and task positive regions, associated with externally directed attention. In human functional magnetic resonance imaging (fMRI) data, this anti-correlated pattern is detectable on the infraslow timescale (<0.1 Hz) as a quasi-periodic pattern (QPP). While the DMN is implicated in creativity and musicality in traditional time-averaged functional connectivity studies, no one has yet explored how creative training may alter dynamic spatiotemporal patterns involving the DMN such as QPPs. In the present study, we compare the outputs of two QPP detection approaches, sliding window algorithm and complex principal components analysis (cPCA). We apply both methods to an existing dataset of musicians captured with resting state fMRI, grouped as either classical, improvisational, or minimally trained non-musicians. The original time-averaged functional connectivity (FC) analysis of this dataset used improvisation as a proxy for creative thinking and found that the DMN and visual networks (VIS) display higher connectivity in improvisational musicians. We expand upon this datasets original study and find that QPP analysis detects convergent results at the group level with both methods. In improvisational musicians, dynamic functional correlation in the group-averaged QPP was found to be increased between the DMN-VIS and DMN-FPN for both the QPP algorithm and complex principal components analysis (cPCA) methods. Additionally, we found an unexpected increase in FC in the group-averaged QPP between the dorsal attention network and amygdala in improvisational musicians; this result was not reported in the original seed-based study of this dataset. The current study represents a novel application of two dynamic FC detection methods with results that replicate and expand upon previous seed-based FC findings. The results show the robustness of both the QPP phenomenon and its detection methods. This study also demonstrates the value of dynamic FC methods in reproducing seed-based findings and their promise in detecting group-wise or individual differences that may be missed by traditional seed-based resting state fMRI studies.

neuroscience↗

bioRxiv2023

Using an ODE model to separate Rest and Task signals in fMRI

Cortical activity results from the interplay between network-connected regions that integrate information and stimulus-driven processes originating from sensory motor networks responding to specific tasks. Separating the information due to each of these components has been challenging, and the relationship as measured by fMRI in each of these cases Rest (network) and Task (stimulus-driven) remains a significant open question in the study of large-scale brain dynamics. In this study, we develop a network ordinary differential equation (ODE) model using advanced system identification tools to analyze fMRI data from both rest and task conditions. We demonstrate that task-specific ODEs are essentially a subset of rest-specific ODEs across four different tasks from the Human Connectome Project. By assuming that task activity is a relative complement of rest activity, our model significantly improves predictions of reaction times on a trial-by-trial basis, leading to a 9 % increase in explanatory power (R2) across the 14 sub-tasks tested. We have additionally shown that these results hold for predicting missing trials and accuracy on a per individual basis as well as classifying Tasks trajectories or resulting dynamic Task functional connectivity. Our findings establish the principle of the Active Cortex Model, which posits that the cortex is always active and that Rest State encompasses all processes, while certain subsets of processes get elevated to perform specific task computations. Thus, this study is an important milestone in the development of the fMRI equation - to causally link large-scale brain activity, brain structural connectivity, and behavioral variables within a single framework.

neuroscience↗

bioRxiv2023

QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity

Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. QPPs have been linked to neural processes such as attention, arousal fluctuations, and cognitive function. Despite their significance, existing QPP analysis tools are limited by study-specific parameters and complex workflows. To address these challenges, we present QPPLab, an open-source MATLAB-based toolbox for detecting, analyzing, and visualizing QPPs from fMRI time series. QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. Processing times vary depending on dataset size and the selected mode, with the fast detection mode completing analyses that can be 4-6 times faster than the robust detection mode. Results include spatiotemporal templates of QPPs, sliding correlation time courses, and functional connectivity maps. By reducing manual parameter adjustments and providing user-friendly tools, QPPLab enables researchers to efficiently study QPPs across diverse datasets and species, advancing our understanding of intrinsic brain dynamics. Metadata O_TBL View this table: org.highwire.dtl.DTLVardef@1f4f44dorg.highwire.dtl.DTLVardef@d31544org.highwire.dtl.DTLVardef@19384corg.highwire.dtl.DTLVardef@1bf2360org.highwire.dtl.DTLVardef@1ab9b80_HPS_FORMAT_FIGEXP M_TBL C_TBL

bioinformatics↗

bioRxiv2023

Time-varying Functional Connectivity Predicts Fluctuations in Sustained Attention in a Serial Tapping Task

The mechanisms for how large-scale brain networks contribute to sustained attention is unknown. Attention fluctuates from moment to moment and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the fronto-parietal control network (FPCN) that drives this difference. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During in-the-zone periods, FPCN synchronized with DAN, while during out-of-the-zone periods, FPCN synchronized with DMN. In contrast, the ventral attention network synchronized more closely with DMN during in-the-zone periods compared to out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity across different brain networks varies with fluctuations in sustained attention.

neuroscience↗

bioRxiv2023

A Unified Physiological Process Links Global Patterns of Functional MRI, Respiratory Activity, and Autonomic Signaling

The brain is closely attuned to visceral signals from the bodys internal environment, as evidenced by the numerous associations between neural, hemodynamic, and peripheral physiological signals. We show that these brain-body co-fluctuations can be captured by a single spatiotemporal pattern. Across several independent samples, as well as single-echo and multi-echo fMRI data acquisition sequences, we identify widespread co-fluctuations in the low-frequency range (0.01 - 0.1 Hz) between resting-state global fMRI signals, neural activity, and a host of autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems. The same brain-body co-fluctuations observed at rest are elicited by arousal induced by cued deep breathing and intermittent sensory stimuli, as well as spontaneous phasic EEG events during sleep. Further, we show that the spatial structure of global fMRI signals is maintained under experimental suppression of end-tidal carbon dioxide (PETCO2) variations, suggesting that respiratory-driven fluctuations in arterial CO2 accompanying arousal cannot explain the origin of these signals in the brain. These findings establish the global fMRI signal as a significant component of the arousal response governed by the autonomic nervous system.

neuroscience↗

bioRxiv2022

Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI

In resting-state fMRI (rs-fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, and has long been used as a regressor to reduce noise during data preprocessing. However, coherent neural activity in spatially-extended functional networks can also contribute to the global signal. The relative contributions of neural and non-neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the blood oxygenation level dependent (BOLD) global signal to an adjacent non-brain tissue signal from the same scan in rs-fMRI obtained from anesthetized rats. In this dataset motion was minimal and ventilation was phase-locked to image acquisition to minimize respiratory fluctuations. In addition to contrasting the spatial and spectral components of these two signals, we also observed these differences across the use of three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane. Here, we report differences in the spectral composition of the two signals as evaluated by a power spectral density (PSD) estimate and a fractional amplitude of low-frequency fluctuations (fALFF) calculation. Additionally, we show spatial selectivity for specific brain structures that show an increased correlation to the global signal both statically and dynamically, through Pearsons correlation and co-activation pattern analysis, respectively. All of the observed differences between the BOLD global signal and the adjacent non-brain tissue signal were maintained across all three anesthetic conditions, showing that the global signal is distinct from the noise contained in the tissue signal. This study provides a unique perspective to the contents of the global signal and their origins.

bioengineering↗

bioRxiv2022

Cortical networks relating to arousal are differentially coupled to neural activity and hemodynamics

Even in the absence of specific sensory input or a behavioral task, the brain produces structured patterns of activity. This organized activity is modulated by changes in arousal. Here, we use wide-field voltage imaging to establish how arousal relates to cortical network voltage and hemodynamic activity in spontaneously behaving head-fixed male and female mice expressing the voltage-sensitive fluorescent FRET sensor Butterfly 1.2. We find that global voltage and hemodynamic signals are both positively correlated with changes in arousal with a maximum correlation of 0.5 and 0.25 respectively at a time lag of 0 seconds. We next show that arousal influences distinct cortical regions for both voltage and hemodynamic signals. These include a broad positive correlation across most sensory-motor cortices extending posteriorly to the primary visual cortex observed in both signals. In contrast, activity in prefrontal cortex is positively correlated to changes in arousal for the voltage signal while it is a slight net negative correlation observed in the hemodynamic signal. Additionally, we show that coherence between voltage and hemodynamic signals relative to arousal is strongest for slow frequencies below 0.15 Hz and is near zero for frequencies greater than 1Hz. We finally show that coupling patterns are dependent on the behavioral state of the animal with correlations being driven by periods of increased orofacial movement. Our results indicate that while hemodynamic signals show strong relations to behavior and arousal, these relations are distinct from those observed by voltage activity. Significance StatementWe leverage wide-field voltage imaging to examine the relation between cortical changes in membrane potential dynamics and hemodynamics. These two signals are then examined with respect to changes in arousal, as measured by pupil diameter, in awake head fixed mice. Our results show similarities as well as important differences in the correlation of arousal with neuronal population activity dynamics and the hemodynamic signal. Further, the spatial activity correlation maps with arousal depended differentially on the behavioral state of the animal in a frequency dependent manner. Our results indicate that the modulation of brain networks by arousal is dynamically regulated, and only partly overlap between functional networks determined from hemodynamic or voltage activity.

neuroscience↗