Hypercapnia dissociates neuronal and hemodynamic responses impairing neurovascular coupling and functional brain connectivity

Domain: neuroscience
Published: 2026-04-13
Editorial outcome: major_revision

Abstract
Neurovascular coupling (NVC) underpins the interpretation of hemodynamic signals as proxies for neural activity, yet its response to metabolic perturbations remains poorly understood. Here, we leverage concurrent fluorescence calcium imaging and functional magnetic resonance imaging in mice expressing genetically encoded calcium indicator to dissect how elevated CO₂ levels reshape the interplay between neural and vascular responses. Our findings indicate that hypercapnia induces opposing trends in calcium and blood-oxygen-level-dependent (BOLD) responses, accompanied by global desynchronization of brain activity. Additionally, 5% CO₂ suppressed sensory-evoked BOLD and hemoglobin responses, while neuronal and astrocytic activity remained unaffected. Dynamic functional connectivity and co-activation pattern analyses further reveal a dissociation in the coordination between BOLD hemodynamic responses and their underlying neural dynamics under hypercapnic conditions. Notably, we observed that hemodynamic responses, normally driven by neuronal signaling, get attenuated with BOLD signals no longer reflecting neural activity patterns in the brain. These findings demonstrate that hypercapnia can override conventional NVC relationships, compelling a reassessment of how BOLD contrast is interpreted under hypercapnic stress in preclinical and clinical settings. This holds particular relevance for chronic hypercapnia-related conditions where a deeper understanding of NVC disruption may inform improved diagnostic and therapeutic strategies.

Introduction
A key concept linking neural activity to vascular responses is neurovascular coupling (NVC), the mechanism by which active neurons receive the necessary supply of oxygenated blood through the dynamic regulation of local blood flow 1. Impairment in NVC is implicated in numerous neurological conditions, including stroke 2, 3, Alzheimer's disease 2, 4, 5, and traumatic brain injury 2, 4, 5. The precise mechanisms underlying NVC remain incompletely understood. Current view favors the hypothesis that glutamate-mediated signaling initiates the release of nitric oxide (NO) from neurons and arachidonic acid metabolites from astrocytes, driven by rising intracellular Ca²⁺ concentrations 6. These vasoactive molecules engage various signaling pathways in vascular smooth muscle cells and pericytes to induce vasodilation and increase cerebral blood flow (CBF) in response to neuronal activity.

One valuable model for probing NVC is hypercapnia, characterized by elevated levels of CO₂ in the blood. Hypercapnia induces widespread vasodilation across the brain’s vasculature, leading to increased CBF and decreased pH levels due to the formation of carbonic acid ¹¹, ¹². However, under hypercapnic conditions, the normal coupling between neural activity and vascular responses can be disrupted ¹³. The impact of hypercapnia on neural activity itself has been a subject of debate, although majority of results point to reduced neuronal excitability 7-10. Exogeneous CO₂ has been shown to occlude functional hyperemia, the increase in blood flow associated with sensory stimulation 11, 12. Conversely, it has recently been reported that hypercapnic hyperemia and functional hyperemia do not activate the same vasodilatory signaling pathway mediating cerebrovascular responses 13, 14. A thorough understanding of this atypical neurovascular environment is essential for accurate interpretation of the blood-oxygen-level-dependent (BOLD) signals in functional magnetic resonance imaging (fMRI), as hypercapnia can result from various medical disorders such as sleep apnea and chronic obstructive pulmonary disease (COPD), as well as from behaviors like breath holding 15-17. Additionally, CO₂ is deliberately elevated in clinical settings for neuroprotective measures and enhancing neuroimaging contrast through calibrated fMRI 18-20.

Studying the key mediators of NVC remains an arduous task as no single imaging modality can fully capture the intricate complexity of brain dynamics 21, 22. Among the available neuroimaging techniques, fluorescence (FL) imaging and fMRI stand out as highly complementary methods. In models expressing genetically encoded calcium indicators, such as GCaMP proteins, FL imaging detects calcium-sensitive fluorescent signals that reflect neuronal excitability with high temporal resolution, making it ideal for capturing rapid, transient neural events 23, 24. In contrast, fMRI leverages the BOLD signal to infer neural activity indirectly through changes in blood oxygenation and cerebral blood flow. Recent work on functional connectivity (FC) have demonstrated that while FL calcium imaging and fMRI both reveal overlapping aspects of cortical functional organization, each modality also captures distinct facets of brain activity that are inaccessible to the other 25, 26. By reading out these multifaceted connectivity patterns simultaneously and examining how they evolve over time may enable the tracking of dynamic brain states—periods characterized by transient synchrony or desynchrony between different brain regions.

Grounded in the hypothesis that spatiotemporal patterns of FC can reveal the coupling and/or uncoupling between hemodynamics and neural activity, we devised a hybrid fluorescence magnetic resonance imaging system and investigated the FC responses in mice during hypercapnia. By employing dynamic functional connectivity (dFC) 27-30, we isolated FC patterns reflective of neural activity, while co-activation pattern analysis (CAP) 31-33 was used to characterize hemodynamic responses driven by neural activity. This integrated approach has allowed studying FL and BOLD responses to hypercapnia with a focus on synchronized brain activity thus providing a multidimensional perspective on neurovascular alignment under physiological stress.

Results
Moderate hypercapnia elicits disparate hemodynamic and neural responses in mouse

A hybrid magnetic resonance-epifluorescence imaging system (Supplementary Figures S1, S2) has been developed to obtain simultaneous BOLD and FL readouts from GCaMP mice exposed to moderate hypercapnia (5% CO₂: air/CO₂ 950:50 mL/min). Fluorescence data were acquired from Thy1-GCaMP6f GP5.17 mice, in which GCaMP6f is expressed in excitatory pyramidal neurons (somata in layer V, apical dendrites in layers I–III) 34. Wide-field epifluorescence thus predominantly reflected dendritic activity. Neuronal population signals were collected through an MRI-compatible fibrescope, synchronized with 9.4 T BOLD-fMRI. This multimodal setup enabled real-time, synchronized acquisition of neural and hemodynamic signals via concurrent BOLD, GCaMP and hemoglobin-weighted intrinsic recordings. Hypercapnia induced robust, bilateral responses in both modalities. The BOLD signal showed widespread increase across the brain spanning cortical and subcortical areas (Supplementary Fig. S3), consistent with previous findings under hypercapnic conditions 7, 8 (Fig. 1a). Meanwhile, the raw FL signal revealed a global decrease (Fig. 1b). To further dissect these effects, we parcellated the FL data using a 3D-to-2D projection of the Allen mouse brain atlas 35, focusing on cortical layers 1 to 4 (Fig. 1c), as these layers are primary contributors to the signal detected in the epifluorescence channel 36, 37. This parcellation yielded 25 distinct cortical regions (see Supplementary Table 1). At the group level (N = 5), hypercapnia induced a 8.95 ± 0.51 % increase in the BOLD signal (Fig. 1d). It is important to note that the raw FL signal was contaminated by hemoglobin-induced and pH-quenching dynamics under hypercapnia (Fig. 1e). To isolate genuine population Ca²⁺ transients, we regressed out the concurrently recorded hemoglobin time course and applied a 0.2–4 Hz band-pass filter to remove low-frequency drifts and high-frequency noise. However, CO₂-driven acidification quenches GCaMP6f fluorescence, underestimating transient amplitude. Hence, we further scaled the filtered traces by the empirically determined pH-dependent attenuation factor (Fig. 1f, Supplementary Figs. S4-S5, Supplementary Note 1). Even after this correction, amplitudes of population Ca²⁺ transients (Supplementary Figs. S6-S7), defined as the peaks of the analytic-signal envelope, remained significantly reduced during hypercapnia.

We next probed neurovascular coupling by delivering electrical forepaw stimuli (0.5 mA, 20 s; 1 mA, 8 s; 0.5 mA, 8 s) under alternating normocapnia and 5% CO₂ (Fig. 1g–j). Under normocapnia, the stimulation evoked robust, graded increases in both BOLD and corrected Ca²⁺ signals across all paradigms. In contrast, hypercapnia suppressed the BOLD response while preserving neuronal calcium transients (Fig. 1g–j, Supplementary Fig. S8). Intrinsic hemoglobin-sensitive (561-nm) responses were likewise attenuated under hypercapnia, consistent with the BOLD findings (Fig. 1k, Supplementary Fig. S8). Because the 561-nm IOSI channel is not strictly isosbestic, its amplitude can be influenced by oxygenation shifts (i.e., changes in the ΔHbO/ΔHbR balance), which may be altered under hypercapnia. Accordingly, we interpret this signal as a hemoglobin-weighted hemodynamic readout rather than an oxygenation-independent HbT/CBV measure. To rule out potential attenuation of the BOLD response across repeated stimulations, we compared mean evoked BOLD amplitudes across successive normocapnia blocks and observed no decline, while BOLD remained suppressed thoroughout hypercapnia blocks (Supplementary Fig. S9). To test for vascular ceiling effects, we increased inhaled CO₂ to 10% resulting in the BOLD amplitude rising further from 5.62 ± 1.12% to 10.64 ± 1.24% (two-sided paired t(4) = 6.56, p = 0.001) in primary somatosensory cortex, excluding saturation of vasodilation (Fig. 1l, Supplementary Figs. S10, S11). A comparable amplification was observed in the IOSI-derived absorbance signal when increasing CO₂ from 5% to 10%, and the across-animal changes closely tracked the corresponding BOLD increases (Supplementary Fig. S12). Conversely, neuronal Ca²⁺ transient amplitudes decreased by 12.18 ± 2.04% at 5% CO₂ and 24.59 ± 3.70% at 10% CO₂ (Fig. 1m), confirming that hypercapnia directly attenuates neuronal excitability despite preserved global vascular responsiveness to inhaled CO₂.

Global desynchronization of brain activity occurs under hypercapnic stress

FC reflects the synchrony (or desynchrony) of low-frequency fluctuations between brain regions, providing measures of inter-regional communication. Building on our approach to characterize neurovascular dynamics by means of functional connectivity, we explored the impact of hypercapnia on FC across the brain. Region-wise FC was assessed by parcellating the brain using the 25-region template, considering identical regions in each modality. Both modalities exhibited strong inter- and intra-hemispheric synchrony under normocapnia, particularly within the somatomotor network, while weaker connectivity emerged between somatosensory and limbic areas (Fig. 2a). Hypercapnia disrupted these FC patterns, causing previously synchronized or opposing regions to become desynchronized, indicating a loss of inter-regional synchrony under metabolic stress (Fig. 2b). Further, the correlation between FC in the BOLD and FL channels decreased during hypercapnia (p= 0.036, paired t-test, Fig. 2c), particularly for specific correlations with Pearson’s r exceeding 0.1 (p= 0.001, paired t-test, Fig. 2d). To capture temporal variations in connectivity, we extended our analysis to dFC. This approach revealed a common reduction in intra-hemispheric synchrony within the somatosensory regions during hypercapnia, as well as weakened inter-hemispheric connectivity between sensory and visual regions in both modalities (Fig. 2e, f). These alterations were not consistently identified with static FC measures (Fig. 2g-j).

Spatiotemporal patterns of multimodal cerebral fluctuations revealed through dFC and CAP analysis

We then set out to test the hypothesis that the temporal dynamics of brain connectivity undergo significant reorganization, particularly in response to conditions such as hypercapnia. Specifically, by examining these temporal dynamics, we aimed to uncover how metabolic stress reshapes connectivity patterns over time and to identify potential mechanisms underlying this network reconfiguration. To capture the transient and evolving shifts in neural and hemodynamic coordination, we employed dFC and CAP analyses. The dFC analysis was used in an attempt to isolate the component of brain signals that reflect the underlying neural activity and capture its alterations to hypercapnia (Supplementary Fig. S13). Time-resolved dFC matrices were generated using a sliding-window technique and clustered to identify recurring network configurations. Unlike dFC, which tracks continuous correlations, CAP analysis identifies discrete periods of global synchronization or desynchronization (Supplementary Fig. S13), thus providing a means to capture the spatiotemporal interaction of neurovascular components underlying the multimodal readings. Through these two approaches, we compared the neurovascular dynamics across modalities and metabolic conditions (normocapnia and hypercapnia).

Hypercapnia disrupts neurovascular coupling and alters calcium dynamics

Clustering the dFC matrices identified six distinct brain states in FL data and five in BOLD data, each characterized by a unique configuration of FC (Supplementary Figs. S14, S15, see details in Methods). Connectivity matrices based on fast transients (>3 Hz) of FL data that reflect underlying calcium dynamics and thus neural population Ca²⁺ transients were further constructed (Fig. 3a). These neural dFC matrices exhibited very high temporal consistency, with correlations exceeding r > 0.99 across all time points (Supplementary Fig. S16). This contrasts with the variability observed in low-frequency FL (< 0.1 Hz) and BOLD dFC matrices (Supplementary Fig. S17). FL state 5 and BOLD state 1 showed the strongest correspondence to neural FC patterns and with each other (Fig. 3b-c, Supplementary Fig. S18). Thus, these states appear to reflect the underlying neural dynamics captured in the low-frequency FL and BOLD signals. However, these states did not consistently coincide (Supplementary Fig. S19). Remaining BOLD states showed lower alignment with neural patterns, suggesting they primarily reflect hemodynamic responses (Fig. 3c). Additionally, FL state 1 and BOLD state 5 exhibited high similarity to all states in the other modality—except for the states that reflect neural activity—suggesting that these baseline states capture broader hemodynamic changes or metabolic processes not directly tied to specific neural activity (Supplementary Fig. S18).

Hypercapnia led to profound changes within the FC dynamics associated with underlying neural activity. The frequency of neural activity-reflective BOLD state decreased under hypercapnia (p = 0.0005, paired t-test, N = 7) (Fig. 3d) and remained active for shorter periods of time (p = 0.0009, paired t-test) (Fig. 3e, Supplementary Fig. S20). Interestingly, no transitions into the BOLD state reflecting neural activity occurred during hypercapnia (p = 0.005, paired t-test, Fig. 3f). In contrast, hypercapnia increased the rate of entries into the baseline state in BOLD readings (p = 0.02, paired t-test, Supplementary Fig. S21). FL data showed a contrasting effect, with heightened transitions into the neural-reflective mode (p = 0.02, paired t-test, Fig. 3f-g). The remaining states did not reveal significant alterations (Supplementary Fig. S22). The alignment of brain state transitions underscored the divergence between FL and BOLD readings as the inter-modal similarity decreased during hypercapnia (p = 1.23 × 10⁻¹⁸, two-sample t-test, Cohen’s d = 0.33 ± 0.07) (Fig. 3h). FL state transitions that precede BOLD state transitions by 1 second under normocapnia (p= 0.006, paired t-test) were further disrupted under hypercapnia with the temporal alignment of neural and vascular FC fluctuations reduced (Fig. 3i).

CO₂ modulates spatiotemporal patterns of brain network coactivations and neural activity-driven hemodynamic responses

Next, we applied CAP analysis to capture dynamic and discrete states characterized by global synchronization or desynchronization. Patterns of synchronized brain activity were clustered into six distinct CAPs in FL data and five in BOLD data (Fig. 4a, see details in Methods). The number of significant frames were consistent across animals (Fig. 4b). While certain CAPs were shared across FL and BOLD with high intermodal similarity, others remained modality-specific (Supplementary Fig. S23). Hypercapnia resulted in reduced occurrence of CAPs associated with somatosensory network activation (FL CAP 5, q = 0.047; BOLD CAP 4, q = 0.034, paired t-test, FDR-corrected at α = 0.05) (Fig. 4c). Conversely, a globally desynchronized cortical pattern (BOLD CAP 2) increased in frequency (q = 0.034, paired t-test, Fig. 4c) and entry rate (q = 0.026, paired t-test; Supplementary Fig. S24) under hypercapnic conditions. CAPs that were simultaneously present across the modalities were distributed relatively uniformly during normocapnia. Under hypercapnia, however, this uniformity was altered to a more selective co-occurrence pattern, as certain CAP pairs became more frequent while others diminished (Fig. 4d).

The temporal alignment of FL and BOLD CAPs was assessed by applying cross-correlation analysis to binary event vectors marking the presence of significant events. The strongest alignment during normocapnia was observed when the FL-captured neural events preceded the corresponding BOLD hemodynamic responses within 1–2 seconds, consistent with established neurovascular coupling delays (Fig. 4e). Moreover, there was no periodic pattern linked to the breathing paradigm indicating that the events were largely driven by endogenous brain activity. Under hypercapnia, the overall correlation decreased, revealing a rhythmic pattern synchronized with the timing of CO₂ inhalation cycles (Fig. 4e). This transition suggests that the global signals in FL and BOLD are becoming increasingly driven by the external CO₂ dynamics rather than spontaneous activity during hypercapnia.

To further characterize the causal relationship of neural and hemodynamic state transitions, we employed Granger causality analysis 38. This method has enabled determining whether FL CAPs could predict subsequent BOLD CAPs. Under normocapnic conditions, FL CAPs 3, 4, and 5 triggered the occurrence of BOLD CAP 5 within a brief (~1 s) temporal window, consistent with established neurovascular coupling delays (3 FL to 5 BOLD, p= 0.001; 4 FL to 5 BOLD, p= 1.64 × 10-6; 5 FL to 5 BOLD, p= 0.005) (see Supplementary Table 2 for details on peak times and F-values) (Fig. 4f, Supplementary Fig. S25). These predictive relationships were diminished under hypercapnia (3 FL to 5 BOLD, p= 0.058; 4 FL to 5 BOLD, p= 0.92; 5 FL to 5 BOLD, p= 0.008). Conversely, FL CAPs 2, 4, and 6 predicted BOLD CAP 3 under hypercapnia (2 FL to 3 BOLD, p= 0.024; 4 FL to 3 BOLD, p= 0.006; 6 FL to 3 BOLD, p= 0.011) whereas these predictive relationships were more variable under normocapnic conditions, often reduced in magnitude (2 FL to 3 BOLD, p= 0.13; 4 FL to 3 BOLD, p= 0.07; 6 FL to 3 BOLD, p= 1.99 × 10-4).

CO₂-induced neurovascular decoupling reveals cell-specific resilience

The previous results highlight a clear discrepancy between the neuronal and hemodynamic responses under hypercapnic stress. To dissect the cellular basis of this neurovascular uncoupling under hypercapnia, in a cohort of C56BL/6 mice, we targeted astrocytes and neurons with AAV-mediated co-expression of GCaMP6s and RCaMP1.07, respectively, in the forelimb subregion of primary somatosensory cortex (Fig. 5a). Confocal ex vivo imaging confirmed robust, cell-type–specific reporter expression (Fig. 5b). Using our hybrid MRI–epifluorescence platform (Supplementary Fig. S2), we recorded simultaneous BOLD, astrocytic (GCaMP6s) and neuronal (RCaMP1.07) Ca²⁺ signals during 0.5 mA, 20 s forepaw stimulation under alternating normocapnia and 5% CO₂ hypercapnia (Supplementary Fig. S26).

Consistent with GCaMP6f recordings, hypercapnia completely abolished the stimulus-evoked BOLD response (Fig. 5c–e, Supplementary Fig. S27), whereas both neuronal and astrocytic Ca²⁺ transients retained their amplitude and kinetics (Fig. 5c–f). Moreover, neither the time-to-peak nor the latency of the RCaMP1.07 or GCaMP6s responses were affected under hypercapnic conditions (Fig. 5f), indicating that moderate CO₂ selectively disrupts hemodynamic responsiveness without impairing neuronal or astrocytic calcium dynamics.

Methods
Hybrid magnetic resonance epifluorescence system

The hybrid system integrates a custom-made MRI-compatible fiberscope (Zibra Corporation, Westport, USA) for optical (epifluorescence) imaging into a 9.4 T high-field MRI scanner (BioSpec 94/20, Bruker BioSpin, Ettlingen, Germany) (Supplementary Figs. S1, S2). The fiberscope features a 1.4 mm diameter optic image guide consisting of 100 000 single-mode fibers used for collecting the fluorescent responses surrounded by 19 multi-mode 600 μm core diameter fibers with 0.4 numerical aperture (NA) for optical excitation (Zibra Corporation, Westport,USA). Epifluorescence imaging was conducted by inserting the fiberscope’s tip into the aperture of a custom-made animal holder cover, aligned parallel to the static magnetic field of the MRI scanner. This setup was combined with a broadband dielectric mirror (BB1-E02, Thorlabs, USA) to redirect the illumination beam and the captured responses. At the output end of the image guide, two emission filters (FL514.5-10, Thorlabs, USA) were cascaded to isolate the fluorescence emission from GCaMP proteins, which was subsequently captured with an EMCCD camera (iXon Life, Andor, UK) at 40 fps. Fluorescence excitation was provided by a continuous wave 488 nm laser (Sapphire LPX 488–500, Coherent, USA). The captured fluorescence images in epi-illumination mode provided a 15 × 15 mm2 field of view (FOV) with ~70 μm lateral resolution. A custom radiofrequency (RF) surface coil, integrated into the MRI setup, was positioned at the bottom of the 3D-printed animal holder, with the axis of the coil oriented perpendicularly to the static magnetic field. An external trigger device (Pulse Pal V2, Sanworks, USA) was used to synchronize the data acquisition of MRI and fluorescence imaging for precise temporal correlation.

For dual-wavelength experiments (simultaneous GCaMP/hemoglobin or GCaMP/RCaMP imaging), a second continuous-wave 561 nm laser (Sapphire LPX, Coherent, USA) was introduced (Supplementary Fig. S2) 41. The same fiberscope was used for excitation light delivery and collection of optical signals. Emitted/returned light was split into two separate detection channels via a dichroic mirror (DM2, Di03-R532-t1-25x36, Semrock). The GCaMP emission, filtered by FF01-525/39-25 (Semrock), was captured by an EMCCD camera (Andor Luca, UK). The red channel was recorded by a CMOS camera at 40 fps (Basler Ace2, Germany) through a long-pass filter (FELH0600, Thorlabs). For neuronal Ca²⁺ imaging in the red channel (RCaMP1.07), the detection path was configured for fluorescence. For hemoglobin-weighted intrinsic-signal optical imaging (IOSI), the same 561 nm channel was operated in reflectance mode by removing the red-channel emission filter, so that changes in detected intensity were dominated by hemoglobin absorption.

At 561 nm, reflectance changes are strongly hemoglobin-weighted but are not strictly isosbestic; therefore, the signal reflects a wavelength-dependent combination of Δ[HbO] and Δ[HbR] rather than an oxygenation-independent measure of HbT/CBV. Accordingly, throughout the manuscript we treat the 561-nm intrinsic signal as a hemoglobin-weighted IOSI readout (ΔR/R), which is used to track stimulus-locked vascular dynamics and as a nuisance regressor for hemodynamic absorption cross-talk in the GCaMP channel, rather than as a quantitative HbT estimate.

MRI data acquisition

MRI experiments were performed on a 9.4 T Bruker BioSpec 94/20 small-animal scanner equipped with custom-built RF coils, following an acquisition framework similar to that used in our previous work, with the parameters specified below 22. T1-weighted anatomical reference images were acquired in the coronal plane using a FLASH sequence (FOV = 20 × 10 mm², matrix = 160 × 80, 11 slices, slice thickness = 0.7 mm, TR = 500 ms, TE = 2.1366 ms, NA = 8). To support coregistration with the fluorescence data, MR angiography images were additionally collected using a 2D time-of-flight sequence (FOV = 20 × 20 mm², 20 slices, slice thickness = 0.3 mm, TR = 13 ms, TE = 1.8904 ms, FA = 80°, NA = 16). Before functional imaging, B0 field maps were used to optimize local magnetic field homogeneity. Functional data were then acquired with a gradient-echo echo-planar imaging sequence (FOV = 20 × 10 mm², matrix = 80 × 40, 11 slices, slice thickness = 0.7 mm, TR = 995 ms, TE = 12 ms, FA = 60°), resulting in a temporal resolution of 1 s over a total scan duration of 900 s.

Animal models

GCaMP6f mice (C57BL/6J-Tg (Thy1-GCaMP6f) GP5.17Dkim/J, Charles River Laboratories, Germany, 24–52 weeks old, n = 13, 7 female, 6 male) and C57BL/6 mice (Charles River Laboratories, 10 weeks-old, n = 4, female) were used in this study. Animals were housed in individually ventilated, temperature-controlled cages under a 12 h dark/light cycle. Pelleted food (3437PXL15, CARGILL) and water were provided ad-libitum. Mouse housing, handling, and experimentation were performed in accordance with the Swiss Federal Act on Animal Protection and were approved by the Cantonal Veterinary Office Zurich (ZH060/22).

In vivo imaging

For a cohort of 13 animals (11 GCaMP6f and 2 C57BL/6 mice), anesthesia was induced by intraperitoneal administration of ketamine (100 mg kg−1 body weight, Pfizer) and xylazine (10 mg kg−1 body weight, Bayer). Supplemental anesthesia was provided intraperitoneally using ketamine (25 mg kg−1 body weight) and xylazine (1.25 mg kg−1 body weight). Twenty minutes after local administration of lidocaine (1%, 20 μL), the scalp was removed and bleeding was controlled using hemostatic sponges (Gelfoam, Pfizer Pharmaceutical). Functional imaging began 15 min after the first maintenance injection, when anesthesia had transitioned from the induction stage to a lighter and more physiologically stable plane.

For a second cohort of 4 animals (2 GCaMP6f and 2 C57BL/6 mice), anesthesia was initiated with 5% isoflurane in air and maintained below 1.5% during preparation. A bolus of medetomidine was then administered intravenously (0.05 mg/kg; Domitor, medetomidine hydrochloride; Pfizer Pharmaceuticals, Sandwich, UK). Five minutes later, isoflurane was reduced to 0.7%, and continuous medetomidine infusion (0.1 mg/kg/h) was started 10 min after the bolus. Functional imaging commenced 40 min after medetomidine administration and after completion of the structural MRI scan.

During imaging, mice were placed prone on a custom 3D-printed animal bed. Respiration and core temperature were continuously monitored using an MRI-compatible pneumatic pillow and rectal probe (SA Instruments, USA). Heart rate and peripheral oxygen saturation were recorded in real time with an MRI-compatible paw-mounted pulse oximeter (PhysioSuite, Kent Scientific Corporation, USA; Supplementary Fig. S28). Body temperature was maintained at approximately 37 °C using a temperature-controlled water-heating system. Concurrent fMRI and FL signals were acquired during a 15 min gas-challenge paradigm. The experiment began with 3 min of normocapnia using air (air/CO2 1000:0 mL/min), followed by two cycles of hypercapnia and recovery, each consisting of 3 min of 5% or 10% CO2 inhalation (air/CO2 900–950:50–100 mL/min) and 3 min of normocapnia.

Sensory stimulation

For sensory stimulation, unipolar rectangular electrical pulses (0.5 ms; 0.5 or 1 mA) were delivered to the left forepaw at 4 Hz. Each stimulation epoch lasted either 8 s or 20 s and recurred every 90 s (onset-to-onset). A schematic overview of the stimulation paradigm is provided in Supplementary Fig. S9. The electric signals were generated using a stimulus isolator device (Model A365R, World Precision Instruments, USA) fed by an external trigger (Pulse Pal V2, Sanworks, USA). During each hypercapnic and normocapnic period, two stimulation cycles were applied; no stimulation was delivered during the baseline normocapnia. All pulses were synchronized to both the camera and MRI acquisitions via the external trigger.

Data preprocessing

For each subject, the first five BOLD volumes were discarded to account for scanner stabilization. Motion correction and smoothing with a Gaussian kernel (FWHM = 0.6 × 0.6 × 0.6 mm3) were performed using SPM12 (Wellcome Trust Centre for Neuroimaging, London, UK). The anatomical and functional MRI images were co-registered and spatially normalized to the Allen Brain Institute reference atlas, following previously described methods 35, 42. Each preprocessing output was visually inspected to ensure accuracy before proceeding with further analysis. The preprocessed BOLD data were analyzed for task-induced responses. For functional connectivity and co-activation analyses, BOLD datasets underwent additional preprocessing steps, including band-pass filtering (0.01-0.1 Hz), detrending, and regression of motion parameters, ventricle signals, and white matter signals. The regression was shown to enhance the specificity of functional connectivity 43. Dataset quality was assessed by examining the simultaneous presence of robust inter-hemispheric connectivity in sensory cortices (left S1BF to right S1BF, r > 0.1) and either weak connectivity or anti-correlation between sensory areas and the anterior cingulate areas (left S1BF to ACA, rFC < 0.1) 44. Datasets meeting this criterion (7 out of 8) were included in further analyses.

Preprocessing of FL data was performed using SPM12 and custom MATLAB scripts. Raw FL images captured at 40 Hz were averaged to 10 Hz, and the first 5 seconds were discarded. Spatial smoothing with a Gaussian kernel (FWHM = 3 pixels) was applied. Data were corrected for motion and spatially normalized to the FL atlas (see ‘Template generation’). The output was visually inspected for accuracy. Data were then spatially resampled to a voxel size of 40 × 40 µm². To correct background fluorescence and ambient light interference, a background subtraction procedure was implemented. A 10 × 10-voxel ROI was manually selected from a non-fluorescent area of each image to represent the background signal. The calculated background signal time-course was subtracted from the fluorescence intensity of each voxel. The background-subtracted images were visually inspected to confirm effectiveness of the subtraction process. FL images were then masked and analyzed for task-induced responses. For analysis of Ca²⁺ transients during hypercapnia, voxel-wise GCaMP6f signals were first coregistered, and concurrent hemoglobin fluctuations were regressed out using a GLM. The residual GCaMP6f time series were then band-pass filtered between 0.2 and 4 Hz to remove low-frequency baseline drift and high-frequency noise. To correct for pH-dependent quenching of the GCaMP6f sensor, Ca²⁺ transients’ amplitudes during hypercapnic challenges were scaled by factors of 1.06 (10% CO₂) and 1.025 (5% CO₂) (see Supplementary Note 1). The envelope of the corrected Ca²⁺ transients was subsequently computed for further quantification. For forepaw stimulation, hemoglobin-corrected fluorescence signals were fed into a voxel-wise GLM to generate activation maps. Estimated motion parameters were included as nuisance regressors to minimize residual movement artifacts. For functional connectivity and co-activation analyses, a different preprocessing pipeline was employed to focus on the low-frequency resting-state fluctuations in the FL data. For this purpose, FL datasets were detrended, band-pass filtered (0.01-0.1 Hz), and regressed for motion parameters and the global signal.

For resting-state analyses, population Ca²⁺ transient amplitude denotes the envelope peak of the hemoglobin-regressed, 0.2–4 Hz band-pass filtered, and (where indicated) pH-scaled fluorescence signal, namely, an ensemble measure rather than single-neuron action potentials (Fig. 1f,l). For evoked responses, fluorescence is reported as ΔF/F relative to a pre-stimulus baseline, whereas activation maps are estimated with a voxel-wise GLM. Traces shown in figures are temporally smoothed with a moving average filter (size: 1 s) for better visualization (Fig. 1j, Fig. 5d).

Template generation

Region definitions of the BOLD template for the FC analyses were based on the Allen Common Coordinate Framework (CCFv3). To enhance the correspondence and comparability with FL signals, only layers 1 to 4 of the cortical areas representing the motor, somatosensory, visual, limbic, and association networks (25 distinct regions from each hemisphere) were included in the 3D BOLD template (see Supplementary Table 1 for the full list of regions). These layer definitions were chosen due to the direct involvement of the superficial cortical layers in the generation and propagation of neural signals detectable by FL imaging 36, 37. 3D to 2D projection of the BOLD template was performed to generate the FL template. Consequently, both BOLD and FL templates contain 25 identical functionally distinct regions from each hemisphere. For the remaining analyses, the images were masked, and ROI time-courses were calculated from the definitions based on the generated templates.

Task-related activation analysis

Task-related responses to hypercapnia and forepaw stimulation were analyzed using MATLAB (R2023b) and SPM12. BOLD and FL task-induced responses were modelled using a general linear model (GLM) to obtain t-value maps 45. The first-order canonical basis set was used, involving the convolution of the hemodynamic response function (HRF) with the stimulation paradigm as a regressor. The default HRF parameters in SPM12, optimized for human studies, were modified with parameters suitable for small animal BOLD responses 46. To account for movement artifacts, six motion parameters calculated during the preprocessing step were included as covariates 47. Activation maps for each animal were obtained by applying an initial threshold of uncorrected p < 0.001 to the t-value maps, and voxels were considered statistically significant after family-wise error (FWE) correction at p < 0.05 using one-sided t-tests. The group-averaged activation maps were calculated by averaging the individual thresholded maps (N = 5). BOLD and FL group activation maps were overlaid on the Allen mouse brain atlas and the FL template, respectively. Exemplary responses were plotted from the regions with the strongest responses in the group activation map (0.4 × 0.4 mm² for FL, 0.4 × 0.4 × 0.4 mm³ for BOLD). The percentile changes at group-level were given as mean ± SEM.

Static functional connectivity analysis

The static FC was assessed using the CONN toolbox for BOLD and FL data. The ROIs were defined by the generated BOLD and FL templates. The preprocessed time-series data were averaged within each ROI. For each subject, region-to-region connectivity matrices were constructed for hypercapnia and normocapnia conditions by computing region-wise Pearson’s correlation coefficients (r). Group-level matrices were computed by averaging the individual subject matrices (N = 7) for normocapnia. The group-level difference matrices (hypercapnia minus normocapnia) were computed. Pearson's correlation coefficient (CC, MATLAB function corr) was used to assess the similarity of connectivity matrices for all connections or only the specific connections exceeding the 0.1 value. Neural correlation matrices were computed by considering high-frequency (> 3Hz) FL signals to capture FC specific to calcium activity.

Dynamic functional connectivity analysis

Dynamic FC (dFC) was assessed using a sliding-window approach 48. Pearson’s correlation coefficients were calculated between region pairs within a time window. The window length was set to 30 seconds, consistent with previous research 49, corresponding to 30 frames for BOLD data and 300 frames for FL data. The window was then shifted forward by one TR frame (1 second), resulting in 866 dFC matrices per animal. The temporal alterations in FC between ROI-pairs were plotted for FL and BOLD data. To identify recurring connectivity patterns (dFC states), k-means clustering was applied to the dFC matrices. The optimal number of clusters was determined by 1) the Silhouette method (Supplementary Fig. S29)50, 2) ensuring that each state was consistently identifiable in at least 6 out of 7 subjects, and 3) confirming that the identified states were sufficiently distinct from one another (with inter-state correlations of r < 0.9). Clustering was performed independently for each modality with 500 iterations and 10 replicates (MATLAB function kmeans). The dFC matrices were grouped into 5 clusters for BOLD and 6 for FL. Neural FC matrices were constructed for each time-window using high-frequency FL data. The similarity between multimodal and neural states was estimated by calculating correlations. To assess the temporal stability, we calculated the correlation across the dFC matrices for neural states and FL/BOLD states. The cross- and within-modality similarities of the states were similarly estimated by Pearson’s correlation.

Several temporal metrics were calculated for each subject during hypercapnia and normocapnia, including the frequency of occurrence (the proportion of presence of each state), dwell time (the average presence duration time of each state), entry rate (how often the brain transitions into each state), and exit rate (how often the brain transitions out of each state). Paired t-tests were used to assess differences between hypercapnia and normocapnia. Transition probability matrices of each animal were constructed by normalizing the transition counts observed during hypercapnia and normocapnia periods. Significant differences between conditions were estimated using paired t-tests. The intermodal similarity was calculated for each dFC window by calculating the Pearson’s correlation between the FL and BOLD dFC matrices. The difference between normocapnia and hypercapnia was assessed by two-sample t-test and Cohen’s d. The temporal alignment between the state transitions across the modalities was analyzed by computing the cross correlation (maximum lag= 6s) across the binary state transition vectors. One-sample t-tests were conducted to determine the significant lags for hypercapnia and normocapnia conditions.

Co-activation pattern (CAP) analysis

CAP analysis was employed to identify and characterize transient, whole-brain patterns of simultaneous activation and deactivation across multiple brain regions 32. This approach enables the association of resting-state activity with discrete events capturing information from relevant time points that would otherwise be spread across a conventional full time-course analysis 33. BOLD and FL data were clustered independently using the same analytical framework. For each subject, significant activation and deactivations were identified at the voxel level by comparing the time-point data against subject-specific thresholds, defined as two standard deviations above or below the mean for both BOLD and FL data. This was conducted for the voxels within BOLD and FL templates. A frame was classified as a significant event if at least 5% of the voxels exhibited significant activation or deactivation at that time-point. The selected frames were then organized into TxN dimensional vectors, where T represents the total number of selected frames across subjects and N corresponds to the number of voxels within the mask. These frames were clustered using k-means (500 iterations, 10 replicates, MATLAB function kmeans), generating centroids representing the average activity patterns for each CAP. The centroids were subsequently reshaped into their original spatial dimensions to create CAPs (Fig. 4a). For the BOLD CAPs, maximum intensity projection (MIP) was used to visualize the patterns. Spatial correlation (Pearson’s r) matrices were calculated between 2D FL and MIP BOLD CAPs to assess the similarity between and within the CAPs identified in the BOLD and FL data. The optimal number of clusters was determined using 1) the Silhouette method (Supplementary Fig. S30) ensuring bilateral spatial maps were obtained, and 3) verifying that all CAPs were identifiable in at least 6 out of 7 subjects. The dynamics of the CAPs from the selected number of clusters (k = 5, k = 6) were further analyzed.

The frequency of occurrence for each CAP was calculated separately for each subject during hypercapnia and normocapnia periods (Fig. 4c). Paired t-tests were conducted to determine whether these frequencies differed between the two conditions with multiple comparisons corrected using the Benjamini-Hochberg method to control the false discovery rate (FDR) at a level of α=0.05. For each CAP, the entry/exit rates were calculated by counting the transitions from any other CAP to the specific CAP during the relevant period (hypercapnia or normocapnia) and normalizing by the total duration of each period. Exit rates were similarly calculated. Significant differences in entry and exit rates between conditions were identified using paired t-tests with FDR-corrected at α=0.05. Simultaneously occurring CAP-pairs were identified by upsampling the BOLD CAP vector to match the FL temporal resolution. This yielded co-occurrence matrices for hypercapnia and normocapnia conditions (each sized kBOLD × kFL), normalized by the total concurrent events for each condition (Fig. 4d). Temporal synchrony of BOLD and FL event vectors were assessed through cross-correlation analysis. Binary event vectors were generated for each modality and condition. To check the temporal correspondence between the identified BOLD and FL events, the normalized cross-correlation of BOLD and FL event vectors was computed (maximum lag = 400s) (Fig. 4e). Granger causality analysis was conducted to assess whether the occurrence of an FL CAP could predict a subsequent BOLD CAP across various time lags 38. For this process, the BOLD event vector was upsampled tenfold by replicating each 1 s sample across ten 100 ms bins to match the 100-ms resolution of the fluorescence data. For each FL-BOLD CAP pair, two models were estimated: a restricted model, where the BOLD CAP occurrence vector was regressed only on its own lagged values, and an unrestricted model, where it was regressed on both its own and the lagged values of the FL CAP vector. The residual sums of squares for each model were then compared to calculate an F-statistic that indicates whether including the FL CAP as a predictor significantly improved the BOLD CAP prediction. F-statistics above 3 were deemed significant, suggesting that the FL CAP provided predictive information about the BOLD CAP. This analysis, performed across lags up to 5 seconds, identified the temporal window where directional influence was strongest to identify the causal pathways between FL and BOLD global synchronization events (Fig. 4f).

Induction of calcium indicator expression

AAV vectors encoding GCaMP6s (AAV-9/2-hGFAP-hHBbI/E-GCaMP6s) and RCaMP1.07 (AAV-9/2-hSYN1-ch1-RCaMP1.07) were mixed 1:1 (v/v) immediately prior to injection, yielding final physical titers of 1.0×10¹³ vg/mL for GCaMP6s and 4.3×10¹² vg/mL for RCaMP1.07. Three weeks before in vivo imaging, the mice were anesthetized with isoflurane (5% induction, 1–2% maintenance) and secured in a stereotaxic frame. Two 0.5 mm burr holes were drilled bilaterally at Bregma +0 mm, lateral ±1.8 mm, and a 33-gauge beveled needle (NF33BV, World Precision Instruments) mounted on a NanoFil 10 μL syringe (World Precision Instruments) was lowered to a depth of 300 μm in the somatosensory (forepaw) cortex. Using a programmable hydraulic pump, 500 nL of the virus mixture was delivered per site at 50 nL/min. Throughout the injection, core body temperature was maintained at 37°C, and physiological parameters were continuously monitored. After infusion, the needle remained in place for 5 min before slow retraction. The scalp was then sutured, and mice received buprenorphine (0.1 mg/kg, s.c.) for 24 h post-operatively. Body weight was recorded daily for three days, then at least thrice weekly until the terminal experiment.

Immunohistochemistry

Mice were anesthetized with ketamine (100 mg/kg), xylazine (10 mg/kg), and acepromazine maleate (2–3 mg/kg) (total volume 100 µL), then transcardially perfused, first with phosphate-buffered saline (PBS) and subsequently with 4% paraformaldehyde (PFA) in PBS. Extracted brains were post-fixed in 4% PFA for 3 h at 4 °C, followed by cryoprotection in 30% sucrose in PBS for 3 days. Specimens were embedded in optimal cutting temperature (OCT) compound, sectioned into 50-μm-thick slices using a cryotome (CM3050S, Leica, Germany), and counterstained with DAPI (4′,6-diamidino-2-phenylindole) for 5 min. Fluorescent images were captured using a Zeiss LSM 900 confocal microscope.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary: This study by Gezginer et al. employs a novel multimodal imaging approach combining fluorescence calcium imaging and functional MRI (fMRI) to investigate neurovascular coupling (NVC) under hypercapnic stress in mice. Using a knock-in mouse in which GCaMP6f is expressed in deep-layer pyramidal neurons, the authors simultaneously measured neuronal activity and hemodynamic responses to elevated inspired CO₂ levels. They observed a dissociation: while BOLD fMRI signals increased during hypercapnia (indicative of vasodilation), neuronal calcium signals concurrently decreased, suggesting suppressed excitability. Furthermore, both static and dynamic functional connectivity analyses revealed widespread desynchronization of brain networks. Co-activation pattern (CAP) analysis and Granger causality revealed that under hypercapnia, the typical temporal alignment between neural activity and hemodynamic responses was disrupted. Notably, while BOLD signals became less reflective of neural dynamics, calcium signals retained neural-specific patterns, highlighting hypercapnia’s selective impact on vascular responsiveness. Thus, the work also shows that neural activity and BOLD fMRI signals do not always correspond with each other. Indeed, hypercapnia decouples neural and vascular signals, which is important for the interpretation of fMRI data. However, the scope of the work here is quite limited for a Nature Communications paper. Essentially, the whole paper is only one experiment (though challenging and technically impressive) on 8 mice and a lot of analysis. There are no manipulations beyond CO2, such as sensory stimulation, pharmacology, or controls. Furthermore, the degree (%) or duration of CO2 is also not altered. Nevertheless, the paper is timely considering the relatively recent Hosford and Tournissac papers on CO2 and neurovascular coupling. While the work as presented could potentially stand alone, there seems to be is a missed opportunity because the authors do not directly address the contradictory results between Hosford (primarily fMRI) and Tournissac (primarily fluorescence imaging). A key experiment in those previous papers is to determine weather or not functional and hypercapnic hyperemia occlude each other, with Hosford saying yes and Tournissac saying no. The authors here are in an ideal position to perform a similar experiment, expand the scope of their study and attempt to help settle this debate. I am hoping the authors will expand their study to increase my enthusiasm. Major Comments: Adding sensory stimulation of different intensities (and potentially durations) would be highly beneficial as the authors can more directly test the relationship between neural activity and hemodynamics with and without hypercapnia. Some of the authors conclusions regard this relationship but are based on resting state activity under anesthesia. It is still unclear if resting state NVC mechanisms are the same or different to sensory-based NVC, and the vast majority of NVC literature is sensory based. Nevertheless, I recognize there may be certain limitations with sensory stimulation in the authors unique experimental setup. While whisker or light stimulation would be ideal, if not, what else would be possible? This would be an excellent way to expand the study. Is the loss of synchronization from hypercapnia and the decoupling of neural activity to the BOLD response due to a ceiling effect? i.e. once the vascular dilates to 3min of CO2, it cannot dilate much more to functional activation. This is critical to determine because all the network connectivity analysis is somewhat meaningless if the main cause is simply maximally dilated blood vessels. I suggest the authors try 10% CO2 as well and compare to the 5% data. Additionally, systemic administration of a vasoconstrictor (such as indomethacin) and seeing the same relationships, would argue against a ceiling effect. Anesthetic used was ketamine/xylazine which is not preferred for NVC studies. Ketamine blocks NMDA receptors, and NMDARs are a major regulator of NO and prostaglandin production in NVC. Xylazine is an alpha2 agonist which causes vascular smooth muscle contraction. While no anesthetic is ideal, it would be important to see if the main effects can be replicated with a different anesthetic. The temporal resolution of fMRI is ~1s, whereas fluorescence calcium imaging ~100 ms, and this limits the direct alignment between neural and hemodynamic events. How was this dealt with regarding the CAP and Granger analysis? Loss of alignment is a major finding in the paper. Most of the authors conclusions rest on correlation, clustering, and causality metrics (e.g., dFC, CAPs, Granger). These methods can be misleading if the underlying assumptions (stationarity, linearity, Gaussian distributions) are violated in health or in the hypercapnic brain. How was this controlled for? If no steps were performed, limitations need to be stated/discussed. Was there any attention to higher frequency signals in the FL data that could be noise? The authors look at greater than 3Hz data. Are there non-neural signals much higher than this that should be filtered out? Spontaneous GCaMP signals beyond 15Hz cannot be faithfully detected due to slow off kinetics; thus, a bandpass filter in this range might be best. Why do the authors speculate that data analysis finds 6 unique states in the FL data but only 5 in the BOLD data? Line 117: The authors say “FL state 5 and BOLD state 1 showed the strongest correspondence to neural FC patterns”. My simple question is: did these two states co-occur? Always? Sometimes? Minor Comments: The authors used Thy-1 mice which predominately express GCaMP6 in layer 5 pyramidal neurons, thus the authors are looking at dendritic signals primarily. This could be briefly discussed. Fig2 appears to be purely an explanation of the dFC and CAP analysis. If there is no real data/result in Fig 2, the authors should consider moving this to the supplement. Did the authors observed any sex differences? Both M and F mice were used (4 each) but don’t recall that being mentioned in the text, though perhaps I missed it. Line 34 should read: “…arachidonic acid metabolites…” Line 42-43: should be revised for clarity, such as: However, opposite findings were reported recently, showing that hypercapnic hyperemia and functional hyperemia do not activate the same vasodilatory signaling pathways that mediate cerebrovascular responses In the results upfront the authors should list the GCaMP mouse model they are using to help the reader understand the cell-type, location, depth of the measurements. Without this, the reader cannot understand or interpret the calcium signal measurement. It would also increase clarity if the authors could briefly mention the fluorescence recording technique too. Line 86. Past tense is probably better. “First, we explored..” I believe there are other place too in the results like this the authors could correct.", "reviewer_2": "Gezginer et al. use state-of-the-art imaging techniques to revisit the interpretation of cerebrovascular reactivity to CO2 in the field of BOLD fMRI. Under anaesthesia, they combine measurements of BOLD contrast and bulk GCaMP6 fluorescence signals in response to 5% CO2. They report that CO2 triggers a global drop of brain fluorescence and conclude that CO2 generates a general decrease in cortical activity concomitant with a BOLD signal increase. While this observation appears to be irrefutable, I suspect that the interpretation is erroneous. It is well-known that CO2 increases CBF and that haemoglobin absorbs light. Therefore, in the absence of correction for the fluorescence contamination by heamoglobin absorption (Ma et al. Philos. Trans. R. Soc. Lond. B Biol. Sci (2016), Scott et al. Neuron (2018)), the observed fluorescence decrease most probably reports a CBF increase. Furthermore, it is well-established that fluorescent reporters, organic dyes and fluorescent proteins, are sensitive to pH. By decreasing brain pH, CO2 most certainly decreases GCaMP6 fluorescence, irrespective of the cell type expressing GCaMP6 (Tournissac et al. 2024). Therefore, it is likely that Gezginer et al. did not monitor a change in neuronal activity, but rather the combined side effects of heamoglobin absorption and GCaMP pH sensitivity. The study cannot be interpreted unless the authors demonstrate that GCaMP6 truly reports neuronal inhibition and not experimental side effects.", "reviewer_3": "This study by Gezginer and colleagues investigates neurovascular coupling by simultaneously measuring neuronal activity and hemodynamic responses using fluorescence calcium imaging and functional MRI (fMRI) in mice exposed to hypercapnia. The authors assess the relationship between neural calcium signals and blood oxygenation level-dependent (BOLD) responses, revealing distinct spatial and temporal patterns. The data reported show that hypercapnia induces a strong hemodynamic response, while neuronal activity is inhibited. This suggests a dissociation between vascular and neural activity, an observation which is important to consider when discussing the relationship between neuronal firing and fMRI signals. By integrating optical imaging with fMRI, the study provides valuable insight into cerebrovascular regulation and its implications for interpreting fMRI data. The study also contributes to an ongoing debate on the role of CO2 as a neuronal signal to the vasculature published in recent years in Nature Communications (PMID: 35440557; PMID: 39223128; PMID: 39223137). The findings support earlier observations (first observed by John Scott Haldane more than 100 years ago) that CO2 inhibits brain neuronal activity and can override NVC relationship. I have the following comments for authors to consider in their revision: 1. While the study clearly demonstrates a neurovascular dissociation, it does not delve into the underlying cellular or molecular mechanisms. Are astrocytes, endothelial cells, or pericytes mediating this effect? Further investigations using pharmacological or genetic tools would be needed to determine the mechanistic basis of these findings. 2. Calcium imaging, though precise, captures activity only in superficial cortical layers, whereas fMRI measures whole-brain hemodynamics, leading to potential spatial mismatches in the data. This needs to be discussed. 3. The authors should consider the pH sensitivity of the GCaMP sensor as fluorescence is reversibly quenching by acidic pH and may not accurately reflect the neuronal activity during hypercapnia."}, "author_response": "Reply: We thank the reviewer for highlighting the opportunity to extend the study. In our revised manuscript, we have broadened the experimental scope in three complementary ways. First, to directly test the open question of whether hypercapnia occludes functional hyperemia—a critical point of contention between prior fMRI and calcium imaging studies—we now present sensory stimulation experiments with three combinations of intensity and duration, delivered both during normocapnia and hypercapnia. This allows us to assess how hypercapnia affects stimulus-evoked neural and hemodynamic responses across a range of conditions, thus directly addressing the functional interplay between hypercapnia and sensory hyperemia. Second, to exclude a vascular-ceiling artefact we added a 10 % CO₂ condition alongside the original 5 % challenge. This new experiment was designed to systematically determine if increasing hypercapnic load results in a true upper limit of vascular response, or if neuronal and vascular signals become further decoupled with escalating CO₂. This approach provides a dose-dependent perspective on neurovascular coupling under stress. Third, given the ongoing debate about the cellular contributors to BOLD-calcium dissociation, we established a new mouse cohort in which RCaMP1.07 is expressed in neurons and GCaMP6f in astrocytes. This enables us to directly compare cell-type-specific calcium dynamics from both neurons and astrocytes under identical gas and sensory conditions, addressing the hypothesis that astrocytic activity may differentially contribute to neurovascular responses during hypercapnia. We believe these additions extend the work beyond a single resting-state paradigm and hope to address the reviewer’s points. Please find the point-by-point responses to the comments below. Major Comments: 1) Adding sensory stimulation of different intensities (and potentially durations) would be highly beneficial as the authors can more directly test the relationship between neural activity and hemodynamics with and without hypercapnia. Some of the authors conclusions regard this relationship but are based on resting state activity under anesthesia. It is still unclear if resting state NVC mechanisms are the same or different to sensory-based NVC, and the vast majority of NVC literature is sensory based. Nevertheless, I recognize there may be certain limitations with sensory stimulation in the authors unique experimental setup. While whisker or light stimulation would be ideal, if not, what else would be possible? This would be an excellent way to expand the study. Reply: We agree that an evoked-response paradigm is the most direct way to probe neurovascular coupling under hypercapnia. Accordingly, in a new cohort of GCaMP6f mice (n = 4) we delivered contralateral electrical forepaw stimulation at three intensity/duration settings (0.5 mA × 8 s, 0.5 mA × 20 s, 1 mA × 8 s) during alternating normocapnia and 5 % CO₂ blocks. Robust calcium transients were observed in all animals under both gas conditions, whereas BOLD responses that were readily detectable in normocapnia were suppressed during hypercapnia. This demonstrates that the resting-state decoupling generalises to stimulus-evoked coupling and that hypercapnia functionally occludes the normal hyperemic response. The new data are shown in Fig. 1g-j, Supplementary Fig. S5, and detailed in the Results (lines 94-97). We then repeated the 0.5 mA × 20 s protocol in a separate cohort (n = 4) co-expressing RCaMP1.07 in neurons and GCaMP6s in astrocytes within the upper-limb region of primary somatosensory cortex. Neuronal and astrocytic calcium responses were indistinguishable between normocapnia and hypercapnia, yet the BOLD response again vanished under 5 % CO₂ (Fig. 5). These results corroborate the GCaMP6f findings and indicate that astrocytic recruitment alone is insufficient to restore functional hyperemia once CO₂ is elevated. The new data are shown in Fig. 5 and detailed in the Results (lines 188–200) and discussed on lines 215–221. 2) Is the loss of synchronization from hypercapnia and the decoupling of neural activity to the BOLD response due to a ceiling effect? i.e. once the vascular dilates to 3min of CO2, it cannot dilate much more to functional activation. This is critical to determine because all the network connectivity analysis is somewhat meaningless if the main cause is simply maximally dilated blood vessels. I suggest the authors try 10% CO2 as well and compare to the 5% data. Additionally, systemic administration of a vasoconstrictor (such as indomethacin) and seeing the same relationships, would argue against a ceiling effect. Reply: We thank the reviewer for this suggestion. We agree that it is essential to exclude the possibility that the neurovascular uncoupling we report merely reflects vessels that are already maximally dilated by 5 % CO₂. We therefore repeated the hypercapnia paradigm in an independent cohort of GCaMP6f mice (n = 5) in which each animal experienced both 5 % and 10 % CO₂. In the primary somatosensory cortex (upper-limb sub-region, the site used for all stimulus trials) the BOLD signal rose from 4.16 ± 0.62 % at 5 % CO₂ to 6.63 ± 0.66 % at 10 % CO₂ (paired t = 9.77, p = 6 × 10⁻⁴). These data demonstrate a substantial remaining vascular reserve at 5 % CO₂ and thus rule out a dilation ceiling as the cause of the observed loss of functional connectivity and calcium–BOLD synchrony. The new results are presented in Fig. 1k-l, Supplementary Fig. S6, and are described in the main text (lines 97–102). 3) Anesthetic used was ketamine/xylazine which is not preferred for NVC studies. Ketamine blocks NMDA receptors, and NMDARs are a major regulator of NO and prostaglandin production in NVC. Xylazine is an alpha2 agonist which causes vascular smooth muscle contraction. While no anesthetic is ideal, it would be important to see if the main effects can be replicated with a different anesthetic. Reply: We appreciate the concern that ketamine/xylazine might bias neurovascular-coupling measurements. To verify that our key results are not regimen-specific we repeated the hypercapnia and sensory-stimulation paradigms in mice anaesthetised with isoflurane/medetomidine (Methods line 321-327). Across outcome measures, the two anaesthetics agreed closely. The reduction in calcium activity was similarly observed under both anesthesia protocols (Supplementary Fig. S7). Additionally, forepaw stimulation elicited robust calcium transients in both regimens, yet the BOLD response disappeared during hypercapnia in each case (new Supplementary Fig. S22). These results demonstrate that the neurovascular decoupling we report is robust to fundamentally different anaesthetic mechanisms. We have added the anesthesia comparison data in Supplementary Fig. S7 and S22 and note this cross-regimen consistency, as well as the remaining caveat that awake animals may behave differently, in the Discussion (lines 254–257). 4) The temporal resolution of fMRI is ~1s, whereas fluorescence calcium imaging ~100 ms, and this limits the direct alignment between neural and hemodynamic events. How was this dealt with regarding the CAP and Granger analysis? Loss of alignment is a major finding in the paper. Reply: To align our CAP and Granger analyses, we up-sampled the BOLD event vector tenfold by replicating each 1 s sample across ten 100 ms bins to match the 100-ms resolution of the fluorescence data. This approach preserves the high temporal fidelity of the calcium signals while ensuring valid frame-by-frame comparison. To verify that our key findings do not depend on this choice, in our revised manuscript, we downsampled the fluorescence event vector to 1 s bins and repeated the Granger causality tests (Supplementary Fig. S20). The same patterns of FL-BOLD directional influence under normocapnia and its loss under hypercapnia persisted, albeit with reduced F-values due to lower statistical power. The upsampling procedure is now detailed in the Methods for clarification (lines 467–468) and discussed (lines 229-231). 5) Most of the authors conclusions rest on correlation, clustering, and causality metrics (e.g., dFC, CAPs, Granger). These methods can be misleading if the underlying assumptions (stationarity, linearity, Gaussian distributions) are violated in health or in the hypercapnic brain. How was this controlled for? If no steps were performed, limitations need to be stated/discussed. Reply: Although we detrended and band‐pass filtered all time series to remove slow drifts and high-frequency noise (helping to approximate quasi-stationary, linear signals), we did not control for the assumptions. dFC and Granger-causality inherently assume that the underlying processes are at least locally stationary, linear, and approximately Gaussian, yet hypercapnia can indeed impose slow, non-linear vascular dynamics that violate these assumptions. Consequently, our connectivity and causality metrics primarily capture the dominant linear components of neurovascular coupling under CO₂ challenge. To acknowledge these caveats, we have revised our discussion (lines 231-233). 6) Was there any attention to higher frequency signals in the FL data that could be noise? The authors look at greater than 3Hz data. Are there non-neural signals much higher than this that should be filtered out? Spontaneous GCaMP signals beyond 15Hz cannot be faithfully detected due to slow off kinetics; thus, a bandpass filter in this range might be best. Reply: Previously, we were relying on conservative filtering to ensure the least amount of contamination from hemoglobin-related dynamics. To eliminate hemoglobin‐related confounds at the source, we now perform simultaneous dual‐wavelength recordings (488 nm for GCaMP; 561 nm for hemoglobin contrast; Supplementary Fig. S2). The 561 nm channel is regressed from the 488 nm trace, removing first‐order vascular absorption artifacts prior to any filtering. On these hemoglobin-corrected traces, we apply a 0.2–4 Hz band-pass filter (Supplementary Fig. S3). The 0.2 Hz high-pass cutoff removes slow drifts (e.g. photobleaching, laser-power fluctuations), while the 4 Hz low-pass cutoff excludes high-frequency noise (photon shot noise, electronic noise, mechanical vibrations) yet remains well below GCaMP6f’s off-kinetics limit (~10–15 Hz) to preserve all physiologically relevant calcium transients. These enhancements are detailed in the revised manuscript (lines 86–93) and reflected in updated panels for Fig. 1e–f. 7) Why do the authors speculate that data analysis finds 6 unique states in the FL data but only 5 in the BOLD data? Reply: We attribute the different state counts to fundamental contrasts in both the spatial and temporal information each modality preserves. Calcium imaging maintains localized neural fluctuations with high temporal fidelity, resolving rapid, subtle shifts in connectivity driven by dendritic events in GCaMP5.17 mice. In contrast, BOLD time-courses carry a dominant global or vascular component that is shared across regions and inherently low-pass–filtered by the haemodynamic response, smoothing over fast transitions. This shared variance and temporal blurring effectively lowers the dimensionality of the BOLD connectivity data, which may cause two finely distinct FL states to merge into a broader BOLD state during clustering. 8) Line 117: The authors say “FL state 5 and BOLD state 1 showed the strongest correspondence to neural FC patterns”. My simple question is: did these two states co-occur? Always? Sometimes? Reply: Both FL5 and BOLD1 are capturing the same core neural connectivity structure, but under very different constraints (fast vs. slow signals) and noise characteristics. The FL signal, being closer to the true neural dynamics, might reflect brief excursions into that connectivity state, whereas the BOLD signal (with its slower, smoothed nature) might gloss over or delay those excursions. Consequently, the two modalities only partially overlap in this state. We quantified this overlap as roughly 20% co-occurrence probability, a clear indication that FL5 and BOLD1 coincide occasionally rather than invariably. We have now reported these conditional co-occurrence probabilities (new Supplementary Fig.S14) and clarified this in the Results (lines139–140). Minor Comments: 1) The authors used Thy-1 mice which predominately express GCaMP6 in layer 5 pyramidal neurons, thus the authors are looking at dendritic signals primarily. This could be briefly discussed. Reply: We thank the reviewer for this important point. For clarification, we have mentioned this in Results (line 76-79). We also acknowledged this in the Discussion (lines 257–259). 2) Fig2 appears to be purely an explanation of the dFC and CAP analysis. If there is no real data/result in Fig 2, the authors should consider moving this to the supplement. Reply: The figure was moved to the supplementary file in the revised manuscript. 3) Did the authors observed any sex differences? Both M and F mice were used (4 each) but don’t recall that being mentioned in the text, though perhaps I missed it. Reply: We did not observe any sex differences. We have clearly indicated this in our revised manuscript (lines 254-255). 4) Line 34 should read: “…arachidonic acid metabolites…” Reply: The sentence was corrected in the revised manuscript. 5) Line 42-43: should be revised for clarity, such as: However, opposite findings were reported recently, showing that hypercapnic hyperemia and functional hyperemia do not activate the same vasodilatory signaling pathways that mediate cerebrovascular responses Reply: We thank the reviewer for pointing out this issue and the suggestion. The sentence was corrected in the revised manuscript (line 46-48). 6) In the results upfront the authors should list the GCaMP mouse model they are using to help the reader understand the cell-type, location, depth of the measurements. Without this, the reader cannot understand or interpret the calcium signal measurement. It would also increase clarity if the authors could briefly mention the fluorescence recording technique too. Reply: We appreciate the suggestion and have added a description of the mouse model and optical read-out at the beginning of the Results section to orient the reader (now lines 76–79). 7) Line 86. Past tense is probably better. “First, we explored..” I believe there are other place too in the results like this the authors could correct. Reply: The sentences with this issue were corrected in the revised manuscript. Reviewer #2: Gezginer et al. use state-of-the-art imaging techniques to revisit the interpretation of cerebrovascular reactivity to CO2 in the field of BOLD fMRI. Under anaesthesia, they combine measurements of BOLD contrast and bulk GCaMP6 fluorescence signals in response to 5% CO2. They report that CO2 triggers a global drop of brain fluorescence and conclude that CO2 generates a general decrease in cortical activity concomitant with a BOLD signal increase. While this observation appears to be irrefutable, I suspect that the interpretation is erroneous. It is well-known that CO2 increases CBF and that haemoglobin absorbs light. Therefore, in the absence of correction for the fluorescence contamination by heamoglobin absorption (Ma et al. Philos. Trans. R. Soc. Lond. B Biol. Sci (2016), Scott et al. Neuron (2018)), the observed fluorescence decrease most probably reports a CBF increase. Furthermore, it is well-established that fluorescent reporters, organic dyes and fluorescent proteins, are sensitive to pH. By decreasing brain pH, CO2 most certainly decreases GCaMP6 fluorescence, irrespective of the cell type expressing GCaMP6 (Tournissac et al. 2024). Therefore, it is likely that Gezginer et al. did not monitor a change in neuronal activity, but rather the combined side effects of heamoglobin absorption and GCaMP pH sensitivity. The study cannot be interpreted unless the authors demonstrate that GCaMP6 truly reports neuronal inhibition and not experimental side effects. Reply: We appreciate the reviewer’s insightful concerns regarding the pH sensitivity of GCaMP and potential contamination of our signals by hemoglobin absorption under hypercapnia. To ensure that our fluorescence measurements faithfully report neuronal calcium dynamics rather than artifactual quenching or hemodynamic effects, we have implemented a suite of experimental and analytical refinements. First, recognizing that CO₂-driven increases in cerebral blood flow alter fluorescence via hemoglobin absorption, we upgraded our imaging platform to a dual-wavelength approach (Supplementary Fig. S2). By simultaneous excitation at 488 nm (GCaMP) and 561 nm (hemoglobin contrast), we capture simultaneous calcium- and blood-volume–sensitive signals. Following coregistration, we regress the 561 nm–derived hemodynamic component from the 488 nm trace and apply a 0.2–4 Hz band-pass filter to remove slow drifts and high-frequency noise. This pipeline produces an Hb-corrected GCaMP6f signal in which residual fluctuations predominantly reflect intracellular calcium spikes (Fig. 1e-f, Supplementary Fig. S3). Second, we quantified the degree of GCaMP6f fluorescence quenching induced by intracellular acidification in response to 5 % and 10 % CO₂ exposure, leveraging GCaMP6f pH-sensitivity curves (Supplementary Note 1). From these, we derived correction factors of 1.025 for 5 % CO₂ and 1.06 for 10 % CO₂. When applied to the measured spike amplitudes during hypercapnic periods, these factors restore the true underlying calcium transient magnitudes. In line with these improvements, we performed new experiments in GCaMP6f mice (n = 5) under both 5 % and 10 % CO₂ challenges. Uncorrected spike amplitudes declined by approximately 13.9 % during 5 % CO₂ and 27.6 % during 10 % CO₂; after applying the appropriate scale factors, these reductions attenuated to 12.1 % and 24.6 %, respectively (Fig. 1k–l, Supplementary Fig. S4). Importantly, even when conservatively applying the larger 10 % CO₂ correction factor to the 5 % data, a significant 9.6 % decrease in amplitude persisted, demonstrating that mild hypercapnia genuinely suppresses neuronal calcium activity (Supplementary Fig. S4). We also note that the extreme acidification required to explain the observed 27 % fluorescence drop (intracellular pH 6.6) or the 14 % drop (pH 6.9) far exceeds the pH shifts measured with up to 20 % inspired CO₂ for 10 min 1. Thus, quenching cannot account for our observed residual amplitude reductions, which we confidently attribute to true decreases in neuronal calcium signaling. Taken together, these quantitative calibrations, artifact‐regression methods, and new in vivo data assure us that our fluorescence measurements accurately reflect hypercapnia-induced neuronal inhibition rather than sensor artifacts. The new results and improvements are given in Fig. 1e-f, Fig. 1l, Supplementary Fig. S2-S4, Supplementary Note 1, and detailed in lines 86-93 and 204-206. [1] Pospelov AS, Puskarjov M, Kaila K, Voipio J. Endogenous brain-sparing responses in brain pH and PO2 in a rodent model of birth asphyxia. Acta Physiol (Oxf). 2020;229(3):e13467. doi:10.1111/apha.13467 Reviewer #3: We thank the reviewer for recognising both the technical advances of our dual-modal platform and the relevance of its application to the long-standing debate on CO₂-driven neurovascular uncoupling. In the revised manuscript we have further strengthened the study along the very lines the reviewer highlighted. Please find the point-by-point responses to the comments below. 1. While the study clearly demonstrates a neurovascular dissociation, it does not delve into the underlying cellular or molecular mechanisms. Are astrocytes, endothelial cells, or pericytes mediating this effect? Further investigations using pharmacological or genetic tools would be needed to determine the mechanistic basis of these findings. Reply: We agree that identifying the cellular players in the hypercapnia-induced neurovascular dissociation is a critical next step. To obtain first evidence, we carried out cell-type–specific calcium imaging in a new cohort of mice. Using a dual-AAV approach, we expressed GCaMP6s under the astrocytic GFAP promoter and RCaMP1.07 under the neuronal hSyn1 promoter in the upper-limb sub-region of primary somatosensory cortex. Cell-type expression was verified by confocal microscopy. During alternating normocapnic and 5 % CO₂ blocks, the animals (n = 4) received fore-paw stimulation (0.5 mA, 20 s) while we simultaneously recorded BOLD, neuronal RCaMP, and astrocytic GCaMP signals with our dual-wavelength enabled hybrid set-up (new Supplementary Figure S2). Neuronal and astrocytic responses were unchanged by hypercapnia: peak ΔF/F, onset latency, and time-to-peak did not differ between gas conditions. In contrast, the stimulus-evoked BOLD responses were suppressed during hypercapnia in every animal. These data show that neither impaired neuronal firing nor diminished astrocytic calcium rises can account for the loss of functional hyperemia. This shows that the decoupling must arise downstream, most plausibly at the level of vasoactive signalling from astrocytes/endothelium to vascular smooth-muscle cells or pericytes. The new experiments are documented in Fig. 5 and described in the Results (lines 188–200) and discussion (lines 215-221). 2. Calcium imaging, though precise, captures activity only in superficial cortical layers, whereas fMRI measures whole-brain hemodynamics, leading to potential spatial mismatches in the data. This needs to be discussed. Reply: We concur that the distinct imaging depths of our two modalities raise the possibility of spatial mismatch. We have addressed this on two levels. We tried to ensure an optimal spatial matching by extracting a three-dimensional mask comprising only layers I–IV from the Allen CCF of the 25 bilateral cortical areas, then orthogonally projected that mask into 2-D to build the fluorescence template shown in Fig. 1c. Every metric that links calcium to BOLD was computed from time-courses averaged over these matched voxels. All deeper-layer and sub-cortical BOLD voxels were excluded. Even with this cortical masking, BOLD signals in layers I–IV inevitably contain contributions from deeper areas, and fluorescence from dendrites may propagate laterally by scattering. Thus, the two read-outs are not in perfect one-to-one correspondence. In the revised manuscript, we acknowledged this potential issue in the Discussion (lines 260-263). 3. The authors should consider the pH sensitivity of the GCaMP sensor as fluorescence is reversibly quenching by acidic pH and may not accurately reflect the neuronal activity during hypercapnia. Reply: We thank the reviewer for drawing attention to the reversible pH‐sensitivity of GCaMP and its potential to confound our interpretation of neural activity under hypercapnia. To ensure that our fluorescence signals truly reflect intracellular calcium transients and not artifactual quenching caused by acidification, we have undertaken a comprehensive set of experimental and analytical improvements, and implemented in the revised manuscript when characterizing the changes in neuronal activity. First, we directly quantify the magnitude of fluorescence quenching in GCaMP6f upon intracellular acidification induced by both 5 % and 10 % CO₂, using the pH shifts and pH-sensitivity curves of GCaMP6f (Supplementary Note 1). We then derive correction factors, S₆f = 1.06 for 10 % CO₂ and S₆f = 1.025 for 5 % CO₂, when applied to measured spike amplitudes, restore the true underlying calcium dynamics (updated Fig. 1e-f, Supplementary Fig. S3). Second, to further isolate genuine calcium signals from hemodynamic and optical artifacts, we have upgraded our imaging setup to a hybrid dual‐wavelength approach. By complementing 488 nm excitation (GCaMP) with 561 nm excitation (hemoglobin absorption contrast), we capture simultaneous calcium‐ and blood‐volume–sensitive signals (Supplementary Fig. S2). We then regress out the hemoglobin‐derived component which accounts for CO₂‐driven cerebral blood flow changes and apply a bandpass filter to remove slow drifts and high‐frequency noise. This preprocessing pipeline yields an Hb‐corrected GCaMP6f trace in which residual fluctuations are dominated by true calcium transients (Fig. 1e-f, Supplementary Fig. S3). Third, we have collected data from a new cohort of GCaMP6f animals (n = 5) under both 5 % and 10 % CO₂ challenges and applied our full correction strategy: hemoglobin regression, bandpass filtering, and pH‐scale‐factor normalization. In these data, we observe that unscaled fluorescence spike amplitudes decrease by ~13.9 % during 5 % CO₂ and ~27.6 % during 10 % CO₂ (Fig. 1k-l, Supplementary Fig. S4). After applying the respective scale factors (1.025 or 1.06), the apparent amplitude drops are reduced to ~12.1 % and ~24.6 %, respectively, confirming that a significant component of the signal change reflects true modulation of neuronal activity rather than purely pH‐driven quenching. Finally, to guard against under‐correction, we also applied the larger 10 % CO₂ scale factor (S₆f = 1.06) to the 5 % CO₂ data. Even with this conservative adjustment, spike amplitudes remained reduced by 9.6 %, demonstrating that the observed decrease in neural activity under mild hypercapnia cannot be attributed solely to pH-dependent quenching (Supplementary Fig. S4). Moreover, explaining the 27 % drop in spike amplitude at 10 % CO₂ would require a scale factor of 1.37, equivalent to an intracellular pH decrease from 7.30 to 6.62 (Supplementary Note 1). Similarly, the 13.9 % loss at 5 % CO₂ would demand a pH drop to 6.88. Yet even a 10 min inhalation of 20 % CO₂ lowers intracellular pH only to 6.85 ¹. Because such extreme acidification never occurs under our conditions, we can confidently conclude that the residual reductions in spike amplitude reflect genuine suppression of neuronal calcium dynamics rather than sensor artifact. Together, these quantitative calibrations, improved optical recording methods, and controls leave us confident that our fluorescence measurements accurately track neuronal calcium dynamics during hypercapnic challenges and the activity changes we report reflect genuine neurophysiological modulation, not sensor artefact. We report the data in updated Fig. 1e-f, Fig. 1k-l and Supplementary Figs. S2-S4. Text is correspondingly updated (lines 86-93 and 204-206). [1] Pospelov AS, Puskarjov M, Kaila K, Voipio J. Endogenous brain-sparing responses in brain pH and PO2 in a rodent model of birth asphyxia. Acta Physiol (Oxf). 2020;229(3):e13467. doi:10.1111/apha.13467"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have done a great job addressing my concerns and expanding the manuscript. They study is more timely and impactful with this new data. Major I have one more major point regarding the new data: the authors need to ensure that the lack of NVC BOLD responses they measured in hypercapnia is not due to run down from successive forepaw stimulations with little time (90sec) between each, in which the system does not have adequate time to fully recover. As the authors are always starting with normocapnia, and then switching to hypercapnia, and giving two stimulations in each condition, run down could be an issue, and the authors do not show washout and recovery back in normocapnia. The exact order of all stimulations and conditions is not provide I believe. Note, the neural and astrocyte calcium response might not run down, but the vessel still could; thus, looking at the calcium data is not an adequate control per se. The authors have this data already (Fig S5) and they just need to look at the two responses given 90 sec apart and see if they are the same amplitude in normocapnia, or compare across different stimulations to determine if there is run down. If there is no run down, great, but if there is run down to the point that the current interpretation is invalid, they could repeat the experiment with a lot more time in between each forepaw stimulation (I recommend around 5 min), or show recovery of NVC by going back to normocapnia and repeating again. Minor I do not love the title of this manuscript. It is not very informative and it focuses more on the method than the finding. The authors could consider focusing on their outstanding method in the abstract and have title read something like this: Hypercapnia uncouples neuronal and hemodynamic responses impairing functional brain connectivity and neurovascular coupling. This is just a suggestion. Line 39, the authors say “These vasoactive molecules engage various signaling pathways in vascular smooth muscle cells to induce vasodilation and increase cerebral blood flow (CBF) in response to neuronal activity.” Please add “and pericytes” after vascular smooth muscle cells. Line 100 the authors say “Conversely, neuronal spike amplitudes decreased by 12.18 ± 2.04% at 5% CO₂ and 24.59 ± 3.70% at 10% CO₂ (Fig. 1l), confirming that hypercapnia directly attenuates neuronal excitability despite unaltered vascular responsiveness.” I do not understand the statement “unaltered vascular responsiveness”. I think the author’s are referring to the global increase in the BOLD signal to CO2 and not the impaired NVC which is discussed in this section. Perhaps the authors want to say: “despite unaltered global vascular responsiveness to inhaled CO2.” Supplemental Fig 1 could also show the new 561 laser line. Or make the laser box more general.", "reviewer_2": "Gezginer et al. present new experiments that add useful information to the manuscript; however, the interpretation of several major observations remains questionable, if not invalid. Previous BOLD fMRI studies (e.g., Stefanovic et al., 2006; Gauthier et al., 2011; and others) have demonstrated the importance of quantifying both resting and evoked CBF to correctly interpret BOLD responses to hypercapnia. These studies should be cited and discussed, as they suggest other explanations for the disappearance of BOLD responses upon CO2. Can Gezginer's work contribute to resolving the ongoing controversy over the effects of hypercapnia on neuronal activity and neurovascular coupling (NVC)? Yes, but only if the authors, which possess all the necessary tools to address this question, perform the key controls which are still missing. First, the effect of CO2 on neuronal activity: bulk GCaMP6 fluorescence is insufficient to draw definitive conclusions, as GCaMP6 is pH-sensitive and thus a poor stand-alone reporter of activity. Corrections for pH sensitivity and hemoglobin absorption are only approximate and should be recalculated (see below). Direct measurements of neuronal activity—via multielectrode recordings or cellular-resolution calcium imaging—are required. A consistent reduction in firing rate or single-cell calcium transient frequency during CO₂ exposure would provide unambiguous evidence of reduced neuronal activity. The second important issue concerns the effect of CO₂ on NVC: Supplementary Figure S2 shows that the authors can measure hemodynamic responses independently of BOLD fMRI. CBV responses to hypercapnia should be recorded, and CBV traces added to Figure 1i, to directly assess NVC. Without these controls, the conclusions remain speculative. Additional detailed comments: 1) In the manuscript, the definition of “spikes” is unclear. With the limited spatial and temporal resolution of the fluorescence acquisition system, the detected events cannot represent true neuronal action potentials. They are more likely spontaneous calcium fluctuations from neuron or astrocyte populations, depending on the expression patterns of GCaMP6 and RCaMP7.01. The term “spikes” should be avoided, as in neurons these signals could reflect synaptic potentials, action potentials, or second messenger–mediated calcium transients; in astrocytes, their meaning is even less certain. Moreover, the presentation of fluorescence data is inconsistent and prone to misinterpretation. In some cases, the authors appear to show the amplitude or envelope of Ca²⁺ “spikes,” while in others they display changes in baseline GCaMP6 fluorescence. Without a clear and consistent definition of what is being measured, most neuroscientists will find the results difficult to interpret. 2) The authors introduced a theoretical correction for the pH-dependent reduction of GCaMP6 fluorescence under hypercapnia. The pKa used is that of GCaMP6 in a saturated Ca²⁺ condition (Chen et al.), which corresponds to a situation with ∼400 times more Ca²⁺ (39µM) than resting physiological condition (∼100 nM). The 'apo' pKa (Ca²⁺-free) - while not fully physiological—is closer to resting neuronal conditions. Using the pKa_apo of 8.77 would yield a scale factor S6f = 0.032/0.022 = 1.45 (instead of 1.06), dramatically increasing the correction required and highlighting the significant impact of acidosis on GCaMP6 fluorescence. Given the high uncertainty of the correction factor, neuronal activity should be corroborated using complementary techniques. 3) The authors report robust BOLD signals in subthalamic regions under hypercapnia, but Figure 1a suggests no cortical response—an unlikely scenario. Given that FL signals are acquired in superficial cortical layers, the authors must show CO₂ and NVC data from the same cortical regions where FL and BOLD signals were acquired, to ensure consistency. 4) Figure S22 (bottom left) shows conflicting results: the AAV-injected group (n=4) appears to show a 2% BOLD increase under hypercapnia following electrical stimulation—higher than their normocapnia response (0.4-1.9%). How to conciliate this with the other experiments? 5) Among the newly presented data, the authors report a marked decrease in astrocytic Ca²⁺ signal (or “spike” amplitude?) during hypercapnia, closely matching in magnitude and dynamic the decrease observed in neurons. In fact, Figure S21 shows a remarkably high correlation coefficient (r = 0.98) between neuronal (RCaMP1.07) and astrocytic (GCaMP6) signals in response to CO₂. While the authors attribute the neuronal GCaMP6 (or “spike amplitude”) decrease to inhibition, no explanation is provided for astrocytes. If not a pH-related artifact, what accounts for such a decrease in astrocytic Ca²⁺? Has such a phenomenon been previously reported?", "reviewer_3": "In their revision, the authors substantially expanded the study with new experiments addressing the main critiques. They (1) performed sensory stimulation at multiple intensities and durations during both normocapnia and hypercapnia, showing that hypercapnia suppresses the BOLD response without reducing neuronal calcium transients, thus demonstrating that functional hyperemia is occluded downstream of neuronal activation; (2) added a 10 % CO₂ condition to test for vascular “ceiling effects,” finding further BOLD signals beyond the 5 % level and ruling out maximal dilation as the cause of neurovascular uncoupling; (3) repeated key tests under a different anesthetic regimen (isoflurane/medetomidine) and found consistent results; (4) implemented dual-wavelength optical recordings with hemoglobin signal regression and pH calibration to correct for optical artefacts and sensor quenching, confirming that hypercapnia genuinely reduces neuronal activity (well described in the earlier literature); and (5) incorporated cell-type–specific imaging (neurons and astrocytes). These additions address prior methodological concerns and strengthen the conclusions. Overall, their arguments appear well-supported: they convincingly show that the observed decoupling is not an artefact of hemodynamic contamination, pH effects, anesthesia choice, or vascular saturation.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Reply: We thank the reviewer for the encouraging words. Please find our point-by-point responses below. Major 1) I have one more major point regarding the new data: the authors need to ensure that the lack of NVC BOLD responses they measured in hypercapnia is not due to run down from successive forepaw stimulations with little time (90sec) between each, in which the system does not have adequate time to fully recover. As the authors are always starting with normocapnia, and then switching to hypercapnia, and giving two stimulations in each condition, run down could be an issue, and the authors do not show washout and recovery back in normocapnia. The exact order of all stimulations and conditions is not provide I believe. Note, the neural and astrocyte calcium response might not run down, but the vessel still could; thus, looking at the calcium data is not an adequate control per se. The authors have this data already (Fig S5) and they just need to look at the two responses given 90 sec apart and see if they are the same amplitude in normocapnia, or compare across different stimulations to determine if there is run down. If there is no run down, great, but if there is run down to the point that the current interpretation is invalid, they could repeat the experiment with a lot more time in between each forepaw stimulation (I recommend around 5 min), or show recovery of NVC by going back to normocapnia and repeating again. Reply: We thank the reviewer for raising this point. We actually designed the paradigm precisely to guard against it. As stated in the Methods (lines 356–357), two stimulation cycles were delivered within each gas block and no stimulation occurred during the initial normocapnic baseline. Hence, critically, the first evoked epoch in each run was under hypercapnia, so any putative rundown from prior stimulation in normocapnia cannot account for the loss of the BOLD NVC response under hypercapnia. To directly test for any potential rundown, we additionally re-analysed the 20 s stimulation condition (i.e., the paradigm with the shortest recovery between successive stimulations) by comparing the forepaw-evoked BOLD responses during the second vs. third normocapnia periods (i.e., the first and second normocapnia periods in which stimulation was applied). We observed no decline in BOLD peak amplitude between these epochs across animals (new Supplementary Fig. S7, lines 100-102). Taken together, the block order and these quantitative checks rule out stimulus rundown as the cause of the absent BOLD response under hypercapnia. We have also clarified the exact order of gas blocks and stimulations by visualizing the stimulation scheme in Supplementary Fig. S7. Minor 2) I do not love the title of this manuscript. It is not very informative and it focuses more on the method than the finding. The authors could consider focusing on their outstanding method in the abstract and have title read something like this: Hypercapnia uncouples neuronal and hemodynamic responses impairing functional brain connectivity and neurovascular coupling. This is just a suggestion. Reply: We thank the reviewer for the great suggestion. We updated the title accordingly to focus on the main finding: “Hypercapnia dissociates neuronal and hemodynamic responses impairing neurovascular coupling and functional brain connectivity.” 3) Line 39, the authors say “These vasoactive molecules engage various signaling pathways in vascular smooth muscle cells to induce vasodilation and increase cerebral blood flow (CBF) in response to neuronal activity.” Please add “and pericytes” after vascular smooth muscle cells. Reply: Manuscript is revised accordingly (line 39). 4) Line 100 the authors say “Conversely, neuronal spike amplitudes decreased by 12.18 ± 2.04% at 5% CO₂ and 24.59 ± 3.70% at 10% CO₂ (Fig. 1l), confirming that hypercapnia directly attenuates neuronal excitability despite unaltered vascular responsiveness.” I do not understand the statement “unaltered vascular responsiveness”. I think the author’s are referring to the global increase in the BOLD signal to CO2 and not the impaired NVC which is discussed in this section. Perhaps the authors want to say: “despite unaltered global vascular responsiveness to inhaled CO2.” Reply: We thank the reviewer for bringing this up. Indeed, we are referring to the global increase in the BOLD signal under hypercapnia. We have revised the sentence accordingly (line 107). 5) Supplemental Fig 1 could also show the new 561 laser line. Or make the laser box more general. Reply: Supplemental Fig. S1 is revised accordingly. Reviewer #2: 1) Gezginer et al. present new experiments that add useful information to the manuscript; however, the interpretation of several major observations remains questionable, if not invalid. Previous BOLD fMRI studies (e.g., Stefanovic et al., 2006; Gauthier et al., 2011; and others) have demonstrated the importance of quantifying both resting and evoked CBF to correctly interpret BOLD responses to hypercapnia. These studies should be cited and discussed, as they suggest other explanations for the disappearance of BOLD responses upon CO2. Can Gezginer's work contribute to resolving the ongoing controversy over the effects of hypercapnia on neuronal activity and neurovascular coupling (NVC)? Yes, but only if the authors, which possess all the necessary tools to address this question, perform the key controls which are still missing. First, the effect of CO2 on neuronal activity: bulk GCaMP6 fluorescence is insufficient to draw definitive conclusions, as GCaMP6 is pH-sensitive and thus a poor stand-alone reporter of activity. Corrections for pH sensitivity and hemoglobin absorption are only approximate and should be recalculated (see below). Direct measurements of neuronal activity—via multielectrode recordings or cellular-resolution calcium imaging—are required. A consistent reduction in firing rate or single-cell calcium transient frequency during CO₂ exposure would provide unambiguous evidence of reduced neuronal activity. The second important issue concerns the effect of CO₂ on NVC: Supplementary Figure S2 shows that the authors can measure hemodynamic responses independently of BOLD fMRI. CBV responses to hypercapnia should be recorded, and CBV traces added to Figure 1i, to directly assess NVC. Without these controls, the conclusions remain speculative. Reply: We agree that evoked hemodynamics should be assessed independently of BOLD under hypercapnia and thank the reviewer for the suggestion. Accordingly, we analyzed the concurrently acquired intrinsic hemoglobin channel (561-nm ISOI, HbT/CBV-weighted) and we now plot these CBV/HbT time courses alongside BOLD and FL (new Fig. 1k, updated Supplementary Fig. S6, line 98-100). The stimulus-evoked HbT/CBV responses were either markedly attenuated or absent at 5% CO 2, while neuronal Ca²⁺ transients were preserved. Thus, the loss of the BOLD NVC response is accompanied by a loss of the CBV/HbT response, directly demonstrating impaired neurovascular coupling under hypercapnia rather than a BOLD-specific dynamic-range artifact. We have added a discussion of prior calibrated-fMRI work that proposed baseline CBF/CBV–driven compression of BOLD (Stefanovic 2006, Gauthier 2011) and explain how the concurrent attenuation of evoked CBV/HbT in our data contradicts interpretations based solely on baseline shifts (see revised Discussion, lines 227–232). We appreciate the reviewer’s concerns about CO effects on neuronal activity and the pH-sensitivity of bulk GcaMP. 2 We address these in detail in our response to point (3). Additional detailed comments: 2) In the manuscript, the definition of “spikes” is unclear. With the limited spatial and temporal resolution of the fluorescence acquisition system, the detected events cannot represent true neuronal action potentials. They are more likely spontaneous calcium fluctuations from neuron or astrocyte populations, depending on the expression patterns of GCaMP6 and RCaMP7.01. The term “spikes” should be avoided, as in neurons these signals could reflect synaptic potentials, action potentials, or second messenger–mediated calcium transients; in astrocytes, their meaning is even less certain. Moreover, the presentation of fluorescence data is inconsistent and prone to misinterpretation. In some cases, the authors appear to show the amplitude or envelope of Ca²⁺ “spikes,” while in others they display changes in baseline GCaMP6 fluorescence. Without a clear and consistent definition of what is being measured, most neuroscientists will find the results difficult to interpret. Reply: We agree that “spikes” is an imprecise term in the context of wide-field calcium imaging. We therefore replaced it with terminology better reflecting what has actually been measured throughout the manuscript and supplementary material. Specifically, we now refer to “(population) Ca²⁺ transients” (neuronal channel). Additionally, we revised the manuscript to explicitly define what is displayed for the envelope of Ca²⁺ transients and event-related responses (line 391-395). 3) The authors introduced a theoretical correction for the pH-dependent reduction of GCaMP6 fluorescence under hypercapnia. The pKa used is that of GCaMP6 in a saturated Ca²⁺ condition (Chen et al.), which corresponds to a situation with ∼400 times more Ca²⁺ (39µM) than resting physiological condition (∼100 nM). The 'apo' pKa (Ca²⁺-free) - while not fully physiological—is closer to resting neuronal conditions. Using the pKa_apo of 8.77 would yield a scale factor S6f = 0.032/0.022 = 1.45 (instead of 1.06), dramatically increasing the correction required and highlighting the significant impact of acidosis on GCaMP6 fluorescence. Given the high uncertainty of the correction factor, neuronal activity should be corroborated using complementary techniques. Reply: We believe there is a misunderstanding where the pH factor has been applied and which biochemical state governs the signal we quantify. We apply pH scaling only to the amplitude of resting-state Ca²⁺ transient peaks after simultaneous hemoglobin regression and band-pass filtering, steps that remove hemoglobin absorption confounds and slow baseline drifts. The residual fast fluctuations (whose peaks we measure) are dominated by the Ca²⁺-bound ensemble, whereas the apo fraction contributes primarily to baseline and slow components that are suppressed by our preprocessing. For peak scaling, the relevant pH sensitivity is therefore the Ca²⁺-saturated pKₐ, not the apo pKₐ that characterizes the Ca²⁺-free chromophore at baseline. This stems directly from the photophysics of GCaMP6 variants, in which Ca²⁺ binding to calmodulin-M13 shifts the chromophore to a brighter, more anionic state with a lower effective pKₐ. Hence, during transient peaks, the photon budget is dominated by the Ca²⁺-bound chromophore. Using the apo pKₐ to rescale peak amplitudes would mis-specify the emitting ensemble. Independent, pH-insensitive measurements consistently show suppression of neuronal activity under moderate hypercapnia in the same regimes we test. In anesthetized macaques, intracortical recordings show reduced multiunit activity and reduced local field potential power [1]; in rodents, hypercapnia reduces multiunit activity and desynchronizes ongoing activity [2,3]; in humans, EEG and MEG show reduced oscillatory power and frequency during mild hypercapnia [4–9]; mechanistically, CO₂-induced acidosis lowers cortical excitability via adenosine and ATP signalling [10]. These observations corroborate reduced neural drive without reliance on fluorescence sensors. Note also that our primary conclusions do not hinge on the exact magnitude of resting state Ca²⁺ attenuation. The abolition of stimulus-evoked hemodynamic responses under hypercapnia despite preserved neuronal and astrocytic Ca²⁺ transients, as well as the loss of cross-modal alignment between fluorescence and BOLD in dynamic functional connectivity and co-activation analyses, are both preserved irrespective of pH-scaling choice. This is because the corresponding inferences are derived from correlation-based and state-based metrics that are insensitive to multiplicative rescaling. We acknowledge that absolute Ca²⁺ transient peak amplitudes carry bounded uncertainty, since hemoglobin regression and pH scaling approximate removal of vascular and acid–base influences, and we now note this limitation in the manuscript (line 270-274). Regarding the request for additional corroboration, it could potentially be done with invasive methods such as two photon calcium imaging or intracranial electrophysiology. Such experiments cannot be done with our current experimental system and preparation, thus would require a new experimental platform that would necessarily sacrifice the simultaneous cortex-wide coverage offered by our current system. Importantly, such corroboration would not affect our central conclusions, which do not depend on the absolute calibration of resting-state Ca²⁺ amplitudes. Note that the literature already provides robust, pH-independent evidence of hypercapnia-related neural suppression [1–10], so there deems to be limited value in repeating this result in a different apparatus as per our stated aims. Accordingly, adding highly invasive stand-alone measurements would not alter any primary conclusions nor materially increase the amount of new information, while substantially increasing invasiveness and animal use. This would therefore amount to a separate study with a different scope, aim, system, and methodology. [1] Zappe AC, Uludağ K, Oeltermann A, Uğurbil K, Logothetis NK. The influence of moderate hypercapnia on neural activity in the anesthetized nonhuman primate. Cereb Cortex. 2008;18(11):2666-2673. doi:10.1093/cercor/bhn023 [2] Jones M, Berwick J, Hewson-Stoate N, Gias C, Mayhew J. The effect of hypercapnia on the neural and hemodynamic responses to somatosensory stimulation. Neuroimage. 2005;27(3):609-623. doi:10.1016/j.neuroimage.2005.04.036 [3] Martin C, Jones M, Martindale J, Mayhew J. Haemodynamic and neural responses to hypercapnia in the awake rat. Eur J Neurosci. 2006;24(9):2601-2610. doi:10.1111/j.1460-9568.2006.05135.x [4] Wang D, Yee BJ, Wong KK, et al. Comparing the effect of hypercapnia and hypoxia on the electroencephalogram during wakefulness. Clin Neurophysiol. 2015;126(1):103-109. doi:10.1016/j.clinph.2014.04.012 [5] Hall EL, Driver ID, Croal PL, et al. The effect of hypercapnia on resting and stimulus induced MEG signals. Neuroimage. 2011;58(4):1034-1043. doi:10.1016/j.neuroimage.2011.06.073 [6] Thesen T, Leontiev O, Song T, et al. Depression of cortical activity in humans by mild hypercapnia. Hum Brain Mapp. 2012;33(3):715-726. doi:10.1002/hbm.21242 [7] Li, S., Zhao, Y., Wang, Q. et al. Electroencephalographic depression after abruptly increasing partial pressure of end-tidal carbon dioxide: a case series. BMC Anesthesiol 24, 373 (2024). https://doi.org/10.1186/s12871-024-02764-7 [8] Babiloni C, Vecchio F, Altavilla R, et al. Hypercapnia affects the functional coupling of resting state electroencephalographic rhythms and cerebral haemodynamics in healthy elderly subjects and in patients with amnestic mild cognitive impairment. Clin Neurophysiol. 2014;125(4):685-693. doi:10.1016/j.clinph.2013.10.002 [9] Driver ID, Whittaker JR, Bright MG, Muthukumaraswamy SD, Murphy K. Arterial CO2 Fluctuations Modulate Neuronal Rhythmicity: Implications for MEG and fMRI Studies of Resting-State Networks. J Neurosci. 2016;36(33):8541-8550. doi:10.1523/JNEUROSCI.4263-15.2016 [10] Dulla CG, Dobelis P, Pearson T, Frenguelli BG, Staley KJ, Masino SA. Adenosine and ATP link PCO2 to cortical excitability via pH. Neuron. 2005;48(6):1011-1023. doi:10.1016/j.neuron.2005.11.009 4) The authors report robust BOLD signals in subthalamic regions under hypercapnia, but Figure 1a suggests no cortical response—an unlikely scenario. Given that FL signals are acquired in superficial cortical layers, the authors must show CO₂ and NVC data from the same cortical regions where FL and BOLD signals were acquired, to ensure consistency. Reply: We appreciate the opportunity to clarify. In Fig. 1a, we apply a strict threshold (t = 6) to highlight the strongest activations, which prominently showcases deep and subcortical structures but inevitably underrepresents widespread cortical responses. Hypercapnia produced robust, positive BOLD changes across cortex in every animal. To make this explicit and to ensure spatial consistency with the fluorescence readout, we now provide (i) an unthresholded group activation map and (ii) the group-level BOLD time course extracted from the same cortical ROI used for FL, showing clear CO₂-evoked increases (new Supplementary Fig. S3, main text line 82). 5) Figure S22 (bottom left) shows conflicting results: the AAV-injected group (n=4) appears to show a 2% BOLD increase under hypercapnia following electrical stimulation—higher than their normocapnia response (0.4-1.9%). How to conciliate this with the other experiments? Reply: We apologize for the confusion and thank the reviewer for catching this error. The purpose of Supplementary Fig. S24 (formerly S22) is to investigate the anaesthesia-dependence of key results and the visualized data on the AAV-injected cohort is a duplicate of Fig. 5e. However, the BOLD panel (bottom left) was inadvertently plotted with incorrect (FL) y-axis values due to a formatting error. We have corrected the axis in the updated Supplementary Fig. S24. 6) Among the newly presented data, the authors report a marked decrease in astrocytic Ca²⁺ signal (or “spike” amplitude?) during hypercapnia, closely matching in magnitude and dynamic the decrease observed in neurons. In fact, Figure S21 shows a remarkably high correlation coefficient (r = 0.98) between neuronal (RCaMP1.07) and astrocytic (GCaMP6) signals in response to CO₂. While the authors attribute the neuronal GCaMP6 (or “spike amplitude”) decrease to inhibition, no explanation is provided for astrocytes. If not a pH-related artifact, what accounts for such a decrease in astrocytic Ca²⁺? Has such a phenomenon been previously reported? Reply: In the dual-indicator cohort (Supplementary Fig. S23; formerly S21), both the neuronal (RCaMP1.07) and astrocytic (GCaMP6s) traces are shown as raw ΔF/F. In the dual-indicator experiments, the 561-nm path was allocated to RCaMP so no separate simultaneous hemoglobin reference was recorded for regression, and no pH normalization was applied in this cohort. During hypercapnia, pH-dependent quenching of both indicators and hemoglobin absorption (stronger in the green band) produce slow downward drifts. These shared optical influences explain the highly correlated neuron–astrocyte co-drifts during CO and are not interpretable as biological suppression of astrocytic Ca²⁺ activity. 2 Accordingly, we do not report or interpret any reduction in astrocytic Ca²⁺ from these baseline drifts. Our inferences for this cohort are drawn from event-locked (GLM) responses, where neuronal and astrocytic Ca²⁺ transients evoked by forepaw stimulation are preserved under 5% CO₂, while the stimulus-evoked BOLD response is abolished (Fig. 5). To avoid any misinterpretation, we revised the caption to Supplementary Fig. S23. Reviewer #3: In their revision, the authors substantially expanded the study with new experiments addressing the main critiques. They (1) performed sensory stimulation at multiple intensities and durations during both normocapnia and hypercapnia, showing that hypercapnia suppresses the BOLD response without reducing neuronal calcium transients, thus demonstrating that functional hyperemia is occluded downstream of neuronal activation; (2) added a 10 % CO₂ condition to test for vascular “ceiling effects,” finding further BOLD signals beyond the 5 % level and ruling out maximal dilation as the cause of neurovascular uncoupling; (3) repeated key tests under a different anesthetic regimen (isoflurane/medetomidine) and found consistent results; (4) implemented dual-wavelength optical recordings with hemoglobin signal regression and pH calibration to correct for optical artefacts and sensor quenching, confirming that hypercapnia genuinely reduces neuronal activity (well described in the earlier literature); and (5) incorporated cell-type–specific imaging (neurons and astrocytes). These additions address prior methodological concerns and strengthen the conclusions. Overall, their arguments appear well-supported: they convincingly show that the observed decoupling is not an artefact of hemodynamic contamination, pH effects, anesthesia choice, or vascular saturation. Reply: We thank the reviewer for the encouraging comments and the thoughtful assessment!"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed all my comments.", "reviewer_2": "Gezginer et al. present some new data to answer our comments. Haemoglobin is known to have an oxygenation dependent absorption spectrum in the visible and near-infrared ranges, a property that underlies intrinsic optical imaging. Typically, to monitor total blood flow with IOSI, one classically uses a high-contrast isosbestic point of the haemoglobin absorption spectra - a wavelength at which both HbO and HbR have the same absorption values. This makes the measurement independent of oxygenation and therefore sensitive only to changes in [HbT] (see Ma et al., Phil.Trans.R.Soc.B, 2016). We therefore do not understand why the authors used the single wavelength of 561 nm, which is precisely a wavelength at which both HbO and HbR vary, in opposite directions, thereby reducing any detectable changes in HbT and thus providing a poor measure of CBV. The authors now show, in Fig. 1k, small CBV responses to hind paw stimulation that decrease during hypercapnia. The point is that the CBV signal is not optimized (due to the wrong wavelength used), and since CO₂ modifies O₂ binding to haemoglobin (Bohr effect) and thus alters the HbO/HbR ratio, no reliable conclusion can be drawn from measurements at only 561 nm. Consequently, the conclusion that “the loss of the BOLD NVC response is accompanied by a loss of the CBV/HbT response” is incorrect. The issue of whether CBV responses accompany or not the “absence” of BOLD responses remains unresolved and crucial for the validity of the paper. The authors must use an appropriate laser wavelength to measure CBV changes during NVC as well as in response to the CO₂ challenge, which is still missing. This later straightforward experiment is essential because the authors regressed calcium transients using the \"hemoglobin\" signal (Fig. 1e,f). Regarding the calcium spikes, which are now referred to as calcium transients, we remain in the dark about their nature. Could the authors at least show, in a supplementary figure, what they are referring to? We suggest simply expanding portions of the traces shown in Fig. 1e and f. This would help the reader understand the shape of these fluctuations. In their current presentation, they appear to represent some form of background noise (which amplitude could be modulated by pH). Are these fluctuations symmetrical? Is it possible to analyze their frequency? These transients seem to contaminate the data. For example, in the upper black trace in Fig. 1j, if one draws a line through the resting period (the first 5 seconds before stimulation), it appears that there is no fluorescence increase and possibly even a decrease after 10 seconds. Could the authors comment on this point? Clearly, more information is needed about these calcium “transients.” Minor points: - Please describe the IOSI methodology in the methods section. - Please clearly indicate in Figure 1 and/or its legend the regions of interest (ROIs) from which the traces in panels i–m were extracted, in relation to panels a–b and g–h. We assume that panels i, j, and k correspond to the same ROI in the somatosensory cortex (as suggested by the heat maps in g–h), but this should be explicitly confirmed. - The ketamine/xylazine anesthesia (100 mg/kg and 10 mg/kg, respectively) cannot be described as “light anesthesia” (lines 67 and 73), as these doses correspond to a surgical level of anesthesia, i.e. a deep anesthesia. - References 11, 13, and 14 are not correctly cited in the introduction. - Error bars are not consistently defined across figures; the only mention of SEM appears in line 418. - Several axes are difficult to read due to missing or misaligned tick marks. Please check carefully (e.g. Fig.1m). - Line 354: the phrase “8/20 s onset time, and 90 s stimulus repetition cycle” is unclear. Based on Figure 1, this likely refers to an 8 or 20 s stimulus duration rather than onset time. - The Methods section states that respiration, heart rate, and SpO₂ were monitored, but no data are shown. These physiological parameters are important to interpret both spontaneous neural activity and baseline vascular tone and examples should be reported in a supplementary figure. - In Figure 5, legends e and f appear inverted.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Reply: We apologize if some of our statements made the reviewer believe that our study was aimed at providing quantitative, oxygenation-independent HbT or CBV measurements. We fully agree that 561-nm is not strictly an isosbestic wavelength with the nearest high-contrast hemoglobin isosbestic region being in the ~570-nm band. Hence, the ISOI readings can be influenced by the relative balance of ΔHbO and ΔHbR. Consequently, hypercapnia-related changes in baseline oxygenation physiology, including shifts in blood gas chemistry and oxygen binding, can alter the composition of a single-wavelength intrinsic signal. As a result, single-wavelength measurement at 561 nm should not be treated as a quantitative, oxygenation-independent HbT or CBV metric. We have revised the manuscript to (1) explicitly refer to this channel as a “hemoglobin-weighted intrinsic reflectance signal” (ΔR/R at 561 nm) rather than “HbT/CBV”, and (2) add a concise description of the spectral weighting and its implications under hypercapnia (lines 100–103, 318–322). At the same time, it is also important to clarify that the 561 nm measurements had a different purpose, which is not affected by the fact that this wavelength cannot be used for retrieving calibrated or oxygenation-independent HbT/CBV traces. Rather, it provides a simultaneously measured intrinsic reflectance signal that tracks hemoglobin-driven optical fluctuations that contaminate wide-field fluorescence through time-varying absorption (and, to a lesser extent, scattering). In wide-field GCaMP imaging, this crosstalk is governed by hemoglobin-dependent optical density changes along the excitation and emission paths and therefore reflects both blood-volume-related and oxygenation-related contributions through ΔHbO and ΔHbR. Accordingly, for regression-based correction, the reference channel need not be HbT-specific; instead, it must capture the dominant absorption-driven variance that couples into the fluorescence channel. In this context, regression is a statistical nuisance-removal step: we remove the component of the GCaMP time series that covaries with the hemoglobin-weighted reflectance signal, without interpreting the reflectance trace itself as HbT/CBV. Because oxygenation-related absorption changes contribute to the very confound we aim to remove from fluorescence, a non-isosbestic hemoglobin-sensitive wavelength remains suitable for regression, whereas strict isosbesticity is primarily required when the goal is to interpret the reflectance signal itself as an oxygenation-independent HbT/CBV metric. We also acknowledge that hypercapnia-induced oxygenation shifts could alter the relative weighting of HbO versus HbR contributions at 561 nm and thus modulate the apparent ΔR/R amplitude. However, this does not undermine the central conclusion of evoked neurovascular uncoupling for two reasons. (i) We quantify ΔR/R relative to the immediate pre-stimulus baseline within each block, which reduces sensitivity to slow baseline offsets between gas conditions and focuses the analysis on stimulus-locked reactivity. (ii) While 561 nm is not HbT-specific and partial ΔHbO/ΔHbR cancellation is possible at a single wavelength, 561 nm remains strongly hemoglobin-weighted; therefore, a robust stimulus-evoked vascular response would be expected to produce a measurable intrinsic reflectance modulation. The near-complete suppression of stimulus-locked intrinsic reflectance transients during hypercapnia, together with preserved neuronal/astrocytic Ca²⁺ responses, is therefore unlikely to be explained solely by spectral weighting and instead supports attenuated stimulus-evoked hemodynamic reactivity under hypercapnic conditions. Importantly, we now describe this optical readout conservatively as hemoglobin-weighted (not quantitative CBV/HbT). 2) Regarding the calcium spikes, which are now referred to as calcium transients, we remain in the dark about their nature. Could the authors at least show, in a supplementary figure, what they are referring to? We suggest simply expanding portions of the traces shown in Fig. 1e and f. This would help the reader understand the shape of these fluctuations. In their current presentation, they appear to represent some form of background noise (which amplitude could be modulated by pH). Are these fluctuations symmetrical? Is it possible to analyze their frequency? These transients seem to contaminate the data. For example, in the upper black trace in Fig. 1j, if one draws a line through the resting period (the first 5 seconds before stimulation), it appears that there is no fluorescence increase and possibly even a decrease after 10 seconds. Could the authors comment on this point? Clearly, more information is needed about these calcium “transients.” Reply: We thank the reviewer for raising this important point. We agree that, in the original submission, the fast fluctuations in the wide-field GCaMP signal were insufficiently visualized and could therefore be misconstrued as background noise. In the revised manuscript, we have expanded both the presentation and analysis to explicitly characterize these events in the time domain and frequency domain, and we now include zoomed-in trace segments as suggested. Wide-field GCaMP imaging does not report single-unit spiking; rather, it measures mesoscale population activity with strong contributions from neuropil/dendritic signals (particularly in Thy1-GCaMP lines). Importantly, cortex-wide GCaMP signals robustly contain a fast component on second time scales (delta-range), which has been repeatedly shown to reflect physiologically meaningful neural dynamics (not camera noise) and to yield reproducible functional connectivity (FC) structure across cortical areas. For example, Wright et al. demonstrated that band-limited wide-field calcium activity in the delta range produces stable FC maps with clear network organization and strong state dependence (awake vs anesthetized) [1]. Likewise, simultaneous wide-field Ca²⁺–fMRI work has shown that separating calcium into a slow component aligned to BOLD and a faster component reveals meaningful and interpretable network structure that is partially shared with, but also distinct from, BOLD-derived organization [2]. These prior results provide a clear framework for interpreting the fast transients in our recordings as an expected component of mesoscale calcium dynamics rather than background noise. Following the reviewer’s suggestion, we now include expanded trace segments focusing on the fast calcium transients (new Supplementary Fig. S6). The expanded view demonstrates that these fluctuations are structured events with stereotyped kinetics (typically a rapid rise followed by a slower decay), rather than isolated outliers or camera noise. Importantly, these events are present under both normocapnia and hypercapnia; in our data, hypercapnia primarily modulates their amplitude envelope, rather than generating an entirely new fluctuation class. This event-like morphology and recurrence on second time scales closely match previously described mesoscale calcium transients in the wide-field literature (e.g., Figs. 6–7 in [1]; Fig. 2 in [3]; Fig. 5 in [4]). Because the traces in Fig. 1f/S6 reflect hemoglobin-regressed and band-pass–filtered signals (0.2–4 Hz), they are effectively zero-mean; hence the signal naturally oscillates around baseline and takes both positive and negative values. This does not indicate artifactual sign flipping; it reflects fluctuations around baseline after removal of slow drifts and vascular confounds. At the event level, perfect symmetry is not expected: GCaMP indicator kinetics and population dynamics typically yield faster rising phases and slower decays, producing the asymmetric waveform shape that is clearly visible in the expanded traces. We now provide a frequency-domain analysis of the fast component (new Supplementary Fig. S7). The power spectral density (PSD) shows broadband power within the passband together with reproducible spectral structure across animals, rather than the flat spectrum expected for white camera noise, consistent with prior mesoscale calcium imaging reports [1]. Based on both prior literature and our data, these fast transients are not a contaminant to be removed; rather, they are a core feature of wide-field calcium dynamics and are widely leveraged to compute biologically meaningful FC. In our study, we compute an FC matrix from the fast calcium component (Fig. 3a) and use it as an empirical neural reference to assess which low-frequency FL/BOLD states best align with neural dynamics (Fig. 3b–c). Critically, we show that the fast-transient FC is highly stable across time windows in our recordings (Supplementary Fig. S15), which mirrors prior findings that calcium-derived FC maps are robust, interpretable, and state dependent [1–3]. Regarding the apparent attenuation and occasional undershoot of the corrected fluorescence trace during the 0.5 mA, 20 s stimulus, we emphasize that this behavior is well within the expected regime for prolonged sensory/electrical drive and does not imply that the response is noise. Sustained stimulation commonly evokes a strong onset response followed by adaptation during the ongoing train, attributable to well-established circuit mechanisms including thalamocortical short-term synaptic depression and activity-dependent recruitment of inhibition/excitation rebalancing [7]. Such adaptation produces a decay in calcium responses during a maintained stimulus and is expected to be more pronounced for long trains (20 s) than for short trains (8 s), consistent with our observation that the late decrease is not prominent in the 1 mA, 8 s and 0.5 mA, 8 s protocols. Finally, post-stimulation suppression (i.e., reduced activity after the train) has been directly observed in calcium imaging studies of prolonged electrical stimulation (including intracortical microstimulation), with suppression detectable in calcium and electrophysiological measures [8–10]; thus these studies demonstrate that prolonged electrical drive can elicit duration-dependent suppression and offset aftereffects consistent with the directionality of the late-phase decrease observed in our longest stimulation condition. [1] Wright PW, Brier LM, Bauer AQ, et al. Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice. PLoS One. 2017;12(10):e0185759. Published 2017 Oct 19. doi:10.1371/journal.pone.0185759 [2] Vafaii H, Mandino F, Desrosiers-Grégoire G, et al. Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization. Nat Commun. 2024;15(1):229. Published 2024 Jan 3. doi:10.1038/s41467-023-44363-z [3] Grienberger C, Adelsberger H, Stroh A, et al. Sound-evoked network calcium transients in mouse auditory cortex in vivo. J Physiol. 2012;590(4):899-918. doi:10.1113/jphysiol.2011.222513 [4] Vanni MP, Murphy TH. Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex. J Neurosci. 2014;34(48):15931-15946. doi:10.1523/JNEUROSCI.1818-14.2014 [5] Musall S, Kaufman MT, Juavinett AL, Gluf S, Churchland AK. Single-trial neural dynamics are dominated by richly varied movements. Nat Neurosci. 2019;22(10):1677-1686. doi:10.1038/s41593-019-0502-4 [6] Chen Z, Zhou Q, Deán-Ben XL, Gezginer I, Ni R, Reiss M, Shoham S, Razansky D. Multimodal Noninvasive Functional Neurophotonic Imaging of Murine Brain-Wide Sensory Responses. Adv Sci (Weinh). 2022 Aug;9(24):e2105588. doi: 10.1002/advs.202105588. Epub 2022 Jul 7. PMID: 35798308; PMCID: PMC9404388. [7] Chung S, Li X, Nelson SB. Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo. Neuron. 2002 Apr 25;34(3):437-46. doi: 10.1016/s0896-6273(02)00659-1. PMID: 11988174. [8] Wu GK, Ardeshirpour Y, Mastracchio C, Kent J, Caiola M, Ye M. Amplitude- and frequency-dependent activation of layer II/III neurons by intracortical microstimulation. iScience. 2023 Oct 6;26(11):108140. doi: 10.1016/j.isci.2023.108140. PMID: 37915592; PMCID: PMC10616374. [9] Hughes, C. L., Stieger, K. C., Chen, K., Vazquez, A. L., & Kozai, T. D. (2025). Spatiotemporal properties of cortical excitatory and inhibitory neuron activation by sustained and bursting electrical microstimulation. iScience, 28(6). [10] Stieger KC, Eles JR, Ludwig KA, Kozai TDY. Intracortical microstimulation pulse waveform and frequency recruits distinct spatiotemporal patterns of cortical neuron and neuropil activation. J Neural Eng. 2022 Mar 31;19(2):10.1088/1741-2552/ac5bf5. doi: 10.1088/1741-2552/ac5bf5. PMID: 35263736; PMCID: PMC9171725. Minor points: 3) Please describe the IOSI methodology in the methods section. Reply: We have revised the Methods to clarify the IOSI methodology (line 308-317). 4) Please clearly indicate in Figure 1 and/or its legend the regions of interest (ROIs) from which the traces in panels i–m were extracted, in relation to panels a–b and g–h. We assume that panels i, j, and k correspond to the same ROI in the somatosensory cortex (as suggested by the heat maps in g–h), but this should be explicitly confirmed. Reply: Indeed, the panels i-k correspond to the same ROI from somatosensory cortex. We now denote the ROI in Figs. 1g, h and explicitly mention it in the Fig. 1 legend. 5) The ketamine/xylazine anesthesia (100 mg/kg and 10 mg/kg, respectively) cannot be described as “light anesthesia” (lines 67 and 73), as these doses correspond to a surgical level of anesthesia, i.e. a deep anesthesia. Reply: This is a valid point. Indeed, the induction dose of ketamine/xylazine (100/10 mg/kg) produces a deep surgical level of anesthesia. Importantly, all imaging was performed under a substantially lower maintenance dose (25/1.25 mg/kg), which corresponds to a lighter and more stable anesthesia plane commonly used in functional imaging studies. We have revised the text to avoid referring to the induction stage as “light anesthesia” and now explicitly state that fMRI acquisition was carried out under the reduced maintenance regimen, where the anesthetic depth is lighter (lines 346–348). 6) References 11, 13, and 14 are not correctly cited in the introduction. Reply: We thank the reviewer for pointing out this issue. After re-examining the cited literature, we confirmed that the placement of Ref. 11 (Hosford et al., 2022) in the first sentence was appropriate, as this study reports the abolition of stimulus-evoked BOLD responses under hypercapnia. However, we agree that its inclusion in the second sentence was not accurate, as the mechanistic dissociation between hypercapnic and functional hyperemia is specifically addressed by Refs. 13 (Tournissac et al., 2024) and 14 (Gordon, 2024). For clarity, the original sentences read: “Exogeneous CO₂ has been shown to occlude functional hyperemia, the increase in blood flow associated with sensory stimulation [11, 12]. Conversely, it has recently been reported that hypercapnic hyperemia and functional hyperemia do not activate the same vasodilatory signaling pathway mediating cerebrovascular responses [11, 13, 14].” To correct this mismatch, we have removed Ref. 11 from the second sentence so that only the studies directly demonstrating mechanistic divergence (Refs. 13 and 14) are cited. 7) Error bars are not consistently defined across figures; the only mention of SEM appears in line 418. Reply: We have now ensured that error bars are explicitly defined in all relevant figures and figure panels, and we confirm that they consistently represent the SEM. 8) Several axes are difficult to read due to missing or misaligned tick marks. Please check carefully (e.g. Fig.1m). Reply: We carefully reviewed all figures and corrected missing or misaligned tick marks. We also adjusted the tick mark placement so that any previously occluded ticks now appear clearly (e.g., by placing them outside the axis when necessary). 9) Line 354: the phrase “8/20 s onset time, and 90 s stimulus repetition cycle” is unclear. Based on Figure 1, this likely refers to an 8 or 20 s stimulus duration rather than onset time. Reply: The phrase “8/20 s onset time” was indeed unclear. We have now revised the text to explicitly state that these values refer to stimulus train durations (line 365-367). 10) The Methods section states that respiration, heart rate, and SpO₂ were monitored, but no data are shown. These physiological parameters are important to interpret both spontaneous neural activity and baseline vascular tone and examples should be reported in a supplementary figure. Reply: The physiological parameters are reported in the new Supplementary Fig. S27. 11) In Figure 5, legends e and f appear inverted. Reply: We thank the reviewer for catching this error – the legends are now corrected in the revised manuscript."}, {"round": 4, "reviewer_comments": {"reviewer_2": "Gezinger et al. have appropriately tempered the conclusions of some of their experiments. We understand that they do not wish to repeat their IOSI experiments using an appropriate laser wavelength, i.e., one at an isosbestic point of the hemoglobin absorption spectrum. Still, we have repeatedly asked the authors to present the IOSI data acquired at 561 nm during CO₂ stimulation. The authors possess the relevant data, and since the \"pseudo CBV\" signal is important to estimate the effect of hemoglobin on fluorescence signals, they should present the IOSI responses to CO₂ in a supplementary figure. If these responses mirror the dynamics of the BOLD responses shown in Supplementary Figure S10, then the use of the “CBV” signal could be considered acceptable. Otherwise and also in light of the effects of CO₂ on the BOLD signal (Kennerley et al., 2012), we continue to find the data difficult to interpret.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Reply: We apologize for the initial misunderstanding; we had interpreted the previous request for “CBV responses” as specifically referring to the electrical stimulation experiments. To address the reviewer’s concern regarding CO 2 stimulation, we now include the IOSI data acquired at 561 nm during hypercapnia in a new Supplementary Fig. S12. This figure illustrates the time-resolved IOSI response during 5% and 10% CO inhalation alongside summary 2 quantification. Critically, the IOSI responses closely mirror the BOLD dynamics previously shown in Supplementary Fig. S10. Furthermore, the across-animal scaling of the IOSI signal from 5% to 10% CO correlates strongly with the 2 corresponding BOLD amplitude increases (new Supplementary Fig. S12, line 108-110). These results validate our use of the 561 nm IOSI-derived signal as a reliable proxy for capturing the hemodynamic changes necessary to interpret and account for hemoglobin contributions to our fluorescence readouts."}, {"round": 5, "reviewer_comments": {"reviewer_2": "The authors have responded to our final question.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter