Explore the vast range of titles available.

Respiration can induce motion and CO 2 fluctuation during resting-state fMRI (rsfMRI) scans, which will lead to non-neural artifacts in the rsfMRI signal. In the meantime, as a crucial physiologic process, respiration can directly drive neural activity change in the brain, and may thereby modulate the rsfMRI signal. Nonetheless, this potential neural component in the respiration–fMRI relationship is largely unexplored. To elucidate this issue, here we simultaneously recorded the electrophysiology, rsfMRI, and respiration signals in rats. Our data show that respiration is indeed associated with neural activity changes, evidenced by a phase-locking relationship between slow respiration variations and the gamma-band power of the electrophysiological signal recorded in the anterior cingulate cortex. Intriguingly, slow respiration variations are also linked to a characteristic rsfMRI network, which is mediated by gamma-band neural activity. In addition, this respiration-related brain network disappears when brain-wide neural activity is silenced at an isoelectrical state, while the respiration is maintained, further confirming the necessary role of neural activity in this network. Taken together, this study identifies a respiration-related brain network underpinned by neural activity, which represents a novel component in the respiration–rsfMRI relationship that is distinct from respiration-related rsfMRI artifacts. It opens a new avenue for investigating the interactions between respiration, neural activity, and resting-state brain networks in both healthy and diseased conditions. What does the brain do when we breathe? Humans and other animals with lungs depend on breathing to supply their cells with oxygen for energy production. Neurons in the brain are supplied oxygen through an intricate system of blood vessels. When active, neurons consume a lot of energy and require a steady supply of oxygen-rich blood. In fact, this relationship between blood vessels and activity of neurons in the brain is so tightly linked that to study neuron activity researchers and clinicians often use an approach called functional magnetic resonance imaging (fMRI) to analyze the flow of oxygenated blood in the brain. This imaging technique allows scientists to map how active different parts of the brain are at any given time without the need for an invasive medical procedure. Unfortunately, fMRI results are affected by the cycles of inhalation and exhalation that take place while breathing, even when an individual is at rest. This is because the rate and depth of respiration can vary, resulting in the body moving unpredictably and in CO 2 levels fluctuating in the brain, which can lead to changes in fMRI signals that do not correlate with neuron activity. Such misleading measurements are called ‘artifacts’. The assumption that these fMRI results do not represent real brain activity has meant that the effects of breathing on neuron activity in different parts of the brain is poorly understood. To solve this issue, Tu and Zhang performed fMRI on rats and combined the results with measurements of the depth and rate of respiration, and with electrophysiology, an approach that allowed them to directly record the electrical properties of neurons. This allowed them to map out the network of neurons that become active in response to breathing. The results show that breathing leads to a specific fMRI signal that can be distinguished from the artifacts introduced by fluctuating CO 2 levels and body movements . The signal correlates with the activity of neurons measured using electrophysiology and with breathing patterns, and it disappears when the electrical activity of neurons in the brain is suppressed, even if the rats are still breathing. This suggests that breathing affects brain activity that is independent of the previously described artifacts. Future studies may focus on how the brain responds to breathing or how respiration itself is controlled by the brain, with the methods developed allowing researchers to explore regions of the brain that increase their activity while breathing. This clears the path towards investigating the neural mechanisms underlying therapies and exercises that focus on breathing.

•BOLD and calcium signals were simultaneously measured in awake rats.•Robust couplings between calcium and BOLD signals were observed.•The efficacy of different rsfMRI data preprocessing pipelines was assessed basedon Ca2+ data. The blood oxygenation level-dependent (BOLD)-based resting-state functional magnetic resonance imaging (rsfMRI) has been widely used as a non-invasive tool to map brain-wide connectivity architecture. However, the neural basis underpinning the resting-state BOLD signal remains elusive. In this study, we combined simultaneous calcium-based fiber photometry with rsfMRI in awake animals to examine the relationship of the BOLD signal and spiking activity at the resting state. We observed robust couplings between calcium and BOLD signals in the dorsal hippocampus as well as other distributed areas in the default mode network (DMN), suggesting that the calcium measurement can reliably predict the rsfMRI signal. In addition, using the calcium signal recorded as the ground truth, we assessed the impacts of different rsfMRI data preprocessing pipelines on functional connectivity mapping. Overall, our results provide important evidence suggesting that spiking activity measured by the calcium signal plays a key role in the neural mechanism of resting-state BOLD signal.

The architecture of brain networks has been extensively studied in multiple species. However, exactly how the brain network reconfigures when a local region, particularly a hub region, stops functioning remains elusive. By combining chemogenetics and resting-state functional magnetic resonance imaging (rsfMRI) in an awake rodent model, we investigated the causal impact of acutely inactivating a hub region (i.e. the dorsal anterior cingulate cortex) on brain network properties. We found that suppressing neural activity in a hub could have a ripple effect that went beyond the hub-related connections and propagated to other neural connections across multiple brain systems. In addition, hub dysfunction affected the topological architecture of the whole-brain network in terms of the network resilience and segregation. Selectively inhibiting excitatory neurons in the hub further changed network integration. None of these changes were observed in sham rats or when a non-hub region (i.e. the primary visual cortex) was perturbed. This study has established a system that allows for mechanistically dissecting the relationship between local regions and brain network properties. Our data provide direct evidence supporting the hypothesis that acute dysfunction of a brain hub can cause large-scale network changes. These results also provide a comprehensive framework documenting the differential impact of hub versus non-hub nodes on network dynamics.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by ‘electrophysiology-invisible’ signals. These findings offer a novel perspective on our understanding of RSN interpretation. The brain contains many cells known as neurons that send and receive messages in the form of electrical signals. The neurons in different regions of the brain must coordinate their activities to enable the brain to operate properly. Researchers often use a method called resting-state functional magnetic resonance imaging (rsfMRI) to study how different areas of the brain work together. This method indirectly measures brain activity by detecting the changes in blood flow to different areas of the brain. Regions that are working together will become active (that is, have higher blood flow and corresponding rsfMRI signal) and inactive (have lower blood flow and a lower rsfMRI signal) at the same time. These coordinated patterns of brain activity are known as “resting-state brain networks” (RSNs). Previous studies have identified RSNs in many different situations, but we still do not fully understand how these changes in blood flow are related to what is happening in the neurons themselves. To address this question, Tu et al. performed rsfMRI while also measuring the electrical activity (referred to as electrophysiology signals) in two distinct regions of the brains of rats. The team then used the data to generate maps of RSNs in those brain regions. This revealed that rsfMRI signals and electrophysiology signals produced almost identical maps in terms of the locations of the RSNs. However, the electrophysiology signals only contributed a small amount to the changes in the local rsfMRI signals over time at the same recording site. This suggests that RSNs may arise from cell activities that are not detectable by electrophysiology but do regulate blood flow to neurons. The findings of Tu et al. offer a new perspective for interpreting how rsfMRI signals relate to the activities of neurons. Further work is needed to explore all the features of the electrophysiology signals and test other methods to compare these features with rsfMRI signals in the same locations.

Rodent models are essential to translational research in health and disease. Investigation in rodent brain function and organization at the systems level using resting-state functional magnetic resonance imaging (rsfMRI) has become increasingly popular. Due to this rapid progress, publicly shared rodent rsfMRI databases can be of particular interest and importance to the scientific community, as inspired by human neuroscience and psychiatric research that are substantially facilitated by open human neuroimaging datasets. However, such databases in rats are still rare. In this paper, we share an open rsfMRI database acquired in 90 rats with a well-established awake imaging paradigm that avoids anesthesia interference. Both raw and preprocessed data are made publicly available. Procedures in data preprocessing to remove artefacts induced by the scanner, head motion and non-neural physiological noise are described in details. We also showcase inter-regional functional connectivity and functional networks obtained from the database.

The brain undergoes a protracted, metabolically expensive maturation process from childhood to adulthood. Therefore, it is crucial to understand how network cost is distributed among different brain systems as the brain matures. To address this issue, here we examined developmental changes in wiring cost and brain network topology using resting-state functional magnetic resonance imaging (rsfMRI) data longitudinally collected in awake rats from the juvenile age to adulthood. We found that the wiring cost increased in the vast majority of cortical connections but decreased in most subcortico-subcortical connections. Importantly, the developmental increase in wiring cost was dominantly driven by long-range cortical, but not subcortical connections, which was consistent with more pronounced increase in network integration in the cortical network. These results collectively indicate that there is a non-uniform distribution of network cost as the brain matures, and network resource is dominantly consumed for the development of the cortex, but not subcortex from the juvenile age to adulthood.

With the rapid development of urban motorized transportation, the narrow and aging streets in historical and cultural districts can no longer meet modern traffic demands. The development of pedestrian systems and the improvement in street walkability have become important issues in the preservation and renewal of these districts. Although walkability research has established a relatively systematic theoretical framework and technical methods, current studies predominantly focus on modern urban roads due to limited attention to the unique characteristics of streets within historical and cultural districts. As a mixed-use area integrating residential, commercial, and tourism functions, the former Russian concession in Hankou features diverse street types and a rich spatial texture, making it a representative case for walkability research in historical districts. This study aimed to construct a walkability evaluation framework suited to the characteristics of such districts. First, relevant literature was reviewed and combined with the actual conditions of streets in the study area to select evaluation indicators and reconstruct the framework. Second, based on multi-source data, a comprehensive evaluation was conducted using spatial syntax, semantic segmentation, and GIS spatial analysis. The results show that streets with high walkability scores are mainly concentrated in the core tourism area and are strongly associated with the distribution of historical buildings. Finally, based on the evaluation results, three groups of representative streets were compared to analyze differences in pedestrian environments. Key issues such as low spatial quality and functional disorder were identified, and targeted optimization strategies are proposed. The findings provide useful references for the future preservation and sustainable renewal of historical and cultural districts.

With the intensification of climate challenges driven by rapid urbanization, the microclimate and thermal comfort of residential blocks have attracted increasing attention. Current research predominantly focuses on isolated morphological factors—such as building orientation, layout patterns, and height-to-width ratios—while neglecting the synergistic effects of multifactorial spatial configurations on outdoor thermal comfort. This study addresses this gap by analyzing 36 residential block samples in Wuhan, a representative city in a hot-summer and cold-winter (HSCW) region. Utilizing the Honeybee plugin in Grasshopper (GH) alongside the Universal Thermal Climate Index (UTCI), we simulate outdoor thermal environments to identify critical influencing elements. The results reveal how multifactor interactions shape thermal performance, providing evidence-based design strategies to optimize microclimate resilience in high-density urban contexts. This work advances the understanding of spatial morphology–thermal dynamics and offers practical insights for sustainable residential planning. This study systematically investigates the thermal performance of residential blocks through parametric prototyping and seasonal simulations. Sixteen morphological prototypes were developed by combining four building layout typologies (arrayed, staggered, enclosed, and hybrid) with three critical variables: the height-to-width ratio (HWR), building orientation deviation angle (θ), and sky visibility factor (SVF). Key findings reveal the following: (1) the hybrid layout demonstrates superior annual thermal adaptability when integrating fixed orientation (θ = 0°), moderate H/W = 1, and SVF = 0.4; (2) increased H/W ratios enhance thermal comfort levels across all layout configurations, particularly in winter wind protection; and (3) moderate orientation deviations (15° < θ < 30°) significantly improve microclimate performance in modular layouts by optimizing solar penetration and aerodynamic patterns. These evidence-based insights provide actionable guidelines for climate-responsive residential design in transitional climate zones, effectively balancing summer heat mitigation and winter cold prevention through spatial configuration optimization.

Studying brain‐wide hemodynamic responses to different stimuli at high spatiotemporal resolutions can help gain new insights into the mechanisms of neuro‐ diseases and ‐disorders. Nonetheless, this task is challenging, primarily due to the complexity of neurovascular coupling, which encompasses interdependent hemodynamic parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), and cerebral oxygen saturation (SO2). The current brain imaging technologies exhibit inherent limitations in resolution, sensitivity, and imaging depth, restricting their capacity to comprehensively capture the intricacies of cerebral functions. To address this, a multimodal functional ultrasound and photoacoustic (fUSPA) imaging platform is reported, which integrates ultrafast ultrasound and multispectral photoacoustic imaging methods in a compact head‐mountable device, to quantitatively map individual dynamics of CBV, CBF, and SO2 as well as contrast agent enhanced brain imaging at high spatiotemporal resolutions. Following systematic characterization, the fUSPA system is applied to study brain‐wide cerebrovascular reactivity (CVR) at single‐vessel resolution via relative changes in CBV, CBF, and SO2 in response to hypercapnia stimulation. These results show that cortical veins and arteries exhibit differences in CVR in the stimulated state and consistent anti‐correlation in CBV oscillations during the resting state, demonstrating the multiparametric fUSPA system's unique capabilities in investigating complex mechanisms of brain functions. In this work, a multimodal functional ultrasound and photoacoustic (fUSPA) imaging system capable of comprehensively mapping brain‐wide hemodynamics (microvascular blood volume, blood flow, oxygen saturation, and vasoreactivity) in response to various stimuli with high spatiotemporal resolutions is presented. The head‐mountable fUSPA device has the potential to gain new insights into the mechanisms of several neurological disorders, including studies on behaving and freely moving animals.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by ‘electrophysiology-invisible’ signals. These findings offer a novel perspective on our understanding of RSN interpretation. The brain contains many cells known as neurons that send and receive messages in the form of electrical signals. The neurons in different regions of the brain must coordinate their activities to enable the brain to operate properly. Researchers often use a method called resting-state functional magnetic resonance imaging (rsfMRI) to study how different areas of the brain work together. This method indirectly measures brain activity by detecting the changes in blood flow to different areas of the brain. Regions that are working together will become active (that is, have higher blood flow and corresponding rsfMRI signal) and inactive (have lower blood flow and a lower rsfMRI signal) at the same time. These coordinated patterns of brain activity are known as “resting-state brain networks” (RSNs). Previous studies have identified RSNs in many different situations, but we still do not fully understand how these changes in blood flow are related to what is happening in the neurons themselves. To address this question, Tu et al. performed rsfMRI while also measuring the electrical activity (referred to as electrophysiology signals) in two distinct regions of the brains of rats. The team then used the data to generate maps of RSNs in those brain regions. This revealed that rsfMRI signals and electrophysiology signals produced almost identical maps in terms of the locations of the RSNs. However, the electrophysiology signals only contributed a small amount to the changes in the local rsfMRI signals over time at the same recording site. This suggests that RSNs may arise from cell activities that are not detectable by electrophysiology but do regulate blood flow to neurons. The findings of Tu et al. offer a new perspective for interpreting how rsfMRI signals relate to the activities of neurons. Further work is needed to explore all the features of the electrophysiology signals and test other methods to compare these features with rsfMRI signals in the same locations.