Interactions between stimuli-evoked cortical activity and spontaneous low frequency oscillations measured with neuronal calcium

From Mechanisms to Medicine: Neurovascular Coupling in the Diagnosis and Treatment of Cerebrovascular Disorders: A Narrative Review

Neurovascular coupling (NVC) refers to the process of local changes in cerebral blood flow (CBF) after neuronal activity, which ensures the timely and adequate supply of oxygen, glucose, and substrates to the active regions of the brain. Recent clinical imaging and experimental technology advancements have deepened our understanding of the cellular mechanisms underlying NVC. Pathological conditions such as stroke, subarachnoid hemorrhage, cerebral small vascular disease, and vascular cognitive impairment can disrupt NVC even before clinical symptoms appear. However, the complexity of the underlying mechanism remains unclear. This review discusses basic and clinical experimental evidence on how neural activity sensitively communicates with the vasculature to cause spatial changes in blood flow in cerebrovascular diseases. A deeper understanding of how neurovascular unit-related cells participate in NVC regulation is necessary to better understand blood flow and nerve activity recovery in cerebrovascular diseases.

ECoG activity distribution patterns detects global cortical responses following weak tactile inputs

Many studies have suggested that the neocortex operates as a global network of functionally interconnected neurons, indicating that any sensory input could shift activity distributions across the whole brain. A tool assessing the activity distribution across cortical regions with high temporal resolution could then potentially detect subtle changes that may pass unnoticed in regionalized analyses. We used eight-channel, distributed electrocorticogram (ECoG) recordings to analyze changes in global activity distribution caused by single pulse electrical stimulations of the paw. We analyzed the temporally evolving patterns of the activity distributions using principal component analysis (PCA). We found that the localized tactile stimulation caused clearly measurable changes in global ECoG activity distribution. These changes in signal activity distribution patterns were detectable across a small number of ECoG channels, even when excluding the somatosensory cortex, suggesting that the method has high sensitivity, potentially making it applicable to human electroencephalography (EEG) for detection of pathological changes.

The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity

One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14~62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.

Identifying the distinct spectral dynamics of laminar-specific interhemispheric connectivity with bilateral line-scanning fMRI

Despite extensive efforts to identify interhemispheric functional connectivity (FC) with resting-state (rs-) fMRI, correlated low-frequency rs-fMRI signal fluctuation across homotopic cortices originates from multiple sources. It remains challenging to differentiate circuit-specific FC from global regulation. Here, we developed a bilateral line-scanning fMRI method to detect laminar-specific rs-fMRI signals from homologous forepaw somatosensory cortices with high spatial and temporal resolution in rat brains. Based on spectral coherence analysis, two distinct bilateral fluctuation spectral features were identified: ultra-slow fluctuation (<0.04 Hz) across all cortical laminae versus Layer (L) 2/3-specific evoked BOLD at 0.05 Hz based on 4 s on/16 s off block design and resting-state fluctuations at 0.08-0.1 Hz. Based on the measurements of evoked BOLD signal at corpus callosum (CC), this L2/3-specific 0.05 Hz signal is likely associated with neuronal circuit-specific activity driven by the callosal projection, which dampened ultra-slow oscillation less than 0.04 Hz. Also, the rs-fMRI power variability clustering analysis showed that the appearance of L2/3-specific 0.08-0.1 Hz signal fluctuation is independent of the ultra-slow oscillation across different trials. Thus, distinct laminar-specific bilateral FC patterns at different frequency ranges can be identified by the bilateral line-scanning fMRI method.

Brain mechanisms of mental processing: from evoked and spontaneous brain activities to enactive brain activity

Within the context of the computer metaphor, evoked brain activity acts as a primary carrier for the brain mechanisms of mental processing. However, many studies have found that evoked brain activity is not the major part of brain activity. Instead, spontaneous brain activity exhibits greater intensity and coevolves with evoked brain activity through continuous interaction. Spontaneous and evoked brain activities are similar but not identical. They are not separate parts, but always dynamically interact with each other. Therefore, the enactive cognition theory further states that the brain is characterized by unified and active patterns of activity. The brain adjusts its activity pattern by minimizing the error between expectation and stimulation, adapting to the ever-changing environment. Therefore, the dynamic regulation of brain activity in response to task situations is the core brain mechanism of mental processing. Beyond the evoked brain activity and spontaneous brain activity, the enactive brain activity provides a novel framework to completely describe brain activities during mental processing. It is necessary for upcoming researchers to introduce innovative indicators and paradigms for investigating enactive brain activity during mental processing.

The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity

One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14∼62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.

Transparent neural interfaces: challenges and solutions of microengineered multimodal implants designed to measure intact neuronal populations using high-resolution electrophysiology and microscopy simultaneously

The aim of this review is to present a comprehensive overview of the feasibility of using transparent neural interfaces in multimodal in vivo experiments on the central nervous system. Multimodal electrophysiological and neuroimaging approaches hold great potential for revealing the anatomical and functional connectivity of neuronal ensembles in the intact brain. Multimodal approaches are less time-consuming and require fewer experimental animals as researchers obtain denser, complex data during the combined experiments. Creating devices that provide high-resolution, artifact-free neural recordings while facilitating the interrogation or stimulation of underlying anatomical features is currently one of the greatest challenges in the field of neuroengineering. There are numerous articles highlighting the trade-offs between the design and development of transparent neural interfaces; however, a comprehensive overview of the efforts in material science and technology has not been reported. Our present work fills this gap in knowledge by introducing the latest micro- and nanoengineered solutions for fabricating substrate and conductive components. Here, the limitations and improvements in electrical, optical, and mechanical properties, the stability and longevity of the integrated features, and biocompatibility during in vivo use are discussed.

Time-frequency cross-coupling between cortical low-frequency neuronal calcium oscillations and blood oxygen metabolism evoked by ultrasound stimulation

Low-intensity transcranial ultrasound stimulation (TUS) can modulate the coupling of high-frequency (160-200 Hz) neural oscillations and cerebral blood oxygen metabolism (BOM); however, the correlation of low-frequency (0-2 Hz) neural oscillations with BOM in temporal and frequency domains under TUS remains unclear. To address this, we monitored the TUS-evoked neuronal calcium oscillations and BOM simultaneously in the mouse visual cortex by using multimodal optical imaging with a high spatiotemporal resolution. We demonstrated that TUS can significantly increase the intensity of the neuronal calcium oscillations and BOM; the peak value, peak time, and duration of calcium oscillations are functionally related to stimulation duration; TUS does not significantly increase the neurovascular coupling strength between calcium oscillations and BOM in the temporal domain; the time differences of the energy peaks between TUS-induced calcium oscillations and BOM depend on their spectral ranges; the frequency differences of the energy peaks between TUS-induced calcium oscillations and BOM depend on their time ranges; and TUS can significantly change the phase of calcium oscillations and BOM from uniform distribution to a more concentrated region. In conclusion, ultrasound stimulation can evoke the time-frequency cross-coupling between the cortical low-frequency neuronal calcium oscillations and BOM in mouse.

Optical imaging of stimulation-evoked cortical activity using GCaMP6f and jRGECO1a

BACKGROUND

Genetically encoded calcium indicators (GECIs), especially the GCaMP-based green fluorescence GECIs have been widely used for in vivo detection of neuronal activity in rodents by measuring intracellular neuronal Ca²⁺ changes. More recently, jRGECO1a, a red shifted GECI, has been reported to detect neuronal Ca²⁺ activation. This opens the possibility of using dual-color GECIs for simultaneous interrogation of different cell populations. However, there has been no report to compare the functional difference between these two GECIs for in vivo imaging. Here, a comparative study is reported on neuronal responses to sensory stimulation using GCaMP6f and jRGECO1a that were virally delivered into the neurons in the somatosensory cortex of two different groups of animals, respectively.

METHODS

GCaMP6f and jRGECO1a GECI were virally delivered to sensory cortex. After 3-4 weeks, the animals were imaged to capture the spatiotemporal changes of neuronal Ca²⁺ and the hemodynamic responses to forepaw electrical stimulation (0.3 mA, 0.3 ms/pulse, 0.03 Hz). The stimulation-evoked neuronal Ca²⁺ transients expressed with GCaMP6f or jRGECO1a were recorded during the baseline period and after an acute cocaine administration (1 mg/kg, i.v.).

RESULTS

Histology confirmed that the efficiency of jRGECO1a and GCaMP6f expression into the cortical neurons was similar, i.e., 34%±3% and 32.7%±1.6%, respectively. Our imaging in vivo showed that the hemodynamic responses to the stimulation were the same between jRGECO1a and GCaMP6f expressed groups. Although the stimulation-evoked fluorescence change (∆F/F) and the time-to-peak of the neuronal Ca²⁺ transients were not significantly different between these two indicators, the full-width-half-maximum (FWHM) duration of the ∆F/F rise in the jRGECO1a-expressed group (0.16±0.02 s) was ~50 ms or 46% longer than that of the GCaMP6f group (0.11±0.003 s), indicating a longer recovery time in jRGECO1a than in GCaMP6f transients (P<0.01). This is likely due to the longer off rate of jRGECO1a than that of GCaMP6f. After cocaine, the time-to-peak of Ca²⁺ transients was delayed and their FWHM duration was prolonged for both expression groups, indicating that these are cocaine's effects on neuronal Ca²⁺ signaling and not artifacts due to the property differences of the GCEIs.

CONCLUSIONS

This study shows that both jRGECO1a and GCaMP6f have sufficient sensitivity for tracking single-stimulation-evoked Ca²⁺ transients to detect neuronal activities from the brain. Since these GECIs are emitted at the different wavelengths, it will be possible to use them together to characterize the activity of different cell types (e.g., neurons and astrocytes) to study brain activation and brain functional changes in normal or diseased brains.

Peripheral sensory stimulation elicits global slow waves by recruiting somatosensory cortex bilaterally

Slow waves (SWs) are globally propagating, low-frequency (0.5- to 4-Hz) oscillations that are prominent during sleep and anesthesia. SWs are essential to neural plasticity and memory. However, much remains unknown about the mechanisms coordinating SW propagation at the macroscale. To assess SWs in the context of macroscale networks, we recorded cortical activity in awake and ketamine/xylazine-anesthetized mice using widefield optical imaging with fluorescent calcium indicator GCaMP6f. We demonstrate that unilateral somatosensory stimulation evokes bilateral waves that travel across the cortex with state-dependent trajectories. Under anesthesia, we observe that rhythmic stimuli elicit globally resonant, front-to-back propagating SWs. Finally, photothrombotic lesions of S1 show that somatosensory-evoked global SWs depend on bilateral recruitment of homotopic primary somatosensory cortices. Specifically, unilateral lesions of S1 disrupt somatosensory-evoked global SW initiation from either hemisphere, while spontaneous SWs are largely unchanged. These results show that evoked SWs may be triggered by bilateral activation of specific, homotopically connected cortical networks.

Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics

Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascular origin of the fMRI signal, it remains challenging to separate the neural correlates of global rs-fMRI signal fluctuations from other confounding sources. However, the slow-oscillation detected from individual vessels by single-vessel fMRI presents strong correlation to neural oscillations. Here, we use recurrent neural networks (RNNs) to predict the future temporal evolution of the rs-fMRI slow oscillation from both rodent and human brains. The RNNs trained with vessel-specific rs-fMRI signals encode the unique brain oscillatory dynamic feature, presenting more effective prediction than the conventional autoregressive model. This RNN-based predictive modeling of rs-fMRI datasets from the Human Connectome Project (HCP) reveals brain state-specific characteristics, demonstrating an inverse relationship between the global rs-fMRI signal fluctuation with the internal default-mode network (DMN) correlation. The RNN prediction method presents a unique data-driven encoding scheme to specify potential brain state differences based on the global fMRI signal fluctuation, but not solely dependent on the global variance.

A deep-learning-based approach for noise reduction in high-speed optical coherence Doppler tomography

Optical coherence Doppler tomography (ODT) increasingly attracts attention because of its unprecedented advantages with respect to high contrast, capillary-level resolution and flow speed quantification. However, the trade-off between the signal-to-noise ratio of ODT images and A-scan sampling density significantly slows down the imaging speed, constraining its clinical applications. To accelerate ODT imaging, a deep-learning-based approach is proposed to suppress the overwhelming phase noise from low-sampling density. To handle the issue of limited paired training datasets, a generative adversarial network is performed to implicitly learn the distribution underlying Doppler phase noise and to generate the synthetic data. Then a 3D based convolutional neural network is trained and applied for the image denoising. We demonstrate this approach outperforms traditional denoise methods in noise reduction and image details preservation, enabling high speed ODT imaging with low A-scan sampling density.