Breath hold effect on cardiovascular brain pulsations - A multimodal magnetic resonance encephalography study

BOLD cardiorespiratory pulsatility in the brain: from noise to signal of interest

Functional magnetic resonance imaging (fMRI) based on the Blood Oxygen Level Dependent (BOLD) contrast has been extensively used to map brain activity and connectivity in health and disease. Standard fMRI preprocessing includes different steps to remove confounds unrelated to neuronal activity. First, this narrative review explores how signal fluctuations due to cardiac and respiratory activity, usually considered as "physiological noise" and regressed out from fMRI time series. However, these signal components bear useful information about some mechanisms of brain functioning (e.g., glymphatic clearance) or cerebrovascular compliance in response to arterial pressure waves. Aging and chronic diseases can cause stiffening of the aorta and other main arteries, with a reduced dampening effect resulting in greater transmission of pressure impulses to the brain. Importantly, the continuous hammering of cardiac pulsations can produce local alterations of the mechanical properties of the small cerebral vessels, with a progressive deterioration that ultimately affects neuronal functionality. Second, the review emphasizes how fMRI can study the brain patterns most affected by cardiac pulsations in health and disease with high spatiotemporal resolution, offering the opportunity to identify much more specific risk markers than systemic factors based on measurements of the vascular compliance of large arteries or other global risk factors. In this regard, modern fast fMRI acquisition techniques allow a better characterization of these pulsatile signal components due to reduced aliasing effects, turning what has been traditionally considered as noise in a signal of interest that can be used to develop novel non-invasive biomarkers in different clinical contexts.

Enhancing diffuse correlation spectroscopy pulsatile cerebral blood flow signal with near-infrared spectroscopy photoplethysmography

Significance

Combining near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) allows for quantifying cerebral blood volume, flow, and oxygenation changes continuously and non-invasively. As recently shown, the DCS pulsatile cerebral blood flow index (pCBF i ) can be used to quantify critical closing pressure (CrCP) and cerebrovascular resistance (CVR i ).

Aim

Although current DCS technology allows for reliable monitoring of the slow hemodynamic changes, resolving pulsatile blood flow at large source-detector separations, which is needed to ensure cerebral sensitivity, is challenging because of its low signal-to-noise ratio (SNR). Cardiac-gated averaging of several arterial pulse cycles is required to obtain a meaningful waveform.

Approach

Taking advantage of the high SNR of NIRS, we demonstrate a method that uses the NIRS photoplethysmography (NIRS-PPG) pulsatile signal to model DCS pCBF i , reducing the coefficient of variation of the recovered pulsatile waveform (pCBF i - fit ) and allowing for an unprecedented temporal resolution (266 Hz) at a large source-detector separation (> 3    cm ).

Results

In 10 healthy subjects, we verified the quality of the NIRS-PPG pCBF i - fit during common tasks, showing high fidelity against pCBF i (R 2 0.98 ± 0.01 ). We recovered CrCP and CVR i at 0.25 Hz, > 10 times faster than previously achieved with DCS.

Conclusions

NIRS-PPG improves DCS pCBF i SNR, reducing the number of gate-averaged heartbeats required to recover CrCP and CVR i .

Increased interictal synchronicity of respiratory related brain pulsations in epilepsy

Respiratory brain pulsations have recently been shown to drive electrophysiological brain activity in patients with epilepsy. Furthermore, functional neuroimaging indicates that respiratory brain pulsations have increased variability and amplitude in patients with epilepsy compared to healthy individuals. To determine whether the respiratory drive is altered in epilepsy, we compared respiratory brain pulsation synchronicity between healthy controls and patients. Whole brain fast functional magnetic resonance imaging was performed on 40 medicated patients with focal epilepsy, 20 drug-naïve patients and 102 healthy controls. Cerebrospinal fluid associated respiratory pulsations were used to generate individual whole brain respiratory synchronization maps, which were compared between groups. Finally, we analyzed the seizure frequency effect and diagnostic accuracy of the respiratory synchronization defect in epilepsy. Respiratory brain pulsations related to the verified fourth ventricle pulsations were significantly more synchronous in patients in frontal, periventricular and mid-temporal regions, while the seizure frequency correlated positively with synchronicity. The respiratory brain synchronicity had a good diagnostic accuracy (ROC_AUC = 0.75) in discriminating controls from medicated patients. The elevated respiratory brain synchronicity in focal epilepsy suggests altered physiological effect of cerebrospinal fluid pulsations possibly linked to regional brain water dynamics involved with interictal brain physiology.

Increased very low frequency pulsations and decreased cardiorespiratory pulsations suggest altered brain clearance in narcolepsy

Background

Narcolepsy is a chronic neurological disease characterized by daytime sleep attacks, cataplexy, and fragmented sleep. The disease is hypothesized to arise from destruction or dysfunction of hypothalamic hypocretin-producing cells that innervate wake-promoting systems including the ascending arousal network (AAN), which regulates arousal via release of neurotransmitters like noradrenalin. Brain pulsations are thought to drive intracranial cerebrospinal fluid flow linked to brain metabolite transfer that sustains homeostasis. This flow increases in sleep and is suppressed by noradrenalin in the awake state. Here we tested the hypothesis that narcolepsy is associated with altered brain pulsations, and if these pulsations can differentiate narcolepsy type 1 from healthy controls.

Methods

In this case-control study, 23 patients with narcolepsy type 1 (NT1) were imaged with ultrafast fMRI (MREG) along with 23 age- and sex-matched healthy controls (HC). The physiological brain pulsations were quantified as the frequency-wise signal variance. Clinical relevance of the pulsations was investigated with correlation and receiving operating characteristic analysis.

Results

We find that variance and fractional variance in the very low frequency (MREG_vlf) band are greater in NT1 compared to HC, while cardiac (MREG_card) and respiratory band variances are lower. Interestingly, these pulsations differences are prominent in the AAN region. We further find that fractional variance in MREG_vlf shows promise as an effective bi-classification metric (AUC = 81.4%/78.5%), and that disease severity measured with narcolepsy severity score correlates with MREG_card variance (R = −0.48, p = 0.0249).

Conclusions

We suggest that our novel results reflect impaired CSF dynamics that may be linked to altered glymphatic circulation in narcolepsy type 1.

The flow of fluid surrounding and inside the human brain is thought to be caused by the movement of brain vessels, breathing and heart rate. These so called brain pulsations are linked to clearing waste from the brain. This process is increased during sleep and suppressed while we are awake. Narcolepsy is a neurological disease where the brain areas regulating being awake and asleep are affected. The diagnosis requires time-consuming hospital tests and is often delayed which has a prolonged negative impact on the patients. Here, we use brain imaging to investigate whether brain pulsations are altered in patients with narcolepsy, and if they can be utilized to differentiate patients with narcolepsy from healthy individuals. We find that narcolepsy affects all brain pulsations, and these findings show promise as an additional diagnostic tool that could help detect the disease earlier.

Järvelä et al. investigate if narcolepsy is associated with altered brain pulsations using ultrafast fMRI. They find differences in the brain pulsations between narcolepsy type 1 patients and healthy controls that may link to altered brain clearance in narcolepsy, have diagnostic potential and correlate with the severity of narcolepsy.

Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping

A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.

Perspective review on applications of optics in skull base surgery

The use of optic technology in skull base surgeries has the potential to revolutionize the field of medicine, particularly neurosurgery and neurology. Here, we briefly present the past, present, and future of skull-base surgery, with an emphasis on the applications of optical topography techniques. We discuss optical topography techniques such as functional near-infrared spectroscopy, optical diffusion tomography, and optical topographical imaging. Optical topography techniques are particularly advantageous when combined with other imaging methods. For instance, optical topography can be combined with techniques such as functional magnetic resonance imaging (fMRI) to combine the temporal resolution of optical topography with the spatial resolution of fMRI. Multimodal approaches will be critical to advance brain-related research as well as medicine. Structured light imaging techniques are also writing the future of 3-dimensional imaging. In short, optical topography can allow for non-invasive, high-resolution imaging that will provide real-time visualizations of the brain that are ideal for neurosurgery. From the limitations of traditional skull base surgeries to the newest developments in optical neuroimaging, here we will discuss the potential applications of optics in skull base procedures.

Blood pressure wave propagation - a multisensor setup for cerebral autoregulation studies

OBJECTIVE

Cerebral autoregulation is critically important to maintain proper brain perfusion and supply the brain with oxygenated blood. Non-invasive measures of blood pressure (BP) are critical in assessing cerebral autoregulation. Wave propogation velocity may be a useful technique to estimate BP but the effect of the location of the sensors on the readings has not been thoroughly examined. In this paper, we were interested to study if propagation velocity of the pressure wave in the direction from the heart to the brain may differ compared with propagation from the heart to the periphery, as well as across different physiological tasks and/or health conditions. Using non-invasive sensors simultaneously placed on different locations of the human body allow for the study of how propagation velocity of the pressure wave, based on pulse transit time (PTT), varies across different directions.

APPROACH

We present multi-sensor BP wave propagation measurement setup aimed for cerebral autoregulation studies. The presented sensor setup consists of three sensors, one each placed on the neck, chest and finger, allowing simultaneous measurement of changes in BP propagation velocity towards the brain and to the periphery. We show how commonly tested physiological tasks affect the relative changes of PTT and correlations with BP.

MAIN RESULTS

We observed that during maximal blow, valsalva and breath hold breathing tasks, the relative changes of PTT were higher when PTT was measured in the direction from the heart to the brain than from the heart to the peripherals. In contrast, during a deep breathing task, the relative change in PTT from the heart to the brain was lower. In addition, we present a short literature review of PTT methods used in brain research.

SIGNIFICANCE

These preliminary data suggest that physiological task and direction of PTT measurement may affect relative PTT changes. Presented three-sensor setup provides an easy and neuroimaging compatible method for cerebral autoregulation studies by allowing to measure BP wave propagation velocity towards the brain vs. towards the periphery.

Spectral analysis of physiological brain pulsations affecting the BOLD signal

Physiological pulsations have been shown to affect the global blood oxygen level dependent (BOLD) signal in human brain. While these pulsations have previously been regarded as noise, recent studies show their potential as biomarkers of brain pathology. We used the extended 5 Hz spectral range of magnetic resonance encephalography (MREG) data to investigate spatial and frequency distributions of physiological BOLD signal sources. Amplitude spectra of the global image signals revealed cardiorespiratory envelope modulation (CREM) peaks, in addition to the previously known very low frequency (VLF) and cardiorespiratory pulsations. We then proceeded to extend the amplitude of low frequency fluctuations (ALFF) method to each of these pulsations. The respiratory pulsations were spatially dominating over most brain structures. The VLF pulsations overcame the respiratory pulsations in frontal and parietal gray matter, whereas cardiac and CREM pulsations had this effect in central cerebrospinal fluid (CSF) spaces and major blood vessels. A quasi‐periodic pattern (QPP) analysis showed that the CREM pulsations propagated as waves, with a spatiotemporal pattern differing from that of respiratory pulsations, indicating them to be distinct intracranial physiological phenomenon. In conclusion, the respiration has a dominant effect on the global BOLD signal and directly modulates cardiovascular brain pulsations.

The variability of functional MRI brain signal increases in Alzheimer's disease at cardiorespiratory frequencies

Biomarkers sensitive to prodromal or early pathophysiological changes in Alzheimer's disease (AD) symptoms could improve disease detection and enable timely interventions. Changes in brain hemodynamics may be associated with the main clinical AD symptoms. To test this possibility, we measured the variability of blood oxygen level-dependent (BOLD) signal in individuals from three independent datasets (totaling 80 AD patients and 90 controls). We detected a replicable increase in brain BOLD signal variability in the AD populations, which constituted a robust biomarker for clearly differentiating AD cases from controls. Fast BOLD scans showed that the elevated BOLD signal variability in AD arises mainly from cardiovascular brain pulsations. Manifesting in abnormal cerebral perfusion and cerebrospinal fluid convection, present observation presents a mechanism explaining earlier observations of impaired glymphatic clearance associated with AD in humans.

Respiratory-related brain pulsations are increased in epilepsy-a two-centre functional MRI study

Resting-state functional MRI has shown potential for detecting changes in cerebral blood oxygen level-dependent signal in patients with epilepsy, even in the absence of epileptiform activity. Furthermore, it has been suggested that coefficient of variation mapping of fast functional MRI signal may provide a powerful tool for the identification of intrinsic brain pulsations in neurological diseases such as dementia, stroke and epilepsy. In this study, we used fast functional MRI sequence (magnetic resonance encephalography) to acquire ten whole-brain images per second. We used the functional MRI data to compare physiological brain pulsations between healthy controls (n = 102) and patients with epilepsy (n = 33) and furthermore to drug-naive seizure patients (n = 9). Analyses were performed by calculating coefficient of variation and spectral power in full band and filtered sub-bands. Brain pulsations in the respiratory-related frequency sub-band (0.11-0.51 Hz) were significantly (P < 0.05) increased in patients with epilepsy, with an increase in both signal variance and power. At the individual level, over 80% of medicated and drug-naive seizure patients exhibited areas of abnormal brain signal power that correlated well with the known clinical diagnosis, while none of the controls showed signs of abnormality with the same threshold. The differences were most apparent in the basal brain structures, respiratory centres of brain stem, midbrain and temporal lobes. Notably, full-band, very low frequency (0.01-0.1 Hz) and cardiovascular (0.8-1.76 Hz) brain pulses showed no differences between groups. This study extends and confirms our previous results of abnormal fast functional MRI signal variance in epilepsy patients. Only respiratory-related brain pulsations were clearly increased with no changes in either physiological cardiorespiratory rates or head motion between the subjects. The regional alterations in brain pulsations suggest that mechanisms driving the cerebrospinal fluid homeostasis may be altered in epilepsy. Magnetic resonance encephalography has both increased sensitivity and high specificity for detecting the increased brain pulsations, particularly in times when other tools for locating epileptogenic areas remain inconclusive.

Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder

This study investigated whole‐brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting‐state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P‐value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default‐mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large‐scale functional brain networks may contribute to the ASD phenotype. Autism Res 2020, 13: 244–258. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.

Lay Summary

Autism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Using an ultra‐fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. We found that ASD individuals had altered information flow patterns across brain regions. Atypical patterns were concentrated in salience, executive, visual, and default‐mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.

Sampling Rate Effects on Resting State fMRI Metrics

Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning.