Measuring intracranial pressure (ICP) is necessary for the treatment of severe head injury but measurement systems are highly invasive and introduce risk of infection and complications. We developed a non-invasive alternative for quantifying ICP using measurements of cerebral blood flow (CBF) by diffuse correlation spectroscopy. The recorded cardiac pulsation waveform in CBF undergoes morphological changes in response to ICP changes. We used the pulse shape to train a randomized regression forest to estimate the underlying ICP and demonstrate in five non-human primates that DCS-based estimation can explain over 90% of the variance in invasively measured ICP.

The brain’s ability to maintain cerebral blood flow approximately constant despite cerebral perfusion pressure changes is known as cerebral autoregulation (CA) and is governed by vasoconstriction and vasodilation. Cerebral perfusion pressure is defined as the pressure gradient between arterial blood pressure and intracranial pressure. Measuring CA is a challenging task and has created a variety of evaluation methods, which are often categorized as static and dynamic CA assessments. Because CA is quantified as the performance of a regulatory system and no physical ground truth can be measured, conflicting results are reported. The conflict further arises from a lack of healthy volunteer data with respect to cerebral perfusion pressure measurements and the variety of diseases in which CA ability is impaired, including stroke, traumatic brain injury and hydrocephalus. To overcome these differences, we present a healthy non-human primate model in which we can control the ability to autoregulate blood flow through the type of anesthesia (isoflurane vs fentanyl). We show how three different assessment methods can be used to measure CA impairment, and how static and dynamic autoregulation compare under challenges in intracranial pressure and blood pressure. We reconstructed Lassen’s curve for two groups of anesthesia, where only the fentanyl anesthetized group yielded the canonical shape. Cerebral perfusion pressure allowed for the best distinction between the fentanyl and isoflurane anesthetized groups. The autoregulatory response time to induced oscillations in intracranial pressure and blood pressure, measured as the phase lag between intracranial pressure and blood pressure, was able to determine autoregulatory impairment in agreement with static autoregulation. Static and dynamic CA both show impairment in high dose isoflurane anesthesia, while low isoflurane in combination with fentanyl anesthesia maintains CA, offering a repeatable animal model for CA studies.

As 3D bioprinting has grown as a fabrication technology, so too has the need for improved analytical methods to characterize engineered constructs. This is especially challenging for engineered tissues composed of hydrogels and cells, as these materials readily deform when trying to assess print fidelity and other properties non-destructively. Establishing that the 3D architecture of the bioprinted construct matches its intended anatomic design is critical given the importance of structure-function relationships in most tissue types. Here we report development of a multimaterial bioprinting platform with integrated optical coherence tomography (OCT) for in situ volumetric imaging, error detection, and 3D reconstruction. We also report improvements to the Freeform Reversible Embedding of Suspended Hydrogels (FRESH) bioprinting process through new collagen bioink compositions, gelatin microparticle support bath optical clearing, and optimized machine pathing. This enables quantitative 3D volumetric imaging with micron resolution over centimeter length scales, the ability to detect a range of print defect types within a 3D volume, and real-time imaging of the printing process at each print layer. These advances provide a comprehensive methodology for print quality assessment, paving the way toward the production and process control required for achieving regulatory approval and ultimately clinical translation of engineered tissues.

Continuous measurements of haemodynamic and oxygenation changes in free living animals remain elusive. However, developments in biomedical technologies may help to fill this knowledge gap. One such technology is continuous-wave near-infrared spectroscopy (CW-NIRS)—a wearable and non-invasive optical technology. Here, we develop a marinized CW-NIRS system and deploy it on elite competition freedivers to test its capacity to function during deep freediving to 107 m depth. We use the oxyhaemoglobin and deoxyhaemoglobin concentration changes measured with CW-NIRS to monitor cerebral haemodynamic changes and oxygenation, arterial saturation and heart rate. Furthermore, using concentration changes in oxyhaemoglobin engendered by cardiac pulsation, we demonstrate the ability to conduct additional feature exploration of cardiac-dependent haemodynamic changes. Freedivers showed cerebral haemodynamic changes characteristic of apnoeic diving, while some divers also showed considerable elevations in venous blood volumes close to the end of diving. Some freedivers also showed pronounced arterial deoxygenation, the most extreme of which resulted in an arterial saturation of 25%. Freedivers also displayed heart rate changes that were comparable to diving mammals both in magnitude and patterns of change. Finally, changes in cardiac waveform associated with heart rates less than 40 bpm were associated with changes indicative of a reduction in vascular compliance. The success here of CW-NIRS to non-invasively measure a suite of physiological phenomenon in a deep-diving mammal highlights its efficacy as a future physiological monitoring tool for human freedivers as well as free living animals. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.

OBJECTIVE Intracranial pressure (ICP) is an important therapeutic target in many critical neuropathologies. The current tools for ICP measurements are invasive; hence, these are only selectively applied in critical cases where the benefits surpass the risks. To address the need for low-risk ICP monitoring, the authors developed a noninvasive alternative. METHODS The authors recently demonstrated noninvasive quantification of ICP in an animal model by using morphological analysis of microvascular cerebral blood flow (CBF) measured with diffuse correlation spectroscopy (DCS). The current prospective observational study expanded on this preclinical study by translating the method to pediatric patients. Here, the CBF features, along with mean arterial pressure (MAP) and heart rate (HR) data, were used to build a random decision forest, machine learning model for estimation of ICP; the results of this model were compared with those of invasive monitoring. RESULTS Fifteen patients (mean age ± SD [range] 9.8 ± 5.1 [0.3–17.5] years; median age [interquartile range] 11 [7.4] years; 10 males and 5 females) who underwent invasive neuromonitoring for any purpose were enrolled. Estimated ICP (ICPest) very closely matched invasive ICP (ICPinv), with a root mean square error (RMSE) of 1.01 mm Hg and 95% limit of agreement of ≤ 1.99 mm Hg for ICPinv 0.01–41.25 mm Hg. When the ICP range (ICPinv 0.01–29.05 mm Hg) was narrowed on the basis of the sample population, both RMSE and limit of agreement improved to 0.81 mm Hg and ≤ 1.6 mm Hg, respectively. In addition, 0.3% of the test samples for ICPinv ≤ 20 mm Hg and 5.4% of the test samples for ICPinv > 20 mm Hg had a limit of agreement > 5 mm Hg, which may be considered the acceptable limit of agreement for clinical validity of ICP sensing. For the narrower case, 0.1% of test samples for ICPinv ≤ 20 mm Hg and 1.1% of the test samples for ICPinv > 20 mm Hg had a limit of agreement > 5 mm Hg. Although the CBF features were crucial, the best prediction accuracy was achieved when these features were combined with MAP and HR data. Lastly, preliminary leave-one-out analysis showed model accuracy with an RMSE of 6 mm Hg and limit of agreement of ≤ 7 mm Hg. CONCLUSIONS The authors have shown that DCS may enable ICP monitoring with additional clinical validation. The lower risk of such monitoring would allow ICP to be estimated for a wide spectrum of indications, thereby both reducing the use of invasive monitors and increasing the types of patients who may benefit from ICP-directed therapies.

Significance: Intracranial pressure (ICP) measurements are important for patient treatment but are invasive and prone to complications. Noninvasive ICP monitoring methods exist, but they suffer from poor accuracy, lack of generalizability, or high cost.Aim: We previously showed that cerebral blood flow (CBF) cardiac waveforms measured with diffuse correlation spectroscopy can be used for noninvasive ICP monitoring. Here we extend the approach to cardiac waveforms measured with near-infrared spectroscopy (NIRS).Approach: Changes in hemoglobin concentrations were measured in eight nonhuman primates, in addition to invasive ICP, arterial blood pressure, and CBF changes. Features of average cardiac waveforms in hemoglobin and CBF signals were used to train a random forest (RF) regressor. Results:The RF regressor achieves a cross-validated ICP estimation of 0.937r 2 , 2.703-mmHg 2 mean squared error (MSE), and 95% confidence interval (CI) of ½−3.064 3.160 mmHg on oxyhemoglobin concentration changes; 0.946r 2 , 2.301-mmHg 2 MSE, and 95% CI of ½−2.841 2.866 mmHg on total hemoglobin concentration changes; and 0.963r 2 , 1.688 mmHg 2 MSE, and 95% CI of ½−2.450 2.397 mmHg on CBF changes. Conclusions:This study provides a proof of concept for the use of NIRS in noninvasive ICP estimation.

Sensory ecology and physiology of free-ranging animals is challenging to study but underpins our understanding of decision-making in the wild. Existing non-invasive human biomedical technology offers tools that could be harnessed to address these challenges. Functional near-infrared spectroscopy (fNIRS), a wearable, non-invasive biomedical imaging technique measures oxy- and deoxyhaemoglobin concentration changes that can be used to detect localized neural activation in the brain. We tested the efficacy of fNIRS to detect cortical activation in grey seals ( Halichoerus grypus ) and identify regions of the cortex associated with different senses (vision, hearing and touch). The activation of specific cerebral areas in seals was detected by fNIRS in responses to light (vision), sound (hearing) and whisker stimulation (touch). Physiological parameters, including heart and breathing rate, were also extracted from the fNIRS signal, which allowed neural and physiological responses to be monitored simultaneously. This is, to our knowledge, the first time fNIRS has been used to detect cortical activation in a non-domesticated or laboratory animal. Because fNIRS is non-invasive and wearable, this study demonstrates its potential as a tool to quantitatively investigate sensory perception and brain function while simultaneously recording heart rate, tissue and arterial oxygen saturation of haemoglobin, perfusion changes and breathing rate in free-ranging animals. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part I)’.

Intracranial pressure (ICP) is typically measured invasively through a sensor placed inside the brain or a needle inserted into the spinal canal, limiting the patient population on which this assessment can be performed. Currently, non-invasive methods are limited due to lack of sensitivity and thus only apply to extreme cases of increased ICP, instead of use in general clinical practice. We demonstrate a novel application for near-infrared spectroscopy (NIRS) to accurately estimate ICP changes over time. Using a non-human primate (Rhesus Macaque) model, we collected optical data while we induced ICP oscillations at multiple ICP levels obtained by manipulating the height of a fluid column connected via a catheter to the lateral ventricle. Hemodynamic responses to ICP changes were measured at the occipital pole and compared to changes detected by a conventional intraparenchymal ICP probe. We demonstrate that hemoglobin concentrations are highly correlated with induced ICP oscillations and that this response is frequency dependent. We translated the NIRS data into non-invasive ICP measurements via a fitted non-parametric transfer function, demonstrating a match in both magnitude and time alignment with an invasively measured reference. Our results demonstrate that NIRS has the potential for non-invasive ICP monitoring.