Neurophotonics

Pulsed infrared neural stimulation (INS, 1875nm the peak of energy absorption by water) is a non-viral method developed to activate single submillimeter sites in the brain. When used in ultrahigh-field fMRI, it reveals functionally specific brain-wide columnar networks comprising synaptically activated nodes. INS is non-damaging and effective for use in the human cortex and is useful for neural and behavioral neuromodulation in primates. To investigate how physiological state influences its cellular effects, we performed in vivo two-photon calcium imaging with GCaMP6s in mice and tracked identified cortical neurons in awake vs anesthetized states. Across the tested radiant exposures, INS-evoked responses differed markedly between states: under anesthesia, stimulation produced predominantly positive-going calcium responses, whereas during wakefulness the same identified neurons exhibited negative-going responses. Pharmacological manipulation of this baseline level by GABAergic signaling shifted response polarity in opposite directions: picrotoxin under anesthesia produced awake-like negative responses, whereas muscimol during wakefulness produced anesthetized-like positive responses. Trial-by-trial analyses further showed that the relationship between pre-stimulus baseline level and INS-evoked response depended on physiological state. Because INS relies on transient tissue heating, we also developed an in situ EGFP-based calibration approach to estimate temperature-related fluorescence changes. These fluoro-thermal contributions were modest at lower radiant exposures and became increasingly important at higher exposures, particularly near the fiber pathway. The low-intensity results are consistent with, but do not establish, a state-dependent feed-forward inhibitory framework. Together, these findings show that the neuronal response to focal INS is shaped by ongoing brain state and provide a practical framework for separating state-dependent calcium responses from temperature-related fluorescence effects, making INS a potentially versatile tool for neuroscience and clinical applications.

Significance: Wearable optical sensors could make continuous cerebral hemodynamic monitoring practical in daily life. Because any surface head sensor also samples extracerebral tissue, demonstrating cerebral relevance requires a reference that separates deep from superficial hemodynamics. Aim: We evaluated whether the Temple Brain Flow Index (Temple-BF), derived from a temple-worn photoplethysmographic wearable, covaried more strongly with the deeper (brain-assigned) than the superficial (scalp-assigned) hemodynamics recovered from time-domain near-infrared spectroscopy (TD-NIRS) during controlled postural challenges. Approach: Sixty sessions from 44 healthy young adult men were analyzed across three physiological challenges: 30{degrees} head-down tilt, stand-to-squat, and stand-to-supine transitions. Optical density and mean time of flight from a three-module TD-NIRS system were inverted with a Monte Carlo-derived two-layer model to obtain scalp- and brain-layer hemoglobin time series. Temple-BF was compared with these model-derived signals using lag-adjusted and zero-lag correlations, paired brain-versus-scalp comparisons, heart rate and short-separation-HbO adjusted partial correlations. Results: Median lag-adjusted correlations between Temple-BF and brain-layer {Delta}HbO traces were 0.912, 0.843, and 0.839 for head-down tilt, stand-to-squat, and stand-to-supine transitions, respectively; corresponding scalp-layer medians were 0.494, 0.700, and 0.767. Paired brain-minus-scalp differences were significant for head-down tilt (median 0.333, p < 0.001) and stand-to-squat (0.110, p < 0.001) but not for stand-to-supine (0.036, p = 0.082) transitions. Zero-lag brain-layer medians were 0.826, 0.701, and 0.796, and the association persisted after adjustment for heart rate (partial r = 0.832, 0.850, 0.654) and for short-separation superficial HbO (0.649, 0.570, 0.438). Transition-window correlations had medians of 0.878, 0.861, and 0.876, with directionally concordant transition responses in 59 of 60 sessions. Conclusions: Temple-BF tracked model-derived brain-layer TD-NIRS {Delta}HbO across all three postural challenges, with larger median correlations against the brain-layer than the scalp compartment in all three protocols, and transition responses in the same direction for almost all sessions. These results support Temple-BF as a relative marker of cerebral hemodynamic change during postural perturbations. Establishing cerebral specificity at the temple, and excluding residual systemic and superficial contributions, will require flow-sensitive references and fuller systemic monitoring.

Diffuse optical measurements of blood flow, blood volume, and blood oxygenation often use separate devices and photon populations. Combining these contrasts within a common photon population would enable their direct co-registration while retaining time-of-flight-based depth discrimination. We introduce dual-wavelength time-of-flight interferometric speckle-contrast optical spectroscopy (TOF-iSCOS), built on an interferometric near-infrared spectroscopy (iNIRS) architecture that temporally multiplexes 780 and 852 nm swept lasers through a shared detection channel. It recovers co-registered flow-sensitive field dynamics and wavelength-resolved attenuation from the same complex-field acquisition. Three 50 ps TOF gates sample increasingly late-arriving photons. Within each gate, g_1 was calculated directly as the normalized temporal autocorrelation of the reconstructed complex field. {kappa}2 was reconstructed by finite-time integration of measured g_1 and finite-integration fits yielded decorrelation rates. No camera speckle-variance contrast or Siegert substitution was used. Gate-integrated TPSF intensities yielded optical-density changes {delta}OD, and division by the baseline-TPSF-weighted mean photon path length gave effective {delta}_a for two-wavelength hemoglobin inversion. In 12 forearm cuff-occlusion recordings from 11 adults, TOF-iSCOS detected flow suppression during occlusion and reactive hyperemia after release at both wavelengths. At 780 nm, the Late-minus-Early reactive-hyperemia amplitude in the relative blood-flow index (rBFI) was +0.647 (95% CI 0.194 to 1.124), whereas the Early-minus-Late cuff {delta}_a contrast was +0.196 cm^{-1} (95% CI 0.123 to 0.280). In the photon-limited 852 nm Late gate, the accepted-frame fraction was 0.994 for {kappa}^2-based fitting versus 0.831 for g_1-based fitting. Exploratory phase-resolved analysis suggested that the initial hemoglobin response was concentrated in early-arriving photons, while later responses differed in magnitude and direction. Wavelength, field dynamics, and photon time-of-flight thus provide complementary physiological information; the largest response need not identify the phase of interest.

Sleep disorders and wakefulness constitute a pronounced and pervasive health risk that contributes to numerous cerebral pathologies such as stroke, depression, and neurodegenerative disorders. The hippocampus (HC) plays vital roles in learning and memory and is especially susceptible to the pathological insults imposed by sleep deprivation (SD). While SD disrupts neuronal network signaling and triggers inflammatory processes, the impact of SD on metabolism within the HC presently remains poorly understood. In this study, we assessed the progressive and cumulative effects of partial SD in the live mouse HC CA1 region using two-photon microscopy. Using a custom-designed implantable cannula enabling direct HC visualization and administration of dyes and pharmacological agents directly to HC tissue, we longitudinally monitored spontaneous neuronal activity and partial pressure of oxygen (pO2) within HC CA1 tissue before and during a 7-day period of partial SD and after a 7- day period of recovery. Our results revealed a progressive decline in spontaneous neural activity, alongside sustained elevation in tissue pO2; during the 7-day partial SD period. Notably, after 7 days of recovery, neuronal activity did not return to baseline, and tissue oxygenation continued to increase. Our findings emphasize chronic SD's cumulative and enduring impairments to neuronal function and metabolism in the HC and reveal uncoupling between metabolic and functional recovery that outlasts the sleep restriction period.

Defining the boundaries of functional cortical areas is increasingly important for targeted electro-physiology, optical imaging, viral delivery, and circuit manipulation. Intrinsic optical signal imaging (IOSI) provides a rapid and minimally invasive approach for mapping stimulus-evoked cortical activity, but its implementation often requires laboratory-specific combinations of stimulus-generation hardware, experiment-control software, synchronization devices, and data-acquisition systems. These requirements limit accessibility and hinder the use of IOSI as a routine functional mapping tool. Here, we present the Arduino-Based Intrinsic Stimulation System (ABISS), an open-source platform that integrates auditory and visual stimulus generation, trial timing, and image-acquisition triggering into a single programmable device. ABISS generates auditory tone stimuli, VGA-based visual stimuli, and tightly synchronized camera-trigger pulses without requiring a dedicated experiment-control system. Stimulus protocols are also fully modifiable in firmware. Performance was evaluated in auditory and visual cortices of mice. Engineering validation demonstrated accurate stimulus generation and synchronization between stimulus delivery and camera triggering over extended recording sessions. Biological validation showed that ABISS output results in auditory and visual intrinsic signal maps comparable to those obtained using highly specialized or commercial platforms. Together, these findings demonstrate the utility of a low-cost open-source platform for experimental control of intrinsic optical signal imaging. By reducing the technical and financial barriers associated with routine intrinsic optical imaging, ABISS facilitates broader adoption of functional cortical mapping as a tool for improved cortical localization in neuroscience experiments. Significance StatementFunctional cortical mapping is an increasingly important element of neuroscience experimental design as anatomical coordinates alone are often insufficient for defining cortical boundaries in individual animals. Intrinsic optical signal imaging provides an effective solution but traditionally requires specialized hardware, commercial stimulus-generation systems, and laboratory-specific synchronization workflows. We developed ABISS, an inexpensive, open-source platform that integrates auditory and visual stimulus generation with synchronized camera triggering in a single programmable device. ABISS produces functional cortical maps comparable to those obtainable with commercial or specialized systems while substantially reducing hardware complexity and cost. By making intrinsic optical signal imaging more accessible, ABISS lowers the practical barriers for routine functional mapping of the brain and promotes adoption of this important neuroscience technique.

Joint optical measurement of hemoglobin and blood-flow dynamics could provide a compact route to richer functional monitoring of tissue physiology. However, hybrid diffuse-optical systems commonly optimize photon time-of-flight (TOF) for individual readouts, even when absorption and flow are recovered from the same acquisition. Here, we asked whether a single TOF window can optimally preserve both signals. Dual-wavelength TOF-iSCOS recovered absorption, hemoglobin and relative blood-flow index (BFI) from the same interferometrically sensed optical field, while Early, Middle and Late photon windows provided progressively greater weighting of longer and deeper photon paths. During forearm cuff occlusion in 11 adults, absorption and flow showed markedly different TOF dependence: Late-gate BFI responses were retained, whereas absorption decreased to a median Late/Early ratio of 0.18. The resulting readout-by-TOF interaction was positive in all 11 participants (median 2.61 octaves, 95% bootstrap CI 2.44-3.22; exact sign-test P = 9.8 x 10-4). Readout-specific gate selection increased held-out response retention by 24.8% (95% CI 8.8-42.5%) relative to a single common gate. We then tested the approach during prefrontal working-memory activation in seven participants using a higher-load 2-back task relative to a low-load 0-back control. Late-gate BFI increased in all seven participants (+5.07%, 95% CI 1.46-8.68%; P = 0.014) and remained positive after adjustment using an Early-TOF signal, whereas hemoglobin responses were less consistent. Layered Monte Carlo modeling showed that the same detected photon histories acquire different absorption and dynamic-scattering sensitivity across TOF, linking the experimental effects to anatomy- and geometry-conditioned tissue weighting. Within the present geometry and photon budget, the central result is therefore not that one readout is intrinsically superior, but that absorption and flow recovered from the same optical field retain physiological information differently across photon time-of-flight.

Studying the role of individual neurons in behavior and disease requires tools for controlling neural activity with high spatiotemporal resolution. Implantable nanophotonic neural probes are capable of delivering targeted photostimulation to enable genetically distinct neurons to be selectively controlled, yet face barriers to achieving scalable emitter densities and lack sensors for monitoring feedback signals relevant to device operation. To address this, we developed laser-integrated nanophotonic neural probes, which feature hybrid-integrated laser diodes (LD) and thermo-optic photonics switches for scalable emitter addressing and on-chip sensors for monitoring optical power and temperature during photo-stimulation. Devices were fabricated at a commercial silicon photonics foundry on 200-mm diameter silicon-on-insulator (SOI) wafers in an active visible-light platform and were controlled using a custom-developed electronic circuit board. Each device features a hybrid-integrated InGaN LD which couples 450-nm light into a reconfigurable photonic switching tree for delivering spatially resolved photostimulation through 16 emitters along a 3-mm implantable shank. Using the on-chip photodetectors, we demonstrate how devices can enable switching tree calibration as well as output power monitoring during photostimulation. Furthermore, using the on-chip temperature sensors, we show how device temperature perturbations resulting from LD and thermo-optic switch activation can be directly monitored during photostimulation to ensure temperature fluctuations remain below 1 {degrees}C. We validate our design by delivering high spatiotemporal photostimulation during an in vivo optogenetic experiment with simultaneous Neuropixels recording to monitor evoked responses. Overall, these scalable integrated devices offer a pathway for neuroscientists to conduct fiberless optogenetic experiments with greater control and precision.

SignificanceDiffuse Correlation Spectroscopy (DCS) is an established technique for non-invasive monitoring of Cerebral Blood Flow (CBF), but existing applications primarily measure CBF changes averaged over cortical tissue volumes. A method capable of targeting blood flow within a particular intracranial artery would expand the utility of DCS for vessel-specific cerebral perfusion monitoring and continuous bedside assessment. AimWe aim to investigate the feasibility of targeted, non-invasive monitoring of blood flow in the Anterior Cerebral Artery (ACA) using DCS through optimization of probe geometry and placement. ApproachA custom-built DCS system operating at 785 nm was used to probe ACA from the glabellar region. Source-detector (SD) separation, probe orientation, and probe location were systematically optimized using lower-limb motor tasks and a mental arithmetic task. The optimized configuration was evaluated using an ACA-mimicking multilayer phantom and validated in forty healthy volunteers during lower-limb activation tasks and postural changes. ResultsAn SD separation of 17 mm, vertical probe orientation, and glabellar placement provided the highest sensitivity to ACA-related blood flow changes. Phantom experiments demonstrated sensitivity to flow changes in a vessel located 45 mm beneath the scalp and showed that the frontal sinus, cerebrospinal fluid, and the absence of cortical tissue beneath the glabella along the longitudinal fissure together provide the best optical window for probing deep ACA flow. Using the optimized probe configuration, significant increases in relative CBF were observed during standing leg marching (66.4 {+/-} 38.6%) and supine leg crunches (39.4 {+/-} 32.2%) (p < 0.01), with consistent responses during supine-to-stand postural transitions. ConclusionThe proposed DCS approach enables targeted measurement of blood flow within the ACA territory. This technique provides a framework for continuous, non-invasive monitoring of ACA perfusion and has potential applications in cerebrovascular monitoring and stroke care.

The development of a new integrated operant system was driven by two challenges in behavioral neuroscience: the high cost and technical complexity of commercial rigs, and their limited adaptability across experiments. We developed the NeuroHab, an integrated behavioral arena for high-fidelity operant conditioning and automated data collection in a single unified system. Food and water reward, conditioned-stimulus presentation, and event recording are tied together programmatically with easy-to-install open-source code to facilitate throughput and reproducibility. All behavioral events are processed by internal microcontrollers and logged with <1 ms latency (typical range 56-728 s). This precise timing is critical for integrating the system with two-photon imaging and electrophysiology, enabling real-time alignment of behavior with brain activity. The NeuroHab uses solenoid-actuated, capacitive-sensing Lickports that let an untethered mouse drink from an automated port, and delivers food via the Kravitz Lab FED3. Conditioned stimuli are presented by dedicated buzzer/LED modules. A central controller (the Core) coordinates all modules and logs event timestamps using TTL-low signaling between two microcontrollers, at a maximum recording rate of 16.67 Hz for single-pulse events. We have deployed the NeuroHab in over 50 behavior trials and over 20 sessions alongside a Mini two-photon microscope. At approximately $1,400, easily modified, and compatible with existing analysis tools, the NeuroHab lowers barriers to multimodal behavioral neuroscience. Significance StatementThe study of how neural activity gives rise to behavior depends on operant systems that are both temporally precise and affordable, yet commercial rigs are costly and difficult to adapt across experiments. We introduce the NeuroHab, an integrated, open-source operant platform that unifies reward delivery, conditioned-stimulus presentation, and event logging with sub-millisecond timing (typical latency 56-728 s). Built for approximately $1,400, the system forwards all behavioral timestamps to external acquisition hardware, enabling millisecond-scale alignment of behavior with two-photon imaging and electrophysiology. By lowering the cost and technical barriers to synchronized behavioral and neural recording, the NeuroHab makes multimodal, reproducible operant neuroscience accessible to a broad range of laboratories and adaptable to diverse experimental paradigms.

Two-photon calcium imaging enables measurements of neural activity from individual neurons and their subcellular processes in awake animals. Axial brain motion can change measured fluorescence independently of neural activity and move small structures out of the imaging plane, confounding two-photon calcium measurements in behaving animals. We developed two complementary post-acquisition methods, selected according to whether the imaged structure remains detectable across the encountered depth range. When it does, z-stack correction estimates imaging depth from a separately acquired reference z-stack and uses an axial fluorescence profile specific to each neural structure to rescale fluorescence to a common reference depth. When a structure can leave a single imaging plane, volume-based z-registration reconstructs a common-depth movie from repeated acquisitions of closely spaced planes. In awake mice, both methods tracked experimentally imposed axial displacements within the sampled range, with depth-estimation root mean squared errors (RMSEs) below 1 m for z-stack correction and 0.69-1.03 m for volume-based z-registration. Correction changed fluorescence substantially for a subset of somata, with larger effects in axonal boutons. Both methods are implemented in suite2p-in-depth, a Python package that integrates with Suite2p and provides diagnostics for depth estimation, correction quality and data reliability. Together, these methods enable axial-motion artifacts to be assessed and corrected within established Suite2p workflows.

Light microscopy is one of the most powerful tools for understanding living systems, but the opacity of tissue prevents visualization of all but superficial layers. Several methods to clarify tissue have been developed, but most require fixed specimens. To address the challenge of improving resolution in functioning neuronal circuits, we developed a biocompatible clearing agent, iodixanol-ACSF, which is capable of increasing the transparency of living neuronal tissue. Brain-cleared mice were motile and unimpaired on a variety of behavioral tasks, and extracellular recordings showed that many cellular and circuit phenomena were well-preserved. In live iodixanol-ACSF cleared mouse brain tissue, both transmission and cellular-resolution fluorescence microscopy indicate improvements of 150-200% in penetration depth with one-third to one-half the laser intensity when compared to untreated tissue. Our results show that iodixanol-ACSF clearing will enable deeper imaging and extend our understanding of neuronal circuit function.

This study presents a systematic investigation of the brain's own microwave emissions in the 0.85-5.0 GHz range, aiming to validate microwave encephalography (MWEG) as a novel tool for cognitive state assessment. Early experiments (2016-2019) using comparisons with background noise, a control phantom, and different antenna types confirmed the endogenous origin of the recorded signals. In a 2021 experiment (118 measurements on one subject, right and left occipital areas), we observed a significant functional hemispheric asymmetry: the number of "substantial spikes" was significantly higher in darkness for the right hemisphere (p<0.001) and higher in light for the left hemisphere (p<0.001). A 2025 group study (three subjects) confirmed the asymmetry (p<0.05 for right, p<0.01 for left). We describe the signal processing pipeline, including spike detection and statistical analysis. Two complementary mathematical models are presented: an energy-based model linking optical input to microwave emission via neural activity modes, and an information-based model interpreting spectral changes in terms of structural pattern overlap with memory engrams. An estimation of the physical dimensions of possible brain resonators (2-22 mm) is provided. The results are discussed in the context of hemispheric specialization and the information commutation theory. This work opens new perspectives for developing low cost, contactless brain monitoring systems.

Background. Hematoporphyrin derivative (HpD, Hiporfin) is used for photodynamic therapy (PDT) of solid tumors but had never been evaluated for fluorescence guided resection (FGR) in glioma. We assessed Hiporfin fluorescence accuracy against histopathology in recurrent glioblastoma (rGBM) and the feasibility of adding cavity PDT to FGR. Methods. In this prospective single arm series, adults with rGBM received a single preoperative intravenous Hiporfin dose of 5 mg/kg before FGR under blue light excitation and 630 nm cavity PDT. Intraoperative fluorescence assessment was the index test and histopathology the reference standard. Specimen level diagnostic accuracy was pooled with bivariate random-effects modeling. Results. Of 63 specimens, 35 were true positive, 7 false positive, 8 false negative and 13 true negative. Pooled sensitivity was 0.751 (95% confidence interval 0.600 to 0.858) and specificity 0.606 (0.407 to 0.775); the crude positive predictive value was 0.833 (0.694 to 0.917) and the negative predictive value 0.619 (0.409 to 0.792). All seven false positive specimens were peritumoral tissue and the eight false negatives were non fluorescent tumor. FGR combined with cavity PDT was completed in all six patients without new neurological deficits. Conclusions. Hiporfin fluorescence identified rGBM with moderate accuracy: fluorescent regions were likely tumor, while non fluorescent tissue often still contained tumor. The main source of error was peritumoral tissue of the treated cavity, and not the drug or the optics. Hiporfin is a new fluorescence agent for glioma. One drug provides both fluorescence guidance during resection and immediate cavity PDT.

Objective. Temperature readings are routinely stratified by race and ethnicity, though significant group differences may reflect physiology, instrument noise, or categorization rather than biology. This study tests whether these categories partition facial infrared thermography measurements beyond individual variation and baseline device noise. Approach. A secondary analysis of thermography data (n = 951 adults) compares inner-canthus maximum (TCEmax) and full-face maximum (Tmax) temperatures across five administrative categories via one-way analysis of variance (ANOVA), mixed-effects modeling, Bland-Altman analysis against oral reference, and Monte Carlo sensitivity analysis. Main results. ANOVA returns significant group differences (p < 0.001; eta-squared = 0.04 for TCEmax, 0.03 for Tmax), but the total spread (0.2 to 0.3 degrees Celsius) is comparable to device accuracy and within-subject variability. Individual variation accounts for 95% of TCEmax variance, and adding simulated Gaussian noise to participant-level temperatures (sigma = 0.10 to 0.30 degrees Celsius) preserves the complete group-mean rank order in as few as 37.5% of iterations. Significance. Testing whether a group difference survives perturbation at the scale of instrument accuracy offers a check against treating significance alone as evidence of physiology. Here, observed group differences are small relative to measurement variability, and their physiological interpretation remains uncertain, with implications for clinical tools that treat administrative categories as biologically meaningful.

PurposePupil diameter in daily life depends on both the light reaching the eye and the observers age, but established prediction formulas require laboratory quantities that are rarely measured in natural environments. We developed a compact age-corrected model that predicts pupil diameter from melanopic equivalent daylight illuminance (mEDI). MethodsWe used an existing field dataset in which binocular pupil diameter and near-corneal spectral irradiance were recorded while 83 adults aged 18-87 years moved through indoor and outdoor environments. The analysis included 10,082 valid paired observations. We fitted a bounded sigmoid relating pupil diameter to mEDI and age, with each participant given equal influence, and assessed prediction in participants excluded from model fitting. Performance was compared with simpler models, a flexible generalised additive model (GAM), and Watson-Yellott predictions based on assumed field geometry. ResultsPupil diameter decreased smoothly as mEDI increased. Age primarily reduced the difference between pupils in dim and bright conditions, by 0.768 mm per decade, while the predicted bright-light diameter changed little with age. In held-out participants, the bounded model had a participant-balanced root mean squared error (RMSE) of 0.630 mm and mean absolute error of 0.537 mm. The GAM had a slightly lower point-estimate RMSE of 0.610 mm, but the difference was small and uncertain. The bounded model outperformed the tested log-linear, reduced, age-only, and Watson-Yellott alternatives. ConclusionAge and mEDI are sufficient to provide useful population-average pupil predictions across the observed adult age and real-world light range. The model is transparent, physiologically bounded, and nearly as accurate as a flexible GAM, but predictions approaching darkness remain uncertain because valid mEDI measurements were not available in that range. Key pointsO_LIA compact equation predicts population-average pupil diameter from age and mEDI alone. C_LIO_LIAge mainly compresses the pupils response range by reducing pupil diameter under dimmer conditions. C_LIO_LIPrediction error in unseen participants was close to that of a flexible GAM, without requiring a fitted smooth object. C_LIO_LIThe model is intended for the observed adult age and field-light range, not for extrapolation into darkness. C_LI

Background: Understanding fetal brain responses to hypoxia-ischemia is of growing interest, as it remains unclear to what extent these responses differ from those of newborns and whether developmental adaptations during gestation contribute to such differences. Materials and methods: Using a combination of broadband near-infrared spectroscopy (bNIRS) and diffuse correlation spectroscopy (DCS), we measured brain microvascular oxygen saturation (StO2), total hemoglobin concentration (THC), relative cerebral blood flow (rCBF), and the oxidation state of cytochrome c oxidase (oxCCO) during an acute ischemic challenge in the brains of five rabbit fetuses in utero. Simultaneous measurements were acquired from four control fetuses located in the contralateral uterine horn. Results: During the hypoxia-ischemia phase, we observed a rapid reduction of CBF (47% +/- 29%) and StO2, (from 64.7% +/- 1.5% at baseline to 40.6% +/- 2.0%) together with an increase of oxCCO (3.7 uM +/- 0.5 uM). Conclusion: The simultaneous increase in oxCCO and decrease in rCBF and StO2 during hypoxia-ischemia reveals a cerebral response pattern that differs from that commonly reported in the newborn brain. These findings support the hypothesis that unique developmental adaptations influence fetal cerebral metabolism during hypoxic-ischemic stress.

Cerebral ischemia is a significant concern during high-risk surgeries, such as carotid endarterectomy (CEA). Continuous electroencephalography, monitored by neurophysiological experts, is used to detect cerebral ischemia during surgery; however, real-time visual interpretation is resource-intensive and error-prone. We evaluated machine learning (ML) models, including random forest (RF), eXtreme Gradient Boosting with a random forest base classifier (XGB), elastic-net logistic regression (LR), support vector classifier (SVC) with a radial basis function kernel, and naive Bayes (NB) classifier, for automated detection of cerebral ischemia during CEA using quantitative electroencephalographic (qEEG) features. RF achieved the highest sensitivity (0.79-0.83) and an area under the precision-recall curve (AUPRC) of 0.44, while XGB demonstrated the highest specificity (0.93-0.96) with an AUPRC of 0.36. Both models showed high negative predictive values and high area under the receiver operating characteristic (AUROC) scores. Feature-importance analysis identified alpha-band activity and hemispheric asymmetry as the most discriminative qEEG predictors of ischemia. These results highlight the potential of ML-assisted monitoring to support neurophysiology experts and enhance patient safety during high-risk surgical procedures.

Objective: To develop and validate a dual-probe Diffuse Correlation Spectroscopy (DCS) system for non-invasive and simultaneous, monitoring of cerebral blood flow (CBF) in the bilateral Middle Cerebral Artery (MCA) territories, and expanding the utility of conventional DCS limited to cortical-volume-based CBF measurements to vessel-specific cerebral perfusion monitoring. Methods: A dual-probe DCS system was designed for non-invasive monitoring of MCA-specific perfusion. Probe placement and protocol optimization study has been performed using anatomical landmarks, motor and speech activation tasks in healthy volunteers. System stability and repeatability were further evaluated in a pilot cohort of 30 healthy (age, 25{+/-}7 years) participants using optimized probe position and protocol. A bilateral MCA ischemic Lacunar Infract stroke case report also validated the feasibility of the system in clinical settings. Results: Measurements demonstrated superior sensitivity towards MCA-territory perfusion at targeted probe locations compared to off-MCA positions. In pilot cohort, significant increase of 30.34 {+/-} 21.56% and 36.48 {+/-} 21.22% in rCBF corresponding to hand squeeze and speech task respectively showed reproducible physiological responsiveness of the system (p<0.001). Measurement done on a patient with bilateral MCA ischemic Lacunar Infract stroke showed a significant change of 30% during speech for both the MCAs but no significant change is observed for hand squeeze tasks. Conclusion: The custom built dual-probe DCS system enables non-invasive, operator-independent, targeted and continuous monitoring of rCBF within bilateral MCA territories. Significance: This approach enables the potential use of DCS system for bilateral and vessel-specific monitoring of cerebral perfusion in the MCA territories.

Abstract Background: Every non-invasive continuous glucose monitoring (NI-CGM) technology introduced into the landscape faces the same skeptical question, from regulators, clinicians, and competing developers alike: is the candidate signal actually specific to glucose, or does an apparently reasonable accuracy figure simply reflect a model fitting to motion, temperature, calibration offset, or trial-duration artifact? Existing evaluation practice does not answer this question directly. NI-CGM performance is instead reported almost exclusively with metrics inherited from minimally invasive, subcutaneous CGM, the Mean Absolute Relative Difference (MARD), Clarke/Parkes error grids, and ISO 15197-style agreement rates, which were designed for sensors whose glucose specificity is already chemically established and which therefore take specificity as a premise rather than treating it as a result to be demonstrated. Methods: We present a methodology for demonstrating NI-CGM technology glucose specificity during the algorithm-development phase, and illustrate it with a case study based on a quantum-cascade-laser (QCL) photoacoustic NI-CGM device (Neogly) evaluated in the SKAMo-2 free-living clinical trial (eight participants with type 1 diabetes). The methodology combines a white-noise control, a constant-glycemia control, a sensor-ablation control that removes the candidate physical signal while retaining auxiliary covariates, and explicit reporting of the train/test generalization level, so that a reported MARD can be read as evidence of specificity rather than taken on faith. Results: Removing the mid-infrared photoacoustic (PA) signal from the model while retaining all auxiliary sensors (accelerometer, skin temperature, hygrometry, PPG) degraded performance at every generalization level tested, inter-patient MARD rose from 35.0% with the PA signal to 43.1% without it, and intra-experimentation MARD rose from 22.5% to 23.9%, providing direct, internal evidence that the PA channel itself, and not merely the auxiliary covariates, carries glucose-specific information. At the same time, an algorithm trained on pure Gaussian noise produced a MARD of 25% over short test windows, and a trivial constant-glycemia predictor outperformed every machine-learning model tested when generalization was extended from a single recording to an unseen patient (MARD 55% for the naive constant model versus 37% for a deep neural network on inter-patient splits). Reported in isolation, any of these MARD values is uninterpretable; reported against one another, they jointly demonstrate that the signal is specific to glucose while also bounding how much of the headline accuracy figure that specificity currently explains. Conclusions: We propose a specificity-demonstration methodology for NI-CGM technology development, comprising (1) signal quality gating prior to any algorithm benchmarking, (2) a white-noise control to test for genuine information content, (3) a constant-glycemia control to expose trial-duration bias, (4) a sensor-ablation control that isolates the contribution of the candidate physical signal from auxiliary covariates, (5) explicit reporting of the data-splitting generalization level (intra-experimentation, intra-patient, inter-patient). This methodology answers a question that precedes clinical accuracy reporting and that recognized clinical frameworks such as the IFCC Working Group on CGM's Dynamic Glucose Regions guideline are not designed to answer: not how accurate is the device, but is the device measuring glucose at all. We argue that without these controls, MARD and error-grid values for NI-CGM are not comparable across studies and may either overstate clinical readiness or undermine promising technologies. We recommend that this specificity methodology be applied routinely once a candidate NI-CGM sensor reaches algorithm-development stage, alongside and as a deliberate complement to IFCC-style clinical accuracy reporting once the device is mature enough for that evaluation. Keywords: non-invasive continuous glucose monitoring; glucose specificity; algorithm validation; MARD; benchmarking; machine learning; photoacoustic spectroscopy; sensor ablation; Clarke error grid

Despite the growing use of functional near-infrared spectroscopy (fNIRS) hyperscanning to record brain activity simultaneously from interacting individuals in naturalistic settings, most analyses quantify functional connectivity separately for each channel pair. The resulting collection of pairwise estimates is difficult to integrate into a network-level characterization of intra- and inter-brain organization. Here, we present an open, configuration-driven Python toolkit that transforms preprocessed fNIRS hyperscanning time series into functional connectivity graphs. The toolkit constructs a bipartite inter-brain network for each dyad and separate intra-brain networks for each participant, computes node- and graph-level measures, and exports adjacency matrices, edge lists, analysis-ready summary tables, reproducibility metadata, and standardized visualizations. Dataset-specific parameters, including directory structure, participant naming, channel selection, epoch extraction, and edge-retention criteria, are defined in a human-readable YAML configuration file, enabling the same workflow to accommodate differently organized datasets without changes to the source code. We illustrate the pipeline using a representative recording from a mother-infant fNIRS hyperscanning dataset and present the resulting network outputs. The toolkit provides a reproducible framework for moving from pairwise functional connectivity estimates to network-level analyses of dyadic and individual brain organization.