The word-with-noise test: development, validation and reference values. / Teste de palavras no ruído: desenvolvimento, validação e valores de referência.

PURPOSE: To propose an instrument for assessing speech recognition in the presence of competing noise. To define its application strategy for use in clinical practice. To obtain evidence of criterion validity and present reference values. METHODS: The study was conducted in three stages: Organization of the material comprising the Word-with-Noise Test (Stage 1); Definition of the instrument's application strategy (Stage 2); Investigation of criterion validity and definition of reference values for the test (Stage 3) through the evaluation of 50 normal-hearing adult subjects and 12 subjects with hearing loss. RESULTS: The Word-with-Noise Test consists of lists of monosyllabic and disyllabic words and speech spectrum noise (Stage 1). The application strategy for the test was defined as the determination of the Speech Recognition Threshold with a fixed noise level at 55 dBHL (Stage 2). Regarding criterion validity, the instrument demonstrated adequate ability to distinguish between normal-hearing subjects and subjects with hearing loss (Stage 3). Reference values for the test were established as cut-off points expressed in terms of signal-to-noise ratio: 1.47 dB for the monosyllabic stimulus and -2.02 dB for the disyllabic stimulus. Conclusion: The Word-with-Noise Test proved to be quick to administer and interpret, making it a useful tool in audiological clinical practice. Furthermore, it showed satisfactory evidence of criterion validity, with established reference values.

OBJETIVO: Propor um instrumento para a avaliação do reconhecimento de fala na presença de ruído competitivo. Definir sua estratégia de aplicação, para ser aplicado na rotina clínica. Obter evidências de validade de critério e apresentar seus valores de referência. MÉTODO: Estudo realizado em três etapas: Organização do material que compôs o Teste de Palavras no Ruído (Etapa 1); Definição da estratégia de aplicação do instrumento (Etapa 2); Investigação da validade de critério e definição dos valores de referência para o teste (Etapa 3), por meio da avaliação de 50 sujeitos adultos normo-ouvintes e 12 sujeitos com perda auditiva. RESULTADOS: O Teste de Palavras no Ruído é composto por listas de vocábulos mono e dissilábicos e um ruído com espectro de fala (Etapa 1). Foi definida como estratégia de aplicação do teste, a realização do Limiar de Reconhecimento de Fala com ruído fixo em 55 dBNA (Etapa 2). Quanto à validade de critério, o instrumento apresentou adequada capacidade de distinção entre os sujeitos normo-ouvintes e os sujeitos com perda auditiva (Etapa 3). Foram definidos como valores de referência para o teste, os pontos de corte expressos em relação sinal/ruído de 1,47 dB para o estímulo monossilábico e de -2,02 dB para o dissilábico. CONCLUSÃO: O Teste de Palavras no Ruído demonstrou ser rápido e de fácil aplicação e interpretação dos resultados, podendo ser uma ferramenta útil a ser utilizada na rotina clínica audiológica. Além disso, apresentou evidências satisfatórias de validade de critério, com valores de referência estabelecidos.

Time-Resolved Dynamic Optical Coherence Tomography for Retinal Blood Flow Analysis.

Purpose: Optical coherence tomography (OCT) representations in clinical practice are static and do not allow for a dynamic visualization and quantification of blood flow. This study aims to present a method to analyze retinal blood flow dynamics using time-resolved structural OCT. Methods: We developed novel imaging protocols to acquire video-rate time-resolved OCT B-scans (1024 × 496 pixels, 10 degrees field of view) at four different sensor integration times (integration time of 44.8 µs at a nominal A-scan rate of 20 kHz, 22.4 µs at 40 kHz, 11.2 µs at 85 kHz, and 7.24 µs at 125 kHz). The vessel centers were manually annotated for each B-scan and surrounding subvolumes were extracted. We used a velocity model based on signal-to-noise ratio (SNR) drops due to fringe washout to calculate blood flow velocity profiles in vessels within five optic disc diameters of the optic disc rim. Results: Time-resolved dynamic structural OCT revealed pulsatile SNR changes in the analyzed vessels and allowed the calculation of potential blood flow velocities at all integration times. Fringe washout was stronger in acquisitions with longer integration times; however, the ratio of the average SNR to the peak SNR inside the vessel was similar across all integration times. Conclusions: We demonstrated the feasibility of estimating blood flow profiles based on fringe washout analysis, showing pulsatile dynamics in vessels close to the optic nerve head using structural OCT. Time-resolved dynamic OCT has the potential to uncover valuable blood flow information in clinical settings.

Signal automatic modulation based on AMC neural network fusion.

With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals.

Time-of-flight MRA of intracranial vessels at 7 T.

BACKGROUND: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is a largely adopted non-invasive technique for assessing cerebrovascular diseases. We aimed to optimize the 7-T TOF-MRA acquisition protocol, confirm that it outperforms conventional 3-T TOF-MRA, and compare 7-T TOF-MRA with digital subtraction angiography (DSA) in patients with different vascular pathologies. METHODS: Seven-tesla TOF-MRA sequences with different spatial resolutions acquired in four healthy subjects were compared with 3-T TOF-MRA for signal-to-noise and contrast-to-noise ratios as well as using a qualitative scale for vessel visibility and the quantitative Canny algorithm. Four patients with cerebrovascular disease (primary arteritis of the central nervous system, saccular aneurism, arteriovenous malformation, and dural arteriovenous fistula) underwent optimized 7-T TOF-MRA and DSA as reference. Images were compared visually and using the complex-wavelet structural similarity index. RESULTS: Contrast-to-noise ratio was higher at 7 T (4.5 ± 0.8 (mean ± standard deviation)) than at 3 T (2.7 ± 0.9). The mean quality score for all intracranial vessels was higher at 7 T (2.89) than at 3 T (2.28). Angiogram quality demonstrated a better vessel border detection at 7 T than at 3 T (44,166 versus 28,720 pixels). Of 32 parameters used for diagnosing cerebrovascular diseases on DSA, 27 (84%) were detected on 7-T TOF-MRA; the similarity index ranged from 0.52 (dural arteriovenous fistula) to 0.90 (saccular aneurysm). CONCLUSIONS: Seven-tesla TOF-MRA outperformed conventional 3-T TOF-MRA in evaluating intracranial vessels and exhibited an excellent image quality when compared to DSA. Seven-tesla TOF-MRA might improve the non-invasive diagnostic approach to several cerebrovascular diseases. RELEVANCE STATEMENT: An optimized TOF-MRA sequence at 7 T outperforms 3-T TOF-MRA, opening perspectives to its clinical use for noninvasive diagnosis of paradigmatic pathologies of intracranial vessels. KEY POINTS: • An optimized 7-T TOF-MRA protocol was selected for comparison with clinical 3-T TOF-MRA for assessing intracranial vessels. • Seven-tesla TOF-MRA outperformed 3-T TOF-MRA in both quantitative and qualitative evaluation. • Seven-tesla TOF-MRA is comparable to DSA for the diagnosis and characterization of intracranial vascular pathologies.

Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes.

We introduce Ultra-Flexible Tentacle Electrodes (UFTEs), packing many independent fibers with the smallest possible footprint without limitation in recording depth using a combination of mechanical and chemical tethering for insertion. We demonstrate a scheme to implant UFTEs simultaneously into many brain areas at arbitrary locations without angle-of-insertion limitations, and a 512-channel wireless logger. Immunostaining reveals no detectable chronic tissue damage even after several months. Mean spike signal-to-noise ratios are 1.5-3x compared to the state-of-the-art, while the highest signal-to-noise ratios reach 89, and average cortical unit yields are ~1.75/channel. UFTEs can track the same neurons across sessions for at least 10 months (longest duration tested). We tracked inter- and intra-areal neuronal ensembles (neurons repeatedly co-activated within 25 ms) simultaneously from hippocampus, retrosplenial cortex, and medial prefrontal cortex in freely moving rodents. Average ensemble lifetimes were shorter than the durations over which we can track individual neurons. We identify two distinct classes of ensembles. Those tuned to sharp-wave ripples display the shortest lifetimes, and the ensemble members are mostly hippocampal. Yet, inter-areal ensembles with members from both hippocampus and cortex have weak tuning to sharp wave ripples, and some have unusual months-long lifetimes. Such inter-areal ensembles occasionally remain inactive for weeks before re-emerging.

Compact and cost-effective laser-powered speckle contrast optical spectroscopy fiber-free device for measuring cerebral blood flow.

Significance: In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF noninvasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. Aim: Our study aims to introduce a compact speckle contrast optical spectroscopy device for noninvasive CBF measurements at long source-to-detector distances, offering cost-effectiveness, and scalability while tracking blood flow (BF) with remarkable sensitivity and temporal resolution. Approach: The wearable sensor module consists solely of a laser diode and a board camera. It can be easily placed on a subject's head to measure BF at a sampling rate of 80 Hz. Results: Compared to the single-fiber-based version, the proposed device achieved a signal gain of about 70 times, showed superior stability, reproducibility, and signal-to-noise ratio for measuring BF at long source-to-detector distances. The device can be distributed in multiple configurations around the head. Conclusions: Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing noninvasive cerebral monitoring technologies.

Pillar data-acquisition strategies for cryo-electron tomography of beam-sensitive biological samples.

For cryo-electron tomography (cryo-ET) of beam-sensitive biological specimens, a planar sample geometry is typically used. As the sample is tilted, the effective thickness of the sample along the direction of the electron beam increases and the signal-to-noise ratio concomitantly decreases, limiting the transfer of information at high tilt angles. In addition, the tilt range where data can be collected is limited by a combination of various sample-environment constraints, including the limited space in the objective lens pole piece and the possible use of fixed conductive braids to cool the specimen. Consequently, most tilt series are limited to a maximum of ±70°, leading to the presence of a missing wedge in Fourier space. The acquisition of cryo-ET data without a missing wedge, for example using a cylindrical sample geometry, is hence attractive for volumetric analysis of low-symmetry structures such as organelles or vesicles, lysis events, pore formation or filaments for which the missing information cannot be compensated by averaging techniques. Irrespective of the geometry, electron-beam damage to the specimen is an issue and the first images acquired will transfer more high-resolution information than those acquired last. There is also an inherent trade-off between higher sampling in Fourier space and avoiding beam damage to the sample. Finally, the necessity of using a sufficient electron fluence to align the tilt images means that this fluence needs to be fractionated across a small number of images; therefore, the order of data acquisition is also a factor to consider. Here, an n-helix tilt scheme is described and simulated which uses overlapping and interleaved tilt series to maximize the use of a pillar geometry, allowing the entire pillar volume to be reconstructed as a single unit. Three related tilt schemes are also evaluated that extend the continuous and classic dose-symmetric tilt schemes for cryo-ET to pillar samples to enable the collection of isotropic information across all spatial frequencies. A fourfold dose-symmetric scheme is proposed which provides a practical compromise between uniform information transfer and complexity of data acquisition.

Probing a neural unreliability account of auditory sensory processing atypicalities in Rett Syndrome.

BACKGROUND: In the search for objective tools to quantify neural function in Rett Syndrome (RTT), which are crucial in the evaluation of therapeutic efficacy in clinical trials, recordings of sensory-perceptual functioning using event-related potential (ERP) approaches have emerged as potentially powerful tools. Considerable work points to highly anomalous auditory evoked potentials (AEPs) in RTT. However, an assumption of the typical signal-averaging method used to derive these measures is "stationarity" of the underlying responses - i.e. neural responses to each input are highly stereotyped. An alternate possibility is that responses to repeated stimuli are highly variable in RTT. If so, this will significantly impact the validity of assumptions about underlying neural dysfunction, and likely lead to overestimation of underlying neuropathology. To assess this possibility, analyses at the single-trial level assessing signal-to-noise ratios (SNR), inter-trial variability (ITV) and inter-trial phase coherence (ITPC) are necessary. METHODS: AEPs were recorded to simple 100 Hz tones from 18 RTT and 27 age-matched controls (Ages: 6-22 years). We applied standard AEP averaging, as well as measures of neuronal reliability at the single-trial level (i.e. SNR, ITV, ITPC). To separate signal-carrying components from non-neural noise sources, we also applied a denoising source separation (DSS) algorithm and then repeated the reliability measures. RESULTS: Substantially increased ITV, lower SNRs, and reduced ITPC were observed in auditory responses of RTT participants, supporting a "neural unreliability" account. Application of the DSS technique made it clear that non-neural noise sources contribute to overestimation of the extent of processing deficits in RTT. Post-DSS, ITV measures were substantially reduced, so much so that pre-DSS ITV differences between RTT and TD populations were no longer detected. In the case of SNR and ITPC, DSS substantially improved these estimates in the RTT population, but robust differences between RTT and TD were still fully evident. CONCLUSIONS: To accurately represent the degree of neural dysfunction in RTT using the ERP technique, a consideration of response reliability at the single-trial level is highly advised. Non-neural sources of noise lead to overestimation of the degree of pathological processing in RTT, and denoising source separation techniques during signal processing substantially ameliorate this issue.

Modeling the Intelligibility Benefit of Active Noise Cancelation in Hearing Devices That Improve Signal-to-Noise Ratio.

The extent to which active noise cancelation (ANC), when combined with hearing assistance, can improve speech intelligibility in noise is not well understood. One possible source of benefit is ANC's ability to reduce the sound level of the direct (i.e., vent-transmitted) path. This reduction lowers the "floor" imposed by the direct path, thereby allowing any increases to the signal-to-noise ratio (SNR) created in the amplified path to be "realized" at the eardrum. Here we used a modeling approach to estimate this benefit. We compared pairs of simulated hearing aids that differ only in terms of their ability to provide ANC and computed intelligibility metrics on their outputs. The difference in metric scores between simulated devices is termed the "ANC Benefit." These simulations show that ANC Benefit increases as (1) the environmental sound level increases, (2) the ability of the hearing aid to improve SNR increases, (3) the strength of the ANC increases, and (4) the hearing loss severity decreases. The predicted size of the ANC Benefit can be substantial. For a moderate hearing loss, the model predicts improvement in intelligibility metrics of >30% when environments are moderately loud (>70 dB SPL) and devices are moderately capable of increasing SNR (by >4 dB). It appears that ANC can be a critical ingredient in hearing devices that attempt to improve SNR in loud environments. ANC will become more and more important as advanced SNR-improving algorithms (e.g., artificial intelligence speech enhancement) are included in hearing devices.

A transfer function based on bilinear transformation for narrow-band seismic record.

For a narrow-brand seismograph with a flat response range limited, it cannot precisely record the signal of a ground motion and output the records with the low-frequency components cut down. A transfer function is usually used to spread the spectrum of the narrow-brand seismic records. However, the accuracy of the commonly used transfer function is not high. The authors derive a new transfer function based on the Laplace transform, bilinear transform, and Nyquist sampling theory to solve this problem. And then, the derived transfer function is used to correct the narrow-band velocity records from the Hi-net. The corrected velocity records are compared with the velocities integrated from the synchronously recorded broad-band acceleration at the same station with Hi-net. Meanwhile, the corrected records are compared with those corrected by the Nakata transfer function. The results show that the calculation accuracy of the derived transfer function is higher than the Nakata transfer function. However, when the signal-to-noise ratio is below 24, its accuracy diminishes, and it is unable to recover signals within the 0.05-0.78Hz frequency band.

Influence of static and dynamic ocular aberrations on full-field optical coherence tomography for in vivo high-resolution retinal imaging.

Under spatially incoherent illumination, time-domain full-field optical coherence tomography (FFOCT) offers the possibility to achieve in vivo retinal imaging at cellular resolution over a wide field of view. Such performance is possible, albeit there is the presence of ocular aberrations even without the use of classical adaptive optics. While the effect of aberrations in FFOCT has been debated these past years, mostly on low-order and static aberrations, we present, for the first time to our knowledge, a method enabling a quantitative study of the effect of statistically representative static and dynamic ocular aberrations on FFOCT image metrics, such as SNR, resolution, and image similarity. While we show that ocular aberrations can decrease FFOCT SNR and resolution by up to 14 dB and fivefold, we take advantage of such quantification to discuss different possible compromises between performance gain and adaptive optics complexity and speed, to optimize both sensor-based and sensorless FFOCT high-resolution retinal imaging.

Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography.

Optical coherence tomography (OCT) is a medical imaging method that generates micron-resolution 3D volumetric images of tissues in-vivo. Photothermal (PT)-OCT is a functional extension of OCT with the potential to provide depth-resolved molecular information complementary to the OCT structural images. PT-OCT typically requires long acquisition times to measure small fluctuations in the OCT phase signal. Here, we use machine learning with a neural network to infer the amplitude of the photothermal phase modulation from a short signal trace, trained in a supervised fashion with the ground truth signal obtained by conventional reconstruction of the PT-OCT signal from a longer acquisition trace. Results from phantom and tissue studies show that the developed network improves signal to noise ratio (SNR) and contrast, enabling PT-OCT imaging with short acquisition times and without any hardware modification to the PT-OCT system. The developed network removes one of the key barriers in translation of PT-OCT (i.e., long acquisition time) to the clinic.

Low illumination fog noise image denoising method based on ACE-GPM.

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image's foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality.

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier.

Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

Single molecule delivery into living cells.

Controlled manipulation of cultured cells by delivery of exogenous macromolecules is a cornerstone of experimental biology. Here we describe a platform that uses nanopipettes to deliver defined numbers of macromolecules into cultured cell lines and primary cells at single molecule resolution. In the nanoinjection platform, the nanopipette is used as both a scanning ion conductance microscope (SICM) probe and an injection probe. The SICM is used to position the nanopipette above the cell surface before the nanopipette is inserted into the cell into a defined location and to a predefined depth. We demonstrate that the nanoinjection platform enables the quantitative delivery of DNA, globular proteins, and protein fibrils into cells with single molecule resolution and that delivery results in a phenotypic change in the cell that depends on the identity of the molecules introduced. Using experiments and computational modeling, we also show that macromolecular crowding in the cell increases the signal-to-noise ratio for the detection of translocation events, thus the cell itself enhances the detection of the molecules delivered.

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.

In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.

Materials and Structural Designs toward Motion Artifact-Free Bioelectronics.

Bioelectronics encompassing electronic components and circuits for accessing human information play a vital role in real-time and continuous monitoring of biophysiological signals of electrophysiology, mechanical physiology, and electrochemical physiology. However, mechanical noise, particularly motion artifacts, poses a significant challenge in accurately detecting and analyzing target signals. While software-based "postprocessing" methods and signal filtering techniques have been widely employed, challenges such as signal distortion, major requirement of accurate models for classification, power consumption, and data delay inevitably persist. This review presents an overview of noise reduction strategies in bioelectronics, focusing on reducing motion artifacts and improving the signal-to-noise ratio through hardware-based approaches such as "preprocessing". One of the main stress-avoiding strategies is reducing elastic mechanical energies applied to bioelectronics to prevent stress-induced motion artifacts. Various approaches including strain-compliance, strain-resistance, and stress-damping techniques using unique materials and structures have been explored. Future research should optimize materials and structure designs, establish stable processes and measurement methods, and develop techniques for selectively separating and processing overlapping noises. Ultimately, these advancements will contribute to the development of more reliable and effective bioelectronics for healthcare monitoring and diagnostics.

Functional ultrasound imaging of human brain activity through an acoustically transparent cranial window.

Visualization of human brain activity is crucial for understanding normal and aberrant brain function. Currently available neural activity recording methods are highly invasive, have low sensitivity, and cannot be conducted outside of an operating room. Functional ultrasound imaging (fUSI) is an emerging technique that offers sensitive, large-scale, high-resolution neural imaging; however, fUSI cannot be performed through the adult human skull. Here, we used a polymeric skull replacement material to create an acoustic window compatible with fUSI to monitor adult human brain activity in a single individual. Using an in vitro cerebrovascular phantom to mimic brain vasculature and an in vivo rodent cranial defect model, first, we evaluated the fUSI signal intensity and signal-to-noise ratio through polymethyl methacrylate (PMMA) cranial implants of different thicknesses or a titanium mesh implant. We found that rat brain neural activity could be recorded with high sensitivity through a PMMA implant using a dedicated fUSI pulse sequence. We then designed a custom ultrasound-transparent cranial window implant for an adult patient undergoing reconstructive skull surgery after traumatic brain injury. We showed that fUSI could record brain activity in an awake human outside of the operating room. In a video game "connect the dots" task, we demonstrated mapping and decoding of task-modulated cortical activity in this individual. In a guitar-strumming task, we mapped additional task-specific cortical responses. Our proof-of-principle study shows that fUSI can be used as a high-resolution (200 µm) functional imaging modality for measuring adult human brain activity through an acoustically transparent cranial window.

Label-free detection and profiling of individual solution-phase molecules.

Most chemistry and biology occurs in solution, in which conformational dynamics and complexation underlie behaviour and function. Single-molecule techniques1 are uniquely suited to resolving molecular diversity and new label-free approaches are reshaping the power of single-molecule measurements. A label-free single-molecule method2-16 capable of revealing details of molecular conformation in solution17,18 would allow a new microscopic perspective of unprecedented detail. Here we use the enhanced light-molecule interactions in high-finesse fibre-based Fabry-Pérot microcavities19-21 to detect individual biomolecules as small as 1.2 kDa, a ten-amino-acid peptide, with signal-to-noise ratios (SNRs) >100, even as the molecules are unlabelled and freely diffusing in solution. Our method delivers 2D intensity and temporal profiles, enabling the distinction of subpopulations in mixed samples. Notably, we observe a linear relationship between passage time and molecular radius, unlocking the potential to gather crucial information about diffusion and solution-phase conformation. Furthermore, mixtures of biomolecule isomers of the same molecular weight and composition but different conformation can also be resolved. Detection is based on the creation of a new molecular velocity filter window and a dynamic thermal priming mechanism that make use of the interplay between optical and thermal dynamics22,23 and Pound-Drever-Hall (PDH) cavity locking24 to reveal molecular motion even while suppressing environmental noise. New in vitro ways of revealing molecular conformation, diversity and dynamics can find broad potential for applications in the life and chemical sciences.

Transcranial volumetric imaging using a conformal ultrasound patch.

Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as -1.51 ± 4.34 cm s-1, -0.84 ± 3.06 cm s-1 and -0.50 ± 2.55 cm s-1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.