Single-trial decoding of movement intentions using functional ultrasound neuroimaging

Functional ultrasound imaging and neuronal activity: how accurate is the spatiotemporal match?

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, though the correlation coefficients vary among visual regions. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

Functional ultrasound neuroimaging reveals mesoscopic organization of saccades in the lateral intraparietal area of posterior parietal cortex

The lateral intraparietal cortex (LIP) located within the posterior parietal cortex (PPC) is an important area for the transformation of spatial information into accurate saccadic eye movements. Despite extensive research, we do not fully understand the functional anatomy of intended movement directions within LIP. This is in part due to technical challenges. Electrophysiology recordings can only record from small regions of the PPC, while fMRI and other whole-brain techniques lack sufficient spatiotemporal resolution. Here, we use functional ultrasound imaging (fUSI), an emerging technique with high sensitivity, large spatial coverage, and good spatial resolution, to determine how movement direction is encoded across PPC. We used fUSI to record local changes in cerebral blood volume in PPC as two monkeys performed memory-guided saccades to targets throughout their visual field. We then analyzed the distribution of preferred directional response fields within each coronal plane of PPC. Many subregions within LIP demonstrated strong directional tuning that was consistent across several months to years. These mesoscopic maps revealed a highly heterogenous organization within LIP with many small patches of neighboring cortex encoding different directions. LIP had a rough topography where anterior LIP represented more contralateral upward movements and posterior LIP represented more contralateral downward movements. These results address two fundamental gaps in our understanding of LIP's functional organization: the neighborhood organization of patches and the broader organization across LIP. These findings were achieved by tracking the same LIP populations across many months to years and developing mesoscopic maps of direction specificity previously unattainable with fMRI or electrophysiology methods.

Flexible Ultrasonic Transducers for Wearable Biomedical Applications: A Review on Advanced Materials, Structural Designs, and Future Prospects

Due to the rapid developments in materials science and fabrication techniques, wearable devices have recently received increased attention for biomedical applications, particularly in medical ultrasound (US) imaging, sensing, and therapy. US is ubiquitous in biomedical applications because of its noninvasive nature, nonionic radiating, high precision, and real-time capabilities. While conventional US transducers are rigid and bulky, flexible transducers can be conformed to curved body areas for continuous sensing without restricting tissue movement or transducer shifting. This article comprehensively reviews the application of flexible US transducers in the field of biomedical imaging, sensing, and therapy. First, we review the background of flexible US transducers. Following that, we discuss advanced materials and fabrication techniques for flexible US transducers and their enabling technology status. Finally, we highlight and summarize some promising preliminary data with recent applications of flexible US transducers in biomedical imaging, sensing, and therapy. We also provide technical barriers, challenges, and future perspectives for further research and development.

Improved Transcranial Plane-Wave Imaging With Learned Speed-of-Sound Maps

Although transcranial ultrasound plane-wave imaging (PWI) has promising clinical application prospects, studies have shown that variable speed-of-sound (SoS) would seriously damage the quality of ultrasound images. The mismatch between the conventional constant velocity assumption and the actual SoS distribution leads to the general blurring of ultrasound images. The optimization scheme for reconstructing transcranial ultrasound image is often solved using iterative methods like full-waveform inversion. These iterative methods are computationally expensive and based on prior magnetic resonance imaging (MRI) or computed tomography (CT) information. In contrast, the multi-stencils fast marching (MSFM) method can produce accurate time travel maps for the skull with heterogeneous acoustic speed. In this study, we first propose a convolutional neural network (CNN) to predict SoS maps of the skull from PWI channel data. Then, use these maps to correct the travel time to reduce transcranial aberration. To validate the performance of the proposed method, numerical, phantom and intact human skull studies were conducted using a linear array transducer (L11-5v, 128 elements, pitch = 0.3 mm). Numerical simulations demonstrate that for point targets, the lateral resolution of MSFM-restored images increased by 65%, and the center position shift decreased by 89%. For the cyst targets, the eccentricity of the fitting ellipse decreased by 75%, and the center position shift decreased by 58%. In the phantom study, the lateral resolution of MSFM-restored images was increased by 49%, and the position shift was reduced by 1.72 mm. This pipeline, termed AutoSoS, thus shows the potential to correct distortions in real-time transcranial ultrasound imaging, as demonstrated by experiments on the intact human skull.

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.

Functional ultrasound imaging of the human spinal cord

Utilizing the first in-human functional ultrasound imaging (fUSI) of the spinal cord, we demonstrate the integration of spinal functional responses to electrical stimulation. We record and characterize the hemodynamic responses of the spinal cord to a neuromodulatory intervention commonly used for treating pain and increasingly used for the restoration of sensorimotor and autonomic function. We found that the hemodynamic response to stimulation reflects a spatiotemporal modulation of the spinal cord circuitry not previously recognized. Our analytical capability offers a mechanism to assess blood flow changes with a new level of spatial and temporal precision in vivo and demonstrates that fUSI can decode the functional state of spinal networks in a single trial, which is of fundamental importance for developing real-time closed-loop neuromodulation systems. This work is a critical step toward developing a vital technique to study spinal cord function and effects of clinical neuromodulation.

Functional organization of posterior parietal cortex circuitry based on inferred information flow

Many studies infer the role of neurons by asking what information can be decoded from their activity or by observing the consequences of perturbing their activity. An alternative approach is to consider information flow between neurons. We applied this approach to the parietal reach region (PRR) and the lateral intraparietal area (LIP) in posterior parietal cortex. Two complementary methods imply that across a range of reaching tasks, information flows primarily from PRR to LIP. This indicates that during a coordinated reach task, LIP has minimal influence on PRR and rules out the idea that LIP forms a general purpose spatial processing hub for action and cognition. Instead, we conclude that PRR and LIP operate in parallel to plan arm and eye movements, respectively, with asymmetric interactions that likely support eye-hand coordination. Similar methods can be applied to other areas to infer their functional relationships based on inferred information flow.

Theta-frequency medial septal nucleus deep brain stimulation increases neurovascular activity in MK-801-treated mice

Introduction

Deep brain stimulation (DBS) has shown remarkable success treating neurological and psychiatric disorders including Parkinson's disease, essential tremor, dystonia, epilepsy, and obsessive-compulsive disorder. DBS is now being explored to improve cognitive and functional outcomes in other psychiatric conditions, such as those characterized by reduced N-methyl-D-aspartate (NMDA) function (i.e., schizophrenia). While DBS for movement disorders generally involves high-frequency (>100 Hz) stimulation, there is evidence that low-frequency stimulation may have beneficial and persisting effects when applied to cognitive brain networks.

Methods

In this study, we utilize a novel technology, functional ultrasound imaging (fUSI), to characterize the cerebrovascular impact of medial septal nucleus (MSN) DBS under conditions of NMDA antagonism (pharmacologically using Dizocilpine [MK-801]) in anesthetized male mice.

Results

Imaging from a sagittal plane across a variety of brain regions within and outside of the septohippocampal circuit, we find that MSN theta-frequency (7.7 Hz) DBS increases hippocampal cerebral blood volume (CBV) during and after stimulation. This effect was not present using standard high-frequency stimulation parameters [i.e., gamma (100 Hz)].

Discussion

These results indicate the MSN DBS increases circuit-specific hippocampal neurovascular activity in a frequency-dependent manner and does so in a way that continues beyond the period of electrical stimulation.

Transcranial Functional Ultrasound Imaging Detects Focused Ultrasound Neuromodulation Induced Hemodynamic Changes in Mouse and Nonhuman Primate Brains In Vivo

Focused ultrasound (FUS) is an emerging noinvasive technique for neuromodulation in the central nervous system (CNS). To evaluate the effects of FUS-induced neuromodulation, many studies used behavioral changes, functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, behavioral readouts are often not easily mapped to specific brain activity, EEG has low spatial resolution limited to the surface of the brain and fMRI requires a large importable scanner that limits additional readouts and manipulations. In this context, functional ultrasound imaging (fUSI) holds promise to directly monitor the effects of FUS neuromodulation with high spatiotemporal resolution in a large field of view, with a comparatively simple and flexible setup. fUSI uses ultrafast Power Doppler Imaging (PDI) to measure changes in cerebral blood volume, which correlates well with neuronal activity and local field potentials. We designed a setup that aligns a FUS transducer with a linear array to allow immediate subsequent monitoring of the hemodynamic response with fUSI during and after FUS neuromodulation. We established a positive correlation between FUS pressure and the size of the activated area, as well as changes in cerebral blood volume (CBV) and found that unilateral sonications produce bilateral hemodynamic changes with ipsilateral accentuation in mice. We further demonstrated the ability to perform fully noninvasive, transcranial FUS-fUSI in nonhuman primates for the first time by using a lower-frequency transducer configuration.

Temporally organized representations of reward and risk in the human brain

The value and uncertainty associated with choice alternatives constitute critical features relevant for decisions. However, the manner in which reward and risk representations are temporally organized in the brain remains elusive. Here we leverage the spatiotemporal precision of intracranial electroencephalography, along with a simple card game designed to elicit the unfolding computation of a set of reward and risk variables, to uncover this temporal organization. Reward outcome representations across wide-spread regions follow a sequential order along the anteroposterior axis of the brain. In contrast, expected value can be decoded from multiple regions at the same time, and error signals in both reward and risk domains reflect a mixture of sequential and parallel encoding. We further highlight the role of the anterior insula in generalizing between reward prediction error and risk prediction error codes. Together our results emphasize the importance of neural dynamics for understanding value-based decisions under uncertainty.

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks

BACKGROUND

Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. Chronic fUS imaging during cognitive tasks in freely moving animals faces multiple exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioral experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts, and validating that the animal can perform cognitive tasks while tethered.

NEW METHOD

We provide validated solutions for those technical challenges. In addition, we present standardized step-by-step reproducible protocols, procedures, and data processing pipelines. Finally, we present proof-of-concept analysis of brain dynamics during a decision making task.

RESULTS

We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored.

COMPARISON WITH EXISTING METHODS

Computational pipelines in fUS are nascent and we present an initial development of a full processing pathway to correct and segment fUS data.

CONCLUSIONS

Our methods provide stable imaging and analysis of behavior with fUS that will enable new experimental paradigms in understanding brain-wide dynamics in naturalistic behaviors.

Decoding motor plans using a closed-loop ultrasonic brain-machine interface

Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.

Human brain mapping using co-registered fUS, fMRI and ESM during awake brain surgeries: A proof-of-concept study

Accurate, depth-resolved functional imaging is key in both understanding and treatment of the human brain. A new sonography-based imaging technique named functional Ultrasound (fUS) uniquely combines high sensitivity with submillimeter-subsecond spatiotemporal resolution available in large fields-of-view. In this proof-of-concept study we show that: (A) fUS reveals the same eloquent regions as found by fMRI while concomitantly visualizing in-vivo microvascular morphology underlying these functional hemodynamics and (B) fUS-based functional maps are confirmed by Electrocortical Stimulation Mapping (ESM), the current gold-standard in awake neurosurgical practice. This unique cross-modality experiment was performed using motor, visual and language-related functional tasks in patients undergoing awake brain tumor resection. The current work serves as an important milestone towards further maturity of fUS as well as a novel avenue to increase our understanding of hemodynamics-based functional brain imaging.

MBGA-Net: A multi-branch graph adaptive network for individualized motor imagery EEG classification

BACKGROUND AND OBJECTIVE

The development of deep learning has led to significant improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification. However, current models are inadequate in ensuring high levels of classification accuracy for an individual. Since MI EEG data is primarily used in medical rehabilitation and intelligent control, it is crucial to ensure that each individual's EEG signal is recognized with precision.

METHODS

We propose a multi-branch graph adaptive network (MBGA-Net), which matches each individual EEG signal with a suitable time-frequency domain processing method based on spatio-temporal domain features. We then feed the signal into the relevant model branch using an adaptive technique. Through an enhanced attention mechanism and deep convolutional method with residual connectivity, each model branch more effectively harvests the features of the related format data.

RESULTS

We validate the proposed model using the BCI Competition IV dataset 2a and dataset 2b. On dataset 2a, the average accuracy and kappa values are 87.49% and 0.83, respectively. The standard deviation of individual kappa values is only 0.08. For dataset 2b, the average classification accuracies obtained by feeding the data into the three branches of MBGA-Net are 85.71%, 85.83%, and 86.99%, respectively.

CONCLUSIONS

The experimental results demonstrate that MBGA-Net could effectively perform the classification task of motor imagery EEG signals, and it exhibits strong generalization performance. The proposed adaptive matching technique enhances the classification accuracy of each individual, which is beneficial for the practical application of EEG classification.

Brain-wide mapping of resting-state networks in mice using high-frame rate functional ultrasound

Functional ultrasound (fUS) imaging is a method for visualizing deep brain activity based on cerebral blood volume changes coupled with neural activity, while functional MRI (fMRI) relies on the blood-oxygenation-level-dependent signal coupled with neural activity. Low-frequency fluctuations (LFF) of fMRI signals during resting-state can be measured by resting-state fMRI (rsfMRI), which allows functional imaging of the whole brain, and the distributions of resting-state network (RSN) can then be estimated from these fluctuations using independent component analysis (ICA). This procedure provides an important method for studying cognitive and psychophysiological diseases affecting specific brain networks. The distributions of RSNs in the brain-wide area has been reported primarily by rsfMRI. RSNs using rsfMRI are generally computed from the time-course of fMRI signals for more than 5 min. However, a recent dynamic functional connectivity study revealed that RSNs are still not perfectly stable even after 10 min. Importantly, fUS has a higher temporal resolution and stronger correlation with neural activity compared with fMRI. Therefore, we hypothesized that fUS applied during the resting-state for a shorter than 5 min would provide similar RSNs compared to fMRI. High temporal resolution rsfUS data were acquired at 10 Hz in awake mice. The quality of the default mode network (DMN), a well-known RSN, was evaluated using signal-noise separation (SNS) applied to different measurement durations of rsfUS. The results showed that the SNS did not change when the measurement duration was increased to more than 210 s. Next, we measured short-duration rsfUS multi-slice measurements in the brain-wide area. The results showed that rsfUS with the short duration succeeded in detecting RSNs distributed in the brain-wide area consistent with RSNs detected by 11.7-T MRI under awake conditions (medial prefrontal cortex and cingulate cortex in the anterior DMN, retrosplenial cortex and visual cortex in the posterior DMN, somatosensory and motor cortexes in the lateral cortical network, thalamus, dorsal hippocampus, and medial cerebellum), confirming the reliability of the RSNs detected by rsfUS. However, bilateral RSNs located in the secondary somatosensory cortex, ventral hippocampus, auditory cortex, and lateral cerebellum extracted from rsfUS were different from the unilateral RSNs extracted from rsfMRI. These findings indicate the potential of rsfUS as a method for analyzing functional brain networks and should encourage future research to elucidate functional brain networks and their relationships with disease model mice.

Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation

The neuromodulation effect of low-intensity focused ultrasound (LIFU) is highly target-specific. Unintended off-target neuronal excitation can be elicited when the beam focusing accuracy and resolution are limited, whereas the resulted side effect has not been evaluated quantitatively. There is also a lack of methods addressing the minimization of such side effects. Therefore, this work introduces a computational model of unintended neuronal excitation during LIFU neuromodulation, which evaluates the off-target activation area (OTAA) by integrating an ultrasound field model with the neuronal spiking model. In addition, a phased array beam focusing scheme called constrained optimal resolution beamforming (CORB) is proposed to minimize the off-target neuronal excitation area while ensuring effective stimulation in the target brain region. A lower bound of the OTAA is analytically approximated in a simplified homogeneous medium, which could guide the selection of transducer parameters such as aperture size and operating frequency. Simulations in a human head model using three transducer setups show that CORB markedly reduces the OTAA compared with two benchmark beam focusing methods. The high neuromodulation resolution demonstrates the capability of LIFU to effectively limit the side effects during neuromodulation, allowing future clinical applications such as treatment of neuropsychiatric disorders.

Mapping Pharmacologically Evoked Neurovascular Activation and Its Suppression in a Rat Model of Tremor Using Functional Ultrasound: A Feasibility Study

Functional ultrasound (fUS), an emerging hemodynamic-based functional neuroimaging technique, is especially suited to probe brain activity and primarily used in animal models. Increasing use of pharmacological models for essential tremor extends new research to the utilization of fUS imaging in such models. Harmaline-induced tremor is an easily provoked model for the development of new therapies for essential tremor (ET). Furthermore, harmaline-induced tremor can be suppressed by the same classic medications used for essential tremor, which leads to the utilization of this model for preclinical testing. However, changes in local cerebral activities under the effect of tremorgenic doses of harmaline have not been completely investigated. In this study, we explored the feasibility of fUS imaging for visualization of cerebral activation and deactivation associated with harmaline-induced tremor and tremor-suppressing effects of propranolol. The spatial resolution of fUS using a high frame rate imaging enabled us to visualize time-locked and site-specific changes in cerebral blood flow associated with harmaline-evoked tremor. Intraperitoneal administration of harmaline generated significant neural activity changes in the primary motor cortex and ventrolateral thalamus (VL Thal) regions during tremor and then gradually returned to baseline level as tremor subsided with time. To the best of our knowledge, this is the first functional ultrasound study to show the neurovascular activation of harmaline-induced tremor and the therapeutic suppression in a rat model. Thus, fUS can be considered a noninvasive imaging method for studying neuronal activities involved in the ET model and its treatment.

Microscale acoustic metamaterials as conformal sonotransparent skull prostheses

Functional ultrasound imaging enables sensitive, high-resolution imaging of neural activity in freely behaving animals and human patients. However, the skull acts as an aberrating and absorbing layer for sound waves, leading to most functional ultrasound experiments being conducted after skull removal. In pre-clinical settings, craniotomies are often covered with a polymethylpentene film, which offers limited longitudinal imaging, due to the film's poor conformability, and limited mechanical protection, due to the film's low stiffness. Here, we introduce a skull replacement consisting of a microstructured, conformal acoustic window based on mechanical metamaterials, designed to offer high stiffness-to-density ratio and sonotransparency. We test the acoustic window in vivo, via terminal and survival experiments on small animals. Long-term biocompatibility and lasting signal sensitivity are demonstrated over a long period of time (> 4 months) by conducting ultrasound imaging in mouse models implanted with the metamaterial skull prosthesis.

Temporally organized representations of reward and risk in the human brain

The value and uncertainty associated with choice alternatives constitute critical features along which decisions are made. While the neural substrates supporting reward and risk processing have been investigated, the temporal organization by which these computations are encoded remains elusive. Here we leverage the high spatiotemporal precision of intracranial electroencephalography (iEEG) to uncover how representations of decision-related computations unfold in time. We present evidence of locally distributed representations of reward and risk variables that are temporally organized across multiple regions of interest. Reward outcome representations across wide-spread regions follow a temporally cascading order along the anteroposterior axis of the brain. In contrast, expected value can be decoded from multiple regions at the same time, and error signals in both reward and risk domains reflect a mixture of sequential and parallel encoding. We highlight the role of the anterior insula in generalizing between reward prediction error (RePE) and risk prediction error (RiPE), within which the encoding of RePE in the distributed iEEG signal predicts RiPE. Together our results emphasize the utility of uncovering temporal dynamics in the human brain for understanding how computational processes critical for value-based decisions under uncertainty unfold.

Co-variations of cerebral blood volume and single neurons discharge during resting state and visual cognitive tasks in non-human primates

To better understand how the brain allows primates to perform various sets of tasks, the ability to simultaneously record neural activity at multiple spatiotemporal scales is challenging but necessary. However, the contribution of single-unit activities (SUAs) to neurovascular activity remains to be fully understood. Here, we combine functional ultrasound imaging of cerebral blood volume (CBV) and SUA recordings in visual and fronto-medial cortices of behaving macaques. We show that SUA provides a significant estimate of the neurovascular response below the typical fMRI spatial resolution of 2mm3. Furthermore, our results also show that SUAs and CBV activities are statistically uncorrelated during the resting state but correlate during tasks. These results have important implications for interpreting functional imaging findings while one constructs inferences of SUA during resting state or tasks.

Decoding behavior from global cerebrovascular activity using neural networks

Functional Ultrasound (fUS) provides spatial and temporal frames of the vascular activity in the brain with high resolution and sensitivity in behaving animals. The large amount of resulting data is underused at present due to the lack of appropriate tools to visualize and interpret such signals. Here we show that neural networks can be trained to leverage the richness of information available in fUS datasets to reliably determine behavior, even from a single fUS 2D image after appropriate training. We illustrate the potential of this method with two examples: determining if a rat is moving or static and decoding the animal's sleep/wake state in a neutral environment. We further demonstrate that our method can be transferred to new recordings, possibly in other animals, without additional training, thereby paving the way for real-time decoding of brain activity based on fUS data. Finally, the learned weights of the network in the latent space were analyzed to extract the relative importance of input data to classify behavior, making this a powerful tool for neuroscientific research.

Functional imaging of the exposed brain

When the brain is exposed, such as after a craniotomy in neurosurgical procedures, we are provided with the unique opportunity for real-time imaging of brain functionality. Real-time functional maps of the exposed brain are vital to ensuring safe and effective navigation during these neurosurgical procedures. However, current neurosurgical practice has yet to fully harness this potential as it pre-dominantly relies on inherently limited techniques such as electrical stimulation to provide functional feedback to guide surgical decision-making. A wealth of especially experimental imaging techniques show unique potential to improve intra-operative decision-making and neurosurgical safety, and as an added bonus, improve our fundamental neuroscientific understanding of human brain function. In this review we compare and contrast close to twenty candidate imaging techniques based on their underlying biological substrate, technical characteristics and ability to meet clinical constraints such as compatibility with surgical workflow. Our review gives insight into the interplay between technical parameters such sampling method, data rate and a technique's real-time imaging potential in the operating room. By the end of the review, the reader will understand why new, real-time volumetric imaging techniques such as functional Ultrasound (fUS) and functional Photoacoustic Computed Tomography (fPACT) hold great clinical potential for procedures in especially highly eloquent areas, despite the higher data rates involved. Finally, we will highlight the neuroscientific perspective on the exposed brain. While different neurosurgical procedures ask for different functional maps to navigate surgical territories, neuroscience potentially benefits from all these maps. In the surgical context we can uniquely combine healthy volunteer studies, lesion studies and even reversible lesion studies in in the same individual. Ultimately, individual cases will build a greater understanding of human brain function in general, which in turn will improve neurosurgeons' future navigational efforts.

Probing the nature of episodic memory in rodents

Episodic memory (EM) specifies the experience of retrieving information of an event at the place and time of occurrence. Whether non-human animals are capable of EM remains debated, whereas evidence suggests that they have a memory system akin to EM. We here trace the development of various behavioral paradigms designed to study EM in non-human animals, in particular the rat. We provide an in-depth description of the available behavioral tests which combine three spontaneous object exploration paradigms, namely novel object preference (for measuring memory for "what"), novel location preference (for measuring memory for "where") and temporal order memory (memory for "when"), into a single trial to gauge a memory akin to EM. Most important, we describe a variation of such a test in which each memory component interacts with the others, demonstrating an integration of diverse mnemonic information. We discuss why a behavioral model of EM must be able to assess the ability to integrate "what", "where" and "when" information into a single experience. We attempt an interpretation of the various tests and review the studies that have applied them in areas such as pharmacology, neuroanatomy, circuit analysis, and sleep. Finally, we anticipate future directions in the search for neural mechanisms of EM in the rat and outline model experiments and methodologies in this pursuit.

Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition

Functional ultrasound (fUS) indirectly measures brain activity by detecting changes in cerebral blood volume following neural activation. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not characterize the whole complexity of the activity-inducing signals that evoke the hemodynamic changes. Furthermore, the HRF is known to vary across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics of the brain function, we model the multivariate fUS time-series as convolutive mixtures and apply block-term decomposition on a set of lagged fUS autocorrelation matrices, revealing both the region-specific HRFs and the source signals that induce the hemodynamic responses. We test our approach on two mouse-based fUS experiments. In the first experiment, we present a single type of visual stimulus to the mouse, and deconvolve the fUS signal measured within the mouse brain's lateral geniculate nucleus, superior colliculus and visual cortex. We show that the proposed method is able to recover back the time instants at which the stimulus was displayed, and we validate the estimated region-specific HRFs based on prior studies. In the second experiment, we alter the location of the visual stimulus displayed to the mouse, and aim at differentiating the various stimulus locations over time by identifying them as separate sources.

High-resolution micro-Doppler imaging during neurosurgical resection of an arteriovenous malformation: illustrative case

OBJECTIVE

Given the high-risk nature of arteriovenous malformation (AVM) resections, accurate pre- and intraoperative imaging of the vascular morphology is a crucial component that may contribute to successful surgical results. Surprisingly, current gold standard imaging techniques for surgical guidance of AVM resections are mostly preoperative, lacking the necessary flexibility to cater to intraoperative changes. Micro-Doppler imaging is a unique high-resolution technique relying on high frame rate ultrasound and subsequent Doppler processing of microvascular hemodynamics. In this paper the authors report the first application of intraoperative, coregistered magnetic resonance/computed tomograpy, micro-Doppler imaging during the neurosurgical resection of an AVM in the parietal lobe.

OBSERVATIONS

The authors applied intraoperative two-dimensional and three-dimensional (3D) micro-Doppler imaging during resection and were able to identify key anatomical features including draining veins, supplying arteries and microvasculature in the nidus itself. Compared to the corresponding preoperative 3D-digital subtraction angiography (DSA) image, the micro-Doppler images could delineate vascular structures and visualize hemodynamics with higher, submillimeter scale detail, even at significant depths (>5 cm). Additionally, micro-Doppler imaging revealed unique microvascular morphology of surrounding healthy vasculature.

LESSONS

The authors conclude that micro-Doppler imaging in its current form has clear potential as an intraoperative counterpart to preoperative contrast-dependent DSA, and the microvascular details it provides could build new ground to further study cerebrovascular pathophysiology.

Targeting neuroplasticity to improve motor recovery after stroke: an artificial neural network model

After a neurological injury, people develop abnormal patterns of neural activity that limit motor recovery. Traditional rehabilitation, which concentrates on practicing impaired skills, is seldom fully effective. New targeted neuroplasticity protocols interact with the central nervous system to induce beneficial plasticity in key sites and thereby enable wider beneficial plasticity. They can complement traditional therapy and enhance recovery. However, their development and validation is difficult because many different targeted neuroplasticity protocols are conceivable, and evaluating even one of them is lengthy, laborious, and expensive. Computational models can address this problem by triaging numerous candidate protocols rapidly and effectively. Animal and human empirical testing can then concentrate on the most promising ones. Here, we simulate a neural network of corticospinal neurons that control motoneurons eliciting unilateral finger extension. We use this network to (i) study the mechanisms and patterns of cortical reorganization after a stroke; and (ii) identify and parameterize a targeted neuroplasticity protocol that improves recovery of extension torque. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal torque recovery. To enhance recovery, we interdigitated standard training with trials in which the network was given feedback only from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on ∼20% of the total trials restored lateralized cortical activation and improved recovery of extension torque. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enable the identification and parameterization of the most promising targeted neuroplasticity protocols. By providing initial guidance, computational models could facilitate and accelerate the realization of new therapies that improve motor recovery.

Functional Ultrasound Neuroimaging

Functional ultrasound (fUS) is a neuroimaging method that uses ultrasound to track changes in cerebral blood volume as an indirect readout of neuronal activity at high spatiotemporal resolution. fUS is capable of imaging head-fixed or freely behaving rodents and of producing volumetric images of the entire mouse brain. It has been applied to many species, including primates and humans. Now that fUS is reaching maturity, it is being adopted by the neuroscience community. However, the nature of the fUS signal and the different implementations of fUS are not necessarily accessible to nonspecialists. This review aims to introduce these ultrasound concepts to all neuroscientists. We explain the physical basis of the fUS signal and the principles of the method, present the state of the art of its hardware implementation, and give concrete examples of current applications in neuroscience. Finally, we suggest areas for improvement during the next few years.

Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data

Functional ultrasound (fUS) is a rapidly emerging modality that enables whole-brain imaging of neural activity in awake and mobile rodents. To achieve sufficient blood flow sensitivity in the brain microvasculature, fUS relies on long ultrasound data acquisitions at high frame rates, posing high demands on the sampling and processing hardware. Here we develop an image reconstruction method based on deep learning that significantly reduces the amount of data necessary while retaining imaging performance. We trained convolutional neural networks to learn the power Doppler reconstruction function from sparse sequences of ultrasound data with compression factors of up to 95%. High-quality images from in vivo acquisitions in rats were used for training and performance evaluation. We demonstrate that time series of power Doppler images can be reconstructed with sufficient accuracy to detect the small changes in cerebral blood volume (~10%) characteristic of task-evoked cortical activation, even though the network was not formally trained to reconstruct such image series. The proposed platform may facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or in clinical scanners.

Neural correlates of blood flow measured by ultrasound

Functional ultrasound imaging (fUSI) is an appealing method for measuring blood flow and thus infer brain activity, but it relies on the physiology of neurovascular coupling and requires extensive signal processing. To establish to what degree fUSI trial-by-trial signals reflect neural activity, we performed simultaneous fUSI and neural recordings with Neuropixels probes in awake mice. fUSI signals strongly correlated with the slow (<0.3 Hz) fluctuations in the local firing rate and were closely predicted by the smoothed firing rate of local neurons, particularly putative inhibitory neurons. The optimal smoothing filter had a width of ∼3 s, matched the hemodynamic response function of awake mice, was invariant across mice and stimulus conditions, and was similar in the cortex and hippocampus. fUSI signals also matched neural firing spatially: firing rates were as highly correlated across hemispheres as fUSI signals. Thus, blood flow measured by ultrasound bears a simple and accurate relationship to neuronal firing.

The science and engineering behind sensitized brain-controlled bionic hands

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

New Insights for Biosensing: Lessons from Microbial Defense Systems

Microorganisms have gained defense systems during the lengthy process of evolution over millions of years. Such defense systems can protect them from being attacked by invading species (e.g., CRISPR-Cas for establishing adaptive immune systems and nanopore-forming toxins as virulence factors) or enable them to adapt to different conditions (e.g., gas vesicles for achieving buoyancy control). These microorganism defense systems (MDS) have inspired the development of biosensors that have received much attention in a wide range of fields including life science research, food safety, and medical diagnosis. This Review comprehensively analyzes biosensing platforms originating from MDS for sensing and imaging biological analytes. We first describe a basic overview of MDS and MDS-inspired biosensing platforms (e.g., CRISPR-Cas systems, nanopore-forming proteins, and gas vesicles), followed by a critical discussion of their functions and properties. We then discuss several transduction mechanisms (optical, acoustic, magnetic, and electrical) involved in MDS-inspired biosensing. We further detail the applications of the MDS-inspired biosensors to detect a variety of analytes (nucleic acids, peptides, proteins, pathogens, cells, small molecules, and metal ions). In the end, we propose the key challenges and future perspectives in seeking new and improved MDS tools that can potentially lead to breakthrough discoveries in developing a new generation of biosensors with a combination of low cost; high sensitivity, accuracy, and precision; and fast detection. Overall, this Review gives a historical review of MDS, elucidates the principles of emulating MDS to develop biosensors, and analyzes the recent advancements, current challenges, and future trends in this field. It provides a unique critical analysis of emulating MDS to develop robust biosensors and discusses the design of such biosensors using elements found in MDS, showing that emulating MDS is a promising approach to conceptually advancing the design of biosensors.

Deep learning for biosignal control: insights from basic to real-time methods with recommendations

Biosignal control is an interaction modality that allows users to interact with electronic devices by decoding the biological signals emanating from the movements or thoughts of the user. This manner of interaction with devices can enhance the sense of agency for users and enable persons suffering from a paralyzing condition to interact with everyday devices that would otherwise be challenging for them to use. It can also improve control of prosthetic devices and exoskeletons by making the interaction feel more natural and intuitive. However, with the current state of the art, several issues still need to be addressed to reliably decode user intent from biosignals and provide an improved user experience over other interaction modalities. One solution is to leverage advances in Deep Learning (DL) methods to provide more reliable decoding at the expense of added computational complexity. This scoping review introduces the basic concepts of DL and assists readers in deploying DL methods to a real-time control system that should operate under real-world conditions. The scope of this review covers any electronic device, but with an emphasis on robotic devices, as this is the most active area of research in biosignal control. We review the literature pertaining to the implementation and evaluation of control systems that incorporate DL to identify the main gaps and issues in the field, and formulate suggestions on how to mitigate them. Additionally, we formulate guidelines on the best approach to designing, implementing and evaluating research prototypes that use DL in their biosignal control systems.

Exploring Cognition with Brain-Machine Interfaces

Traditional brain-machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.

Functional ultrasound imaging: A useful tool for functional connectomics?

Functional ultrasound (fUS) is a hemodynamic-based functional neuroimaging technique, primarily used in animal models, that combines a high spatiotemporal resolution, a large field of view, and compatibility with behavior. These assets make fUS especially suited to interrogating brain activity at the systems level. In this review, we describe the technical capabilities offered by fUS and discuss how this technique can contribute to the field of functional connectomics. First, fUS can be used to study intrinsic functional connectivity, namely patterns of correlated activity between brain regions. In this area, fUS has made the most impact by following connectivity changes in disease models, across behavioral states, or dynamically. Second, fUS can also be used to map brain-wide pathways associated with an external event. For example, fUS has helped obtain finer descriptions of several sensory systems, and uncover new pathways implicated in specific behaviors. Additionally, combining fUS with direct circuit manipulations such as optogenetics is an attractive way to map the brain-wide connections of defined neuronal populations. Finally, technological improvements and the application of new analytical tools promise to boost fUS capabilities. As brain coverage and the range of behavioral contexts that can be addressed with fUS keep on increasing, we believe that fUS-guided connectomics will only expand in the future. In this regard, we consider the incorporation of fUS into multimodal studies combining diverse techniques and behavioral tasks to be the most promising research avenue.

Functional ultrasound imaging of the spreading activity following optogenetic stimulation of the rat visual cortex

Optogenetics has revolutionized neurosciences by allowing fine control of neuronal activity. An important aspect for this control is assessing the activation and/or adjusting the stimulation, which requires imaging the entire volume of optogenetically-induced neuronal activity. An ideal technique for this aim is fUS imaging, which allows one to generate brain-wide activation maps with submesoscopic spatial resolution. However, optical stimulation of the brain with blue light might lead to non-specific activations at high irradiances. fUS imaging of optogenetic activations can be obtained at these wavelengths using lower light power (< 2mW) but it limits the depth of directly activatable neurons from the cortical surface. Our main goal was to report that we can detect specific optogenetic activations in V1 even in deep layers following stimulation at the cortical surface. Here, we show the possibility to detect deep optogenetic activations in anesthetized rats expressing the red-shifted opsin ChrimsonR in V1 using fUS imaging. We demonstrate the optogenetic specificity of these activations and their neuronal origin with electrophysiological recordings. Finally, we show that the optogenetic response initiated in V1 spreads to downstream (LGN) and upstream (V2) visual areas.