Real-time Neuroimaging Research Articles

Audiovisualization of real-time neuroimaging data.

Advancements in brain imaging techniques have significantly expanded the size and complexity of real-time neuroimaging and behavioral data. However, identifying patterns, trends and synchronies within these datasets presents a significant computational challenge. Here, we demonstrate an approach that can translate time-varying neuroimaging data into unique audiovisualizations consisting of audible representations of dynamic data merged with simplified, color-coded movies of spatial components and behavioral recordings. Multiple variables can be encoded as different musical instruments, letting the observer differentiate and track multiple dynamic parameters in parallel. This representation enables intuitive assimilation of these datasets for behavioral correlates and spatiotemporal features such as patterns, rhythms and motifs that could be difficult to detect through conventional data interrogation methods. These audiovisual representations provide a novel perception of the organization and patterns of real-time activity in the brain, and offer an intuitive and compelling method for complex data visualization for a wider range of applications.

Suppressing physiological interferences and physical noises in functional diffuse optical tomography via tandem inversion filtering and LSTM classification.

For performance enhancement of functional diffuse optical tomography (fDOT), we propose a tandem method that takes advantage of the inversion filtering and the long short term memory (LSTM) classification to simultaneously suppress the physiological interferences and physical noises in fDOT. In the former phase, the absorption perturbation maps over the scalp-skull (SS) and cerebral-cortex (CC) layers are firstly pre-reconstructed using a two-layer topography scheme. Then, the recovered SS-map is inversed into measurement space by the forward calculation to estimate the intensity changes associated with the physiological interferences. Finally, the raw measurements are adaptively filtered with reference to the estimated intensity changes for accomplishing the model-based full three-dimension (3D) reconstruction. In the later phase, for further removing the randomly distributed physical noises, mainly instrumental noise, a LSTM network based fusion strategy is applied, where a pixel-wise binary mask of "activated" or "inactive" state is generated by performing LSTM classification and then fused with the 3D reconstruction. The results of the simulative investigation and in-vivo experiment show the proposed tandem scheme achieves improved performance in fDOT using a cost-effective and physically explicable way. Thus, the proposed method can be promisingly applied in real-time neuroimaging to acquire cortical neural activation information with improved reliability, quantification and resolution.

Application of new techniques in mechanistic study of general anesthesia

Background The mechanisms underlying the actions of general anesthetics are poorly understood. The application of breakthrough techniques in neuroscience research could advance our understanding of general anesthesia. Objective To review the application of research techniques to study the mechanisms underlying the actions of general anesthetics. Content Studies using conventional neuroscience research techniques revealed that general anesthetics may cause deep anesthesia by targeting at least three parts, thalamocortical pathway, classical sleep-wake circuitry, and cortical fragmentation circuits. The combination of viral vector-assisted retrograde and anterograde neuronal tracing and transgenic Cre-mouse lines allows tracing neural pathways involving particular neuron types. Application of optogenetics, chemogenetics, in vivo calcium imaging, and microdialysis and neuroimaging techniques would overcome the obstacles unconquerable to conventional techniques and fulfill in vivo real-time neuroimaging, neurochemical and behavioral monitoring, and neuromodulations as well. Trend More investigations incorporate the multidisciplinary techniques to systemically study the potential targets of general anesthetics, and evaluate the involvement of the targets in various aspects of anesthesia. These studies could pave the avenue to eventually solve the mystery of general anesthesia. Key words: Anesthetics, general; Mechanism; New techniques

Fast and robust Block-Sparse Bayesian learning for EEG source imaging

We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of noisy measurements. Current implementations of the SBL framework are computationally expensive and typically handle fluctuations in the measurement noise using different heuristics that are unsuitable for real-time imaging applications. We address these shortcomings by decoupling the estimation of the sensor noise covariance and the sparsity profile of the sources, thereby yielding an efficient two-stage algorithm. In the first stage, we optimize a simplified non-sparse generative model to get an estimate of the sensor noise covariance and a good initialization of the group-sparsity profile of the sources. Sources obtained at this stage are equivalent to those estimated with the popular inverse method LORETA. In the second stage, we apply a fast SBL algorithm with the noise covariance fixed to the value obtained in the first stage to efficiently shrink to zero groups of sources that are irrelevant for explaining the EEG measurements. In addition, we derive an initialization to the first stage of the algorithm that is optimal in the least squares sense, which prevents delays due to suboptimal initial conditions. We validate our method on both simulated and real EEG data. Simulations show that the method is robust to measurement noise and performs well in real-time, with faster performance than two state of the art SBL solvers. On real error-related negativity EEG data, we obtain source images in agreement with the experimental literature. The method shows promise for real-time neuroimaging and brain-machine interface applications.

Cognitive and Affective Probing for Neuroergonomics

Cognitive and Affective Probing for Neuroergonomics

Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design.

The relatively young field of Brain-Computer Interfaces has promoted the use of electrophysiology and neuroimaging in real-time. In the meantime, cognitive neuroscience studies, which make extensive use of functional exploration techniques, have evolved toward model-based experiments and fine hypothesis testing protocols. Although these two developments are mostly unrelated, we argue that, brought together, they may trigger an important shift in the way experimental paradigms are being designed, which should prove fruitful to both endeavors. This change simply consists in using real-time neuroimaging in order to optimize advanced neurocognitive hypothesis testing. We refer to this new approach as the instantiation of an Active SAmpling Protocol (ASAP). As opposed to classical (static) experimental protocols, ASAP implements online model comparison, enabling the optimization of design parameters (e.g., stimuli) during the course of data acquisition. This follows the well-known principle of sequential hypothesis testing. What is radically new, however, is our ability to perform online processing of the huge amount of complex data that brain imaging techniques provide. This is all the more relevant at a time when physiological and psychological processes are beginning to be approached using more realistic, generative models which may be difficult to tease apart empirically. Based upon Bayesian inference, ASAP proposes a generic and principled way to optimize experimental design adaptively. In this perspective paper, we summarize the main steps in ASAP. Using synthetic data we illustrate its superiority in selecting the right perceptual model compared to a classical design. Finally, we briefly discuss its future potential for basic and clinical neuroscience as well as some remaining challenges.

Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG.

We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification. The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system. Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ±0.09) and LCMV (0.72 ±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 ±0.16) but significantly better for LCMV (0.82 ±0.12) . We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.

Consciousness and the 21st Century Operating Room

Consciousness and the 21st Century Operating Room

Smartphones as pocketable labs: Visions for mobile brain imaging and neurofeedback

Mobile brain imaging solutions, such as the Smartphone Brain Scanner, which combines low cost wireless EEG sensors with open source software for real-time neuroimaging, may transform neuroscience experimental paradigms. Normally subject to the physical constraints in labs, neuroscience experimental paradigms can be transformed into dynamic environments allowing for the capturing of brain signals in everyday contexts. Using smartphones or tablets to access text or images may enable experimental design capable of tracing emotional responses when shopping or consuming media, incorporating sensorimotor responses reflecting our actions into brain machine interfaces, and facilitating neurofeedback training over extended periods. Even though the quality of consumer neuroheadsets is still lower than laboratory equipment and susceptible to environmental noise, we show that mobile neuroimaging solutions, like the Smartphone Brain Scanner, complemented by 3D reconstruction or source separation techniques may support a range of neuroimaging applications and thus become a valuable addition to high-end neuroimaging solutions.

Kruppel-like factor 2 protects against ischemic stroke by regulating endothelial blood brain barrier function

During an ischemic stroke normal brain endothelial function is perturbed, resulting in blood brain barrier (BBB) breakdown with subsequent infiltration of activated inflammatory blood cells, ultimately leading to neuronal cell death. Kruppel-like factor 2 (KLF2) is regulated by flow, is highly expressed in vascular endothelial cells (ECs), and serves as a key molecular switch regulating endothelial function and promoting vascular health. In this study we sought to determine the role of KLF2 in cerebrovascular function and the pathogenesis of ischemic stroke. Transient middle cerebral artery occlusion was performed in KLF2-deficient (KLF2(-/-)), KLF2 overexpressing (KLF2(tg)), and control mice, and stroke volume was analyzed. BBB function was assessed in vivo by real-time neuroimaging using positron emission tomography and Evan's blue dye assay. KLF2(-/-) mice exhibited significantly larger strokes and impairment in BBB function. In contrast, KLF2(tg) mice were protected against ischemic stroke and demonstrated preserved BBB function. In concordance, gain- and loss-of-function studies in primary brain microvascular ECs using transwell assays revealed KLF2 to be BBB protective. Mechanistically, KLF2 was demonstrated, both in vitro and in vivo, to regulate the critical BBB tight junction factor occludin. These data are first to identify endothelial KLF2 as a key regulator of the BBB and a novel neuroprotective factor in ischemic stroke.

A simple framework (ASF) for behavioral and neuroimaging experiments based on the psychophysics toolbox for MATLAB

The cognitive neurosciences combine behavioral experiments with acquiring physiological data from different modalities, such as electroencephalography, magnetoencephalography, transcranial magnetic stimulation, and functional magnetic resonance imaging, all of which require excellent timing. A simple framework is proposed in which uni- and multimodal experiments can be conducted with minimal adjustments when one switches between modalities. The framework allows the beginner to quickly become productive and the expert to be flexible and not constrained by the tool by building on existing software such as MATLAB and the Psychophysics Toolbox, which already are serving a large community. The framework allows running standard experiments but also supports and facilitates exciting new possibilities for real-time neuroimaging and state-dependent stimulation.

Quantification of Gd-DTPA concentration in neuroimaging using T 1 3D MP-RAGE sequence at 3.0 T

Purpose To propose a simple and accurate quantitative method based on the linear relationship between magnetic resonance (MR) signal enhancement (Δ SI= SI postcontrast− SI precontrast) and gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA) concentration ( C) by using T 1-weighted three-dimensional magnetization-prepared rapid acquisition gradient-echo (T 1 3D MP-RAGE) sequence for the in vivo measurement of Gd-DTPA concentration in real-time neuroimaging at 3.0 T. Methods Phantom experiment was carried out to study the linear fitting of signal intensity change vs. Gd-DTPA concentration (Δ SI– C) curve. A goodness-of-fit test was performed to compare the accuracy between the proposed method and the conventional method based on longitudinal relaxation rate ( R 1=1/ T 1) measurement. The influences on the goodness of fit ( R 2) and the signal-to-noise ratio (SNR) by sequence parameters were explored. Six human subjects with different brain tumors, who underwent a Gd-DTPA-enhanced MRI, were enrolled for in vivo application of the novel method. Results A good linear relationship between Δ SI and Gd-DTPA concentration existed over the concentration range of 0–1 mM ( R 2=0.985). The linearity of the Δ SI– C curve was as good as that of the 1/ T 1– C curve ( R 2=0.988). Concentrations calculated by both methods had a strong correlation ( R 2=0.920). An improved linearity of the Δ SI– C curve and an increased SNR can be achieved using sequences with a shorter inversion time (TI) and a higher flip angle. The concentration range of Gd-DTPA in human brain tumors was within the quantitative scope of 0–1 mM. Conclusions The proposed quantitative method based on Δ SI measurement is accurate and applicable for real-time neuroimaging at 3.0 T.

Advances in transcranial doppler ultrasonography

Transcranial Doppler ultrasonography (TCD) is the only noninvasive real-time neuroimaging modality for the evaluation of characteristics of blood flow in basal intracerebral vessels that adds physiologic information to structural imaging. TCD has been rapidly evolving from a simple noninvasive diagnostic tool to an imaging modality with a broad spectrum of clinical applications. In acute stroke, TCD can provide rapid information about vascular stenosis and occlusion, the hemodynamic status of the cerebral circulation, and real-time monitoring of recanalization. Extended applications such as vasomotor reactivity testing, emboli monitoring, and right-to-left shunt detection help clinicians ascertain stroke mechanisms at the bedside, plan and monitor treatment, and determine prognosis. In the neurointensive care unit, TCD is useful for detecting increased intracranial pressure and confirming cerebral circulatory arrest. TCD is of established value for screening children with sickle cell disease and detecting and monitoring vasospasm after spontaneous subarachnoid hemorrhage.