Real-time functional magnetic resonance imaging

Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review

Brain-computer interface (BCI) technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices. The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies. This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years. Our review synthesizes insights from both clinical and engineering viewpoints, delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs. We delineate nine discrete categories of technologies, furnishing exemplars for each and delineating the salient challenges pertinent to these modalities. This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI, and deliberates on the paramount issues presently confronting the field. Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives. Achieving equilibrium between signal fidelity, invasiveness, biocompatibility, and other pivotal considerations is imperative. By doing so, we can propel BCI technology forward, bolstering its effectiveness, safety, and dependability, thereby contributing to an auspicious future for human-technology integration.

Real-Time Functional MRI in the Treatment of Mental Health Disorders

Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health.

Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Pyneal: Open Source Real-Time fMRI Software

Increasingly, neuroimaging researchers are exploring the use of real-time functional magnetic resonance imaging (rt-fMRI) as a way to access a participant's ongoing brain function throughout a scan. This approach presents novel and exciting experimental applications ranging from monitoring data quality in real time, to delivering neurofeedback from a region of interest, to dynamically controlling experimental flow, or interfacing with remote devices. Yet, for those interested in adopting this method, the existing software options are few and limited in application. This presents a barrier for new users, as well as hinders existing users from refining techniques and methods. Here we introduce a free, open-source rt-fMRI package, the Pyneal toolkit, designed to address this limitation. The Pyneal toolkit is python-based software that offers a flexible and user friendly framework for rt-fMRI, is compatible with all three major scanner manufacturers (GE, Siemens, Phillips), and, critically, allows fully customized analysis pipelines. In this article, we provide a detailed overview of the architecture, describe how to set up and run the Pyneal toolkit during an experimental session, offer tutorials with scan data that demonstrate how data flows through the Pyneal toolkit with example analyses, and highlight the advantages that the Pyneal toolkit offers to the neuroimaging community.

Fast detection and reduction of local transient artifacts in resting-state fMRI

Image quality control (QC) is a critical and computationally intensive component of functional magnetic resonance imaging (fMRI). Artifacts caused by physiologic signals or hardware malfunctions are usually identified and removed during data processing offline, well after scanning sessions are complete. A system with the computational efficiency to identify and remove artifacts during image acquisition would permit rapid adjustment of protocols as issues arise during experiments. To improve the speed and accuracy of QC and functional image correction, we developed Fast Anatomy-Based Image Correction (Fast ANATICOR) with newly implemented nuisance models and an improved pipeline. We validated its performance on a dataset consisting of normal scans and scans containing known hardware-driven artifacts. Fast ANATICOR's increased processing speed may make real-time QC and image correction feasible as compared with the existing offline method.

Quality assurance of human functional magnetic resonance imaging: a literature review

Functional magnetic resonance imaging (fMRI) has been a popular approach in brain research over the past 20 years. It offers a noninvasive method to probe the brain and uses blood oxygenation level dependent (BOLD) signal changes to access brain function. However, the BOLD signal only represents a small fraction of the total MR signal. System instability and various noise have a strong impact on the BOLD signal. Additionally, fMRI applies fast imaging technique to record brain cognitive process over time, requiring high temporal stability of MR scanners. Furthermore, data acquisition, image quality, processing, and statistical analysis methods also have a great effect on the results of fMRI studies. Quality assurance (QA) programs for fMRI can test the stability of MR scanners, evaluate the quality of fMRI and help to find errors during fMRI scanning, thereby greatly enhancing the success rate of fMRI. In this review, we focus on previous studies which developed QA programs and methods in SCI/SCIE citation peer-reviewed publications over the last 20 years, including topics on existing fMRI QA programs, QA phantoms, image QA metrics, quality evaluation of existing preprocessing pipelines and fMRI statistical analysis methods. The summarized studies were classified into four categories: QA of fMRI systems, QA of fMRI data, quality evaluation of data processing pipelines and statistical methods and QA of task-related fMRI. Summary tables and figures of QA programs and metrics have been developed based on the comprehensive review of the literature.

Neu3CA-RT: A framework for real-time fMRI analysis

Real-time functional magnetic resonance imaging (rtfMRI) allows visualisation of ongoing brain activity of the subject in the scanner. Denoising algorithms aim to rid acquired data of confounding effects, enhancing the blood oxygenation level-dependent (BOLD) signal. Further image processing and analysis methods, like general linear models (GLM) or multivariate analysis, then present application-specific information to the researcher. These processes are typically applied to regions of interest but, increasingly, rtfMRI techniques extract and classify whole brain functional networks and dynamics as correlates for brain states or behaviour, particularly in neuropsychiatric and neurocognitive disorders. We present Neu³CA-RT: a Matlab-based rtfMRI analysis framework aiming to advance scientific knowledge on real-time cognitive brain activity and to promote its translation into clinical practice. Design considerations are listed based on reviewing existing rtfMRI approaches. The toolbox integrates established SPM preprocessing routines, real-time GLM mapping of fMRI data to a basis set of spatial brain networks, correlation of activity with 50 behavioural profiles from the BrainMap database, and an intuitive user interface. The toolbox is demonstrated in a task-based experiment where a subject executes visual, auditory and motor tasks inside a scanner. In three out of four experiments, resulting behavioural profiles agreed with the expected brain state.

Stuttering treatment and brain research in adults: A still unfolding relationship

PURPOSE

Brain imaging and brain stimulation procedures have now been used for more than two decades to investigate the neural systems that contribute to the occurrence of stuttering in adults, and to identify processes that might enhance recovery from stuttering. The purpose of this paper is to review the extent to which these dual lines of research with adults who stutter have intersected and whether they are contributing towards the alleviation of this impairment.

METHOD

Several areas of research are reviewed in order to determine whether research on the neurology of stuttering is showing any potential for advancing the treatment of this communication disorder: (a) attempts to discover the neurology of stuttering, (b) neural changes associated with treated recovery, and (c) direct neural intervention.

RESULTS AND CONCLUSIONS

Although much has been learned about the neural underpinnings of stuttering, little research in any of the reviewed areas has thus far contributed to the advancement of stuttering treatment. Much of the research on the neurology of stuttering that does have therapy potential has been largely driven by a speech-motor model that is designed to account for the efficacy of fluency-inducing strategies and strategies that have been shown to yield therapy benefits. Investigations on methods that will induce neuroplasticity are overdue. Strategies profitable with other disorders have only occasionally been employed. However, there are signs that investigations on the neurology of adults who have recovered from stuttering are slowly being recognized for their potential in this regard.

OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.

An Extended Bayesian-FBP Algorithm

Recently we developed a Bayesian-FBP (Filtered Backprojection) algorithm for CT image reconstruction. This algorithm is also referred to as the FBP-MAP (FBP Maximum a Posteriori) algorithm. This non-iterative Bayesian algorithm has been applied to real-time MRI, in which the k-space is under-sampled. This current paper investigates the possibility to extend this Bayesian-FBP algorithm by introducing more controlling parameters. Thus, our original Bayesian-FBP algorithm became a special case of the extended Bayesian-FBP algorithm. A cardiac patient data set is used in this paper to evaluate the extended Bayesian-FBP algorithm, and the result from a well-establish iterative algorithm with L₁-norms is used as the gold standard. If the parameters are selected properly, the extended Bayesian-FBP algorithm can outperform the original Bayesian-FBP algorithm.

Dynamic adjustment of stimuli in real time functional magnetic resonance imaging

The conventional fMRI image analysis approach to associating stimuli to brain activation is performed by carrying out a massive number of parallel univariate regression analyses. fMRI blood-oxygen-level dependent (BOLD) signal, the basis of these analyses, is known for its low signal-noise-ratio and high spatial and temporal signal correlation. In order to ensure accurate localization of brain activity, stimulus administration in an fMRI session is often lengthy and repetitive. Real-time fMRI BOLD signal analysis is carried out as the signal is observed. This method allows for dynamic, real-time adjustment of stimuli through sequential experimental designs. We have developed a voxel-wise sequential probability ratio test (SPRT) approach for dynamically determining localization, as well as decision rules for stopping stimulus administration. SPRT methods and general linear model (GLM) approaches are combined to identify brain regions that are activated by specific elements of stimuli. Stimulus administration is dynamically stopped when sufficient statistical evidence is collected to determine activation status across regions of interest, following predetermined statistical error thresholds. Simulation experiments and an example based on real fMRI data show that scan volumes can be substantially reduced when compared with pre-determined, fixed designs while achieving similar or better accuracy in detecting activated voxels. Moreover, the proposed approach is also able to accurately detect differentially activated areas, and other comparisons between task-related GLM parameters that can be formulated in a hypothesis-testing framework. Finally, we give a demonstration of SPRT being employed in conjunction with a halving algorithm to dynamically adjust stimuli.

High-throughput optogenetic functional magnetic resonance imaging with parallel computations

Optogenetic functional magnetic resonance imaging (of MRI) technology enables cell-type-specific, temporally precise neuronal control and the accurate, in vivo readout of the resulting activity across the entire brain. With the ability to precisely control excitation and inhibition parameters and accurately record the resulting activity, there is an increased need for a high-throughput method to bring the of MRI studies to their full potential. In this paper, an advanced system facilitating real-time fMRI with interactive control and analysis in a fraction of the MRI acquisition repetition time (TR) is proposed. With high-processing speed, sufficient time will be available for the integration of future developments that further enhance of MRI data or streamline the study. We designed and implemented a highly optimised, massively parallel system using graphics processing units (GPUs), which achieves the reconstruction, motion correction, and analysis of 3D volume data in approximately 12.80 ms. As a result, with a 750 ms TR and 4 interleaf fMRI acquisition, we can now conduct sliding window reconstruction, motion correction, analysis and display in approximately 1.7% of the TR. Therefore, a significant amount of time can now be allocated to integrating advanced but computationally intensive methods that improve image quality and enhance the analysis results within a TR. Utilising the proposed high-throughput imaging platform with sliding window reconstruction, we were also able to observe the much-debated initial dips in our of MRI data. Combined with methods to further improve SNR, the proposed system will enable efficient real-time, interactive, high-throughput of MRI studies.

RLS adaptive filtering for physiological interference reduction in NIRS brain activity measurement: a Monte Carlo study

The non-invasive measurement of cerebral functional haemodynamics using near-infrared spectroscopy (NIRS) instruments is often affected by physiological interference. The suppression of this interference is crucial for reliable recovery of brain activity measurements because it can significantly affect the signal quality. In this study, we present a recursive least-squares (RLS) algorithm for adaptive filtering to reduce the magnitude of the physiological interference component. To evaluate it, we implemented Monte Carlo simulations based on a five-layer slab model of a human adult head with a multidistance source-detector arrangement, of a short pair and a long pair, for NIRS measurement. We derived measurements by adopting different interoptode distances, which is relevant to the process of optimizing the NIRS probe configuration. Both RLS and least mean squares (LMS) algorithms were used to attempt the removal of physiological interference. The results suggest that the RLS algorithm is more capable of minimizing the effect of physiological interference due to its advantages of faster convergence and smaller mean squared error (MSE). The influence of superficial layer thickness on the performance of the RLS algorithm was also investigated. We found that the near-detector position is an important variable in minimizing the MSE and a short source-detector separation less than 9 mm is robust to superficial layer thickness variation.

Spatio-temporal activity in real time (STAR): optimization of regional fMRI feedback

The use of real-time feedback has expanded fMRI from a brain probe to include potential brain interventions with significant therapeutic promise. However, whereas time-averaged blood oxygenation level-dependent (BOLD) signal measurement is usually sufficient for probing a brain state, the real-time (frame-to-frame) BOLD signal is noisy, compromising feedback accuracy. We have developed a new real-time processing technique (STAR) that combines noise-reduction properties of multi-voxel (e.g., whole-brain) techniques with the regional specificity critical for therapeutics. Nineteen subjects were given real-time feedback in a cognitive control task (imagining repetitive motor activity vs. spatial navigation), and were all able to control a visual feedback cursor based on whole-brain neural activity. The STAR technique was evaluated, retrospectively, for five a priori regions of interest in these data, and was shown to provide significantly better (frame-by-frame) classification accuracy than a regional BOLD technique. In addition to regional feedback signals, the output of the STAR technique includes spatio-temporal activity maps (movies) providing insight into brain dynamics. The STAR approach offers an appealing optimization for real-time fMRI applications requiring an anatomically-localized feedback signal.

Monte Carlo study of global interference cancellation by multidistance measurement of near-infrared spectroscopy

The performance of near-infrared spectroscopy is sometimes degraded by the systemic physiological interference in the extracerebral layer. There is some systemic interference, which is highly correlated with the functional response evoked by a task execution. This kind of interference is difficult to remove by using ordinary techniques. A multidistance measurement method is one of the possible solutions for this problem. The multidistance measurement method requires estimation parameters derived from partial pathlength values of tissue layers to calculate an absorption coefficient change from a temporal absorbance change. Because partial path lengths are difficult to obtain, experimentally, we estimated them by a Monte Carlo simulation based on a five-layered slab model of a human adult head. Model parameters such as thickness and the transport scattering coefficient of each layer depend on a subject and a measurement position; thus, we assumed that these parameters obey normal distributions around standard parameter values. We determined the estimation parameters that provide a good separation performance in average for the model parameter distribution. The obtained weighting is robust to model parameter deviation and provides smaller errors on average compared to the parameters, which are determined without considering parameter distribution.

Single echo acquisition MRI using RF encoding

Encoding of spatial information in magnetic resonance imaging is conventionally accomplished by using magnetic field gradients. During gradient encoding, the position in k-space is determined by a time-integral of the gradient field, resulting in a limitation in imaging speed due to either gradient power or secondary effects such as peripheral nerve stimulation. Partial encoding of spatial information through the sensitivity patterns of an array of coils, known as parallel imaging, is widely used to accelerate the imaging, and is complementary to gradient encoding. This paper describes the one-dimensional limit of parallel imaging in which all spatial localization in one dimension is performed through encoding by the radiofrequency (RF) coil. Using a one-dimensional array of long and narrow parallel elements to localize the image information in one direction, an entire image is obtained from a single line of k-space, avoiding rapid or repeated manipulation of gradients. The technique, called single echo acquisition (SEA) imaging, is described, along with the need for a phase compensation gradient pulse to counteract the phase variation contained in the RF coil pattern which would otherwise cause signal cancellation in each imaging voxel. Image reconstruction and resolution enhancement methods compatible with the speed of the technique are discussed. MR movies at frame rates of 125 frames per second are demonstrated, illustrating the ability to monitor the evolution of transverse magnetization to steady state during an MR experiment as well as demonstrating the ability to image rapid motion. Because this technique, like all RF encoding approaches, relies on the inherent spatially varying pattern of the coil and is not a time-integral, it should enable new applications for MRI that were previously inaccessible due to speed constraints, and should be of interest as an approach to extending the limits of detection in MR imaging.

Real-time functional magnetic resonance imaging (rt-fMRI) in patients with brain tumours: preliminary findings using motor and language paradigms

Functional MRI (fMRI) shows areas of the brain that are active during a task, but the standard approach (offline analysis after the imaging has finished) precludes tailoring of the imaging to the individual patient, e.g. for assessing normal function around an individual lesion. The aims of the study were to explore the technical feasibility of acquiring functional images in real-time (rt-fMRI), develop the necessary software interfaces and protocols for image acquisition, and to compare images of functional activation acquired in real-time with the standard offline statistical parametric method in patients with solitary brain tumours. Patients with a solitary supratentorial lesion were studied. The rt-fMRI paradigms were sequential finger opposition, ankle movement and language function (correct recognition of grammatically violated sentences). Datasets were analysed using AFNI software (National Institute of Mental Health, Bethesda, Maryland, USA) for the real-time analysis and SPM99 (Functional Imaging Laboratory, University College, London, UK) for the offline analysis. From 11 patients, useful data were obtained in nine. The finger tapping task produced most consistent activation between real-time and offline analysis with good anatomic localization to the primary motor cortex contralateral to the tapping finger. Ankle movement produced weaker activation and correlation with real-time analysis. For the language task the offline analysis provided reproducible activation patterns, but the real-time method showed no activation at the chosen threshold of p = 0.001. Tumourous areas of brain did not show any activation with either method of analysis during any task. rt-fMRI is feasible and could be a valuable functional evaluation tool in the planning of surgery for tumours in motor regions of the brain. Further paradigm development is required for evaluation of language, and possibly other more complex executive functions.

Applications of real-time fMRI

For centuries people have aspired to understand and control the functions of the mind and brain. It has now become possible to image the functioning of the human brain in real time using functional MRI (fMRI), and thereby to access both sides of the mind-brain interface--subjective experience (that is, one's mind) and objective observations (that is, external, quantitative measurements of one's brain activity)--simultaneously. Developments in neuroimaging are now being translated into many new potential practical applications, including the reading of brain states, brain-computer interfaces, communicating with locked-in patients, lie detection, and learning control over brain activation to modulate cognition or even treat disease.

Likelihood-based hypothesis tests for brain activation detection from MRI data disturbed by colored noise: a simulation study

Functional magnetic resonance imaging (fMRI) data that are corrupted by temporally colored noise are generally preprocessed (i.e., prewhitened or precolored) prior to functional activation detection. In this paper, we propose likelihood-based hypothesis tests that account for colored noise directly within the framework of functional activation detection. Three likelihood-based tests are proposed: the generalized likelihood ratio (GLR) test, the Wald test, and the Rao test. The fMRI time series is modeled as a linear regression model, where one regressor describes the task-related hemodynamic response, one regressor accounts for a constant baseline and one regressor describes potential drift. The temporal correlation structure of the noise is modeled as an autoregressive (AR) model. The order of the AR model is determined from practical null data sets using Akaike's information criterion (with penalty factor 3) as order selection criterion. The tests proposed are based on exact expressions for the likelihood function of the data. Using Monte Carlo simulation experiments, the performance of the proposed tests is evaluated in terms of detection rate and false alarm rate properties and compared to the current general linear model (GLM) test, which estimates the coloring of the noise in a separate step. Results show that theoretical asymptotic distributions of the GLM, GLR, and Wald test statistics cannot be reliably used for computing thresholds for activation detection from finite length time series. Furthermore, it is shown that, for a fixed false alarm rate, the detection rate of the proposed GLR test statistic is slightly, but (statistically) significantly improved compared to that of the common GLM-based tests. Finally, simulations results reveal that all tests considered show seriously inferior performance if the order of the AR model is not chosen sufficiently high to give an adequate description of the correlation structure of the noise, whereas the effects of (slightly) overmodeling are observed to be less harmful.

Real-time animal functional magnetic resonance imaging and its application to neuropharmacological studies

In pharmacological magnetic resonance imaging (phMRI) with anesthetized animals, there is usually only a single time window to observe the dynamic signal change to an acute drug administration since subsequent drug injections are likely to result in altered response properties (e.g., tolerance). Unlike the block-design experiments in which fMRI signal can be elicited with multiple repetitions of a task, these single-event experiments require stable baseline in order to reliably identify drug-induced signal changes. Such factors as subject motion, scanner instability and/or alterations in physiological conditions of the anesthetized animal could confound the baseline signal. The unique feature of such functional MRI (fMRI) studies necessitates a technique that is able to monitor MRI signal in a real-time fashion and to interactively control certain experimental procedures. In the present study, an approach for real-time MRI on a Bruker scanner is presented. The custom software runs on the console computer in parallel with the scanner imaging software, and no additional hardware is required. The utility of this technique is demonstrated in manganese-enhanced MRI (MEMRI) with acute cocaine challenge, in which temporary disruption of the blood-brain barrier (BBB) is a critical step for MEMRI experiments. With the aid of real-time MRI, we were able to assess the outcome of BBB disruption following bolus injection of hyperosmolar mannitol in a near real-time fashion prior to drug administration, improving experimental success rate. It is also shown that this technique can be applied to monitor baseline physiological conditions in conventional fMRI experiments using blood oxygenation level-dependent (BOLD) contrast, further demonstrating the versatility of this technique.

Reading and controlling human brain activation using real-time functional magnetic resonance imaging

Understanding how to control how the brain's functioning mediates mental experience and the brain's processing to alter cognition or disease are central projects of cognitive and neural science. The advent of real-time functional magnetic resonance imaging (rtfMRI) now makes it possible to observe the biology of one's own brain while thinking, feeling and acting. Recent evidence suggests that people can learn to control brain activation in localized regions, with corresponding changes in their mental operations, by observing information from their brain while inside an MRI scanner. For example, subjects can learn to deliberately control activation in brain regions involved in pain processing with corresponding changes in experienced pain. This may provide a novel, non-invasive means of observing and controlling brain function, potentially altering cognitive processes or disease.

Progress in visualizing turbulent flow using single-echo acquisition imaging

MRI of flow remains a challenging problem despite significant improvements in imaging speeds. For periodic flow the acquisition can be gated, synchronizing data acquisition with the flow. However, this method fails to work if the flow is sufficiently fast that turbulence occurs, or when it is sufficiently fast that blurring occurs during the excitation of the spins or the acquisition of the signal. This paper describes recent progress in employing a very fast MR imaging technique, Single Echo Acquisition Imaging (SEA-MRI) and spin-tagging to visualize very rapid and turbulent flow patterns. Demonstrations are done on a separating channel phantom with input flow rates ranging from zero to over 100 cm/sec. Spin-tagging enables a "texture" to be placed on the spins, enabling clear visualization of the complex flow patterns, and in some cases measurement of the flow velocity.

Real-time functional MR imaging (fMRI) for presurgical evaluation of paediatric epilepsy

BACKGROUND

The role of fMRI in the presurgical evaluation of children with intractable epilepsy is being increasingly recognized. Real-time fMRI allows the clinician to visualize functional brain activation in real time. Since there is no off-line data analysis as in conventional fMRI, the overall time for the procedure is reduced, making it clinically feasible in a busy clinical sitting.

OBJECTIVE

(1) To study the accuracy of real-time fMRI in comparison to conventional fMRI with off-line processing; (2) to determine its effectiveness in mapping the eloquent cortex and language lateralization in comparison to invasive procedures such as intraoperative cortical stimulation and Wada testing; and (3) to evaluate the role of fMRI in presurgical decision making in children with epilepsy.

MATERIALS AND METHODS

A total of 23 patients (age range 6-18 years) underwent fMRI with sensorimotor, visual and language paradigms. Data processing was done in real time using in-line BOLD.

RESULTS

The results of real-time fMRI matched those of off-line processing done using the well-accepted standard technique of statistical parametric mapping (SPM) in all the initial ten patients in whom the two techniques were compared. Coregistration of the fMRI data on a 3-D FLAIR sequence rather than a T1-weighted image gave better information regarding the relationship of the lesion to the area of activation. The results of intraoperative cortical stimulation and fMRI matched in six out of six patients, while the Wada test and fMRI had similar results in four out of five patients in whom these techniques were performed. In the majority of patients in this series the technique influenced patient management.

CONCLUSION

Real-time fMRI is an easily performed and reliable technique in the presurgical workup of children with epilepsy.

Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study

The sensitivity of near-infrared spectroscopy (NIRS) to evoked brain activity is reduced by physiological interference in at least two locations: 1. the superficial scalp and skull layers, and 2. in brain tissue itself. These interferences are generally termed as "global interferences" or "systemic interferences," and arise from cardiac activity, respiration, and other homeostatic processes. We present a novel method for global interference reduction and real-time recovery of evoked brain activity, based on the combination of a multiseparation probe configuration and adaptive filtering. Monte Carlo simulations demonstrate that this method can be effective in reducing the global interference and recovering otherwise obscured evoked brain activity. We also demonstrate that the physiological interference in the superficial layers is the major component of global interference. Thus, a measurement of superficial layer hemodynamics (e.g., using a short source-detector separation) makes a good reference in adaptive interference cancellation. The adaptive-filtering-based algorithm is shown to be resistant to errors in source-detector position information as well as to errors in the differential pathlength factor (DPF). The technique can be performed in real time, an important feature required for applications such as brain activity localization, biofeedback, and potential neuroprosthetic devices.

Real-time functional magnetic resonance imaging: methods and applications

Functional magnetic resonance imaging (fMRI) has been limited by time-consuming data analysis and a low signal-to-noise ratio, impeding online analysis. Recent advances in acquisition techniques, computational power and algorithms increased the sensitivity and speed of fMRI significantly, making real-time analysis and display of fMRI data feasible. So far, most reports have focused on the technical aspects of real-time fMRI (rtfMRI). Here, we provide an overview of the different major areas of applications that became possible with rtfMRI: online analysis of single-subject data provides immediate quality assurance and functional localizers guiding the main fMRI experiment or surgical interventions. In teaching, rtfMRI naturally combines all essential parts of a neuroimaging experiment, such as experimental design, data acquisition and analysis, while adding a high level of interactivity. Thus, the learning of essential knowledge required to conduct functional imaging experiments is facilitated. rtfMRI allows for brain-computer interfaces (BCI) with a high spatial and temporal resolution and whole-brain coverage. Recent studies have shown that such BCI can be used to provide online feedback of the blood-oxygen-level-dependent signal and to learn the self-regulation of local brain activity. Preliminary evidence suggests that this local self-regulation can be used as a new paradigm in cognitive neuroscience to study brain plasticity and the functional relevance of brain areas, even being potentially applicable for psychophysiological treatment.

The Quantitative Assessment of Gd-DTPA in Contrast Enhanced Magnetic Resonance Angiography

Recently, the use of MRI contrast agents has been proven to be substantially improved sensitivity and specificity in many clinical applications. CE-MRA has higher blood signal based on the T1 and T2-shortening property of contrast agents, so that even the small vessels can be visualized. The use of contrast agents can improve lesion detection and characterization. The routinely used dose of contrast agents in the routine MRI examinations only relies on the weight of the subject. The purpose of this study is to obtain the clinically optimal dose for 3D-TOF (time-of-flight) pulse sequences for CE-MRA examinations. In the phantom study, ten test tubes were filled with saline mixed with different dose of Gd-DTPA. It is found that the optimal dose of Gd-DTPA for saline phantom by using 3D-TOF pulse sequences is 20 mM. Also, there has no differences of optimal doses between Omniscan and Magnivist contrast agents Gd-DTPA. The results show that consistent high quality CE-MRA images might be obtained by using 0.25M Gd-DTPA (half of the routine dose) with 3~4 cc/sec injection rate for all clinical cases. The benefits of this study might be to minimize dose and potential toxicity. Additionally, the decrease of the cost of contrast agents might be achieved. It is expected to provide the recommended dose of Gd-DTPA for contrast enhanced MRA in clinical routine diagnosis.

Head motion suppression using real-time feedback of motion information and its effects on task performance in fMRI

A voluntary head motion suppression method using feedback to subjects of their own head motion information is demonstrated. A real-time fMRI system was developed on standard MR imaging hardware for this purpose. The head motion information was simplified as a four-way arrow display that changed color from green to red when a composite head motion index went beyond a specified threshold. The arrow indicators were integrated into a version of the commonly used visual N-BACK task. Results suggest a significant suppression of head motion consistently in all subjects while the influence on task performance and brain activation was minimal. It is proposed that under certain experimental conditions, voluntary head motion suppression may feasibly be employed without significant compromise of fMRI data.

K-space in the clinic

Magnetic resonance imaging (MRI) sequences are characterized by both radio frequency (RF) pulses and time-varying gradient magnetic fields. The RF pulses manipulate the alignment of the resonant nuclei and thereby generate a measurable signal. The gradient fields spatially encode the signals so that those arising from one location in an excited slice of tissue may be distinguished from those arising in another location. These signals are collected and mapped into an array called k-space that represents the spatial frequency content of the imaged object. Spatial frequencies indicate how rapidly an image feature changes over a given distance. It is the action of the gradient fields that determines where in the k-space array each data point is located, with the order in which k-space points are acquired being described by the k-space trajectory. How signals are mapped into k-space determines much of the spatial, temporal, and contrast resolution of the resulting images and scan duration. The objective of this article is to provide an understanding of k-space as is needed to better understand basic research in MRI and to make well-informed decisions about clinical protocols. Four major classes of trajectories-echo planar imaging (EPI), standard (non-EPI) rectilinear, radial, and spiral-are explained. Parallel imaging techniques SMASH (simultaneous acquisition of spatial harmonics) and SENSE (sensitivity encoding) are also described.