Brain Imaging Device Research Articles

Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review.

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops.

Progress of near-infrared spectroscopy in cerebral blood oxygenation detection: A mini review

In contrast to conventional oximeters, near-infrared spectroscopy-based brain tissue oximetry monitoring devices are capable of non-invasive, continuous, and real-time quantitative monitoring of cerebral oximetry parameters. Initially, these devices were utilized for intensive care or surgical monitoring of oxygen saturation. Due to the rapid advancement of optoelectronic sensing and measurement technologies over the past decade, the derived functional near-infrared brain imaging devices have been widely used in a variety of fields. This paper first introduces the basic principles of near-infrared spectroscopy-based cerebral oxygenation parameter detection, then focuses on the most recent developments in this field of study. Finally, a prospect on their future application in practical settings is also provided.

Kernel Flow: a high channel count scalable time-domain functional near-infrared spectroscopy system.

.Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems.Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device.Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head.Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results.Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.

Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data.

Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman’s general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.

Prefrontal functional connectivity analysis of cognitive decline for early diagnosis of mild cognitive impairment: a functional near-infrared spectroscopy study.

Cognitive decline (CD) is a major symptom of mild cognitive impairment (MCI). Patients with MCI have an increased likelihood of developing Alzheimer's disease (AD). Although a cure for AD is currently lacking, medication therapies and/or daily training in the early stage can alleviate disease progression and improve patients' quality of life. Accordingly, investigating CD-related biomarkers via brain imaging devices is crucial for early diagnosis. In particular, "portable" brain imaging devices enable frequent diagnostic checks as a routine clinical tool, and therefore increase the possibility of early AD diagnosis. This study aimed to comprehensively investigate functional connectivity (FC) in the prefrontal cortex measured by a portable functional near-infrared spectroscopy (fNIRS) device during a working memory (WM) task known as the delayed matching to sample (DMTS) task. Differences in prefrontal FC between healthy control (HC) (n = 23) and CD groups (n = 23) were examined. Intra-group analysis (one-sample t-test) revealed significantly greater prefrontal FC, especially left- and inter-hemispheric FC, in the CD group than in the HC. These observations could be due to a compensatory mechanism of the prefrontal cortex caused by hippocampal degeneration. Inter-group analysis (unpaired two-sample t-test) revealed significant intergroup differences in left- and inter-hemispheric FC. These attributes may serve as a novel biomarker for early detection of MCI. In addition, our findings imply that portable fNIRS devices covering the prefrontal cortex may be useful for early diagnosis of MCI.

Investigation of the source-detector separation in near infrared spectroscopy for healthy and clinical applications.

Understanding near infrared light propagation in tissue is vital for designing next generation optical brain imaging devices. Monte Carlo (MC) simulations provide a controlled mechanism to characterize and evaluate contributions of diverse near infrared spectroscopy (NIRS) sensor configurations and parameters. In this study, we developed a multilayer adult digital head model under both healthy and clinical settings and assessed light-tissue interaction through MC simulations in terms of partial differential pathlength, mean total optical pathlength, diffuse reflectance, detector light intensity and spatial sensitivity profile of optical measurements. The model incorporated four layers: scalp, skull, cerebrospinal-fluid and cerebral cortex with and without a customizable lesion for modeling hematoma of different sizes and depths. The effect of source-detector separation (SDS) on optical measurements' sensitivity to brain tissue was investigated. Results from 1330 separate simulations [(4 lesion volumes × 4 lesion depths for clinical +3 healthy settings) × 7 SDS × 10 simulation = 1330)] each with 100 million photons indicated that selection of SDS is critical to acquire optimal measurements from the brain and recommended SDS to be 25 to 35 mm depending on the wavelengths to obtain optical monitoring of the adult brain function. The findings here can guide the design of future NIRS probes for functional neuroimaging and clinical diagnostic systems.

Data-driven forward model inference for EEG brain imaging

Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain imaging device. The quality of the source reconstruction depends on the forward model which details head geometry and conductivities of different head compartments. These person-specific factors are complex to determine, requiring detailed knowledge of the subject's anatomy and physiology. In this proof-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models. Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging is possible, even when the head geometry and conductivities are unknown.

Evaluation of Hand-Eye Coordination Based on Brain Activity

Surgical robots have improved considerably in recent years, but their intuitive operability, and thus their user interoperability, has yet to be quantitatively evaluated. Thus, we propose a method for measuring a user’s brain activity while operating such a robot, to better enable the design of a robot with intuitive operability. The objective of this study was to determine the angle and radius between an endoscope and manipulator that best allows the user to perceive the manipulator as being part of their own body. In the experiments, a subject operated a hand controller to position the tip of a virtual slave manipulator onto a target in a surgical simulator while his/her brain activity was measured using a brain imaging device. The experiment was carried out several times with the virtual slave manipulator configured in a variety of ways. The results show that the amount of brain activity is significantly greater with a particular slave manipulator configuration. We concluded that the hand-eye coordination between the body image and the robot should be closely matched in the design of a robot having intuitive operability.

Intuitive Operability Evaluation of Robotic Surgery Using Brain Activity Measurements to Clarify Immersive Reality

Surgical robots have undergone considerable improvement in recent years. But intuitive operability, which represents user interoperability, has not been quantitatively evaluated. With the aim of designing a robot with intuitive operability, we thus propose a method for measuring brain activity to determine intuitive operability. The purpose of this paper is to clarify the master configuration against the position of the monitor that best allows user to perceive the manipulator as part of his own body. We assume that the master configuration provides immersive reality to user as if he puts own arm into the monitor. In our experiments, subjects controlled the hand controller to position the tip of the virtual slave manipulator on a target in the surgical simulator and we measured brain activity using brain imaging devices. We carried out experiments a number of times with themastermanipulator configured in a variety of ways and the position of the monitor fixed. We found that the brain was significantly activated in all subjects when the master manipulator was located behind the monitor. We concluded that the master configuration produces immersive reality through body images related to visual and somatic sensory feedback.

Neuroscience examined by the clinical and the social science

The scientific, historical, political, cultural and media development of neuroscience is remarkable. Promoted to overcome brain diseases, they promise to answer the old questions of the relationship between thought, body and brain in a definitive way thanks to new techniques of observation. The thematic dossier mobilizes historians, psychiatrists, anthropologists, psychologists, sociologists, neuroscientists and philosophers to shed light on how neurosciences are reconfiguring care practices and to question their ambitions. The contributions testify to the development of critical analyses of the neurosciences and the epistemic and political tensions they maintain with other disciplines. A first set of texts deals with brain imaging devices, the inflation of objects produced and their shaping of the human being. The second set deals with attempts to understand the cerebral bases of mental pathology and the instruments, substances and methods aimed at modifying mental states. It also deals with the transformation of the role of experts and clinical research with evidence-based medicine. The third set raises the question of the articulation between modes of knowledge production, including analysis inherited from feminist studies, denaturalizing the brain to re-politicize gender differences. The articles question the development of neuroscience and its epistemic and practical effects on the human and social sciences, as well as the dynamics of compatibility and tension between different analyses.

Design and inital evaluation of a 4-layer DOI-PET system: the jPET-D4.

PET scanners are an important component in functional brain imaging devices. Several PET scanners have been developed during the last two decades for brain research studies. We are developing a high-performance brain PET scanner, jPET-D4. This scanner is designed to achieve not only high spatial resolution but also high sensitivity using four-layered depth-of-interaction (DOI) detectors. The scanner has five block detector rings with the ring diameter of 390 mm and each block detector ring consists of 24 DOI detectors. In previous work, we have demonstrated that 3 mm FWHM uniform spatial resolution within the field-of-view could be realized. In this paper, we describe the jPET-D4 system and evaluate its performance. The average energy resolution for 120 DOI detectors is optimized to 16 % +/- 1.0 %. The scatter fraction for this system is 40 % with an energy window of 400-600 keV. The sensitivity for the point source is 102 kcps/MBq (10.2 %) with a 400 keV LLD. Maximum noise equivalent count rate (NECR) is 154 kcps at 11 kBq/ml with a 10 ns coincidence time window. We evaluated scatter fraction and NECR following procedures based on NEMA NU2-1994. These first evaluation measurements indicate the jPET-D4 has good performance.

1P1-O02 脳活動計測に基づいた手術支援ロボットの直観的な操作性評価(医療ロボティクス・メカトロニクス(1))

The intuitive operability, representing user interoperability, has not been quantitatively evaluated in surgical robot. We propose a method for measuring brain activity to determine intuitive operability so as to design a robot with intuitive operability. The objective of this paper is to determine the angle and radius between the endoscope and the manipulator that allows users to perceive the manipulator as part of their body. In the experiments, while subjects moved the hand controller to position the tip of the virtual slave manipulator on the target in the surgical simulator, we measured the brain activity through brain imaging devices. The results show that brain activation is significantly greater with a particular slave manipulator configuration. We conclude that the hand-eye coordination between the body image and the robot should be closely matched in the design of a robot with intuitive operability.

J165033 脳活動計測に基づいた手術支援ロボットの直感的な操作性評価

Surgical robot needs the intuitive operability. However conventional method cannot evaluate user inter-operability. We propose the evaluation method using brain activity measurement to design the robot which perceive user to be part of user's own body. In this paper, the objective is to clarify the master configuration against the monitor that facilitates users to perceive the manipulator as part of their own body. We assume that the master configuration produces the immersive reality of the user putting his own arm into the monitor. In our experiments, as subjects controlled the virtual slave manipulator on the target in a surgical simulator, we measured brain activity through brain-imaging devices. We carried out our experiments a number of times with the master manipulator configured in a variety of ways with the position of the monitor fixed. In all subjects, we found that the brain was activated significantly when the master manipulator was located behind the monitor. We conclude that the master configuration produces immersive reality through body image.

New Trend of Pain Study by Brain Imaging Devices

New Trend of Pain Study by Brain Imaging Devices

Urgent Need for Secondary Stroke Prevention After Transient Ischemic Attack

To review symptom awareness, diagnostic techniques, and therapeutic interventions for the recognition and treatment of transient ischemic attack (TIA) and to discuss secondary stroke prevention after TIA. Published information related to recognition and treatment of TIA and the prevention of a secondary cerebrovascular event after TIA. Literature review reveals high risk for an early, secondary cerebrovascular event after TIA. Historically, TIA was not treated urgently; today, it is understood that TIA is a sign of a serious condition and may be an indication of impending disability and death. Prompt initiation of treatment may significantly reduce morbidity and mortality. However, many factors, such as low public awareness of symptoms, lack of urgency, or unavailability of brain imaging devices, are responsible for treatment delay. Delay between symptom onset and symptom diagnosis may preclude the use of tissue-plasminogen activator, which must be administered within a three-hour window from time of onset. Other therapeutic interventions for secondary stroke prevention after a TIA include the early use of antiplatelet agents (aspirin, clopidogrel, dipyridamole), and may involve procedures such as carotid endarterectomy and stenting. Risk reduction may also be achieved by treating hypertension and hypercholesterolemia. TIA and stroke must be recognized and treated urgently. Through public education, readily available information, access to diagnostic imaging devices, and aggressive preventive strategies and treatments, the frequency and severity of secondary cerebrovascular events, as well as the economic burden of secondary stroke, could be substantially reduced.

What aspects of experience can functional neuroimaging be expected to reveal?

The success of functional neuroimaging methods in picturing distinct cerebral activation profiles for different psychological functions has led many specialists and many more non-specialists to speculate that it will soon be possible for neuroimaging experts to sit in front of the screen of a functional brain imaging device and “read” in the changing patterns of brain activity, displayed there in real-time, what the person whose brain is imaged is experiencing from one moment to the next. This apparently reasonable scenario presupposes that each concrete experience is associated with a distinct and unique brain activity pattern, and that these patterns are, at least in principle, discernible through functional neuroimaging. It will be argued that, for reasons of an epistemological order, even if the first assumption was true, that pattern could not be discernible, and therefore readable, even with ideal neuroimaging devices. It will also be argued that the only epistemologically, therefore, in principle, also technically feasible feat is to discern and decipher patterns of the brain activity corresponding not to concrete experiences, but to types or “kinds” of experiences, that is, to general concepts. Moreover, it will be shown that we could, in principle, discern only such patterns for the very same reason that we can know objectively only concepts, that is, the invariant features common to sets of concrete, fleeting and unrepeatable single experiences.

Estimation of Cortical Connectivity in Humans: Advanced Signal Processing Techniques

In the last ten years many different brain imaging devices have conveyed a lot of information about the brain functioning in different experimental conditions. In every case, the biomedical engineers, together with mathematicians, physicists and physicians are called to elaborate the signals related to the brain activity in order to extract meaningful and robust information to correlate with the external behavior of the subjects. In such attempt, different signal processing tools used in telecommunications and other field of engineering or even social sciences have been adapted and re-used in the neuroscience field. The present book would like to offer a short presentation of several methods for the estimation of the cortical connectivity of the human brain. The methods here presented are relatively simply to implement, robust and can return valuable information about the causality of the activation of the different cortical areas in humans using non invasive electroencephalographic r cordings. The knowledge of such signal processing tools will enrich the arsenal of the computational methods that a engineer or a mathematician could apply in the processing of brain signals. Table of Contents: Introduction / Estimation of the Effective Connectivity from Stationary Data by Structural Equation Modeling / Estimation of the Functional Connectivity from Stationary Data by Multivariate Autoregressive Methods / Estimation of Cortical Activity by the use of Realistic Head Modeling / Application: Estimation of Connectivity from Movement-Related Potentials / Application to High-Resolution EEG Recordings in a Cognitive Task (Stroop Test) / Application to Data Related to the Intention of Limb Movements in Normal Subjects and in a Spinal Cord Injured Patient / The Instantaneous Estimation of the Time-Varying Cortical Connectivity by Adaptive Multivariate Estimators / Time-Varying Connectivity from Event-Related Potentials

Adaptive focusing for transcranial ultrasound imaging using dual arrays

Ultrasonic brain imaging remains difficult and limited because of the strong aberrating effects of the skull (absorption, diffusion and refraction of ultrasounds): high resolution transcranial imaging would require adaptive focusing techniques in order to correct the defocusing effect of the skull. In this paper, a noninvasive brain imaging device is presented. It is made of two identical linear arrays of 128 transducers located on each side of the skull. It is possible to separate the respective influence of the two bone windows on the path of an ultrasonic wave propagating from one array to the other, and thus estimate at each frequency the attenuation and phase shift locally induced by each bone window. The information obtained on attenuation and phase is used to correct the wave fronts that have to be sent through the skull in order to obtain a good focusing inside the skull. Compared to uncorrected wave fronts, the spatial shift of the focal spot is corrected, the width of the focal spot is reduced, and the sidelobes level is decreased up to 17 dB. Transcranial images of a phantom are presented and exhibit the improvement in image quality provided by this new noninvasive adaptive focusing method.

Fetal magnetoencephalography

The assessment of the neurological integrity of the human fetus in utero is a technically challenging problem. New brain imaging devices can substantially improve our capabilities to describe functional brain activity in the fetus. It has been well established by fetal behavioral studies and investigations in preterm and term newborns that the brain is functional in utero. The major effort required to perform effective neurological functional investigations is designation of an integrated approach to neurological assessment and the generation of normative data. Currently, it is possible to record evoked brain activity elicited by auditory and visual stimulation from the fetus. In addition, there is growing evidence that spontaneous brain activity can be recorded in the fetus. This paper explores the current status of the brain-imaging field for fetal investigations and currently available data.

Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG

Different brain imaging devices are presently available to provide images of the human functional cortical activity, based on hemodynamic, metabolic or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions are interconnected. The concept of brain connectivity plays a central role in the neuroscience, and different definitions of connectivity, functional and effective, have been adopted in literature. While the functional connectivity is defined as the temporal coherence among the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally among cortical sites. The structural equation modeling (SEM) is the most used method to estimate effective connectivity in neuroscience, and its typical application is on data related to brain hemodynamic behavior tested by functional magnetic resonance imaging (fMRI), whereas the directed transfer function (DTF) method is a frequency-domain approach based on both a multivariate autoregressive (MVAR) modeling of time series and on the concept of Granger causality. This study presents advanced methods for the estimation of cortical connectivity by applying SEM and DTF on the cortical signals estimated from high-resolution electroencephalography (EEG) recordings, since these signals exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. To estimate correctly the cortical signals, we used a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual MRI, a distributed source model and a regularized linear inverse source estimates of cortical current density. Before the application of SEM and DTF methodology to the cortical waveforms estimated from high-resolution EEG data, we performed a simulation study, in which different main factors (signal-to-noise ratio, SNR, and simulated cortical activity duration, LENGTH) were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). The statistical analysis returned that during simulations, both SEM and DTF estimators were able to correctly estimate the imposed connectivity patterns under reasonable operative conditions, that is, when data exhibit an SNR of at least 3 and a LENGTH of at least 75 s of nonconsecutive EEG recordings at 64 Hz of sampling rate. Hence, effective and functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in any practical EEG recordings, by combining high-resolution EEG techniques and linear inverse estimation with SEM or DTF methods. We conclude that the estimation of cortical connectivity can be performed not only with hemodynamic measurements, but also with EEG signals treated with advanced computational techniques.