Brain Imaging Technologies Research Articles

Vowel Sound Synthesis from Electroencephalography during Listening and Recalling

Recent advances in brain imaging technology have furthered our knowledge of the neural basis of auditory and speech processing, often via contributions from invasive brain signal recording and stimulation studies conducted intraoperatively. Herein, an approach for synthesizing vowel sounds straightforwardly from scalp‐recorded electroencephalography (EEG), a noninvasive neurophysiological recording method is demonstrated. Given cortical current signals derived from the EEG acquired while human participants listen to and recall (i.e., imagined) two vowels, /a/ and /i/, sound parameters are estimated by a convolutional neural network (CNN). The speech synthesized from the estimated parameters is sufficiently natural to achieve recognition rates >85% during a subsequent sound discrimination task. Notably, the CNN identifies the involvement of the brain areas mediating the “what” auditory stream, namely the superior, middle temporal, and Heschl's gyri, demonstrating the efficacy of the computational method in extracting auditory‐related information from neuroelectrical activity. Differences in cortical sound representation between listening versus recalling are further revealed, such that the fusiform, calcarine, and anterior cingulate gyri contributes during listening, whereas the inferior occipital gyrus is engaged during recollection. The proposed approach can expand the scope of EEG in decoding auditory perception that requires high spatial and temporal resolution.

Neuro - Geometry in Managerial Decision Dynamics

New brain imaging technologies have motivated neuromanagerial studies of internal order of mind. We are only at beginning of an enterprise. Its promise suggests a fundamental change in how we think, observe and model decision in context. Brain absorbs information, recognises and frames problematic situations towards appropriate responses. Decision-making in neuromanagement is based on complexly interlinked imaging technologies with links to bandwidth of human decision. Manager often fail to design ‘rational’ decisions. Processes by which Managers reach decisions embody conflicting tenets. Question is how Manager makes decisions. How are decisions carried out in brain? Interest is on assumptions, beliefs, habits and tactics that Manager uses to make decisions. How do parts of brain govern decision-making, coordinate, face obscurity and engage in strategic simplification while deciding? What happens in brain or is activated? Is study of decision-making geometry via neuromanagement relevant? What are the limits? How does previous experience alter behaviour? There are unsolved problems in (managerial) cognition. What are the general implications? Managerial cognition plays a role. Sensing is as an important aspect and there is evidence of how evolutionary patterns are shaped by beliefs and attention allocation. How does Manager decide in state of VUCA (Uncertainty, Vulnerability, Complexity and Ambiguity? In this, neuromanagement seeks to explain decision-making, ability to process alternatives and choose optimal course of action. It studies how management behaviour shape understanding of brain and guide coherent brain geometry towards decision-making? This paper attempts to explore above phenomenon to put forward a model for neuro-management decision, in which interaction are addressed through measurements of brain activity. Attempt is to model decision-making linking neuro-management of decision behaviour. This provides conceptual geometry for conducting neuro (managerial) research at intersection of neuro (managerial) science, raises a few fundamental issues and offer solution through measurements of brain activity at management levels of analysis.

Toward neuroinformatics of neuroimaging data sharing and open brain science

In the late 1980s, fusions of advances in both neuroscience and information science gave birth to neuroinformatics with a core mission to informationize the complex brain structure and function and build the database. Three decades of neuroinformatics has witnessed the rapid progression on the in-vivo brain mapping technologies for monitoring and recording high-resolution spatial and temporal properties of such a complex informatic system non-invasively. Comparable to “Apollo Lunar Exploration” and “Manhattan Project” in the last century, brain projects became a national-wide arena for scientific frontiers in early 2000s. The most recent decade paralleled the remarkable development of the non-invasive and in-vivo human brain imaging technologies. Magnetic resonance imaging has made significant progresses on the basic research for understanding the human behavior and brain. This generated huge amount of in-vivo neuroimaging data and waved a new cycle of neuroinformatic research frontiers in life sciences, “open neuroscience” or “open brain science” featured by neuroimaging data sharing. In this review, we surveyed the history, state and future of neuroimaging data-sharing and the open brain science.

Comparative Analysis to Identify Efficient Technique for Interfacing BCI System

Brain Computer Interfacing (BCI) allows user to control certain devices or objects using human brain. With the availability of various sensors, it is possible to obtain the data from the human brain which controls the physical activities in the brain itself. BCI users can access a system capable of communicating efficiently in a special manner that works based on the brain signals. BCI gives a better platform for physically challenged people to do certain significant functions in their daily routine. The use of brain imaging technologies in BCI helps to enhance the signal quality during the communication among the machines and the human beings. The paper describes some of the research works carried on BCI and its advancements to perform a better communication between the computers and the human brain, by using emerging technologies. This paper also covers the applications, challenges, and future directions of BCI.

Assessing the depth of language processing in patients with disorders of consciousness.

Assessing residual consciousness and cognitive abilities in unresponsive patients is a major clinical concern and a challenge for cognitive neuroscience. Although neuroimaging studies have demonstrated a potential for informing diagnosis and prognosis in unresponsive patients, these methods involve sophisticated brain imaging technologies, which limit their clinical application. In this study, we adopted a new language paradigm that elicited rhythmic brain responses tracking the single-word, phrase and sentence rhythms in speech, to examine whether bedside electroencephalography (EEG) recordings can help inform diagnosis and prognosis. EEG-derived neural signals, including both speech-tracking responses and temporal dynamics of global brain states, were associated with behavioral diagnosis of consciousness. Crucially, multiple EEG measures in the language paradigm were robust to predict future outcomes in individual patients. Thus, EEG-based language assessment provides a new and reliable approach to objectively characterize and predict states of consciousness and to longitudinally track individual patients' language processing abilities at the bedside.

Imaging through highly scattering human skulls with ultrasound-modulated optical tomography.

Advances in human brain imaging technologies are critical to understanding how the brain works and the diagnosis of brain disorders. Existing technologies have different drawbacks, and the human skull poses a great challenge for pure optical and ultrasound imaging technologies. Here we demonstrate the feasibility of using ultrasound-modulated optical tomography, a hybrid technology that combines both light and sound, to image through human skulls. Single-shot off-axis holography was used to measure the field of the ultrasonically tagged light. This Letter paves the way for imaging the brain noninvasively through the skull, with optical contrast and a higher spatial resolution than that of diffuse optical tomography.

Survey on Early Detection of Alzhiemer’s Disease Using Capsule Neural Network

Alzheimer's disease (AD) is an disorder which is irreversible of the brain related to memory loss, mostly found in the old and aged population. Alzheimer's dementia results from the degeneration or loss of brain cells. The brain-imaging technologies most often used to diagnose AD is Magnetic resonance imaging (MRI). MRI or structural magnetic resonance is a very popular and actual technique used to diagnose AD. An MRI uses magnets and powerful radio waves to create a complete view of your brain. To actually detect the presence of Alzheimer’s, the MRI should me studied carefullyImplementation of CBIR Content Based Image Retrival which is a revolutionary computer aided diagnosis technique will create new abilities in MRI Magnetic resonance imaging in related image retrieval and training for recognition of development of AD in early stages

Brain imaging technologies as source for Extrospection: self-formation through critical self-identification

Brain imaging technologies are increasingly used to find networks and brain regions that are specific to the functional realization of particular aspects of the self. In this paper, we aim to show how neuroscientific research and techniques could be used in the context of self-formation without treating them as representations of an inner realm. To do so, we show first how a Cartesian framework underlies the interpretation and usage of brain imaging technologies as functional evidence. To illustrate how material-technological inventions and developments can have a significant and lasting impact on views of the self, we show how this framework was influenced by another technology: the camera obscura. Subsequently, we show that brain imaging technologies challenge the idea that privileged access to the self can be obtained merely through introspection, indicating a strong discontinuity between the Cartesian and the current neuroscientific framework. Building on these insights, we reframe the self in terms of self-formation. This view neither regards the brain as an independent realizer of aspects of the self, nor assumes that self-knowledge can be obtained through introspection. From this perspective, self-formation is realized through critical self-identification: instead of offering representational knowledge of an ‘inner self,’ the potential use of brain imaging technologies within this framework lies in their capacity to offer what we call ‘extrospective knowledge’ that pragmatically can contribute to self-formation. Brain imaging technologies contribute to this process because they foreground our neurophysiology, which helps to critically integrate biological aspects into self-formation.

Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer\u2019s Disease

Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimer’s disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimer’s Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimer’s disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimer’s disease.

Neuromanagement Under the Light of Maqasid Sharia

Technology advancement and Industrial revolution 4.0 engagement are increasingly seen as powerful tool for making innovation, managerial efficiency of the company. New brain imaging technologies have motivated neuromanagement studies of the internal order of the mind and its links with the spectrum of human decisions from harmful and injustice decision. The main achievement of this study is to explain how neuromanagement works under light of Maqasid al Shariah. As spirit of Shariah is based on wisdom and achieving people’s welfare in this life and the afterlife. Shariah is all about justice, wisdom, and good. This paper contributes to the literature by explaining neuromanagement and Maqasid al Shariah. In fact, that there is a lack of literature related to neuromanagement and Maqasid al Shariah. This research will, therefore, provide to the neuromanagement decision making in accordance with the Maqasid al Shariah.

Constituting ‘Visual Attention’: On the Mediating Role of Brain Stimulation and Brain Imaging Technologies in Neuroscientific Practice

ABSTRACT An important development within cognitive neuroscience is the use of Non-Invasive Brain Stimulation (NIBS), a technique which holds the promise of establishing causal relationships between brain processes and cognitive processes. However, NIBS does not allow researchers to observe neurophysiological processes, and must be coupled with imaging technologies such as Electroencephalography (EEG) for the visualization of neurophysiological change. Technologies such as NIBS and EEG are not neutral intermediaries between scientists and the world, but actively mediate the reality that scientists investigate. How these technologies shape the objects of neuroscientific study becomes clear when analyzing real-life conversations between neuroscientists researching visual attention. During the constitution of visual attention, neuroscientists need to manage a tension between the epistemic norms of ‘causality’ and ‘reality’, and how this tension is managed differs when different technologies are used. In the case of NIBS, the tension between reality and causality is managed in terms of the relation between experimental results obtained within the laboratory and the ‘real’ world outside of the laboratory. When NIBS and EEG are combined, neuroscientist orient to the norms of causality and reality in a different way: now, the reality of the causality obtained within scientific experiments needs to managed. This indicates that neuroscientists cannot straightforwardly assume the causal efficacy of the brain on human behavior. Instead, the coming into being of neuroscientific objects such as visual attention is both dependent on technological mediations and on how the tension between the norms of ‘causality’ and ‘reality’ is managed.

From Brain Science to Artificial Intelligence

Reviewing the history of the development of artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs in AI, such as deep learning. At present, although the developmental trend in AI and its applications has surpassed expectations, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI research, including a link from brain science to AI, and a connection from knowing the brain to simulating the brain. The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology; to establish a dynamic connection diagram of the brain; and to integrate neuroscience experiments with theory, models, and statistics. Based on these steps, a new generation of AI theory and methods can be studied, and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established. This article discusses the opportunities and challenges of adapting brain science to AI.

Why do students fall into webtoon viewing while they give up mathematics - An fMRI study

The purpose of this study was to identify brain activation regions related to flow and motivation during mathematics problem solving and webtoon viewing based on brain imaging technology. Researchers measured brain activation using functional magnetic resonance imaging (fMRI) and investigated flow and motivation through questionnaires. Significant differences between mathematics problem solving and webtoon viewing were found in high school students' brain activation regions. Based on the functions of these brain activation regions, result-oriented reward pursuit could play an important role in students' motivation-inducing during mathematics problem solving. However, investigation of flow and motivation from webtoon viewing were ascertained to be caused from pleasure from new information presented in the story and picture. Results of this study could help devise a specific strategy for mathematics problem solving instruction.

Neuroanatomic and Functional Neuroimaging Findings.

The search for brain morphology findings that could explain behavioral disorders has gone through a long path in the history of psychiatry. With the advance of brain imaging technology, studies have been able to identify brain morphology and neural circuits associated with the pathophysiology of mental illnesses, such as bipolar disorders (BD). Promising results have also shown the potential of neuroimaging findings in the identification of outcome predictors and response to treatment among patients with BD. In this chapter, we present brain imaging structural and functional findings associated with BD, as well as their hypothesized relationship with the pathophysiological aspects of that condition and their potential clinical applications.

Implicit processing of emotional words by children with Post-Traumatic Stress Disorder: An fMRI investigation

Background/ObjectiveIn the last decade, socio-political violence in Colombia (South America) has created an environment of extreme/chronic stress. In this study, brain imaging technology (fMRI) and behavioral task performance were used to measure potential deficits in executive functioning for emotional processing in Colombian children. MethodParticipants (22 Post-Traumatic Stress Disorder, PTSD and 22 neurotypical, NT) were asked to perform a word task with implicit emotional salience, which required them to report the color of the ink in which a positive, negative or neutral word was printed. ResultsMixed design analysis of variance showed no group differences in accuracy for determining ink color when presented as a positive or neutral word. However, PTSD children were significantly less accurate (negative words) and notably slower (both positive and negative words) at determining ink color when presented in the context of an emotional word. PTSD processing of positive and negative words was associated with hypoactivation in the superior and middle frontal gyri of the right hemisphere in comparison to NT children. ConclusionsThese results may reflect a deficit in executive functioning for emotionally laden stimuli, perhaps induced as a by-product of their traumatic experiences.

Structure and Optical Properties of Co-Sputtered Amorphous Silicon Tin Alloy Films for NIR-II Region Sensor.

Near-infrared brain imaging technology has great potential as a non-invasive, real-time inspection technique. Silicon-tin (SiSn) alloy films could be a promising material for near-infrared brain detectors. This study mainly reports on the structure of amorphous silicon tin alloy thin films by Raman spectroscopy to investigate the influence of doped-Sn on an a-Si network. The variations in TO peak caused by the increase in Sn concentration indicate a decrease in the short-range order of the a-Si network. A model has been proposed to successfully explain the non-linear variation in Raman parameters of ITA/ITO and ILA+LO/ITO. The variations of Raman parameters of the films with a higher deposition temperature indicate the presence of SiSn nanocrystals, though the SiSn nanocrystals present no Raman peaks in Raman spectra. XRD and TEM analysis further illustrate the existence of nanocrystals. The ratio of photo/dark conductivity and optical bandgap results demonstrate that the films can be selected as a sensitive layer material for NIR-II region sensors.

A Review of Functional Near-Infrared Spectroscopy Studies of Motor and Cognitive Function in Preterm Infants.

Preterm infants are vulnerable to brain injuries, and have a greater chance of experiencing neurodevelopmental disorders throughout development. Early screening for motor and cognitive functions is critical to assessing the developmental trajectory in preterm infants, especially those who may have motor or cognitive deficits. The brain imaging technology functional near-infrared spectroscopy (fNIRS) is a portable and low-cost method of assessing cerebral hemodynamics, making it suitable for large-scale use even in remote and underdeveloped areas. In this article, we review peer-reviewed, scientific fNIRS studies of motor performance, speech perception, and facial recognition in preterm infants. fNIRS provides a link between hemodynamic activity and the development of brain functions in preterm infants. Research using fNIRS has shown different patterns of hemoglobin change during some behavioral tasks in early infancy. fNIRS helps to promote our understanding of the developmental mechanisms of brain function in preterm infants when performing motor or cognitive tasks in a less-restricted environment.

Towards a Multimodal Model of Cognitive Workload Through Synchronous Optical Brain Imaging and Eye Tracking Measures.

Recent advances in neuroimaging technologies have rendered multimodal analysis of operators’ cognitive processes in complex task settings and environments increasingly more practical. In this exploratory study, we utilized optical brain imaging and mobile eye tracking technologies to investigate the behavioral and neurophysiological differences among expert and novice operators while they operated a human-machine interface in normal and adverse conditions. In congruence with related work, we observed that experts tended to have lower prefrontal oxygenation and exhibit gaze patterns that are better aligned with the optimal task sequence with shorter fixation durations as compared to novices. These trends reached statistical significance only in the adverse condition where the operators were prompted with an unexpected error message. Comparisons between hemodynamic and gaze measures before and after the error message indicated that experts’ neurophysiological response to the error involved a systematic increase in bilateral dorsolateral prefrontal cortex (dlPFC) activity accompanied with an increase in fixation durations, which suggests a shift in their attentional state, possibly from routine process execution to problem detection and resolution. The novices’ response was not as strong as that of experts, including a slight increase only in the left dlPFC with a decreasing trend in fixation durations, which is indicative of visual search behavior for possible cues to make sense of the unanticipated situation. A linear discriminant analysis model capitalizing on the covariance structure among hemodynamic and eye movement measures could distinguish experts from novices with 91% accuracy. Despite the small sample size, the performance of the linear discriminant analysis combining eye fixation and dorsolateral oxygenation measures before and after an unexpected event suggests that multimodal approaches may be fruitful for distinguishing novice and expert performance in similar neuroergonomic applications in the field.

The Structure and Properties of Sn/SiSn-nanodisperse Alloy Thin Films

Near-infrared brain imaging technology has great advantages in brain imaging and inspection of brain disorders compared to traditional brain imaging technology. Silicon-tin (SiSn) alloys are expected to be the material for infrared brain imaging detectors. The structure and properties of the SiSn alloy thin films with relatively low Sn concentration, which are the key for it to be used in near-infrared brain imaging technology, have not been reported yet. Here, we report the deposition time, growth temperature, microstructure, resistivity, and transmittance of amorphous silicon-tin (a-Si1-xSnx) alloy thin films prepared by radio frequency (RF) magnetron sputtering. Reasonable deposition time and growth temperature for the preparation of the films are given in this paper. Sn nanocrystals are observed in the a-Si1-xSnx alloy thin films. The variations in resistivity and transmittance indicate that it has excellent electrical and optical properties so that it can be used as a near-infrared brain imaging detector material.

Using neuroscience to understand the impact of premium digital out-of-home media

In this article, Heather Andrew and Dr Shaun Seixas of Neuro-Insight, and Helen Haines from Ocean Outdoor explain how modern brain imaging technology can be applied to measure people’s emotional, subconscious responses to different forms of outdoor media, from static paper and paste posters to multisensory advertising screens. With reference to visual attention, emotional intensity, desirability, and memorability, they describe a program of research that investigated brain response to the latest generation of outdoor advertising locations, and which explains the cognitive processes that underlie their success, not just in the context of out–of-home advertising, but in the broader digital world. The conclusions have implications that are fundamental to the planning, design, and development of media campaigns, identifying the role that outdoor advertising can play vis a vis other media, and how different media platforms can work together to the benefit of advertisers.