Field Of Neuroimaging Research Articles

A Narrative Review on Clinical Applications of fNIRS.

Functional near-infrared spectroscopy (fNIRS) is a relatively new imaging modality in the functional neuroimaging research arena. The fNIRS modality non-invasively investigates the change of blood oxygenation level in the human brain utilizing the transillumination technique. In the last two decades, the interest in this modality is gradually evolving for its real-time monitoring, relatively low-cost, radiation-less environment, portability, patient-friendliness, etc. Including brain-computer interface and functional neuroimaging research, this technique has some important application of clinical perspectives such as Alzheimer's disease, schizophrenia, dyslexia, Parkinson's disease, childhood disorders, post-neurosurgery dysfunction, attention, functional connectivity, and many more can be diagnosed as well as in some form of assistive modality in clinical approaches. Regarding the issue, this review article presents the current scopes of fNIRS in medical assistance, clinical decision making, and future perspectives. This article also covers a short history of fNIRS, fundamental theories, and significant outcomes reported by a number of scholarly articles. Since this review article is hopefully the first one that comprehensively explores the potential scopes of the fNIRS in a clinical perspective, we hope it will be helpful for the researchers, physicians, practitioners, current students of the functional neuroimaging field, and the related personnel for their further studies and applications.

Hemorrhagic Cerebrovascular Pathology in the Pediatric Population.

Hemorrhagic cerebrovascular disease in the pediatric population can have devastating and long-term effects. Progress in the fields of genetics, neuroimaging, pharmacology, and surgical techniques has led to improved diagnosis and management of pediatric cerebrovascular diseases. In this review we discuss the current etiologies and medical and surgical treatments of hemorrhagic cerebrovascular pathology affecting infants and children. A special emphasis is placed on neuroendovascular treatment options. Increased knowledge about this unique pathology and the medical and therapeutic options will empower practitioners to more quickly and accurately identify and accurately treat hemorrhagic diseases in the pediatric population.

Evolution of International Psychiatry

International psychiatry is currently facing serious challenges triggered by the global economic crisis and the COVID-19 pandemic. These global events lead to the need to broaden our nosographic and therapeutic horizons, and to make use of the newest psychological approaches and the latest neuroscience acquisitions. The focus should be on the psychological consequences of the pandemic, not only on people suffering from mental disorders, but also on the general population, for which the risk of developing psychic symptoms appears to be increased. A population that needs special attention is that of health workers involved in the management of the pandemic. In facing these problems, psychiatry today can use numerous new clinical applications and technologies in the fields of precision medicine. These include genomics, neuroimaging, and microbiomics, which can also be integrated with each other through machine learning systems. They can provide new contributions both in treatment personalization and in the evolution of nosographic systems. Besides this, the contribution of psychotherapies and dynamic and clinical psychology appears to be indispensable for a complete understanding of the clinical and personological aspects of patients. This journal aims to include innovative studies deriving from original, clinical, and basic research in the fields of mental health, precision psychiatry, genomics, neuroimaging, neuropsychopharmacology, and dynamic and clinical psychology.

PRZEGLĄD METOD NEUROOBRAZOWYCH DO OCENY PROCESÓW PRZETWARZANIA SŁUCHOWEGO W KORZE MÓZGU

In this review we describe several methods that can be used to study auditory processing in the cerebral cortex, including functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and positron emission tomography (PET). We explain the principles of each technique and list the characteristics that make them suitable for certain research applications. For each method we give a broad range of examples that have already helped uncover various aspects of cortical auditory processing. We compare and summarise the characteristics of each method in order to help the reader choose one that is best suited to answer a specific research question. We also give perspectives on multimodal imaging – collecting functional brain data with two or more techniques during one study – as a means for overcoming the limitations of each method alone by examining complementary information. This article aims to be a short introductory guide and source of reference for researchers in the field of auditory neuroimaging.

Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity.

Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert–Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and non-imagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding approaches and suggest the possibility of a modality-independent imagery-related mechanism. The use of time evolution of temporal complexity in neural decoding might extend our knowledge of the neural bases of hierarchical functions in the human brain.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.

Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.

DeepNeuro: an open-source deep learning toolbox for neuroimaging.

Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines that can load and preprocess data, design and train various neural network architectures, and evaluate and visualize the results of trained networks on evaluation data. We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces.

Reliability of BOLD signals in chronic stroke-induced aphasia.

Investigating the neurobiology of language impairment and treatment in chronic stroke aphasia using fMRI requires an understanding of measurement variability within and between participants. In this multicenter study, we evaluated the scan-rescan reliability of an auditory and visual (written) story comprehension paradigm in stroke participants with aphasia (N=65) and healthy controls (N=22). The multi-modal task was conducted twice (~1week apart) on separate visits upon study enrolment and twice again at completion three months later. A non-language visuomotor task was studied in the aphasia group only, which was conducted once per time point (3months apart). While participants were asked to make responses during the comprehension task, these in-scanner responses were not recorded. Reliability was assessed using intraclass correlation coefficient (ICC) at both group and individual participant levels. The visual story comprehension condition had higher reliability than the auditory condition in both groups, with participants with aphasia exhibiting lower reliability than controls in both conditions (stroke ICC=.43, healthy ICC=.81). Differences in reliability within the group of participants with aphasia were found to be partially explained by overall language impairment as well as greater head motion. In the participants with aphasia, the visuomotor paradigm was found to have greater reliability than the story comprehension task at equivalent interscan intervals (visuomotor=0.50, comprehension=0.34), and its reliability was not associated with language impairment. This work highlights the importance of considering the reliability of fMRI tasks in aphasia research, provides strategies to improve reliability and has potential implications for the field of clinical neuroimaging in general.

Brain development and neurocircuit modeling at the interface between genetic/environmental risk factors and eating disorders

Research in the field of neuroimaging, connectomics and neuropsychology is growing in the field of eating disorders. However, few studies to date have attempted to explore the effects of malnutrition on the developmental trajectories of brain and cognition. In this presentation, I will review the recent advances of neuroscience research conducted by our group of research with a particular attention to the role of genetic and early environmental risk factors and to those aspects that have direct or indirect clinical implications. Neurodevelopmental trajectories of women with anorexia nervosa are probably influenced both by genetic and early environmental risk factors for these disorders and by the direct and indirect effects of malnutrition after the onset of the disease. In this presentation, I will discuss the evidence of this by means of a series of neuropsychological, functional and brain structural measures. Finally, although a lot of new knowledge is available in the neuroimaging of eating disorders, it is still difficult to translate research findings into practice and clinical implications. I will report about neuroimaging and neuropsychological studies that demonstrated that neuropsychological and brain morphological characteristics have a significant link with clinical characteristics and a predictive effect on the outcome.

Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network.

In recent years, deep learning (DL) has become more widespread in the fields of cognitive and clinical neuroimaging. Using deep neural network models to process neuroimaging data is an efficient method to classify brain disorders and identify individuals who are at increased risk of age-related cognitive decline and neurodegenerative disease. Here we investigated, for the first time, whether structural brain imaging and DL can be used for predicting a physical trait that is of significant clinical relevance—the body mass index (BMI) of the individual. We show that individual BMI can be accurately predicted using a deep convolutional neural network (CNN) and a single structural magnetic resonance imaging (MRI) brain scan along with information about age and sex. Localization maps computed for the CNN highlighted several brain structures that strongly contributed to BMI prediction, including the caudate nucleus and the amygdala. Comparison to the results obtained via a standard automatic brain segmentation method revealed that the CNN-based visualization approach yielded complementary evidence regarding the relationship between brain structure and BMI. Taken together, our results imply that predicting BMI from structural brain scans using DL represents a promising approach to investigate the relationship between brain morphological variability and individual differences in body weight and provide a new scope for future investigations regarding the potential clinical utility of brain-predicted BMI.

Neurobiology of Anxiety Disorders

In recent years, advances in the fields of neuroimaging and experimental psychology increased our understanding of the basic mechanisms of classical conditioning and learning, contributing to our knowledge of the neurobiology of anxiety disorders. Research has shown that the amygdala is the cornerstone of fear circuitry and that abnormalities in amygdala pathways can affect the acquisition and expression of fear conditioning. Activation of the amygdala in response to disorder-relevant stimuli has been observed in anxiety disorders. The roles of the hippocampus, nucleus accumbens, periaqueductal gray, and insular and medial prefrontal cortices in response to fear have been identified as well. Neurotransmitters such as serotonin, dopamine, γ-aminobutyric acid, glutamate, and some neurosteroids play an important part in the neurobiology of anxiety disorders. Neuropeptides such as oxytocin, neuropeptide Y, galanin, and cholecystokinin have been shown to modulate stress response. Drugs such as N-methyl-d-aspartate (NMDA) antagonists and blockers of voltage-gated calcium channels in the amygdala are anxiolytic. Fear extinction, which entails new learning of fear inhibition, is the mechanism of effective antianxiety treatments such as d-cycloserine, a partial NMDA agonist. Extinction is thought to occur by the medial prefrontal cortex, which inhibits the lateral amygdala under hippocampal modulation. Harnessing extinction to delink neutral stimuli from aversive responses is an important goal of the psychotherapy and pharmacotherapy of anxiety disorders. Discovery of the role of microRNAs in the etiology of anxiety disorders and their possible utility as targets to treat these disorders is fascinating. In this review, we discuss the neurobiology of anxiety disorders, which will help us better manage them clinically. This review contains 5 figures, 6 tables, and 39 references. Key words: Amygdala, anxiety disorders, neurobiology, fear conditioning, neurocircuitry, neurotransmitters, neuropeptides, neurosteroids, endogenous opioids.

Neurobiology of Anxiety Disorders

In recent years, advances in the fields of neuroimaging and experimental psychology increased our understanding of the basic mechanisms of classical conditioning and learning, contributing to our knowledge of the neurobiology of anxiety disorders. Research has shown that the amygdala is the cornerstone of fear circuitry and that abnormalities in amygdala pathways can affect the acquisition and expression of fear conditioning. Activation of the amygdala in response to disorder-relevant stimuli has been observed in anxiety disorders. The roles of the hippocampus, nucleus accumbens, periaqueductal gray, and insular and medial prefrontal cortices in response to fear have been identified as well. Neurotransmitters such as serotonin, dopamine, γ-aminobutyric acid, glutamate, and some neurosteroids play an important part in the neurobiology of anxiety disorders. Neuropeptides such as oxytocin, neuropeptide Y, galanin, and cholecystokinin have been shown to modulate stress response. Drugs such as N-methyl-d-aspartate (NMDA) antagonists and blockers of voltage-gated calcium channels in the amygdala are anxiolytic. Fear extinction, which entails new learning of fear inhibition, is the mechanism of effective antianxiety treatments such as d-cycloserine, a partial NMDA agonist. Extinction is thought to occur by the medial prefrontal cortex, which inhibits the lateral amygdala under hippocampal modulation. Harnessing extinction to delink neutral stimuli from aversive responses is an important goal of the psychotherapy and pharmacotherapy of anxiety disorders. Discovery of the role of microRNAs in the etiology of anxiety disorders and their possible utility as targets to treat these disorders is fascinating. In this review, we discuss the neurobiology of anxiety disorders, which will help us better manage them clinically. This review contains 5 figures, 6 tables, and 39 references. Key words: Amygdala, anxiety disorders, neurobiology, fear conditioning, neurocircuitry, neurotransmitters, neuropeptides, neurosteroids, endogenous opioids.

Preface

The International Seminar on Medical Physics (ISMP) 2019 is a scientific conference organised by the Malaysian Association of Medical Physics (MAMP). This year’s 11th conference was held from 7 to 8 of November 2019 at Istana Hotel, Kuala Lumpur, Malaysia in conjunction with the 2019 Medical Physics Day, which is celebrated on the 7th of November every year. The theme for this year’s seminar is “Medical Physics: Together We Make an Impact”.This conference converges recent advancements and trends in the area of medical physics to facilitate knowledge sharing, networking and interaction among scientists, academicians, researchers and biomedical engineers as well as exchanging information on the emerging trends and new challenges in this field.This conference also provides an opportunity for medical physicists and researchers in the related fields to explore the research conducted by their colleagues in other places and to present their research findings. The conference accepted research papers in the field of diagnostic radiology, radiation therapy, nuclear medicine, radiation protection, neuroimaging and other fields related to medical physics.

Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study.

In the field of neuroimaging and cognitive neuroscience, functional Magnetic Resonance Imaging (fMRI) has been widely used to study the functional localization and connectivity of the brain. However, the inherently low signal-to-noise ratio (SNR) of the fMRI signals greatly limits the accuracy and resolution of current studies. In addressing this fundamental challenge in fMRI analytics, in this work we develop and implement a denoising method for task fMRI (tfMRI) data in order to delineate the high-resolution spatial pattern of the brain activation and functional connectivity via dictionary learning and sparse coding (DLSC). In addition to the traditional unsupervised dictionary learning model which has shown success in image denoising, we further utilize the prior knowledge of task paradigm to learn a dictionary consisting of both data-driven and model-driven terms for a more stable sparse representation of the data. The proposed method is applied to preprocess the motor tfMRI dataset from Human Connectome Project (HCP) for the purpose of brain activation detection and functional connectivity estimation. Comparison between the results from original and denoised fMRI data shows that the disruptive brain activation and functional connectivity patterns can be recovered, and the prominence of such patterns is improved through denoising. The proposed method is then compared with the temporal non-local means (tNLM)-based denoising method and shows consistently superior performance in various experimental settings. The promising results show that the proposed DLSC-based fMRI denoising method can effectively reduce the noise level of the fMRI signals and increase the interpretability of the inferred results, therefore constituting a crucial part of the preprocessing pipeline and provide the foundation for further high-resolution functional analysis.

A comparison of random-field-theory and false-discovery-rate inference results in the analysis of registered one-dimensional biomechanical datasets.

BackgroundThe inflation of falsely rejected hypotheses associated with multiple hypothesis testing is seen as a threat to the knowledge base in the scientific literature. One of the most recently developed statistical constructs to deal with this problem is the false discovery rate (FDR), which aims to control the proportion of the falsely rejected null hypotheses among those that are rejected. FDR has been applied to a variety of problems, especially for the analysis of 3-D brain images in the field of Neuroimaging, where the predominant form of statistical inference involves the more conventional control of false positives, through Gaussian random field theory (RFT). In this study we considered FDR and RFT as alternative methods for handling multiple testing in the analysis of 1-D continuum data. The field of biomechanics has recently adopted RFT, but to our knowledge FDR has not previously been used to analyze 1-D biomechanical data, nor has there been a consideration of how FDR vs. RFT can affect biomechanical interpretations.MethodsWe reanalyzed a variety of publicly available experimental datasets to understand the characteristics which contribute to the convergence and divergence of RFT and FDR results. We also ran a variety of numerical simulations involving smooth, random Gaussian 1-D data, with and without true signal, to provide complementary explanations for the experimental results.ResultsOur results suggest that RFT and FDR thresholds (the critical test statistic value used to judge statistical significance) were qualitatively identical for many experimental datasets, but were highly dissimilar for others, involving non-trivial changes in data interpretation. Simulation results clarified that RFT and FDR thresholds converge as the true signal weakens and diverge when the signal is broad in terms of the proportion of the continuum size it occupies. Results also showed that, while sample size affected the relation between RFT and FDR results for small sample sizes (<15), this relation was stable for larger sample sizes, wherein only the nature of the true signal was important.DiscussionRFT and FDR thresholds are both computationally efficient because both are parametric, but only FDR has the ability to adapt to the signal features of particular datasets, wherein the threshold lowers with signal strength for a gain in sensitivity. Additional advantages and limitations of these two techniques as discussed further. This article is accompanied by freely available software for implementing FDR analyses involving 1-D data and scripts to replicate our results.

Confronting the grey zone after severe brain injury

In recent years, rapid technological developments in the field of neuroimaging have provided several new methods for revealing thoughts, actions and intentions based solely on the pattern of activity that is observed in the brain. In specialized centres, these methods are now being employed routinely to assess residual cognition, detect consciousness and even communicate with some behaviorally non-responsive patients who clinically appear to be comatose or in a vegetative state. In this article, we consider some of the ethical issues raised by these developments and the profound implications they have for clinical care, diagnosis, prognosis and medical-legal decision-making after severe brain injury.

A review of childhood maltreatment, latent vulnerability and the brain: implications for clinical practice and prevention

There is a well-established association between childhood maltreatment and later poor mental health and increasing recognition that we need to find ways to support children following such experiences to improve long-term outcomes. We suggest that the rationale for such a preventive approach is directly informed by the emerging findings from the field of functional neuroimaging. Here, we review the evidence from four neurocognitive systems: threat processing, reward processing, emotion regulation and executive control. We briefly summarise what is known about each system, review the evidence that altered functioning is implicated in common mental health problems and describe how the functioning of each system is altered following maltreatment. Across domains, these neurocognitive alterations following child maltreatment are in line with those seen in adults presenting with mental health problems yet most maltreated children studied do not have a presenting ‘disorder’. This suggests that these neurocognitive alterations may potentiate the risk of future psychopathology. We discuss this possibility in the context of the theory of latent vulnerability ( McCrory and Viding, 2015 ). According to this model, children may respond to early adverse environments in ways that are potentially adaptive in the short term but which create vulnerability to future mental health problems in the long term. We also consider the clinical implications of the neuroimaging evidence ‒ in particular, the growing need for a more preventive clinical approach.

Psychoanalysis and Neuroscience: The Bridge Between Mind and Brain.

In 1895 in the Project for a Scientific Psychology, Freud tried to integrate psychology and neurology in order to develop a neuroscientific psychology. Since 1880, Freud made no distinction between psychology and physiology. His papers from the end of the 1880s to 1890 were very clear on this scientific overlap: as with many of his contemporaries, Freud thought about psychology essentially as the physiology of the brain. Years later he had to surrender, realizing a technological delay, not capable of pursuing its ambitious aim, and until that moment psychoanalysis would have to use its more suitable clinical method. Also, he seemed skeptical about phrenology drift, typical of that time, in which any psychological function needed to be located in its neuroanatomical area. He could not see the progresses of neuroscience and its fruitful dialogue with psychoanalysis, which occurred also thanks to the improvements in the field of neuroimaging, which has made possible a remarkable advance in the knowledge of the mind-brain system and a better observation of the psychoanalytical theories. After years of investigations, deriving from research and clinical work of the last century, the discovery of neural networks, together with the free energy principle, we are observing under a new light psychodynamic neuroscience in its exploration of the mind-brain system. In this manuscript, we summarize the important developments of psychodynamic neuroscience, with particular regard to the free energy principle, the resting state networks, especially the Default Mode Network in its link with the Self, emphasizing our view of a bridge between psychoanalysis and neuroscience. Finally, we suggest a discussion by approaching the concept of Alpha Function, proposed by the psychoanalyst Wilfred Ruprecht Bion, continuing the association with neuroscience.

Advances in neurodegenerative and psychiatric imaging: introductory editorial.

Advances in neurodegenerative and psychiatric imaging: introductory editorial.