Brain Function Research Research Articles

Reproducible brain PET data analysis: easier said than done.

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future.

Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on 18F-FDG PET

The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography ( 18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.

Modeling the stress and forces on multi-channel TMS coil arrays in high-field MRI scanners

Transcranial magnetic stimulation (TMS) is a non-invasive method for stimulating the cortex. Concurrent functional magnetic resonance imaging can show changes in TMS-induced activity in the whole brain, with the potential to inform brain function research and to guide the development of TMS therapy. However, the interaction of the strong current pulses in the TMS coil in the static main magnetic field of the MRI produces high Lorentz forces, which may damage the coil enclosure and compromise the patient’s safety. We studied the time-dependent mechanical behavior and durability of two multi-locus TMS (mTMS) coil arrays inside a high-field MRI bore with finite element modeling. In addition, coil arrays were built and tested based on the simulation results. We found that the current pulses produce shock waves and time-dependent stress distribution in the coil plates. The intensity and location of the maximum stress depend on the current waveform, the coil combination, and the transducer orientation relative to the MRI magnetic field. We found that 30% glass-fiber-filled polyamide is the most durable material out of the six options studied. In addition, novel insights for more durable TMS coil designs were obtained. Our study contributes to a comprehensive understanding of the underlying mechanisms responsible for the structural failure of mTMS coil arrays during stimulation within high static magnetic fields. This knowledge is essential for developing mechanically stable and safe mTMS-MRI transducers.

Dynamic causal model application on hierarchical human motor control estimation in visuomotor tasks.

In brain function research, each brain region has been investigated independently, and how different parts of the brain work together has been examined using the correlations among them. However, the dynamics of how different brain regions interact with each other during time-varying tasks, such as voluntary motion tasks, are still not well-understood. To address this knowledge gap, we conducted functional magnetic resonance imaging (fMRI) using target tracking tasks with and without feedback. We identified the motor cortex, cerebellum, and visual cortex by using a general linear model during the tracking tasks. We then employed a dynamic causal model (DCM) and parametric empirical Bayes to quantitatively elucidate the interactions among the left motor cortex (ML), right cerebellum (CBR) and left visual cortex (VL), and their roles as higher and lower controllers in the hierarchical model. We found that the tracking task with visual feedback strongly affected the modulation of connection strength in ML → CBR and ML↔VL. Moreover, we found that the modulation of VL → ML, ML → ML, and ML → CBR by the tracking task with visual feedback could explain individual differences in tracking performance and muscle activity, and we validated these findings by leave-one-out cross-validation. We demonstrated the effectiveness of our approach for understanding the mechanisms underlying human motor control. Our proposed method may have important implications for the development of new technologies in personalized interventions and technologies, as it sheds light on how different brain regions interact and work together during a motor task.

SWsnn: A Novel Simulator for Spiking Neural Networks.

Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other fields and can also be applied to artificial intelligence, machine learning, and other fields. At present, many simulators using central processing unit (CPU) or graphics processing unit (GPU) have been developed. However, due to the randomness of connections between neurons and spiking events in SNN simulation, this causes a lot of memory access time. To alleviate this problem, we developed an SNN simulator SWsnn based on the new Sunway SW26010pro processor. The SW26010pro processor consists of six core groups, each with 16 MB of local data memory (LDM). LDM has the characteristics of high-speed read and write, which is suitable for performing simulation tasks similar to SNNs. Experimental results show that SWsnn runs faster than other mainstream GPU-based simulators when simulating a certain scale of neural network, showing a strong performance advantage. To conduct larger scale simulations, SWsnn designed a simulation computation based on a large shared model of Sunway processor and developed a multiprocessor version of SWsnn based on this mode, achieving larger scale SNN simulations.

Evaluation of Brain Source Localization Methods Based on Test-Retest Reliability With Multiple Session EEG Data.

Various EEG source localization methods have been proposed for functional brain research. The evaluation and comparison of these methods are usually based on simulated data but not real EEG data, as the ground truth of source localization is unknown. In this study, we aim to evaluate source localization methods quantitatively under the real situation. We examined the test-retest reliability of the source signals reconstructed from a public six-session EEG data of 16 subjects performing face recognition tasks by five mainstream methods, including weighted minimum norm estimation (WMN), dynamical Statistical Parametric Mapping (dSPM), Standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA), dipole modeling and linearly constrained minimum variance (LCMV) beamformers. All methods were evaluated in terms of peak localization reliability and amplitude reliability of source signals. In the two brain regions responsible for static face recognition, all methods have promising peak localization reliability, with WMN showing the smallest peak dipole distance between session pairs. The spatial stability of source localization in the familiar face condition is better than those in the unfamiliar face and the scrambled face conditions in the face recognition areas in the right hemisphere. In addition, the test-retest reliability of source amplitude by all methods is good to excellent under the familiar face condition. Stable and reliable results for source localization can be obtained in the presence of evident EEG effects. Due to different levels of a priori knowledge, different source localization methods have different applicable scenarios. These findings provide new evidence for the validity of source localization analysis and a new perspective for the evaluation of source localization methods on real EEG data.

A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer's Patients.

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging tool, used in brain function research and is also a low-frequency signal, showing brain activation by means of Oxygen consumption. One of the reliable methods in brain functional connectivity analysis is the correlation method. In correlation analysis, the relationship between two time-series has been investigated. In fMRI analysis, the Pearson correlation is used while there are other methods. This study aims to investigate the different correlation methods in functional connectivity analysis. In this analytical research, based on fMRI signals of Alzheimer's Disease (AD) and healthy individuals from the ADNI database, brain functional networks were generated using correlation techniques, including Pearson, Kendall, and Spearman. Then, the global and nodal measures were calculated in the whole brain and in the most important resting-state network called Default Mode Network (DMN). The statistical analysis was performed using non-parametric permutation test. Results show that although in nodal analysis, the performance of correlation methods was almost similar, in global features, the Spearman and Kendall were better in distinguishing AD subjects. Note that, nodal analysis reveals that the functional connectivity of the posterior areas in the brain was more damaged because of AD in comparison to frontal areas. Moreover, the functional connectivity of the dominant hemisphere was disrupted more. Although the Pearson method has limitations in capturing non-linear relationships, it is the most prevalent method. To have a comprehensive analysis, investigating non-linear methods such as distance correlation is recommended.

Electroencephalography source localization.

Electroencephalography (EEG) has been and is still widely used in brain function research. EEG has advantages over other neuroimaging modalities. First, it not only directly images the electrical activity of neurons; it has a higher temporal resolution. Furthermore, current advanced technologies enable accurate mathematical calculations and sophisticated localization from EEG data. Several important factors should be considered for EEG analysis using these advanced technologies. First, raw EEG data contain physiological or nonphysiological artifacts. Therefore, preprocessing methods and algorithms to detect and remove these artifacts have been proposed and developed. In the analysis of preprocessed EEG, forward and inverse problems require solving and several proposed models have been applied. To solve the forward problem, the source information and matrix parameters from which the EEG originates are essential. Therefore, an accurate head model is required. In contrast, the possible combinations of the current sources computed inversely from EEG measured at a limited number of electrodes are infinite, referring to the inverse problem. The inverse problem can be solved by setting limits based on assumptions made of the anatomy and physiology on the generation and propagation of the current sources. Thus, methods such as dipole source models and distributed source models have been proposed. Source localization requires the consideration of many factors such as the preprocessing of raw EEG data, artifact removal, accurate head models and forward problems, and inverse computation problems. This review summarizes the methods and considerations applied to the above EEG source localization process. It also introduces the applications of EEG source localization for epilepsy and other diseases as well as brain function studies and discusses future directions.

A pilot study of contrast-enhanced electrical impedance tomography for real-time imaging of cerebral perfusion.

Real-time detection of cerebral blood perfusion can prevent adverse reactions, such as cerebral infarction and neuronal apoptosis. Our previous clinical trial have shown that the infusion of therapeutic fluid can significantly change the impedance distribution in the brain. However, whether this alteration implicates the cerebral blood perfusion remains unclear. To explore the feasibility of monitoring cerebral blood perfusion, the present pilot study established a novel cerebral contrast-enhanced electrical impedance tomography (C-EIT) technique. Rabbits were randomly divided into two groups: the internal carotid artery non-occlusion (ICAN) and internal carotid artery occlusion (ICAO) groups. Both of groups were injected with glucose, an electrical impedance-enhanced contrast agent, through the right internal carotid artery under EIT monitoring. The C-EIT reconstruction images of the rabbits brain were analyzed according to the collected raw data. The paired and independent t-tests were used to analyze the remodeled impedance values of the left and right cerebral hemispheres within and between studied groups, respectively. Moreover, pathological examinations of brain were performed immediately after C-EIT monitoring. According to the reconstructed images, the impedance value of the left cerebral hemisphere in the ICAN group did not change significantly, whereas the impedance value of the right cerebral hemisphere gradually increased, reaching a peak at approximately 10 s followed by gradually decreased. In the ICAO group, the impedance values of both cerebral hemispheres increased gradually and then began to decrease after reaching the peak value. According to the paired t-test, there was a significant difference (P < 0.001) in the remodeling impedance values between the left and right hemispheres in the ICAN group, and there was also a significant difference (P < 0.001) in the ICAO group. According to the independent t-test, there was a significant difference (P < 0.001) of the left hemispheres between the ICAN and ICAO groups. The cerebral C-EIT proposed in this pilot study can reflect cerebral blood perfusion. This method has potential in various applications in the brain in the future, including disease progression monitoring, collateral circulation judgment, tumor-specific detection, and brain function research.

Multi-nutrient interventions and cognitive ageing: are we barking up the right tree?

As we continue to elucidate the mechanisms underlying age-related brain diseases, the reductionist strategy in nutrition–brain function research has focused on establishing the impact of individual foods. However, the biological processes connecting diet and cognition are complex. Therefore, consideration of a combination of nutritional compounds may be most efficacious. One barrier to establishing the efficacy of multi-nutrient interventions is that the area lacks an established set of evidence-based guidelines for studying their effect on brain health. This review is an output of the International Life Sciences Institute (ILSI) Europe. A multi-disciplinary expert group was assembled with the aim of developing a set of considerations to guide research into the effects of multi-nutrient combinations on brain functions. Consensus recommendations converged on six key issues that should be considered to advance research in this area: (1) establish working mechanisms of the combination and contributions of each individual compound; (2) validate the relevance of the mechanisms for the targeted human condition; (3) include current nutrient status, intake or dietary pattern as inclusion/exclusion criteria in the study design; (4) select a participant population that is clinically and biologically appropriate for all nutritional components of the combination; (5) consider a range of cognitive outcomes; (6) consider the limits of reductionism and the ‘gold standard’ randomised controlled trial. These guiding principles will enhance our understanding of the interactive/complementary activities of dietary components, thereby strengthening the evidence base for recommendations aimed at delaying cognitive decline.

Reporter Genes for Brain Imaging Using MRI, SPECT and PET.

The use of molecular imaging technologies for brain imaging can not only play an important supporting role in disease diagnosis and treatment but can also be used to deeply study brain functions. Recently, with the support of reporter gene technology, optical imaging has achieved a breakthrough in brain function studies at the molecular level. Reporter gene technology based on traditional clinical imaging modalities is also expanding. By benefiting from the deeper imaging depths and wider imaging ranges now possible, these methods have led to breakthroughs in preclinical and clinical research. This article focuses on the applications of magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) reporter gene technologies for use in brain imaging. The tracking of cell therapies and gene therapies is the most successful and widely used application of these techniques. Meanwhile, breakthroughs have been achieved in the research and development of reporter genes and their imaging probe pairs with respect to brain function research. This paper introduces the imaging principles and classifications of the reporter gene technologies of these imaging modalities, lists the relevant brain imaging applications, reviews their characteristics, and discusses the opportunities and challenges faced by clinical imaging modalities based on reporter gene technology. The conclusion is provided in the last section.

Decoding Digital Visual Stimulation From Neural Manifold With Fuzzy Leaning on Cortical Oscillatory Dynamics.

A crucial point in neuroscience is how to correctly decode cognitive information from brain dynamics for motion control and neural rehabilitation. However, due to the instability and high dimensions of electroencephalogram (EEG) recordings, it is difficult to directly obtain information from original data. Thus, in this work, we design visual experiments and propose a novel decoding method based on the neural manifold of cortical activity to find critical visual information. First, we studied four major frequency bands divided from EEG and found that the responses of the EEG alpha band (8–15 Hz) in the frontal and occipital lobes to visual stimuli occupy a prominent place. Besides, the essential features of EEG data in the alpha band are further mined via two manifold learning methods. We connect temporally consecutive brain states in the t distribution random adjacency embedded (t-SNE) map on the trial-by-trial level and find the brain state dynamics to form a cyclic manifold, with the different tasks forming distinct loops. Meanwhile, it is proved that the latent factors of brain activities estimated by t-SNE can be used for more accurate decoding and the stable neural manifold is found. Taking the latent factors of the manifold as independent inputs, a fuzzy system-based Takagi-Sugeno-Kang model is established and further trained to identify visual EEG signals. The combination of t-SNE and fuzzy learning can highly improve the accuracy of visual cognitive decoding to 81.98%. Moreover, by optimizing the features, it is found that the combination of the frontal lobe, the parietal lobe, and the occipital lobe is the most effective factor for visual decoding with 83.05% accuracy. This work provides a potential tool for decoding visual EEG signals with the help of low-dimensional manifold dynamics, especially contributing to the brain–computer interface (BCI) control, brain function research, and neural rehabilitation.

Shining a Light also Casts a Shadow: Neuroimaging Incidental Findings in Neuromarketing Research

Rapid growth in structural and functional brain research has led to increasing ethical discussion of what to do about incidental findings within the brains of healthy neuroimaging research participants that have potential health importance, but which are beyond the original aims of the study. This dilemma has been widely debated with respect to general neuroimaging research but has attracted little attention in the context of neuromarketing studies. In this paper, I argue that neuromarketing researchers owe participants the same ethical obligations as other neuroimaging researchers. The financial resources available to neuromarketing firms and the social value of neuromarketing studies should command greater attention to the elucidation and management of incidental findings. However, this needs to be balanced against finite resources available within most public health systems.

Neural Basis and Motor Imagery Intervention Methodology Based on Neuroimaging Studies in Children With Developmental Coordination Disorders: A Review.

Although the neural bases of the brain associated with movement disorders in children with developmental coordination disorder (DCD) are becoming clearer, the information is not sufficient because of the lack of extensive brain function research. Therefore, it is controversial about effective intervention methods focusing on brain function. One of the rehabilitation techniques for movement disorders involves intervention using motor imagery (MI). MI is often used for movement disorders, but most studies involve adults and healthy children, and the MI method for children with DCD has not been studied in detail. Therefore, a review was conducted to clarify the neuroscientific basis of the methodology of intervention using MI for children with DCD. The neuroimaging review included 20 magnetic resonance imaging studies, and the neurorehabilitation review included four MI intervention studies. In addition to previously reported neural bases, our results indicate decreased activity of the bilateral thalamus, decreased connectivity of the sensory-motor cortex and the left posterior middle temporal gyrus, bilateral posterior cingulate cortex, precuneus, cerebellum, and basal ganglia, loss of connectivity superiority in the abovementioned areas. Furthermore, reduction of gray matter volume in the right superior frontal gyrus and middle frontal gyrus, lower fractional anisotropy, and axial diffusivity in regions of white matter pathways were found in DCD. As a result of the review, children with DCD had less activation of the left brain, especially those with mirror neurons system (MNS) and sensory integration functions. On the contrary, the area important for the visual space processing of the right brain was activated. Regarding of characteristic of the MI methods was that children observed a video related to motor skills before the intervention. Also, they performed visual-motor tasks before MI training sessions. Adding action observation during MI activates the MNS, and performing visual-motor tasks activates the basal ganglia. These methods may improve the deactivated brain regions of children with DCD and may be useful as conditioning before starting training. Furthermore, we propose a process for sharing the contents of MI with the therapist in language and determining exercise strategies.

Aktuelnost biosocijalnih istraživanja u kriminologiji

New scientific and technological developments have provided a clearer insight into the functioning of the human body. Research into human genetic predispositions, genome mapping, and brain function research have all contributed to explaining human behavior. The results of studies in recent years have unequivocally shown that genetic and social factors of crime act individually but also in interaction. Biosocial criminology is considered to be a more recent paradigm in the explanation of criminal behavior, which underlines the mechanism of the cooperation of biological and social factors in the explanation of crime. The aim of the paper is to review the conceptualization and key aspects of biosocial criminology, key factors in the explanation of crime, and the most important biosocial interventions that are applied. Genetic, neuropsychological, neurophysiological and endocrinological factors are crucial in explaining criminal behavior within the biosocial approach. The results show that programs resulting from the biosocial paradigm are more effective in preventing criminal behavior than interventions implemented within traditional criminology and neglecting the influence of biological factors.

Analysis of neurite length of hippocampal neurons cultured into 3D artificial network patterned microfluidic chips

Purpose: The study aims to lay a foundational probe for the thorough application microfluidic chips in brain function research with microfluidic chips. Neuron slide culture is a common culture method in vitro, and the microfluidic chip with the artificial network pattern not only can realize neuron cells 3 D culture in vitro, but also limit the extension space of neurite outgrow.Materials and Methods: In order to analyze the differences of hippocampal cells neurite growth length between the 3 D chips and the common 2 D culture, the experiments utilized statistical analysis method analyzing the length of the hippocampus neuron neurite of 3 days, 5 days and 7 days, respectively, with the common glass slide 2 D culture method and the microfluidic chip 3 D culture in vitro.Results: The results showed that there was no significant difference in the neurite length after 3 days. However, there was a significant difference after 5 days and 7 days. It can be seen that the microfluidic chip with artificial network pattern has limitations to the growth of neurite after 5 days.Conclusions: We concluded that the growth state of hippocampal cells in the restricted 3 D space is different from that of conventional 2 D culture.It showed that the artificial network pattern design has limited the growth space of the dendrites but also affected its growth.

Chapter 1 - Human brain function and brain-computer interfaces

Human brain function research has evolved dramatically in the last decades. In this chapter the role of modern methods of recording brain activity in understanding human brain function is explained. Current knowledge of brain function relevant to brain-computer interface (BCI) research is detailed, with an emphasis on the motor system which provides an exceptional level of detail to decoding of intended or attempted movements in paralyzed beneficiaries of BCI technology and translation to computer-mediated actions. BCI technologies that stand to benefit the most of the detailed organization of the human cortex are, and for the foreseeable future are likely to be, reliant on intracranial electrodes. These evolving technologies are expected to enable severely paralyzed people to regain the faculty of movement and speech in the coming decades.

Early intervention and perspectives for children with autism spectrum disorder in Japan

Early detection and early diagnosis of autism spectrum disorder (ASD) are promoted throughout the world. Early detection and early intervention for children with ASD is known to have certain effects. Consistent with global trends, systems to provide children with appropriate environments and educational opportunities in Japan are improving; features of ASD are being detected by the age of approximately 2 years through health checkups for infants and children. However, the early screening of autistic infants and children in Japan, subsequent follow-up, and early intervention methods differ greatly among residential areas and local governments. In this article, we outline well-constructed models of systems that include aspects such as early detection, early diagnosis, early intervention, parent support, and longitudinal follow-up of ASD and are used in some areas in Japan. Furthermore, we note the current efforts of the Early Start Denver Model (ESDM), which the authors are working to apply in Japan. Early detection and diagnosis systems for children with ASD in Japan will continue to develop, and the validation of their effects through randomized controlled trials (RCTs) is desired in research on early intervention for children with ASD. Furthermore, the brain function research that the author of this article is working on may be one approach for objectively evaluating the effects of early intervention from both behavioral and physiological perspectives.

Modeling of stochastic brain function in artificial intelligence

Objectives -research of stochastic brain function in respect to creation of artificial intelligence. Material and methods. Mathematical modeling principles were used for simulation of brain functioning in a stochastic mode. Results. Two types of brain activity were considered: determinated type, usually modeled using the perceptron, and stochastic type. It is shown, that stochastic brain function modeling is the necessary condition for AI to become capable of creativity, generation of new knowledge. Mathematical modeling of a neural network of the cerebral cortex, consisting of the set of the cyclic neuronal circuits (memory units), was performed for the stochastic mode of brain functioning. Models of "two-dimensional" and "one-dimensional" brain were analyzed. The pattern of excitation in memory units was calculated in the "one-dimensional" brain model. Conclusion. Relying on the knowledge of the stochastic mode of brain function, a way of creation of AI can be offered. а-rhythm of a patient is a recommended focus of the therapist's attention in diagnostics and treatment of brain disorders. It was noted, that the alpha wave amplitude and frequency could indicate the cognitive, creative and intuitive abilities of a person.

Research Imaging of Brain Structure and Function After Concussion.

Even when concussions are associated with prolonged physical and cognitive sequelae, concussions are typically "invisible" on diagnostic brain imaging, indicating that the neuropathology associated with concussion lies under the detection threshold of routine imaging. However, data from brain structural and functional research imaging studies using diffusion tensor imaging, resting-state functional magnetic resonance imaging, and brain perfusion imaging indicate that these imaging sequences have a role in identifying concussion-related neuropathology. These advanced imaging techniques provide insights into concussion neuropathology and might be useful for differentiating concussed patients from healthy controls. In this review article, we provide an overview of research findings from brain structural and functional imaging studies of concussion, and discuss the accuracy of classification models developed via machine-learning algorithms for identifying individual patients with concussion based on imaging data.