Auditory cerebral activation patterns of Chinese English learners by fMRI

Objective:To identify the cerebral activation patterns associated with the processes that occur during viewing Chinese and English words in native Chinese English learners. Methods:12 right-handed Chinese English learners were divided into two groups equally,namely English majors and non-English majors,and took semantic judgement tasks of both English and Chinese words, for whom the fMRI images were collected.Results:To various degrees, all subjects demonstrated activation of associated cerebral regions in both hemispheres and the left hemisphere activation was more significant for most subjects. Except for classical regions involved in language processing,such as Wernicke areas and Broca areas,there were other activated cerebral regions, including cerebellum, limbic system and basal ganglia nucleus, etc. To sum up,there were apparent overlap for cerebral activation distribution and no specific processing areas for both tasks. The analysis of ROI(region of interest)suggested that subjects in specialized group were more dependent on right hemisphere to perform English words task. Conclusion:Language cognition is dominated by left hemisphere,which is also shared by the right hemisphere to various degrees and thus two hemispheres work by ways of both dissociation and coordination. It is possible that working strategy of the right hemisphere in English task is related to proficiency of the second language. A variety of distinctions are shared by each subject for language cognitive patterns.

Time Slice Analysis Method Based on OTCA Used in fMRI Weak Signal Function Extraction

The original temporal clustering analysis (OTCA) is an effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown, but its deficiency of sensitivity is exposed in processing brain activation signal which is relatively weak. The time slice analysis method based on OTCA is proposed considering the weakness of the functional magnetic resonance imaging (fMRI) signal of the rat model. By dividing the stimulation period into several time slices and analyzing each slice to detect the activated pixels respectively after the background removal, the sensitivity is significantly improved. The inhibitory response in the hypothalamus after glucose loading is detected successfully with this method in the experiment on rat. Combined with the OTCA method, the time slice analysis method based on OTCA is effective on detecting when, where and which type of response will happen after stimulation, even if the fMRI signal is weak.

An fMRI Study of Words Processing in Chinese Language

Using the blood oxygen levels dependent technology of magnetic resonance imaging (BLOD-fMRI), we aimed to explore the brain activation after visual stimulation by Chinese words. In the current study, 24 healthy volunteers (12 males, 12 females, right-handed, mean age 26 ± 2 years) were prospectively included. The event related design was used in the current fMRI study when participants silently read all words appearing in the middle of the screen. Images were processed with Statistical Parametric Mapping 8 (SPM8) software, by using a general linear model (GLM). Group activations were extracted from the 2nd level group analysis with a threshold of p < 0.001, and it was shown that the main activated areas by silent reading tasks were regions involved in brain semantic processing, including middle temporal gyrus, fusiform gyrus, supplementary motor area, inferior frontal gyrus, cingulate gyrus, superior parietal lobule and inferior parietal lobule. It was also learnt that superior parietal lobule and middle temporal gyrus are related with semantic understanding, lenticular nucleus are related with semantic processing. This means, in addition to the cerebral cortex, subcortical nuclei is also very important to the processing of words in Chinese language.

Estimation of the Hemodynamic Response during Motor Imagery Using Bayesian RBF Neural Network

Hemodynamic response during motor imagery （MI） is studied extensively by functional magnetic resonance imaging （fMRI） technologies. To further understand the human brain functions under MI, a more precise classification of the brain regions corresponding to each brain function is desired. In this study, a Bayesian trained radial basis function （RBF） neural network, which determines the weights and regularization parameters automatically by Bayesian learning, is applied to make a precise classification of the hemodynamic response to the tasks during the MI experiment. To illustrate the proposed method, data with MI task performance from 1 subject was used. The results demonstrate that this approach splits the hemodynamic response to different tasks successfully.

Integrating Various Neural Features Based on Mechanism of Intricate Balance and Ongoing Activity: Unified Neural Account Underlying and Correspondent to Mental Phenomena

In recent decades, brain science has been enriched from both empirical and computational approaches. Interesting emerging neural features include power-law distribution, chaotic behavior, self-organized criticality, variance approach, neuronal avalanches, difference-based and sparse coding, optimized information transfer, maximized dynamic range for information processing, and reproducibility of evoked spatio-temporal motifs in spontaneous activities, and so on. These intriguing findings can be largely categorized into two classes: complexity and regularity. This article would like to highlight that the above-mentioned properties although look diverse and unrelated, but actually may be rooted in a common foundation—excitatory and inhibitory balance (EIB) and ongoing activities (OA). To be clear, description and observation of neural features are phenomena or epiphenomena, while EIB-OA is the underlying mechanism. The EIB is maintained in a dynamic manner and may possess regional specificity, and importantly, EIB is organized along the boundary of phase transition which has been called criticality, bifurcation or edge of chaos. OA is composed of spontaneous organized activity, physiological noise, non-physiological noise and the interacting effect between OA and evoked activities. Based on EIB-OA, the brain may accommodate the property of chaos and regularity. We propose “virtual brain space” to bridge brain dynamics and mental space, and “code driving complexity hypothesis” to integrate regularity and complexity. The functional implication of oscillation and energy consumption of the brain are discussed.

A new method for fMRI data processing: Neighborhood independent component correlation algorithm and its preliminary application

Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is an area of active research and widespread interest. Therefore, the development of an ICA based fMRI data processing method is of obvious value both theoretically and in potential applications. In this paper, analyzed firstly is the drawback of the extant popular ICA-fMRI method where the adopted signal model assumes the independence of spatial distributions of the signals and noise. Then presented is a new fMRI signal model, which assumes the independence of temporal courses of signal and noise in a tiny spatial domain. Consequently we get a novel fMRI data processing method: Neighborhood independent component correlation algorithm. The effectiveness is elucidated through theoretical analysis and simulation tests, and finally a real fMRI data test is presented.

fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

One of the most significant challenges in the neuroscience community is to understand how the human brain works.Recent progress in neuroimaging techniques have validated that it is possible to decode a person′s thoughts,memories,and emotions via functional magnetic resonance imaging(i.e.,fMRI)since it can measure the neural activation of human brains with satisfied spatiotemporal resolutions.However,the unprecedented scale and complexity of the fMRI data have presented critical computational bottlenecks requiring new scientific analytic tools.Given the increasingly important role of machine learning in neuroscience,a great many machine learning algorithms are presented to analyze brain activities from the fMRI data.In this paper,we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects,i.e.,brain image functional alignment,brain activity pattern analysis,and visual stimuli reconstruction.In addition,online resources and open research problems on brain pattern analysis are also provided for the convenience of future research.

Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation

Functional connectivity has emerged as a promising approach to study the functional organisation of the brain and to define features for prediction of brain state. The most widely used method for inferring functional connectivity is Pearson＇s correlation, but it cannot differentiate direct and indirect effects. This disadvantage is often avoided by computing the partial correlation between two regions controlling all other regions, but this method suffers from Berkson＇s paradox. Some advanced methods, such as regularised inverse covariance, have been applied. However, these methods usually depend on some parameters. Here we propose use of minimum partial correlation as a parameter-free measure for the skeleton of functional connectivity in functional magnetic resonance imaging （flVIRI）. The minimum partial correlation between two regions is the minimum of absolute values of partial correlations by controlling all possible subsets of other regions. Theoretically, there is a direct effect between two regions if and only if their minimum partial correlation is non-zero under faithfulness and Gaussian assumptions. The elastic PC-algorithm is designed to efficiently approximate minimum partial correlation within a computational time budget. The simulation study shows that the proposed method outperforms others in most cases and its application is illustrated using a resting-state fMRI dataset from the human connectome project.

Data-driven multimodal fusion:approaches and applications in psychiatric research

In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion methods.These methods aim to integrate diverse neuroimaging per-spectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders.However,analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data.This is where data-driven multi-modal fusion techniques come into play.By combining information from multiple modalities in a synergistic manner,these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed.In this paper,we present an extensive overview of data-driven multimodal fusion approaches with or without prior information,with specific emphasis on canonical correlation analysis and independent component analysis.The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics,environment,cognition,and treatment outcomes across various brain disorders.After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications,we further discuss the emerging neuroimaging analyzing trends in big data,such as N-way multimodal fusion,deep learning approaches,and clinical translation.Overall,multimodal fusion emerges as an imperative approach providing valuable insights into the under-lying neural basis of mental disorders,which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

合谷穴与面口部特异性联系的脑网络功能研究

Objective:This study was designed to observe the effect of electroacupuncture at Hégǔ(合谷L14),or Hòuxī(后溪SI3) or Wàiguān(外关TE5) acupoints on the function of the brain regions of a healthy human,and to explore the neural information mechanism of a specific connection between LI4 and the facialoral area from the point of view of the brain function connection network.A further objective was to enrich knowledge of the specific connection between the body surface and the meridian route,and the specificity of acupoint effects.Methods:A total of 30 healthy volunteers were randomly assigned to LI4,SI3,and TE5 groups.The members of each group were stimulated with electroacupuncture,and their heads were scanned using fMRI.DPARSFA 2.4 and REST 1.8 software were used for data preprocessing and statistical analysis.The paired t-test was used within group and the double sample t-test was used for two-group comparisons.Results:In healthy people,left LI4 with electroacupuncture mainly caused a decrease of the functional connection of the right orofacial motor area of the brain,which remained decreased after removing the needle.When LI4 was compared with SI3 and TE5,LI4 caused a more significant decline in the functional connection of the right side of the brain during acupuncture.Conclusions:Acupuncture at LI4 has a significant effect on the function of the face-and mouth-related areas in the brain,and there is a continuous effect.It is suggested that LI4 and the face and mouth have a specific relationship in the brain,which is most obvious during acupuncture.

针刺治疗原发性痛经作用机制研究进展

Through searching the literature on the mechanism of acupuncture for primary dysmenorrhea in the last 15 years,this paper summarizes systematically the current study progress on the analgesic mechanism of acupuncture for primary dysmenorrhea,from the perspectives of animal experiment and human trials.In terms of research methods,the behavioral observation,evaluation of central and peripheral biochemical indexes,and molecular biological techniques were mainly used in the animal experiments;whilst the clinical symptoms and evaluation of peripheral serum biochemical indexes were focused on in human trials.As for the research results,the animal experiments showed that acupuncture may play an analgesic role by regulating endocrine,promoting the release of central and peripheral neurotransmitters,regulating immune function,and relieving uterine smooth muscle spasm.However,the human trials have found that acupuncture can produce analgesic effects by regulating serum prostaglandin and ovarian hormone levels,promoting the release of peripheral β-endorphin,improving the uterine artery flow status,and relieving uterine smooth muscle spasm.Although different research methods were used in animal and human studies,the similar results were obtained in acupuncture regulating endocrine levels,promoting release of peripheral neurotransmitters,and alleviating uterine smooth muscle spasms.In addition,at present stage,animal experiments are more than human trials in numbers,and there are relatively few studies on the central mechanism of acupuncture analgesia in human trials.Therefore,in view of this phenomenon,this paper proposes to use functional magnetic resonance imaging(fMRI) technology to explore the central mechanism of acupuncture analgesia in human body,so as to provide a new idea for further exploring the mechanism of acupuncture analgesia in treating primary dysmenorrhea.

Graph-based semi-supervised learning

The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear neighborhood propagation(LNP)which can automatically construct the optimal graph;2)a novel multilevel scheme to make our algorithm scalable for large data sets;3)a generalized point charge scheme for GBSSL;4)a multilabel GBSSL method by solving a Sylvester equation;5)an information fusion framework for GBSSL;and 6)an application of GBSSL on fMRI image segmentation.

Computational Decision Support System for ADHD Identification

Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adulthood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for effective and reliable mechanisms for the early identification of ADHD.This paper presents a decision support system for the ADHD identification process.The proposed system uses both functional magnetic resonance imaging(fMRI)data and eye movement data.The classification processes contain enhanced pipelines,and consist of pre-processing,feature extraction,and feature selection mechanisms.fMRI data are processed by extracting seed-based correlation features in default mode network(DMN)and eye movement data using aggregated features of fixations and saccades.For the classification using eye movement data,an ensemble model is obtained with 81%overall accuracy.For the fMRI classification,a convolutional neural network(CNN)is used with 82%accuracy for the ADHD identification.Both ensemble models are proved for overfitting avoidance.