Functional neuroimaging of inner fields-of-view with 2D-selective RF excitations

Echo-planar imaging is widely used in functional neuroimaging but suffers from its pronounced sensitivity to field inhomogeneities that cause geometric distortions and image blurring which both limit the effective in-plane resolution achievable. In this work, it is shown how inner-field-of-view techniques based on 2D-selective RF excitations (2DRF) can be applied to reduce the field-of-view in the phase-encoding direction without aliasing and increase the in-plane resolution accordingly. Free-induction-decay (FID) EPI and echo-train-shifted (T2*-weighted) and standard (T2-weighted) spin-echo (SE) EPI with in-plane resolutions of up to 0.5×1.0 mm² (slice thickness 5 mm) were acquired at 3 T. Unwanted signal contributions of 2DRF side excitations were shifted out of the object (FID-EPI) or of the refocusing plane by tilting the excitation plane (SE-EPI). Brain activation in healthy volunteers was investigated with checkerboard and finger-tapping block-design paradigms. Brain activation could be detected with all sequences and contrasts, most reliably with FID-EPI due to its higher signal amplitude and the longer 2DRF excitation that are more sensitive to magnetic field inhomogeneities. In conclusion, inner-FOV EPI based on 2DRF excitations could help to improve the spatial resolution of fMRI of focal target regions, e.g. for applications in the spinal cord.     

PET Imaging of the Neuropeptide Y System: A Systematic Review

Neuropeptide Y (NPY) is a vastly studied biological peptide with numerous physiological functions that activate the NPY receptor family (Y₁, Y₂, Y₄ and Y₅). Moreover, these receptors are correlated with the pathophysiology of several diseases such as feeding disorders, anxiety, metabolic diseases, neurodegenerative diseases, some types of cancers and others. In order to deepen the knowledge of NPY receptors’ functions and molecular mechanisms, neuroimaging techniques such as positron emission tomography (PET) have been used. The development of new radiotracers for the different NPY receptors and their subsequent PET studies have led to significant insights into molecular mechanisms involving NPY receptors. This article provides a systematic review of the imaging biomarkers that have been developed as PET tracers in order to study the NPY receptor family.     

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Alzheimer’s disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey–Osterrieth complex figure and Raven’s progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.     

A new nonparametric approach for multiplicity control:Optimal Subset procedures

Summary  A new approach for multiplicity control (Optimal Subset) is presented. This is based on the selection of the best subset of partial (univariate) hypotheses producing the minimal p-value. In this work, we show how to perform this new procedure in the permutation framework, choosing suitable combining functions and permutation strategies. The optimal subset approach can be very useful in exploratory studies because it performs a weak control for multiplicity which can be a valid alternative to the False Discovery Rate (FDR). A comparative simulation study and an application to neuroimaging real data shows that it is particularly useful in presence of a high number of hypotheses. We also show how stepwise regression may be a special case of Optimal Subset procedures and how to adjust the p-value of the selected model taking into account for the multiplicity arising from the possible different models selected by a stepwise regression.     

脑网络组学构建分析及应用研究

大脑是自然界最为复杂的系统之一。脑网络作为复杂网络理论在神经科学中的重要应用,极大程度上表现了不同尺度的脑结构或功能连接模型,提供了解释人脑这一复杂系统在结构组织及信息加工模式等问题的重要工具。同时,脑网络在脑疾病的临床应用研究中,也已证明很多脑疾病,在网络层面中均体现了不同程度的拓扑结构差异。这些成果为在系统水平上揭示脑疾病的病理机制提供了新的思路。笔者将以脑网络研究为重点,介绍脑网络的构建方法,包括不同影像类型下节点及边的定义方法;关联矩阵的阈值选择及稀疏度的划分方法;网络度量指标的计算,包括度、小世界属性、模块化等;脑网络的比较方法及其在抑郁症的临床应用及未来可能的研究方向。     

Recent advances in the data analysis method of functional magnetic resonance imaging and its applications in neuroimaging

Functional magnetic resonance imaging (fMRI) has opened a new area to explore the human brain. The fMRI can reveal the deep insights of spatial and temporal changes underlying a broad range of brain function, such as motor, vision, memory and emotion, all of which are helpful in the clinical investigation. In this paper, we introduce some recent-developed algorithms for fMRI signal detection such as model-driven method (general linear model, deconvolution model, non-linear model, etc.) and data-driven method (principle component analysis, independent component analysis, self-organization mapping, clustered constrained non-negative matrix factorization, etc.). We also propose several important applications of neuroimaging and point out their shortcomings and future perspectives.     

探索思维的力量：脑机接口研究现状与展望

 脑机接口通过解码人类思维活动过程中的脑神经活动信息,构建人脑与外部世界的直接信息传输通路。近20年来,脑机接口领域迎来了快速发展,取得了一系列重要研究与应用成果。本文从信息交流与控制、功能康复与增强、状态识别与监测3个主要发展方向综述了脑机接口的研究现状,并对脑机接口的发展所面临的机遇和挑战进行了展望。     

杏仁核参与情绪记忆的脑功能成像研究述评

脑功能成像技术近年来已应用于情绪记忆的研究,相关的研究结果扩展了依据动物实验所得到的有关结论.作者主要对近年来情绪记忆的编码和提取阶段的脑成像研究进行了综述.研究表明,杏仁核在情绪记忆中的作用不仅局限在巩固阶段,它的作用在编码过程的初期已显示出来,并参与记忆的提取过程.脑区相关和联结模型强调了杏仁核与内侧颞叶等之间的交互作用.已有的研究还表明,刺激类型是影响情绪记忆的重要因素之一.杏仁核还参与对情绪的刺激类型、情绪记忆的生动感和主观性的加工.在今后的研究中将多种神经科学的技术相结合会更深入地揭示情绪记忆的脑机制.     

Neuroeconomics: Opening the “black box” behind the economic behavior

This paper introduces an emerging interdisciplinary research field,namely neuroeconomics,which uses the neuroscientific methods to investigate the neural systems supporting economically relevant behaviors. Traditional economic research is restricted to the level of describing decision behaviors,leaving the cognitive mechanisms behind them unknown. It also fails to predict many decision behav-iors in real life. The combination of neuroscience and economics makes it possible to uncover the underlying mental and neural processes of economic decision making. This paper reviews the findings from the neuroeconomic literature on encoding of utility in the brain,showing that neuroeconomic re-search can test the validity of economic concepts and theories and provide new explanations to eco-nomic phenomenon. It discusses the important role the emotion plays in economic decision making and the associated neural evidence,suggesting the possibility of understanding the impact of emotion upon decision making by measuring the neural activity of emotion-related brain regions. This paper also summarizes neuroscientific studies on cooperation and trust in monetary games,pointing out that the trend of neuroeconomic research is to model the real life decision making in the laboratory with solid ecological validity. Neuroeconomics provides not only neuroscientific evidence for economic theories,such as the prospect theory,the regret theory and the game theory,but also foundations for more comprehensive and powerful economic models.     

Information theoretic approaches to functional neuroimaging

Information theory is a probabilistic framework that allows the quantification of statistical non-independence between signals of interest. In contrast to other methods used for this purpose, it is model free, i.e., it makes no assumption about the functional form of the statistical dependence or the underlying probability distributions. It thus has the potential to unveil important signal characteristics overlooked by classical data analysis techniques. In this review, we discuss how information theoretic concepts have been applied to the analysis of functional brain imaging data such as functional magnetic resonance imaging and magneto/electroencephalography. We review studies from a number of imaging domains, including the investigation of the brain's functional specialization and integration, neurovascular coupling and multimodal imaging. We demonstrate how information theoretical concepts can be used to answer neurobiological questions and discuss their limitations as well as possible future developments of the framework to advance our understanding of brain function.