多模态MRI技术和数值模拟技术用于脑血流灌注研究进展

脑血管疾病发病机制复杂,包括脑实质损伤、脑血流和脑脊液循环异常等。MRI脑血流灌注成像可评估脑结构和功能,结合数值模拟技术及流体间的差异,更准确地描述脑的病理变化。本文就多模态MRI技术结合数值模拟对脑血管疾病的脑血流灌注研究进展进行综述。     

现代脑功能磁共振诠释传统针刺机制

功能磁共振成像(functional MRI,fMRI)近年在针灸脑效应、针灸治病脑机制研究的应用逐渐增多,取得了新的进展。研究在健康受试者及患者的治疗前后进行,采用任务态、静息态、脑结构等多模态成像方法,观察针刺后的脑形态、功能激活及网络的变化,初步发现了人体经穴针刺的脑效应特征,特别是针灸疗效相关的脑功能及网络变化规律,丰富了针灸脑科学的内涵。     

基于多模态磁共振成像探讨针刺治疗卒中后失语症的研究进展

卒中后失语症（post-stroke aphasia, PSA）是指因脑血管或言语中枢神经功能紊乱引起的后天性语言功能障碍。针刺治疗PSA已取得较好的临床疗效,但其作用机制尚未明确。近年来,多模态MRI飞速发展,此项技术具有无辐射、多参数、多序列成像等优点,已被众多学者应用于PSA中枢效应机制研究。本研究基于功能MRI、弥散张量成像、结构性MRI、磁共振波谱成像等多模态成像技术,从脑功能、脑结构及脑代谢等角度探讨了针刺对PSA患者中枢效应机制,旨在为针刺治疗PSA临床个体化诊疗方案提供新方向,为研究针刺治疗PSA的潜在机制提供新思路。     

多模态影像学研究帕金森病伴左旋多巴诱导异动症进展

帕金森病(PD)是常见神经退行性疾病;左旋多巴诱导异动症(LID)是PD患者长期服用左旋多巴药物所致常见致残性运动并发症。MR结构成像、功能MRI及PET显像可观察LID患者脑结构、功能及代谢改变,为阐述脑相关发病机制提供多模态影像学依据,有助于临床早期诊断。本文就多模态影像学研究PD伴LID进展进行综述。     

基于多模态MRI在帕金森病冻结步态的研究进展

冻结步态（freezing of gait, FOG）是一种可见于帕金森病中晚期患者的阵发性步态障碍,极大地影响了患者的生活质量,但针对其发病机制以及病程中脑结构功能变化至今尚无明确的阐述。近年来以扩散张量成像（diffusion tensor imaging,DTI）、三维T1加权成像（3D T1-weighted imaging, 3D-T1WI）和功能MRI(functional MRI, fMRI)为主的多模态MRI技术广泛运用于神经性疾病发病机制的探究,为探索FOG的深层机制提供了新思路。近年来研究显示,视觉、运动和认知网络的改变与FOG的发生紧密相关。笔者旨在通过查阅分析近年国内外相关文献,对多模态MRI探索帕金森病FOG患者脑结构和功能变化的研究进行综述,讨论当前FOG研究仍值得商榷之处,为今后通过多模态MRI更全面解释其发病机制提供新的思路。     

2型糖尿病患者脑默认网络损伤的多模态功能磁共振研究进展

2型糖尿病(T2DM)是以胰岛素抵抗为主要病因以高血糖为主要特征的全身代谢性疾病。默认网络(DMN)相关脑区是T2DM在中枢神经系统中最易受累的部位之一。多模态功能磁共振(f MRI)对T2DM脑DMN损伤的早期发现及预后评估是近年来研究的热点。作者对T2DM脑损伤的机制及病理学改变、运用多模态f MRI对T2DM脑DMN损伤的研究现状进行综述。     

中医康复疗法干预膝骨关节炎的脑网络机制研究进展

膝骨关节炎(knee osteoarthritis, KOA)是一种多发于老年人的骨关节性疾病,传统运动、针灸、推拿等中医康复疗法是缓解KOA的临床方法。MRI显示KOA与脑皮质变薄、脑区功能连接异常等大脑结构和功能网络变化相关,而中医康复疗法通过恢复KOA患者脑功能、结构的异常变化来发挥作用。因此,本综述基于MRI技术在以下方面进行回顾总结:(1)KOA疼痛会导致皮质变薄、灰质体积、密度缩小等大脑结构网络异常改变;(2)KOA会出现特定脑区异常激活、不同脑区功能连接异常改变等功能网络变化;(3)规范的运动训练可以调节大脑网络;(4)针灸、推拿可以改善多个脑区的功能结构。以期通过本综述为促进中医康复与影像学技术结合,及为通过中医康复缓解KOA症状、减轻患者痛苦提供新思路。     

视神经脊髓炎患者默认网络及额顶网络功能连接的研究

目的采用独立成分分析(independent component analysis,ICA)方法分析静息态功能磁共振成像(resting-state functional magnetic resonance imaging,rsf MRI)数据,观察视神经脊髓炎(neuromyelitis optica,NMO)患者大脑默认网络(default mode network,DMN)及额顶网络(frontoparietal network,FPN)功能连接的异常以及与临床评分的相关性。材料与方法对我院20例NMO患者(NMO组)及20名健康对照者(正常对照组)行静息态f MRI扫描,所得数据利用DPARSFA软件预处理,然后利用GIFT软件行ICA分析,并用SPM8比较两组默认网络及额顶网络功能连接的差异,同时分析有差异脑区的时间序列信号与临床扩展残疾状态量表评分及病程的相关性。结果与对照组比较,NMO组DMN功能连接增强的脑区包括双侧舌回,延伸到右侧顶上小叶及左侧辅助运动区;功能连接减弱的脑区包括右侧额中回及右侧枕中回;NMO组FPN功能连接减弱的脑区为双侧楔叶,无功能连接增强的脑区。右侧舌回与病程呈正相关(r=0.682,P0.05)。结论 NMO患者静息态DMN、FPN均存在功能连接异常,提示患者的脊髓及视神经病变不仅引起患者相应的临床症状,局部结构损伤所致的功能改变也不仅仅局限于病变对应的区域,脑功能网络是一个复杂的互相关联的网络,存在损伤与代偿的复杂过程。     

原发全面性癫痫丘脑皮层网络的神经影像学研究进展

原发全面性癫痫患者脑内无器质性病变,常规影像学检查不能发现异常。目前普遍认为丘脑皮层网络在原发全面性癫痫的发病中起着重要作用。多模态神经影像学可从结构、功能、血流及代谢等方面观察大脑异常,为研究原发全面性癫痫的发病机制提供方法。本文对原发全面性癫痫的丘脑皮层网络的神经影像学研究进展进行综述。     

多变量模式分析观察慢性颈肩痛患者静息态下脑功能连接变化

目的 基于多变量模式分析(MVPA)观察慢性颈肩痛(CNSP)患者静息态下全脑功能连接改变。方法 对27例CNSP患者(CNSP组)及15名健康受试者(对照组)采集头部静息态功能MRI(rs-fMRI),对CNSP组行视觉模拟量表(VAS)评分。以偏相关法基于rs-fMRI构建脑网络，以MVPA法对CNSP及健康受试者进行分类，定位组间存在差异的功能连接，分析CNSP组上述功能连接强度与VAS评分的相关性。结果 以MVPA法区分CNSP进行分类的准确率为90.48%。组间脑功能连接强度存在差异脑区涉及默认网络、额顶网络、边缘网络及感觉运动网络等。CNSP患者右侧眶部额下回-左侧缘上回功能连接强度与VAS评分呈负相关(r=-0.496,P=0.009),左侧眶部额中回-左侧角回、左侧枕中回-左侧枕上回功能连接强度与VAS均呈正相关(r=0.398、0.461,P=0.039、0.015)。结论 CNSP患者脑网络内与疼痛感受及情绪异常相关的眶部额下回、眶部额中回、角回、枕中回及枕上回等多个脑区存在功能连接异常。     

Does function follow form?: Methods to fuse structural and functional brain images show decreased linkage in schizophrenia

When both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) data are collected they are typically analyzed separately and the joint information is not examined. Techniques that examine joint information can help to find hidden traits in complex disorders such as schizophrenia. The brain is vastly interconnected, and local brain morphology may influence functional activity at distant regions. In this paper we introduce three methods to identify inter-correlations among sMRI and fMRI voxels within the whole brain. We apply these methods to examine sMRI gray matter data and fMRI data derived from an auditory sensorimotor task from a large study of schizophrenia. In Method 1 the sMRI–fMRI cross-correlation matrix is reduced to a histogram and results show that healthy controls (HC) have stronger correlations than do patients with schizophrenia (SZ). In Method 2 the spatial information of sMRI–fMRI correlations is retained. Structural regions in the cerebellum and frontal regions show more positive and more negative correlations, respectively, with functional regions in HC than in SZ. In Method 3 significant sMRI–fMRI inter-regional links are detected, with regions in the cerebellum showing more significant positive correlations with functional regions in HC relative to SZ. Results from all three methods indicate that the linkage between gray matter and functional activation is stronger in HC than SZ. The methods introduced can be easily extended to comprehensively correlate large data sets.     

Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment

Multimodal fusion of different types of neural image data provides an irreplaceable opportunity to take advantages of complementary cross-modal information that may only partially be contained in single modality. To jointly analyze multimodal data, deep neural networks can be especially useful because many studies have suggested that deep learning strategy is very efficient to reveal complex and non-linear relations buried in the data. However, most deep models, e.g., convolutional neural network and its numerous extensions, can only operate on regular Euclidean data like voxels in 3D MRI. The interrelated and hidden structures that beyond the grid neighbors, such as brain connectivity, may be overlooked. Moreover, how to effectively incorporate neuroscience knowledge into multimodal data fusion with a single deep framework is understudied. In this work, we developed a graph-based deep neural network to simultaneously model brain structure and function in Mild Cognitive Impairment (MCI): the topology of the graph is initialized using structural network (from diffusion MRI) and iteratively updated by incorporating functional information (from functional MRI) to maximize the capability of differentiating MCI patients from elderly normal controls. This resulted in a new connectome by exploring “deep relations” between brain structure and function in MCI patients and we named it as Deep Brain Connectome. Though deep brain connectome is learned individually, it shows consistent patterns of alteration comparing to structural network at group level. With deep brain connectome, our developed deep model can achieve 92.7% classification accuracy on ADNI dataset.     

Weight-conserving characterization of complex functional brain networks

Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.     

Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5-10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the architecture of neural circuitry and developing imaging biomarkers of poor neurodevelopment outcome in infants with prenatal diseases.     

基于功能MRI机器学习用于诊断和治疗精神分裂症研究进展

精神分裂症(SZ)是一种严重精神疾病,采用传统方法进行诊断易漏、误诊。利用机器学习(ML)算法可从功能MRI(fMRI)数据中提取SZ相关特征,并进行诊断及疗效预测。本文就基于fMRI的ML用于诊断和治疗SZ的研究进展进行综述。     

Characterization of the anterior cingulate's role in the at-risk mental state using graph theory

The onset of positive symptoms in schizophrenia is often preceded by a prodromal phase characterized by neurocognitive abnormalities as well as changes in brain structure and function. Increasing efforts have been made to identify individuals at elevated risk of developing schizophrenia, as early intervention may help prevent progression towards psychosis. The present study uses functional MRI and graph theoretical analysis to characterize the organization of a functional brain network in at-risk mental state patients with varying symptoms assessed with the PANSS and healthy volunteers during performance of a verbal fluency task known to recruit frontal lobe networks and to be impaired in psychosis. We first examined between-groups differences in total network connectivity and global network compactness/efficiency. We then addressed the role of specific brain regions in the network organization by calculating the node-specific "betweeness centrality", "degree centrality" and "local average path length" metrics; different ways of assessing a region's importance in a network. We focused our analysis on the anterior cingulate cortex (ACC); a region known to support executive function that is structurally and functionally impaired in at-risk mental state patients. Although global network connectivity and efficiency were maintained in at-risk patients relative to the controls, we report a significant decrease in the contribution of the ACC to task-relevant network organization in at risk subjects with elevated symptoms (PANSS ≥ 45) relative to both the controls and the less symptomatic at-risk subjects, as reflected by a reduction in the topological centrality of the ACC. These findings provide evidence of network abnormalities and anterior cingulate cortex dysfunction in people with prodromal signs of schizophrenia.     

功能MRI在肝性脑病诊断和研究中的进展

肝性脑病是由急、慢性肝功能障碍或门—体分流引起的神经精神综合征,可导致不同程度精神异常,影响患者生活质量。近年来,MR功能成像技术飞速发展,可无创显示大脑结构及代谢功能变化,为早期发现、诊断肝性脑病及治疗监测提供依据。本文对功能MRI在肝性脑病诊断和研究中的进展进行综述。     

Schizophrenia, neuroimaging and connectomics

Schizophrenia is frequently characterized as a disorder of brain connectivity. Neuroimaging has played a central role in supporting this view, with nearly two decades of research providing abundant evidence of structural and functional connectivity abnormalities in the disorder. In recent years, our understanding of how schizophrenia affects brain networks has been greatly advanced by attempts to map the complete set of inter-regional interactions comprising the brain's intricate web of connectivity; i.e., the human connectome. Imaging connectomics refers to the use of neuroimaging techniques to generate these maps which, combined with the application of graph theoretic methods, has enabled relatively comprehensive mapping of brain network connectivity and topology in unprecedented detail. Here, we review the application of these techniques to the study of schizophrenia, focusing principally on magnetic resonance imaging (MRI) research, while drawing attention to key methodological issues in the field. The published findings suggest that schizophrenia is associated with a widespread and possibly context-independent functional connectivity deficit, upon which are superimposed more circumscribed, context-dependent alterations associated with transient states of hyper- and/or hypo-connectivity. In some cases, these changes in inter-regional functional coupling dynamics can be related to measures of intra-regional dysfunction. Topological disturbances of functional brain networks in schizophrenia point to reduced local network connectivity and modular structure, as well as increased global integration and network robustness. Some, but not all, of these functional abnormalities appear to have an anatomical basis, though the relationship between the two is complex. By comprehensively mapping connectomic disturbances in patients with schizophrenia across the entire brain, this work has provided important insights into the highly distributed character of neural abnormalities in the disorder, and the potential functional consequences that these disturbances entail.     

Discriminant analysis of functional connectivity patterns on Grassmann manifold

The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders. Rather than analyzing each network encoded by a spatial independent component separately, we propose a novel algorithm for discriminant analysis of functional brain networks jointly at an individual level. The functional brain networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based Riemannian distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional brain networks that are informative for schizophrenia diagnosis.