Spontaneous pain and brain activity in neuropathic pain: Functional MRI and pharmacologic functional MRI studies

Functional brain imaging studies in chronic neuropathic pain patients have lagged far behind equivalent studies in acute pain. In the past few years, this trend has begun to shift. This article discusses the novel approach of studying brain activity for spontaneous pain and its modulation by pharmacologic manipulation. We argue that the approach provides a solid methodology for studying clinical (especially neuropathic) pain and patient populations, and moreover, that the latest results using this approach imply that distinct clinical chronic pain conditions seem to involve specific brain circuitry, which is also distinct from the brain activity commonly observed in acute pain.     

屏气在磁共振脑功能成像中的应用研究现状

fMRI可间接反应神经元的激活,其结果受脑血管生理及病理状态的影响。通过检测脑血管反应能力(CVR)可提高fMRI结果的准确性。屏气过程中血液内的二氧化碳分压升高,引起脑血管扩张,脑血流量增加,导致BOLD信号升高。屏气条件下fMRI检测CVR,可克服基础及临床研究中CVR的个体差异,提高结果的准确性。本文就屏气在磁共振脑功能成像中的应用研究现状进行综述。     

经典三叉神经痛患者脑局部自发活动的静息态功能MRI

目的 观察经典三叉神经痛（CTN）患者静息状态下脑局部自发活动的改变。方法 对27例CTN患者（CTN组）和27名健康对照者（对照组）行静息态脑功能MRI数据采集,采用局部一致性（ReHo）数据分析方法获得CTN组ReHo差异脑区,并对组间差异脑区ReHo值分别与患者视觉模拟评分（VAS）和病程行相关性分析。结果 与对照组比较（P<0.05,高斯随机场校正）,CTN患者双侧初级感觉运动皮层,右侧辅助运动区、颞下皮层和小脑,左侧丘脑、边缘叶/海马旁回和颞上/中皮层ReHo值增高;双侧前额皮层/眶额皮层和脑岛,右侧额内侧皮层和颞上皮层,左侧前扣带回、缘上回和小脑ReHo值减低。右额内侧皮层ReHo值与病程呈负相关（r=-0.45,P=0.03）;左侧初级感觉运动皮层ReHo值与VAS评分（r=0.46,P=0.02）呈正相关。结论 CTN患者存在疼痛相关功能脑区自发功能活动一致性的异常,有助于对CTN发生机制的理解。     

Partial correlation for functional brain interactivity investigation in functional MRI

Examination of functional interactions through effective connectivity requires the determination of three distinct levels of information: (1) the regions involved in the process and forming the spatial support of the network, (2) the presence or absence of interactions between each pair of regions, and (3) the directionality of the existing interactions. While many methods exist to select regions (Step 1), very little is available to complete Step 2. The two main methods developed so far, structural equation modeling (SEM) and dynamical causal modeling (DCM), usually require precise prior information to be used, while such information is sometimes lacking. Assuming that Step 1 was successfully completed, we here propose a data-driven method to deal with Step 2 and extract functional interactions from fMRI datasets through partial correlations. Partial correlation is more closely related to effective connectivity than marginal correlation and provides a convenient graphical representation for functional interactions. As an instance of brain interactivity investigation, we consider how simple hand movements are processed by the bihemispheric cortical motor network. In the proposed framework, Bayesian analysis makes it possible to estimate and test the partial statistical dependencies between regions without any prior model on the underlying functional interactions. We demonstrate the interest of this approach on real data.     

Reconstruction of central cortical surface from brain MRI images: method and application

Reconstruction of the central surface representation of the cerebral cortex is an important means to study the structure and function of the human brain. In this paper, we propose a novel method based on an elastic transform vector field to drive a deformable model for the reconstruction of the central cortical surface. Both simulated brain cortexes and real brain images are used to evaluate this approach. We applied the surface reconstruction method and a hybrid volumetric and surface registration algorithm to detect simulated brain atrophy. Experimental results show that the central cortical surface representation has better performance in detecting simulated atrophy than the traditionally used inner or outer cortical surface representations.     

TWave: high-order analysis of functional MRI

The traditional approach to functional image analysis models images as matrices of raw voxel intensity values. Although such a representation is widely utilized and heavily entrenched both within neuroimaging and in the wider data mining community, the strong interactions among space, time, and categorical modes such as subject and experimental task inherent in functional imaging yield a dataset with "high-order" structure, which matrix models are incapable of exploiting. Reasoning across all of these modes of data concurrently requires a high-order model capable of representing relationships between all modes of the data in tandem. We thus propose to model functional MRI data using tensors, which are high-order generalizations of matrices equivalent to multidimensional arrays or data cubes. However, several unique challenges exist in the high-order analysis of functional medical data: na?ve tensor models are incapable of exploiting spatiotemporal locality patterns, standard tensor analysis techniques exhibit poor efficiency, and mixtures of numeric and categorical modes of data are very often present in neuroimaging experiments. Formulating the problem of image clustering as a form of Latent Semantic Analysis and using the WaveCluster algorithm as a baseline, we propose a comprehensive hybrid tensor and wavelet framework for clustering, concept discovery, and compression of functional medical images which successfully addresses these challenges. Our approach reduced runtime and dataset size on a 9.3GB finger opposition motor task fMRI dataset by up to 98% while exhibiting improved spatiotemporal coherence relative to standard tensor, wavelet, and voxel-based approaches. Our clustering technique was capable of automatically differentiating between the frontal areas of the brain responsible for task-related habituation and the motor regions responsible for executing the motor task, in contrast to a widely used fMRI analysis program, SPM, which only detected the latter region. Furthermore, our approach discovered latent concepts suggestive of subject handedness nearly 100× faster than standard approaches. These results suggest that a high-order model is an integral component to accurate scalable functional neuroimaging.     

Multivariate brain network graph identification in functional MRI

Motivated by recent interest in identification of functional brain networks, we develop a new multivariate approach for functional brain network identification and name it as Multivariate Vector Regression-based Connectivity (MVRC). The proposed MVRC method regresses time series of all regions to those of other regions simultaneously and estimates pairwise association between two regions with consideration of influence of other regions and builds the adjacency matrix. Next, modularity method is applied on the adjacency matrix to detect communities or functional brain networks. We compare the proposed MVRC method with existing methods ranging from simple Pearson correlation to advanced Multivariate Adaptive Sparse Representation (ASR) methods. Experimental results on simulated and real fMRI dataset demonstrate that MVRC is able to extract functional brain networks that are consistent with the literature. Also, the proposed MVRC method is 650–750 times faster compared to the existing ASR method on 90 node network.     

主观耳鸣患者大脑局部神经功能改变的静息态功能磁共振研究

目的利用局部一致性(regional homogeneity,ReHo)和分数低频振幅(fractional amplitude of low-frequency fluctuations,fALFF)方法相结合探讨主观耳鸣患者大脑局部自发神经功能的变化。材料与方法选取25例符合纳入标准的主观耳鸣患者和25名与其相匹配的健康对照者,采集所有受试者临床、量表资料并行磁共振扫描,数据预处理后进行两组间ReHo和fALFF值比较,提取差异脑区ReHo、fALFF值与病程和耳鸣残疾量表(tinnitus handicap inventory,THI)评分进行相关分析。结果与健康对照组相比,患者组在右侧颞中回和楔叶ReHo值增高,在右侧额中回、左侧小脑前叶ReHo值降低,同时在右侧颞中回和楔叶fALFF值增高,差异有统计学意义(P0.05,GRF校正)。另外患者组右侧颞中回ReHo值与THI评分成正比(r=0.576,P=0.003)。结论通过ReHo和fALFF相结合,发现主观耳鸣患者在听觉和非听觉的部分脑区发生不同程度的局部脑功能活动改变,并主要累及默认模式网络及视听觉网络,初步证实了主观耳鸣大脑局部自发神经功能存在改变。     

Evolution and current challenges in the teaching of functional MRI and functional brain imaging

The report of any new and successful method for studying the world triggers the need to train people in the use of that method. In the case of functional magnetic resonance imaging and its use for examining human brain function in vivo, expertise is required in a greater collection of domains than usual. Development of fMRI training programs started shortly after the announcement of BOLD-based fMRI in humans. These programs had a variety of durations and primary content areas. All programs had to deal with the challenge of bringing interested researchers from a wide variety of areas-many of whom had little or no understanding of MR physics, and/or experimental psychology, and/or the nuances of data analysis and modeling-to a sufficiently detailed level of knowledge that both the funding agencies, and the existing proprietors of the technology (often radiologists or MR physicists at hospitals) would take the research proposals of new investigators seriously. Now that fMRI-based research is well established, there are new educational challenges. Some have to do with the growing list of technologies used to study human brain function in vivo. But perhaps more daunting is the challenge of training consumers of the reports and claims based on fMRI and other brain imaging modalities. As fMRI becomes influential in contexts beyond the research environment-from the clinic to the courtroom to the legislature-training consumers of fMRI-based claims will take on increasing importance, and represents its own unique challenges for education.     

静息态fMRI评价神经梅毒脑功能改变

目的 采用静息态fMRI平均低频振幅率(mfALFF)算法评价神经梅毒患者静息态脑功能改变。方法 分别对17例神经梅毒患者(神经梅毒组)和17名健康志愿者(正常对照组)行静息态fMRI,采用mfALFF算法获得并比较两组mfALFF差异的脑区。结果 静息状态下,神经梅毒组左中央后回、左海马、左缘上回及角回、右额上回内侧/右额中回、右楔前叶mfALFF值低于正常对照组(P均<0.01);神经梅毒组右苍白球/岛叶/丘脑、右海马尾部、右后扣带回、右颞下回、右楔叶mfALFF值高于正常对照组(P均<0.01)。结论 静息态fMRI可以检测神经梅毒患者脑功能变化。     

磁共振检查的运动、运动伪影与PROPELLER技术

MR临床检查中受检对象运动时常发生,运动类型种类繁多且关系复杂,运动造成的各种图像伪影直接影响影像资料的质量和医生阅片的效果,给影像学诊断和研究工作带来各种干扰。所以,正确区分MR检查的运动类型,认识伪影产生的基本原理有利于影像工作者正确识别MR图像中的各种伪影,提高临床和科研质量。本文对MR检查过程中运动伪影的分类、伪影的产生做一阐述,并介绍了一种新的伪影消除技术PROPELLER技术。     

弥漫性创伤性脑损伤功能磁共振成像的研究进展

随着人类社会工业化、现代化进程的快速发展,创伤性脑损伤(Traumatic brain injury,TBI)的发生率大大增加,在许多国家已经或将要成为人类死亡和致残的首要原因.     

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data

Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNN^AM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.     

刺激相关冲突与任务相关冲突在脑内的并行处理

目的探讨大脑对刺激相关和任务相关冲突的处理机制。方法受试者分别默数数值相同和不同的数字对出现的次数,并记录头皮事件相关电位。数字对分为4种情况没有冲突、刺激相关冲突、任务相关冲突、刺激相关和任务相关双重冲突。结果3种冲突的数字对在第2个数字后引起事件相关电位N270,没有冲突的数字对则无该负波。N270的起始潜伏期终止时间在3种冲突之间没有显著差异。结论大脑并行处理刺激相关和任务相关冲突。     

数据仓库在脑功能成像中的应用

本文从神经信息学的角度出发,分析了脑功能成像数据的特点,给出了用数据仓库建立脑功能成像数据模型的方案.探讨了该数据仓库的总体框架、数据抽取、维度和事件的建立方法,以及其在神经信息学研究中的应用.     

LOFA: software for individualized localization of functional MRI activity

Although PET, SPECT, and fMRI studies have led to significant advances in functional mapping of the human brain, precise localization and quantification of activity in individual brains require additional procedures. Difficulties to be addressed by a localization strategy are: resolution of individual anatomic differences, differentiation of functional activity in closely juxtaposed brain regions, and management of multiple intricately shaped 3D anatomic structures. In this paper, we describe a localization tool, LOFA, which addresses these problems by forming ROIs with a user-driven interface. Using LOFA, complex 3D anatomy can be defined through open or closed loops and anatomic landmarks. Resulting partitions can be overlaid on top of each other to form multiple regions of interest (ROIs), and functional activity in these ROIs can be extracted individually, one after the other. LOFA introduces important paradigmatic advances over the other ROI analysis methods. The toolbox is interactive, fully compatible with AFNI (MCW), and requires Pv-Wave (VNI Inc.) license to run.     

Discriminating imagined from perceived information engages brain areas implicated in schizophrenia

Some of the symptoms of schizophrenia may reflect a difficulty discriminating between information that was perceived from the outside world and information that was imagined. This study used fMRI to examine the brain regions associated with this reality monitoring ability in healthy volunteers, who recollected whether information had previously been perceived or imagined, or whether information had been presented on the left or right of a monitor screen. Recent studies have suggested that schizophrenia may be associated particularly with dysfunction in medial anterior prefrontal cortex, thalamus, and cerebellum. In our data, activation in all three of these regions of interest was significantly greater during recollection of whether stimuli had been perceived or imagined versus recollection of stimulus position. In addition, reduced prefrontal activation was associated with the same misattribution error that has been observed in schizophrenia. These results indicate a possible link between the brain areas implicated in schizophrenia and the regions supporting the ability to discriminate between perceived and imagined information.     

Combined structural and resting-state functional MRI analysis of sexual dimorphism in the young adult human brain: an MVPA approach

There has been growing interest recently in the use of multivariate pattern analysis (MVPA) to decode information from high-dimensional neuroimaging data. The present study employed a support vector machine-based MVPA approach to identify the complex patterns of sex differences in brain structure and resting-state function. We also aimed to assess the role of anatomy on functional sex differences during rest. One hundred and forty healthy young Chinese adults (70 men and 70 women) underwent structural and resting-state functional MRI scans. Gray matter density and regional homogeneity (ReHo) were used to map brain structure and resting-state function, respectively. After combining these two feature vectors into one union-vector, a pattern classifier was designed using principal component analysis and linear support vector machine to identify brain areas that had distinct characteristics between the groups. We found that: (1) male and female brains were different with a mean classification accuracy of 89%; (2) sex differences in gray matter density were widely distributed in the brain, notably in the occipital lobe and the cerebellum; (3) men primarily showed higher ReHo in their right hemispheres and women tended to show greater ReHo in their left hemispheres; (4) about 50% of brain areas with functional sex differences exhibited significant positive correlations between gray matter density and ReHo. Our results suggest that sex is an important factor that account for interindividual variability in the healthy brain.     

Mapping of functional brain activity in freely behaving rats during voluntary running using manganese-enhanced MRI: Implication for longitudinal studies

Magnetic resonance imaging (MRI) is widely used in basic and clinical research to map the structural and functional organization of the brain. An important need of MR research is for contrast agents that improve soft-tissue contrast, enable visualization of neuronal tracks, and enhance the capacity of MRI to provide functional information at different temporal scales. Unchelated manganese can be such an agent, and manganese-enhanced MRI (MEMRI) can potentially be an excellent technique for localization of brain activity (for review see Silva et al., 2004). Yet, the toxicity of manganese presents a major limitation for employing MEMRI in behavioral paradigms. We have tested systematically the voluntary wheel running behavior of rats after systemic application of MnCl₂ in a dose range of 16–80 mg/kg, which is commonly used in MEMRI studies. The results show a robust dose-dependent decrease in motor performance, which was accompanied by weight loss and decrease in food intake. The adverse effects lasted for up to 7 post-injection days. The lowest dose of MnCl₂ (16 mg/kg) produced minimal adverse effects, but was not sufficient for functional mapping. We have therefore evaluated an alternative method of manganese delivery via osmotic pumps, which provide a continuous and slow release of manganese. In contrast to a single systemic injection, the pump method did not produce any adverse locomotor effects, while achieving a cumulative concentration of manganese (80 mg/kg) sufficient for functional mapping. Thus, MEMRI with such an optimized manganese delivery that avoids toxic effects can be safely applied for longitudinal studies in behaving animals.     

Differential coding of hyperalgesia in the human brain: a functional MRI study

Neuropathic pain can be both ongoing or stimulus-induced. Stimulus-induced pain, also known as hyperalgesia, can be differentiated into primary and secondary hyperalgesia. The former results from sensitization of peripheral nociceptive structures, the latter involves sensitization processes within the central nervous system (CNS). Hypersensitivity towards heat stimuli, i.e. thermal hyperalgesia, is a key feature of primary hyperalgesia, whereas secondary hyperalgesia is characterized by hypersensitivity towards mechanical (e.g. pin-prick) stimulation. Using functional magnetic resonance imaging (fMRI), we investigated if brain activation patterns associated with primary and secondary hyperalgesia might differ. Thermal and pin-prick hyperalgesia were induced on the left forearm in 12 healthy subjects by topical capsaicin (2.5%, 30 min) application. Equal pain intensities of both hyperalgesia types were applied during fMRI experiments, based on previous quantitative sensory testing. Simultaneously, subjects had to rate the unpleasantness of stimulus-related pain. Pin-prick hyperalgesia (i.e. subtraction of brain activations during pin-prick stimulation before and after capsaicin exposure) led to activations of primary and secondary somatosensory cortices (S1 and S2), associative-somatosensory cortices, insula and superior and inferior frontal cortices (SFC, IFC). Brain areas activated during thermal hyperalgesia (i.e. subtraction of brain activations during thermal stimulation before and after capsaicin exposure) were S1 and S2, insula, associative-somatosensory cortices, cingulate cortex (GC), SFC, middle frontal cortex (MFC) and IFC. When compared to pin-prick hyperalgesia, thermal hyperalgesia led to an increased activation of bilateral anterior insular cortices, MFC, GC (Brodmann area 24' and 32') and contralateral SFC and IFC, despite equal pain intensities. Interestingly, stronger activations of GC, contralateral MFC and anterior insula significantly correlated to higher ratings of the stimulus-related unpleasantness. We conclude that thermal and mechanical hyperalgesia produce substantially different brain activation patterns. This is linked to different psychophysical properties.