Brain Regions Responsible for Tinnitus Distress and Loudness: A Resting-State fMRI Study

Subjective tinnitus is characterized by the perception of phantom sound without an external auditory stimulus. We hypothesized that abnormal functionally connected regions in the central nervous system might underlie the pathophysiology of chronic subjective tinnitus. Statistical significance of functional connectivity (FC) strength is affected by the regional autocorrelation coefficient (AC). In this study, we used resting-state functional MRI (fMRI) and measured regional mean FC strength (mean cross-correlation coefficient between a region and all other regions without taking into account the effect of AC (rGC) and with taking into account the effect of AC (rGCa) to elucidate brain regions related to tinnitus symptoms such as distress, depression and loudness. Consistent with previous studies, tinnitus loudness was not related to tinnitus-related distress and depressive state. Although both rGC and rGCa revealed similar brain regions where the values showed a statistically significant relationship with tinnitus-related symptoms, the regions for rGCa were more localized and more clearly delineated the regions related specifically to each symptom. The rGCa values in the bilateral rectus gyri were positively correlated and those in the bilateral anterior and middle cingulate gyri were negatively correlated with distress and depressive state. The rGCa values in the bilateral thalamus, the bilateral hippocampus, and the left caudate were positively correlated and those in the left medial superior frontal gyrus and the left posterior cingulate gyrus were negatively correlated with tinnitus loudness. These results suggest that distinct brain regions are responsible for tinnitus symptoms. The regions for distress and depressive state are known to be related to depression, while the regions for tinnitus loudness are known to be related to the default mode network and integration of multi-sensory information.     

Biological Motion Coding in the Brain: Analysis of Visually Driven EEG Functional Networks

Herein, we address the time evolution of brain functional networks computed from electroencephalographic activity driven by visual stimuli. We describe how these functional network signatures change in fast scale when confronted with point-light display stimuli depicting biological motion (BM) as opposed to scrambled motion (SM). Whereas global network measures (average path length, average clustering coefficient, and average betweenness) computed as a function of time did not discriminate between BM and SM, local node properties did. Comparing the network local measures of the BM condition with those of the SM condition, we found higher degree and betweenness values in the left frontal (F7) electrode, as well as a higher clustering coefficient in the right occipital (O2) electrode, for the SM condition. Conversely, for the BM condition, we found higher degree values in central parietal (Pz) electrode and a higher clustering coefficient in the left parietal (P3) electrode. These results are discussed in the context of the brain networks involved in encoding BM versus SM.     

An Adaptive Complex Network Model for Brain Functional Networks

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.     

Visual Feedback Dominates the Sense of Agency for Brain-Machine Actions

Recent advances in neuroscience and engineering have led to the development of technologies that permit the control of external devices through real-time decoding of brain activity (brain-machine interfaces; BMI). Though the feeling of controlling bodily movements (sense of agency; SOA) has been well studied and a number of well-defined sensorimotor and cognitive mechanisms have been put forth, very little is known about the SOA for BMI-actions. Using an on-line BMI, and verifying that our subjects achieved a reasonable level of control, we sought to describe the SOA for BMI-mediated actions. Our results demonstrate that discrepancies between decoded neural activity and its resultant real-time sensory feedback are associated with a decrease in the SOA, similar to SOA mechanisms proposed for bodily actions. However, if the feedback discrepancy serves to correct a poorly controlled BMI-action, then the SOA can be high and can increase with increasing discrepancy, demonstrating the dominance of visual feedback on the SOA. Taken together, our results suggest that bodily and BMI-actions rely on common mechanisms of sensorimotor integration for agency judgments, but that visual feedback dominates the SOA in the absence of overt bodily movements or proprioceptive feedback, however erroneous the visual feedback may be.     

编者按: 脑成像与脑网络

揭示脑的奥秘是人类面临的最大挑战之一。神经元是构成神经系统结构与功能的基本单位。神经元与神经元之间通过突触实现信息交互,并构成神经环路或神经网络。神经环路有局部的,也有跨脑区或长程的,甚至全脑尺度的。神经环路则是脑实现神经信息处理的基本单元。若干神经环路构成脑网络。脑网络研究已经成为脑功能与脑疾病研究领域的热点。 在国家自然科学基金委员会和科技部“973计划”等项目的支持下,我国科学家在这一领域已经开展了卓有成效的工作。2011年第393次香山科学会议“脑网络组及其临床应用的前沿科学问题”曾对此进行过比较深入的研讨。为促进对该领域现状及发展的了解,本期汇集了2篇述评和2篇研究论文,作为脑成像与脑网络专题发表,以飨读者。 利用9.4T功能磁共振成像(fMRI)获得轻度麻醉状态下大鼠静息状态及刺激激活的数据,通过互相关分析构建节点之间的相关系数矩阵并计算相应的网络参数,赖永秀等人报道了大鼠感觉运动系统静息态脑网络的研究成果,发现感觉运动系统在静息态时的脑网络具有小世界属性。 扩散磁共振成像(dMRI)的出现为大脑结构与功能研究提供了全新的检测手段,雷皓等报道了小动物高分辨扩散磁共振成像数据分析方法,为小动物脑dMRI研究提供了统一图像模板与完善的计算方法,对于检测神经纤维微观结构的变化,以及临床诊断,将具有极其重要的意义。 神经环路功能变化的实时在体监测是研究脑网络不可或缺的手段,曾绍群等评述了基于声光偏转器的快速无惯性随机扫描双光子显微成像技术的研究进展及发展趋势,指出该技术的进一步发展将为神经活动观测提供一种全新的方法,从而极大地推动脑科学研究的发展。 针对哺乳动物全脑的神经元网络成像,龚辉等从空间分辨率、探测范围、数据配准和成像速度等方面评述了光学显微水平全脑成像方法的研究进展,并讨论所面临的挑战。他们指出,要在全脑尺度获取突起水平分辨率的结构与功能数据,光学成像方法最为成熟。华中科技大学研制的MOST系统,率先获得了一系列高分辨率的完整大脑解剖数据集,该成果将在神经元网络的构建和脑功能与疾病研究中发挥重要作用。 我们期待更多、更好的有关脑成像与脑网络的论文发表,以更广泛和深入地促进我国脑科学研究领域的学术交流。     

Oxidative Inactivation of Brain Alkaline Phosphatase Responsible for Hydrolysis of Phosphocholine

Abstract : Alkaline phosphatase, one of the enzymes responsible for the conversion of phosphocholine into choline, was purified from bovine brain membrane, where the phosphatase is bound as glycosylphosphatidylinositollinked protein, and subjected to oxidative inactivation. The phosphatase activity, based on the hydrolysis of p-nitrophenyl phosphate and phosphocholine, decreased slightly after the exposure to H₂O₂. Inclusion of Cu²⁺ in the incubation with 1 mM H₂O₂ led to a rapid decrease of activity in a time- and concentration-dependent manner. In comparison, the H₂O₂/Cu²⁺ system was much more effective than the H₂O₂/Fe²⁺ system in inactivating brain phosphatase. In a further study, it was observed that the hydroxy radical scavengers mannitol, ethanol, or benzoate failed to prevent against H₂O₂/Cu²⁺-induced inactivation of the phosphatase, excluding the involvement of extraneous hydroxy radicals in metalcatalyzed oxidation. In addition, it was found that both substrates, p-nitrophenyl phosphate and phosphocholine, and an inhibitor, phosphate ion, at their saturating concentrations exhibited a remarkable, although incomplete, protection against the inactivating action of H₂O₂/Cu²⁺. A similar protection was also expressed by divalent metal ions such as Mg²⁺ or Mn²⁺. Separately, it was found that H₂O₂/Fe²⁺-induced inactivation was prevented by p-nitrophenyl phosphate or Mg²⁺ but not phosphate ions. Thus, it is implied that phosphocholine-hydrolyzing alkaline phosphatase in brain membrane might be one of enzymes susceptible to metal-catalyzed oxidation.     

Brain oscillations: phase-locked EEG alpha controls perception

New research suggests that auditory stimuli can reset human oscillatory activity in visual cortex. This change in rhythmical brain activity leads to modulation of visual perception.     

Landmarking the Brain for Geometric Morphometric Analysis: An Error Study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm–5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm–3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes.     

An Electrochemical Study of the Factors Responsible for Modulating the Reduction Potential of Putidaredoxin

 The gene coding for putidaredoxin has been synthesized using a combination of chemical and enzymatic methods and subsequently expressed in Escherichia coli. The recombinant protein characterized by electronic spectroscopy, mass spectrometry, and electrochemistry was found to be identical to putidaredoxin obtained from Pseudomonas putida. Polylysine was found to promote the fast and reversible electrochemistry of putidaredoxin at negatively charged electrodes such as indium-doped tin oxide or gold surfaces modified with mercaptoalkanoate groups. The value of the heterogeneous electron transfer rate constant obtained from solutions containing a mixture of putidaredoxin and polylysine (k _s =1.3×10^–3 cm/s) is one order of magnitude larger than the values reported previously at gold electrodes modified with mercaptoethylamine or at antimony-doped tin oxide semiconductor electrodes. It was observed that when the reduction potential of putidaredoxin is measured by cyclic voltammetry, the resultant value is consistently more positive (64 mV) than the reduction potential measured with potentiometric titrations. A comparison between the electrochemical responses of putidaredoxin and spinach ferredoxin, combined with the examination of their corresponding three-dimensional structures, indicates that the positive shift in the reduction potential of putidaredoxin originates from the formation of a transient complex between putidaredoxin and polylysine at the electrode surface. The formation of this transient complex modulates the reduction potential of putidaredoxin by lowering the value of the dielectric constant around its iron-sulfur cluster microenvironment, specifically by neutralizing negative charges surrounding the active site and by excluding water from the solvent exposed iron sulfur cluster. The observed positive shift in E°′, which is induced by complexation with polylysine at the electrode-surface, suggests that similar factors are likely to contribute to the anodic shift in the E°′ of cytochrome P450_cam-bound putidaredoxin (+44 mV) with respect to the E°′ measured for free putidaredoxin. Received: 14 June 1999 / Accepted: 6 August 1999     

40例慢性脑型血吸虫病的脑电图分析

目的:了解脑型血吸虫病脑电图(EEG)的脑电活动状况,为临床诊断与治疗提供参考。方法:收集1997~2004年临床诊断为脑型血吸虫病的40例EEG资料,主要分析异常EEG的脑电活动状况与异常程度、临床分型及预后的关系。结果:31例出现不同程度的EEG异常改变,异常率为77.5%,其中癫痫性为70%;脑瘤型为100%。绝大部分EEG检查是患者经治疗后作的。治疗前后均作了EEG检查的仅9例。治疗前,9例均有不同程度异常,治疗后7例有不同程度改善,2例恢复正常。结论:EEG对脑型血吸虫病的诊断及预后的评价有参考价值。     

Study of the Brain Organization of Creativity: III. Brain Activation Assessed by the Local Cerebral Blood Flow and EEG

In this article, a polymethodological approach was applied to the analysis of the brain organization of creative thinking. The electroencephalographic studies and the investigation of the local cerebral blood flow by means of positron-emission tomography in the same testing conditions substantially validate and supplement each other. The physiological indices of subjects were recorded during the composition of stories with the given words belonging to the same or different semantic fields. The reconstruction of correct grammatical forms in a presented text and memorizing of a set of words were used as control tasks. The fundamental importance of the processes taking place in both frontal lobes (Brodmann's areas (BA) 8–11 and 44–47) and the interhemispheric interaction was demonstrated.     

Identification of a Topological Characteristic Responsible for the Biological Robustness of Regulatory Networks

Attribution of biological robustness to the specific structural properties of a regulatory network is an important yet unsolved problem in systems biology. It is widely believed that the topological characteristics of a biological control network largely determine its dynamic behavior, yet the actual mechanism is still poorly understood. Here, we define a novel structural feature of biological networks, termed ‘regulation entropy’, to quantitatively assess the influence of network topology on the robustness of the systems. Using the cell-cycle control networks of the budding yeast (Saccharomyces cerevisiae) and the fission yeast (Schizosaccharomyces pombe) as examples, we first demonstrate the correlation of this quantity with the dynamic stability of biological control networks, and then we establish a significant association between this quantity and the structural stability of the networks. And we further substantiate the generality of this approach with a broad spectrum of biological and random networks. We conclude that the regulation entropy is an effective order parameter in evaluating the robustness of biological control networks. Our work suggests a novel connection between the topological feature and the dynamic property of biological regulatory networks.     

Neuroimaging Study of Alpha and Beta EEG Biofeedback Effects on Neural Networks

Neural networks interaction was studied in healthy men (20–35 years old) who underwent 20 sessions of EEG biofeedback training outside the MRI scanner, with concurrent fMRI–EEG scans at the beginning, middle, and end of the course. The study recruited 35 subjects for EEG biofeedback, but only 18 of them were considered as “successful” in self-regulation of target EEG bands during the whole course of training. Results of fMRI analysis during EEG biofeedback are reported only for these “successful” trainees. The experimental group (N?=?23 total, N?=?13 “successful”) upregulated the power of alpha rhythm, while the control group (N?=?12 total, N?=?5 “successful”) beta rhythm, with the protocol instructions being as for alpha training in both. The acquisition of the stable skills of alpha self-regulation was followed by the weakening of the irrelevant links between the cerebellum and visuospatial network (VSN), as well as between the VSN, the right executive control network (RECN), and the cuneus. It was also found formation of a stable complex based on the interaction of the precuneus, the cuneus, the VSN, and the high level visuospatial network (HVN), along with the strengthening of the interaction of the anterior salience network (ASN) with the precuneus. In the control group, beta enhancement training was accompanied by weakening of interaction between the precuneus and the default mode network, and a decrease in connectivity between the cuneus and the primary visual network (PVN). The differences between the alpha training group and the control group increased successively during training. Alpha training was characterized by a less pronounced interaction of the network formed by the PVN and the HVN, as well as by an increased interaction of the cerebellum with the precuneus and the RECN. The study demonstrated the differences in the structure and interaction of neural networks involved into alpha and beta generating systems forming and functioning, which should be taken into account during planning neurofeedback interventions. Possibility of using fMRI-guided biofeedback organized according to the described neural networks interaction may advance more accurate targeting specific symptoms during neurotherapy.