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.     

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.     

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.     

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia.     

Task and Spatial Frequency Modulations of Object Processing: An EEG Study

Visual object processing may follow a coarse-to-fine sequence imposed by fast processing of low spatial frequencies (LSF) and slow processing of high spatial frequencies (HSF). Objects can be categorized at varying levels of specificity: the superordinate (e.g. animal), the basic (e.g. dog), or the subordinate (e.g. Border Collie). We tested whether superordinate and more specific categorization depend on different spatial frequency ranges, and whether any such dependencies might be revealed by or influence signals recorded using EEG. We used event-related potentials (ERPs) and time-frequency (TF) analysis to examine the time course of object processing while participants performed either a grammatical gender-classification task (which generally forces basic-level categorization) or a living/non-living judgement (superordinate categorization) on everyday, real-life objects. Objects were filtered to contain only HSF or LSF. We found a greater positivity and greater negativity for HSF than for LSF pictures in the P1 and N1 respectively, but no effects of task on either component. A later, fronto-central negativity (N350) was more negative in the gender-classification task than the superordinate categorization task, which may indicate that this component relates to semantic or syntactic processing. We found no significant effects of task or spatial frequency on evoked or total gamma band responses. Our results demonstrate early differences in processing of HSF and LSF content that were not modulated by categorization task, with later responses reflecting such higher-level cognitive factors.     

An approach for representing EEG activity

A method for the representation of EEG-parameters by means of a bidimensional plot of two signals is described. A block diagram is given for the recording of figures with phase-sensitive parameter variations, using an oscilloscope or an X-Y graphic recorder as a display. Charts for the determination of the setting parameters of a digital as well as an analog phase-shifting arrangement are submitted. The method is capable of reflecting individual properties of a single wave and typical characteristics of the epoch as well. It is felt that it may be of benefit in the immediate estimation of EEG-activity changes.