936 resultados para brain network


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Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test-retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.

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Objectives: Recent anatomical-functional studies have transformed our understanding of cerebral motor control away from a hierarchical structure and toward parallel and interconnected specialized circuits. Subcortical electrical stimulation during awake surgery provides a unique opportunity to identify white matter tracts involved in motor control. For the first time, this study reports the findings on motor modulatory responses evoked by subcortical stimulation and investigates the cortico-subcortical connectivity of cerebral motor control. Experimental design: Twenty-one selected patients were operated while awake for frontal, insular, and parietal diffuse low-grade gliomas. Subcortical electrostimulation mapping was used to search for interference with voluntary movements. The corresponding stimulation sites were localized on brain schemas using the anterior and posterior commissures method. Principal observations: Subcortical negative motor responses were evoked in 20/21 patients, whereas acceleration of voluntary movements and positive motor responses were observed in three and five patients, respectively. The majority of the stimulation sites were detected rostral of the corticospinal tract near the vertical anterior-commissural line, and additional sites were seen in the frontal and parietal white matter. Conclusions: The diverse interferences with motor function resulting in inhibition and acceleration imply a modulatory influence of the detected fiber network. The subcortical stimulation sites were distributed veil-like, anterior to the primary motor fibers, suggesting descending pathways originating from premotor areas known for negative motor response characteristics. Further stimulation sites in the parietal white matter as well as in the anterior arm of the internal capsule indicate a large-scale fronto-parietal motor control network. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.

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This study describes the influence of age, sex, and working memory (WM) performance on the visuospatial WM network. Thirty-nine healthy children (7-12 years) completed a dot location functional magnetic resonance imaging (fMRI) task. Percent signal change measured the intensity and laterality indices measured the asymmetry of activation in frontal and parietal brain regions. Old children showed greater intensity of activation in parietal regions than young children but no differences in lateralization were observed. Intensity of activation was similar across sex and WM performance groups. Girls and high WM performers showed more right-sided lateralization of parietal regions than boys and low WM performers.

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The deterioration of performance over time is characteristic for sustained attention tasks. This so-called "performance decrement" is measured by the increase of reaction time (RT) over time. Some behavioural and neurobiological mechanisms of this phenomenon are not yet fully understood. Behaviourally, we examined the increase of RT over time and the inter-individual differences of this performance decrement. On the neurophysiological level, we investigated the task-relevant brain areas where neural activity was modulated by RT and searched for brain areas involved in good performance (i.e. participants with no or moderate performance decrement) as compared to poor performance (i.e. participants with a steep performance decrement). For this purpose, 20 healthy, young subjects performed a carefully designed task for simple sustained attention, namely a low-demanding version of the Rapid Visual Information Processing task. We employed a rapid event-related functional magnetic resonance imaging (fMRI) design. The behavioural results showed a significant increase of RT over time in the whole group, and also revealed that some participants were not as prone to the performance decrement as others. The latter was statistically significant comparing good versus poor performers. Moreover, high BOLD-responses were linked to longer RTs in a task-relevant bilateral fronto-cingulate-insular-parietal network. Among these regions, good performance was associated with significantly higher RT-BOLD correlations in the pre-supplementary motor area (pre-SMA). We concluded that the task-relevant bilateral fronto-cingulate-insular-parietal network was a cognitive control network responsible for goal-directed attention. The pre-SMA in particular might be associated with the performance decrement insofar that good performers could sustain activity in this brain region in order to monitor performance declines and adjust behavioural output.

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Recovery from eye movement deficits after cortical lesions is amazingly rapid and almost complete, which is in sharp contrast to most other neurological deficits of cerebral lesions. The underlying mechanisms of this successful recovery remain uncertain. We had the rare opportunity to examine two patients with recovery from saccade deficits after a lesion restricted to the frontal eye field (FEF) by means of transcranial magnetic stimulation (TMS). The results provide direct evidence that recovery depended on the integrity of the oculomotor regions of the nonlesioned contralesional hemisphere, and that the compensatory network is task-specific.

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The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

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Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.

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PURPOSE: The aim of this study was to assess the outcome of patients with primary spinal myxopapillary ependymoma (MPE). MATERIALS AND METHODS: Data from a series of 85 (35 females, 50 males) patients with spinal MPE were collected in this retrospective multicenter study. Thirty-eight (45%) underwent surgery only and 47 (55%) received postoperative radiotherapy (RT). Median administered radiation dose was 50.4 Gy (range, 22.2-59.4). Median follow-up of the surviving patients was 60.0 months (range, 0.2-316.6). RESULTS: The 5-year progression-free survival (PFS) was 50.4% and 74.8% for surgery only and surgery with postoperative low- (<50.4 Gy) or high-dose (>or=50.4 Gy) RT, respectively. Treatment failure was observed in 24 (28%) patients. Fifteen patients presented treatment failure at the primary site only, whereas 2 and 1 patients presented with brain and distant spinal failure only. Three and 2 patients with local failure presented with concomitant spinal distant seeding and brain failure, respectively. One patient failed simultaneously in the brain and spine. Age greater than 36 years (p = 0.01), absence of neurologic symptoms at diagnosis (p = 0.01), tumor size >or=25 mm (p = 0.04), and postoperative high-dose RT (p = 0.05) were variables predictive of improved PFS on univariate analysis. In multivariate analysis, only postoperative high-dose RT was independent predictors of PFS (p = 0.04). CONCLUSIONS: The observed pattern of failure was mainly local, but one fifth of the patients presented with a concomitant spinal or brain component. Postoperative high-dose RT appears to significantly reduce the rate of tumor progression.

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Phase locking or synchronization of brain areas is a key concept of information processing in the brain. Synchronous oscillations have been observed and investigated extensively in EEG during the past decades. EEG oscillations occur over a wide frequency range. In EEG, a prominent type of oscillations is alpha-band activity, present typically when a subject is awake, but at rest with closed eyes. The spectral power of alpha rhythms has recently been investigated in simultaneous EEG/fMRI recordings, establishing a wide-range cortico-thalamic network. However, spectral power and synchronization are different measures and little is known about the correlations between BOLD effects and EEG synchronization. Interestingly, the fMRI BOLD signal also displays synchronous oscillations across different brain regions. These oscillations delineate so-called resting state networks (RSNs) that resemble the correlation patterns of simultaneous EEG/fMRI recordings. However, the nature of these BOLD oscillations and their relations to EEG activity is still poorly understood. One hypothesis is that the subunits constituting a specific RSN may be coordinated by different EEG rhythms. In this study we report on evidence for this hypothesis. The BOLD correlates of global EEG synchronization (GFS) in the alpha frequency band are located in brain areas involved in specific RSNs, e.g. the 'default mode network'. Furthermore, our results confirm the hypothesis that specific RSNs are organized by long-range synchronization at least in the alpha frequency band. Finally, we could localize specific areas where the GFS BOLD correlates and the associated RSN overlap. Thus, we claim that not only the spectral dynamics of EEG are important, but also their spatio-temporal organization.

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The central nervous system (CNS) is tightly sealed from the changeable milieu of blood by the blood-brain barrier (BBB) and the blood-cerebrospinal fluid (CSF) barrier (BCSFB). While the BBB is considered to be localized at the level of the endothelial cells within CNS microvessels, the BCSFB is established by choroid plexus epithelial cells. The BBB inhibits the free paracellular diffusion of water-soluble molecules by an elaborate network of complex tight junctions (TJs) that interconnects the endothelial cells. Combined with the absence of fenestrae and an extremely low pinocytotic activity, which inhibit transcellular passage of molecules across the barrier, these morphological peculiarities establish the physical permeability barrier of the BBB. In addition, a functional BBB is manifested by a number of permanently active transport mechanisms, specifically expressed by brain capillary endothelial cells that ensure the transport of nutrients into the CNS and exclusion of blood-borne molecules that could be detrimental to the milieu required for neural transmission. Finally, while the endothelial cells constitute the physical and metabolic barrier per se, interactions with adjacent cellular and acellular layers are prerequisites for barrier function. The fully differentiated BBB consists of a complex system comprising the highly specialized endothelial cells and their underlying basement membrane in which a large number of pericytes are embedded, perivascular antigen-presenting cells, and an ensheathment of astrocytic endfeet and associated parenchymal basement membrane. Endothelial cell morphology, biochemistry, and function thus make these brain microvascular endothelial cells unique and distinguishable from all other endothelial cells in the body. Similar to the endothelial barrier, the morphological correlate of the BCSFB is found at the level of unique apical tight junctions between the choroid plexus epithelial cells inhibiting paracellular diffusion of water-soluble molecules across this barrier. Besides its barrier function, choroid plexus epithelial cells have a secretory function and produce the CSF. The barrier and secretory function of the choroid plexus epithelial cells are maintained by the expression of numerous transport systems allowing the directed transport of ions and nutrients into the CSF and the removal of toxic agents out of the CSF. In the event of CNS pathology, barrier characteristics of the blood-CNS barriers are altered, leading to edema formation and recruitment of inflammatory cells into the CNS. In this review we will describe current knowledge on the cellular and molecular basis of the functional and dysfunctional blood-CNS barriers with focus on CNS autoimmune inflammation.

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Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.

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Structural and functional connectivity are intrinsic properties of the human brain and represent the amount of cognitive capacities of individual subjects. These connections are modulated due to development, learning, and disease. Momentary adaptations in functional connectivity alter the structural connections, which in turn affect the functional connectivity. Thus, structural and functional connectivity interact on a broad timescale. In this study, we aimed to explore distinct measures of connectivity assessed by functional magnetic resonance imaging and diffusion tensor imaging and their association to the dominant electroencephalogram oscillatory property at rest: the individual alpha frequency (IAF). We found that in 21 healthy young subjects, small intraindividual temporal IAF fluctuations were correlated to increased blood oxygenation level-dependent signal in brain areas associated to working memory functions and to the modulation of attention. These areas colocalized with functionally connected networks supporting the respective functions. Furthermore, subjects with higher IAF show increased fractional anisotropy values in fascicles connecting the above-mentioned areas and networks. Hence, due to a multimodal approach a consistent functionally and structurally connected network related to IAF was observed.

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Anelis Kaiser is associate researcher at the Center for Cognitive Science at the University of Freiburg, Germany. Dr. Kaiser recently co-edited a special issue of the journal Neuroethics on gender and brain science. She is co-founder (with Isabelle Dussauge) of the interdisciplinary network NeuroGenderings, which brings together experts from the brain sciences, the humanities and science studies (STS) to critically study the sexed brain. She has published on sex and gender as constructed categories in science as well as on the topics of multilingualism and language processing in the brain. Co-sponsored with the Center for Lesbian and Gay Studies. - See more at: http://www.gc.cuny.edu/Page-Elements/Academics-Research-Centers-Initiatives/Centers-and-Institutes/Center-for-the-Study-of-Women-and-Society/Center-Events#sthash.bDeBg5fk.dpuf