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News Article | September 27, 2017

Some 10 million people worldwide suffer from Parkinson's disease -- a debilitating condition that causes degeneration of brain nerve cells that control movement. The exact reasons for this degeneration remain unknown. A study published in open-access journal Frontiers in Computational Neuroscience uses a new approach to model the strength of connections within the brain's basal ganglia. Determining how these differ between healthy and Parkinsonian patients could help scientists understand why individual brains malfunction -- and lead to customized therapies specific to the particular pattern of neural degeneration in an individual Parkinson's sufferer. The basal ganglia are a system of neuron clusters, or nuclei, connected to each other and to many other parts of the brain. These ganglia are associated with a number of functions including control of voluntary movements, procedural learning, and habit formation, and their dysfunction leads to neurological disorders such as Parkinson's disease and Tourette's syndrome. "The brain is a complicated organ with many interconnecting systems. At its center, the basal ganglia is one of its most interesting and interconnected regions. Improved experimental methods are now enabling us to discover more about it, revealing the great complexity of the basal ganglia," says Abigail Morrison of the Institute of Neuroscience and Medicine, Jülich Research Center, Germany. Every brain is different Scientists can examine physical connections within the basal ganglia, but many knowledge gaps remain about their strength. "We need to understand how the strength of connections in the brain's basal ganglia change when someone develops Parkinson's disease," says Morrison. "It's also important to remember that every brain is different -- what is true for one Parkinsonian patient might not be true for another." To understand what's going on in an individual brain, the team devised a way to model this variability. They hope this can be used to develop a computational approach that is complementary to clinical practice. This could lead to the creation of customized therapies, specific to the particular pattern of neural degeneration in an individual Parkinson's sufferer. There are two main obstacles to accurate modelling of the basal ganglia: the lack of reliable data describing the strength of these connections, and the large variability between subjects. To take these into account, the team decided to generate the parameters for their basal ganglia model using a genetic algorithm. Genetic algorithms are based on natural selection processes that mimic evolution. In nature, a bird with a harder beak has an advantage foraging for insects in the bark of a tree, and so has a higher probability of reproducing. After many generations, this process can ultimately result in a woodpecker. Analogously, in a genetic algorithm, large numbers of potential solutions are generated, combined, and adapted, until a desired genetic product is reached. "We turned to the genetic algorithm to generate connections between the nuclei and their strengths computationally. This allowed us to make lots of different network configurations that represent different possible brain connections in individuals," explains co-author Jyotika Bahuguna. The team used electrophysiological data from rat brains as criteria for healthy and Parkinsonian conditions. The genetic algorithm generated more than 1,000 possible configurations for the activity occurring in each condition, which were then analyzed for both groups. "The approach is similar to a weather forecasting technique where predictions for a variety of weather systems are made using different initial configurations of weather conditions," explains Morrison. The team found a broad overlap between the strength of individual neural connections in healthy and Parkinsonian brains. However, when they looked at the global network activity in response to stimulus, it was easy to determine whether a network configuration was healthy or Parkinsonian. A detailed analysis of these networks, especially the ones which lie on the boundaries between healthy and diseased, might shed light on how a brain transitions from a healthy to a Parkinsonian state. More importantly, it could help identify therapeutically feasible options that would allow transition from a Parkinsonian state back to a healthy state. "It is important to realize that Parkinson's disease is dynamic. We have a stylized picture of the effects of neurodegeneration, but we need to remember that the patterns of degeneration are variable and are different in each individual," concludes Morrison. "Future clinical approaches incorporating computational methods could incorporate the variability present among the patients. This would allow treatment to be customized to compensate for the specific pattern of degeneration exhibited by a patient, to try to restore healthy dynamics."

Keil F.,Institute of Neuroscience and Medicine | Oros-Peusquens A.-M.,Institute of Neuroscience and Medicine | Shah N.J.,Institute of Neuroscience and Medicine | Shah N.J.,RWTH Aachen
NeuroImage | Year: 2012

A novel method for the quantification of heterogeneity and spatial correlation in 3D MP-RAGE images of white matter is presented. The technique is based on the variogram, a tool commonly used in geosciences for the analysis of spatial data, and was tailored to the special requirements of MR image analysis. Influences from intensity non-uniformities, noise and arbitrary greyscale were quantified and considered in the calculations. The obtained variograms were fitted with spherical model functions to infer parameters that quantify heterogeneity and size of the correlation structures of the tissue. Numerically generated samples with well-defined correlation properties were employed to validate the estimation process and to provide an interpretation of the parameters obtained. It is shown that the method gives reliable results in an interval of correlation structures sized between 2mm and 20mm. The method was applied to 24 MP-RAGE datasets of healthy female volunteers ranging in age from 19 to 73years. White matter was found to have two prominent correlation structures with sizes of approximately 3mm and 23mm. The heterogeneity of the smaller structure increases significantly with age (r=0.83, p<10-6). © 2012 Elsevier Inc.

Zimmermann E.,Institute of Neuroscience and Medicine | Morrone M.C.,University of Pisa | Morrone M.C.,Scientific Institute Stella Maris | Burr D.C.,University of Florence | Burr D.C.,National Research Council Italy
Journal of Neuroscience | Year: 2013

One of the more enduring mysteries of neuroscience is how the visual system constructs robust maps of the world that remain stable in the face of frequent eye movements. Here we show that encoding the position of objects in external space is a relatively slow process, building up over hundreds of milliseconds. We display targets to which human subjects saccade after a variable preview duration. As they saccade, the target is displaced leftwards or rightwards, and subjects report the displacement direction. When subjects saccade to targets without delay, sensitivity is poor; but if the target is viewed for 300-500 ms before saccading, sensitivity is similar to that during fixation with a strong visual mask to dampen transients. These results suggest that the poor displacement thresholds usually observed in the "saccadic suppression of displacement" paradigm are a result of the fact that the target has had insufficient time to be encoded in memory, and not a result of the action of special mechanisms conferring saccadic stability. Under more natural conditions, trans-saccadic displacement detection is as good as in fixation, when the displacement transients are masked. © 2013 the authors.

Watanabe T.,CNS Drug Discovery Unit | Hikichi Y.,Oncology Drug Discovery Unit | Willuweit A.,Evotec | Willuweit A.,Institute of Neuroscience and Medicine | And 2 more authors.
Journal of Neuroscience | Year: 2012

The ubiquitin-proteasome pathway is a major protein degradation pathway whose dysfunction is now widely accepted as a cause of neurodegenerative diseases, including Alzheimer's disease. Here we demonstrate that the F-box and leucine rich repeat protein2 (FBL2), a component of the E3 ubiquitin ligase complex, regulates amyloid precursor protein (APP) metabolism through APP ubiquitination. FBL2 overexpression decreased the amount of secreted amyloid β (Aβ) peptides and sAPPβ, whereas FBL2 mRNA knockdown by siRNA increased these levels. FBL2 overexpression also decreased the amount of intracellular Aβ in Neuro2a cells stably expressing APP with Swedish mutation. FBL2 bound with APP specifically at its C-terminal fragment (CTF), which promoted APP/CTF ubiquitination. FBL2 overexpression also accelerated APP proteasome-dependent degradation and decreased APP protein localization in lipid rafts by inhibiting endocytosis. These effects were not observed in an F-box-deleted FBL2 mutant that does not participate in the E3 ubiquitin ligase complex. Furthermore, a reduced insoluble Aβ and Aβ plaque burden was observed in the hippocampus of 7-month-old FBL2 transgenic mice crossed with double-transgenic mice harboring APPswe and PS1 M146V transgenes. These findings indicate that FBL2 is a novel and dual regulator of APP metabolism through FBL2-dependent ubiquitination of APP. © 2012 the authors.

Silchenko A.N.,Institute of Neuroscience and Medicine | Adamchic I.,Institute of Neuroscience and Medicine | Hauptmann C.,Institute of Neuroscience and Medicine | Tass P.A.,Institute of Neuroscience and Medicine | Tass P.A.,University of Cologne
NeuroImage | Year: 2013

Chronic subjective tinnitus is an auditory phantom phenomenon characterized by abnormal neuronal synchrony in the central auditory system. As recently shown in a proof of concept clinical trial, acoustic coordinated reset (CR) neuromodulation causes a significant relief of tinnitus symptoms combined with a significant decrease of pathological oscillatory activity in a network comprising auditory and non-auditory brain areas. The objective of the present study was to analyze whether CR therapy caused an alteration of the effective connectivity in a tinnitus related network of localized EEG brain sources. To determine which connections matter, in a first step, we considered a larger network of brain sources previously associated with tinnitus. To that network we applied a data-driven approach, combining empirical mode decomposition and partial directed coherence analysis, in patients with bilateral tinnitus before and after 12. weeks of CR therapy as well as in healthy controls. To increase the signal-to-noise ratio, we focused on the good responders, classified by a reliable-change-index (RCI). Prior to CR therapy and compared to the healthy controls, the good responders showed a significantly increased connectivity between the left primary cortex auditory cortex and the posterior cingulate cortex in the gamma and delta bands together with a significantly decreased effective connectivity between the right primary auditory cortex and the dorsolateral prefrontal cortex in the alpha band. Intriguingly, after 12. weeks of CR therapy most of the pathological interactions were gone, so that the connectivity patterns of good responders and healthy controls became statistically indistinguishable. In addition, we used dynamic causal modeling (DCM) to examine the types of interactions which were altered by CR therapy. Our DCM results show that CR therapy specifically counteracted the imbalance of excitation and inhibition. CR significantly weakened the excitatory connection between posterior cingulate cortex and primary auditory cortex and significantly strengthened inhibitory connections between auditory cortices and the dorsolateral prefrontal cortex. The overall impact of CR therapy on the entire tinnitus-related network showed up as a qualitative transformation of its spectral response, in terms of a drastic change of the shape of its averaged transfer function. Based on our findings we hypothesize that CR therapy restores a silence based cognitive auditory comparator function of the posterior cingulate cortex. © 2013 Elsevier Inc.

Kuzmanovic B.,Max Planck Institute for Metabolism Research | Kuzmanovic B.,Institute of Neuroscience and Medicine | Kuzmanovic B.,University of Cologne | Jefferson A.,University of Birmingham | And 2 more authors.
NeuroImage | Year: 2016

People are motivated to adopt the most favorable beliefs about their future because positive beliefs are experienced as rewarding. However, it is so far inconclusive whether brain regions known to represent reward values are involved in the generation of optimistically biased belief updates. To address this question, we investigated neural correlates of belief updates that result in relatively better future outlooks, and therefore imply a positive subjective value of the judgment outcome. Participants estimated the probability of experiencing different adverse future events. After being provided with population base rates of these events, they had the opportunity to update their initial estimates. Participants made judgments concerning themselves or a similar other, and were confronted with desirable or undesirable base rates (i.e., lower or higher than their initial estimates).Belief updates were smaller following undesirable than desirable information, and this optimism bias was stronger for judgments regarding oneself than others. During updating, the positive value of self-related updates was reflected by neural activity in the subgenual ventromedial prefrontal cortex (vmPFC) that increased both with increasing sizes of favorable updates, and with decreasing sizes of unfavorable updates. During the processing of self-related undesirable base rates, increasing activity in a network including the dorsomedial PFC, hippocampus, thalamus and ventral striatum predicted decreasing update sizes.Thus, key regions of the neural reward circuitry contributed to the generation of optimistically biased self-referential belief updates. While the vmPFC tracked subjective values of belief updates, a network including the ventral striatum was involved in neglecting information calling for unfavorable updates. © 2016 Elsevier Inc.

Florin E.,University of Cologne | Florin E.,Institute of Neuroscience and Medicine | Gross J.,University of Glasgow | Pfeifer J.,University of Bonn | And 3 more authors.
NeuroImage | Year: 2010

In the past, causality measures based on Granger causality have been suggested for assessing directionality in neural signals. In frequency domain analyses (power or coherence) of neural data, it is common to preprocess the time series by filtering or decimating. However, in other fields, it has been shown theoretically that filtering in combination with Granger causality may lead to spurious or missed causalities. We investigated whether this result translates to multivariate causality methods derived from Granger causality with (a) a simulation study and (b) an application to magnetoencephalographic data. To this end, we performed extensive simulations of the effect of applying different filtering techniques and evaluated the performance of five different multivariate causality measures in combination with two numerical significance measures (random permutation and leave one out method). The analysis included three of the most widely used filters (high-pass, low-pass, notch filter), four different filter types (Butterworth, Chebyshev I and II, elliptic filter), variation of filter order, decimating and interpolation. The simulation results suggest that preprocessing without a strong prior about the artifact to be removed disturbs the information content and time ordering of the data and leads to spurious and missed causalities. Only if apparent artifacts like a current or movement artifact are present, filtering out the respective disturbance seems advisable. While oversampling poses no problem, decimation by a factor greater than the minimum time shift between the time series may lead to wrong inferences. In general, the multivariate causality measures are very sensitive to data preprocessing. © 2009 Elsevier Inc. All rights reserved.

Chase H.W.,University of Nottingham | Eickhoff S.B.,Institute of Neuroscience and Medicine | Eickhoff S.B.,RWTH Aachen | Laird A.R.,University of Texas Health Science Center at San Antonio | Hogarth L.,University of Nottingham
Biological Psychiatry | Year: 2011

Background: The capacity of drug cues to elicit drug-seeking behavior is believed to play a fundamental role in drug dependence; yet the neurofunctional basis of human drug cue-reactivity is not fully understood. We performed a meta-analysis to identify brain regions that are consistently activated by presentation of drug cues. Studies involving treatment-seeking and nontreatment-seeking substance users were contrasted to determine whether there were consistent differences in the neural response to drug cues between these populations. Finally, to assess the neural basis of craving, consistency across studies in brain regions that show correlated activation with craving was assessed. Methods: Appropriate studies, assessing the effect of drug-related cues or manipulations of drug craving in drug-user populations across the whole brain, were obtained via the PubMed database and literature search. Activation likelihood estimation, a method of quantitative meta-analysis that estimates convergence across experiments by modeling the spatial uncertainty of neuroimaging data, was used to identify consistent regions of activation. Results: Cue-related activation was observed in the ventral striatum (across both subgroups), amygdala (in the treatment-seeking subgroup and overall), and orbitofrontal cortex (in the nontreatment-seeking subgroup and overall) but not insula cortex. Although a different pattern of frontal and temporal lobe activation between the subgroups was observed, these differences were not significant. Finally, right amygdala and left middle frontal gyrus activity were positively associated with craving. Conclusions: These results substantiate the key neural substrates underlying reactivity to drug cues and drug craving. © 2011 Society of Biological Psychiatry.

Zimmermann E.,Institute of Neuroscience and Medicine | Bremmer F.,University of Marburg
Current Biology | Year: 2016

Our world appears stable, although our eyes constantly shift its image across the retina. What brain mechanisms allow for this perceptual stability? A recent study has brought us a step closer to answering this millennial question. © 2016 Elsevier Ltd. All rights reserved.

Zimmermann E.,Institute of Neuroscience and Medicine | Fink G.,Institute of Neuroscience and Medicine | Fink G.,University of Cologne | Cavanagh P.,Attention
Journal of Vision | Year: 2013

We report a strong compression of space around a visual anchor presented in the near visual periphery (<5°). While subjects kept fixation, a salient visual stimulus (from now on referred to as "anchor") was presented, followed by a brief whole-field mask. At various times around mask onset a probe dot was flashed. Subjects estimated the position of the probe dot in relation to a subsequently presented comparison bar. The probe dot location was perceived nearly veridically when presented long before or after mask onset. However, when the probe dot was presented simultaneously with the mask it appeared shifted toward the anchor by as much as 50% of their separation. The anchor had to appear briefly before mask onset to attract the probe dot. No compression occurred when the anchor was presented long before or after the mask. When the probe dot and anchor were presented with similar brief duration, the more peripheral stimulus always shifted toward the more foveal stimulus independently of their temporal order. We hypothesize that the attraction might be explained by the summation of the neural activity distributions of probe and anchor. © 2013 ARVO.

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