Differential processing of laser stimuli by Aδ and C fibres in major depression

Terhaar, Janneke; Viola, Filipa Campos; Franz, Marcel; Berger, Sandy; Bär, Karl‐Jürgen; Weiß, Thomas

doi:10.1016/j.pain.2011.03.027

Cited by 20 publications

(11 citation statements)

References 56 publications

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“…Single subject averages were computed for each participant using a baseline from −100 to 0 ms. To quantify the quality of AEPs, signal-to-noise ratios (SNRs) were calculated for the stimulus NA in both groups (n = 30) based on a single subject level at electrode Fz. SNRs were obtained by dividing the absolute of the N1 peak amplitude by the root mean square of the baseline (−100 to 0 ms) in a time window of 100 −180 ms. AEPs were further analyzed only for those participants who had SNRs above 10 dB (N = 12), as well as peak morphologies discernible as N1 [43]. All statistical analyses were carried out on a fronto-central electrode (Fz), which is known to show strong N1 and MMN responses [27].…”

Section: Methodsmentioning

confidence: 99%

Temporal Feature Perception in Cochlear Implant Users

et al. 2012

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For the perception of timbre of a musical instrument, the attack time is known to hold crucial information. The first 50 to 150 ms of sound onset reflect the excitation mechanism, which generates the sound. Since auditory processing and music perception in particular are known to be hampered in cochlear implant (CI) users, we conducted an electroencephalography (EEG) study with an oddball paradigm to evaluate the processing of small differences in musical sound onset. The first 60 ms of a cornet sound were manipulated in order to examine whether these differences are detected by CI users and normal-hearing controls (NH controls), as revealed by auditory evoked potentials (AEPs). Our analysis focused on the N1 as an exogenous component known to reflect physical stimuli properties as well as on the P2 and the Mismatch Negativity (MMN). Our results revealed different N1 latencies as well as P2 amplitudes and latencies for the onset manipulations in both groups. An MMN could be elicited only in the NH control group. Together with additional findings that suggest an impact of musical training on CI users’ AEPs, our findings support the view that impaired timbre perception in CI users is at partly due to altered sound onset feature detection.

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Section: Methodsmentioning

confidence: 99%

Temporal Feature Perception in Cochlear Implant Users

et al. 2012

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“…It is known that chronic pain and major depression are correlated since depression is a common comorbidity of chronic pain and often chronic pain is an additional symptom of depressed patients [42, 43]. It has been confirmed by some studies that thresholds for acute painful stimulation are lower in depressed patients than in healthy controls [44, 45], whereas other studies found the opposite, namely, increased thresholds in depressed patients [42, 46–49]. The physiological basis for pain perception, pain processing, and the sensitivity to painful stimuli of depressed patients remains unclear.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Approach to Network Motif Detection in Samples of Networks with Pairwise Different Vertex Labels

Schmidt

Weiß

Komusiewicz

et al. 2012

Computational and Mathematical Methods in Medicine

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Network motifs, overrepresented small local connection patterns, are assumed to act as functional meaningful building blocks of a network and, therefore, received considerable attention for being useful for understanding design principles and functioning of networks. We present an extension of the original approach to network motif detection in single, directed networks without vertex labeling to the case of a sample of directed networks with pairwise different vertex labels. A characteristic feature of this approach to network motif detection is that subnetwork counts are derived from the whole sample and the statistical tests are adjusted accordingly to assign significance to the counts. The associated computations are efficient since no simulations of random networks are involved. The motifs obtained by this approach also comprise the vertex labeling and its associated information and are characteristic of the sample. Finally, we apply this approach to describe the intricate topology of a sample of vertex-labeled networks which originate from a previous EEG study, where the processing of painful intracutaneous electrical stimuli and directed interactions within the neuromatrix of pain in patients with major depression and healthy controls was investigated. We demonstrate that the presented approach yields characteristic patterns of directed interactions while preserving their important topological information and omitting less relevant interactions.

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“…Another somewhat surprising finding is that there are slightly more motifs in the post-stimulus period found in MD patients. It was previously found that MD patients compared to HC exhibit higher pain thresholds to external stimulation including electrical stimulation [48], [58], lower sensitivity to C-fiber activation [59], and/or lower sensitivity to experimental nociceptive stimulation [58], [60]. However, it should be mentioned that the stimulation was performed with stimuli that were adjusted for subjective pain ratings (i.e., moderately painful in both groups).…”

Section: Resultsmentioning

confidence: 99%

Extracting Labeled Topological Patterns from Samples of Networks

et al. 2013

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An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investigation promises to yield more detailed information about the represented system. Often patterns of directed edges in sample element networks are too intractable for a direct evaluation and interpretation. The new approach addresses the problem of simplifying topological information and characterizes such a sample of networks by finding its locatable characteristic topological patterns. These patterns, essentially sample-specific network motifs with vertex labeling, might represent the essence of the intricate topological information contained in all sample element networks and provides as well a means of differentiating network samples. Central to the accurateness of this approach is the null model and its properties, which is needed to assign significance to topological patterns. As a proof of principle the proposed approach has been applied to the analysis of networks that represent brain connectivity before and during painful stimulation in patients with major depression and in healthy subjects. The accomplished reduction of topological information enables a cautious functional interpretation of the altered neuronal processing of pain in both groups.

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Differential processing of laser stimuli by Aδ and C fibres in major depression

Cited by 20 publications

References 56 publications

Temporal Feature Perception in Cochlear Implant Users

Temporal Feature Perception in Cochlear Implant Users

An Analytical Approach to Network Motif Detection in Samples of Networks with Pairwise Different Vertex Labels

Extracting Labeled Topological Patterns from Samples of Networks

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