Evidence map›Paper›PMID 24179776›Full record

ArticleNeuroImage. Clinical2013

Electrophysiological responses to emotional prosody perception in cochlear implant users.

D Agrawal, J D Thorne, F C Viola, L Timm, S Debener, A Büchner, R Dengler, M Wittfoth

Abstract read
In one paragraph

Article in NeuroImage. Clinical, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

D AgrawalDepartment of Neurology, Hannover Medical School, Hannover, Germany.
J D Thorne
F C Viola
L Timm
S Debener
A Büchner
R Dengler
M Wittfoth

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present electroencephalographic (EEG) study investigated the ability of cochlear implant (CI) users to recognize emotional prosody. Two CI speech-processing strategies were compared: the ACE (Advance Combination Encoder) and the newly developed MP3000. Semantically neutral sentences spoken in three different emotional prosodies (neutral, angry, happy) were presented to 20 post-lingually deafened CI users and age-matched normal-hearing controls. Event related potentials (ERPs) were recorded to study the N100 and the P200 responses. In addition, event-related spectral power modulations were calculated to study the brain activity corresponding to the recognition of prosody in earlier (0-400) as well as later (600-1200) part of the stimuli where the prosodic features differed maximally. CI users with MP3000 strategy showed a higher proportion of correctly recognized prosodic information compared to the ACE strategy users. Our ERP results demonstrated that emotional prosody elicited significant N100 and P200 peaks. Furthermore, the P200 amplitude in response to happy prosodic information was significantly more positive for the MP3000 strategy compared to the ACE strategy. On spectral power analysis, two typical gamma activities were observed in the MP3000 users only: (1) an early gamma activity in the 100-250 ms time window reflecting bottom-up attention regulation; and (2) a late gamma activity between 900 and 1100 ms post-stimulus onset, probably reflecting top-down cognitive control. Our study suggests that the MP3000 strategy is better than ACE in regard to happy prosody perception. Furthermore, we show that EEG is a useful tool that, in combination with behavioral analysis, can reveal differences between two CI processing strategies for coding of prosody-specific features of language.

Indexed as

Cochlear implantsEmotional prosodyERPGamma band powerP200

Identifiers

PMID24179776
PMCPMC3777796

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.