2018
DOI: 10.1038/s41386-018-0014-z
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Alterations in interhemispheric gamma-band connectivity are related to the emergence of auditory verbal hallucinations in healthy subjects during NMDA-receptor blockade

Abstract: Auditory verbal hallucinations (AVH) are a common positive symptom of schizophrenia. Excitatory-to-inhibitory (E/I) imbalance related to disturbed N-methyl-D-aspartate receptor (NMDAR) functioning has been suggested as a possible mechanism underlying altered connectivity and AVH in schizophrenia. The current study examined the effects of ketamine, a NMDAR antagonist, on glutamate-related mechanisms underlying interhemispheric gamma-band connectivity, conscious auditory perception during dichotic listening (DL)… Show more

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Cited by 25 publications
(17 citation statements)
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References 47 publications
(79 reference statements)
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“…This is of special interest, because even though the majority (80–85%) of right-handed humans exhibit a clear REA 76 , some do not, and in particular those who suffer from AVH in schizophrenia 7779 . Further, emerging evidence emphasized that abnormal gamma rhythm in AVH patients are due to an E/I imbalance related to glutamate NMDA receptor hypofunction 80,81 . In support, initial 1 H MR spectroscopy studies reported glutamate over-activation in cortical prefrontal and auditory brain areas 82,83 , which in turn goes along with the typical observed neural hyperexcitation of auditory cortices during AVH 84–86 .…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…This is of special interest, because even though the majority (80–85%) of right-handed humans exhibit a clear REA 76 , some do not, and in particular those who suffer from AVH in schizophrenia 7779 . Further, emerging evidence emphasized that abnormal gamma rhythm in AVH patients are due to an E/I imbalance related to glutamate NMDA receptor hypofunction 80,81 . In support, initial 1 H MR spectroscopy studies reported glutamate over-activation in cortical prefrontal and auditory brain areas 82,83 , which in turn goes along with the typical observed neural hyperexcitation of auditory cortices during AVH 84–86 .…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, a multitude of different measures to quantify functional connectivity are currently available that differ concerning the weighting of phase and amplitude or regarding the removal of zero-phase lag components. We used lagged phase synchronization as this metric removes confounding zero-phase lag contributions, is especially well-established in the auditory domain using dichotic listening tasks 1,15,81 , and commonly used in the clinical field 32,8991 . However, there might be alternative methods to examine the structure-function relationship, although no metric has emerged as being superior 92 .…”
Section: Discussionmentioning
confidence: 99%
“…The attenuated task-evoked gamma-band response has been demonstrated across all stages of schizophrenia [ 63 - 66 ] and is suggested to constitute a pathomechanism of cognitive dysfunction in schizophrenia [ 67 , 68 ]. Notably, a recent study demonstrated a relationship between increased interhemispheric gamma-band connectivity and the emergence of auditory verbal distortions and hallucinations under ketamine [ 69 ]. Using a visuomotor paradigm in a single-blind, placebo-controlled MEG study, Shaw et al [ 70 ] revealed increased amplitudes of movement-related gamma synchrony and ipsilateral movement-related beta desynchrony elicited by ketamine within the motor cortex.…”
Section: Resultsmentioning
confidence: 99%
“…The development of qualitative diagnostic and therapeutic efficacy prediction models in patients with AVHs who have various diagnoses may improve the accuracy and repeatability of prediction models, and may, ultimately, facilitate the delivery of early, potentially curative treatments. 7 , 52 60 …”
Section: Discussionmentioning
confidence: 99%
“…The development of qualitative diagnostic and therapeutic efficacy prediction models in patients with AVHs who have various diagnoses may improve the accuracy and repeatability of prediction models, and may, ultimately, facilitate the delivery of early, potentially curative treatments. 7,[52][53][54][55][56][57][58][59][60] Data related to genomic characteristics, brain characterizations (endophenotypes), clinical manifestations, and prognostic characteristics (phenotypes) may be submitted to machine learning algorithms to establish disease optimization classifiers that reveal characteristics that are specific to particular diagnoses or common across neurological conditions accompanied by AVHs. This approach may provide a basis for early AVH diagnosis and treatment models in different diagnostic contexts based on imaging and genetic features.…”
Section: Differentiable Neuroimaging Presentations Of Different Disormentioning
confidence: 99%