Non-invasive Brain Stimulation and Auditory Verbal Hallucinations: New Techniques and Future Directions

Moseley, Peter; Alderson-Day, Ben; Ellison, Amanda; Jardri, Renaud; Fernyhough, Charles

doi:10.3389/fnins.2015.00515

Cited by 21 publications

(9 citation statements)

References 125 publications

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“…In particular, auditory hallucinations in schizophrenia have been suggested to reflect an over-coupling between auditory and frontal areas [83,90]. The present findings underline the idea that tACS might be an ideal candidate for potential treatment of network disorders [91,92].…”

Section: Discussionsupporting

confidence: 69%

Intrinsic 40Hz-phase asymmetries predict tACS effects during conscious auditory perception

et al. 2019

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Synchronized oscillatory gamma-band activity (30-100Hz) has been suggested to constitute a key mechanism to dynamically orchestrate sensory information integration across multiple spatio-temporal scales. We here tested whether interhemispheric functional connectivity and ensuing auditory perception can selectively be modulated by high-density transcranial alternating current stimulation (HD-tACS). For this purpose, we applied multi-site HD-tACS at 40Hz bilaterally with a phase lag of 180° and recorded a 64-channel EEG to study the oscillatory phase dynamics at the source-space level during a dichotic listening (DL) task in twenty-six healthy participants. In this study, we revealed an oscillatory phase signature at 40Hz which reflects different temporal profiles of the phase asymmetries during left and right ear percept. Here we report that 180°-tACS did not affect the right ear advantage during DL at group level. However, a follow-up analysis revealed that the intrinsic phase asymmetries during sham-tACS determined the directionality of the behavioral modulations: While a shift to left ear percept was associated with augmented interhemispheric asymmetry (closer to 180°), a shift to right ear processing was elicited in subjects with lower asymmetry (closer to 0°). Crucially, the modulation of the interhemispheric network dynamics depended on the deviation of the tACS-induced phase-lag from the intrinsic phase asymmetry. Our characterization of the oscillatory network trends is giving rise to the importance of phase-specific gamma-band coupling during ambiguous auditory perception, and emphasizes the necessity to address the inter-individual variability of phase asymmetries in future studies by tailored stimulation protocols.

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Section: Discussionsupporting

confidence: 69%

Intrinsic 40Hz-phase asymmetries predict tACS effects during conscious auditory perception

et al. 2019

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“…49 The hypothesized synaptic efficacy aberrations may be linked to NMDA receptor abnormalities. 49 In this context it is of interest that effects on temporoparietal-prefrontal circuitry through transcranial Direct Current Stimulation (possibly via NMDA-dependent mechanisms 50 ) has been shown to ameliorate severity of auditory hallucinations, 51,52 possibly through “correction” of functional dysconnectivity. 53 It is likely that further systematic application of machine learning techniques to analysis of brain connectivity may be useful for developing prognostic markers for schizophrenia that might predict differential responses to clinical interventions.…”

Section: Discussionmentioning

confidence: 99%

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

et al. 2019

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In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia ( N = 81) as well as age- and sex-matched healthy controls ( N = 93). We present an ensemble model -- EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples ( N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

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“…There are three main challenges. First, AVH arise from dysfunction in a brain network (Figure 1 ) rather than a single brain region which leads to uncertainty about which regions within the AVH-related network may constitute ideal targets for tDCS ( 46 ). Moreover, commonly used montages often target left-sided AVH-ROIs while evidence from the meta-analyses (Figure 1 ; Table S11 in Supplementary Material) and from individual studies point to additional involvement of right-sided brain regions to the pathophysiology of AVH ( 47 – 51 ).…”

Section: Discussionmentioning

confidence: 99%

A Computational Assessment of Target Engagement in the Treatment of Auditory Hallucinations with Transcranial Direct Current Stimulation

et al. 2018

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We use auditory verbal hallucinations (AVH) to illustrate the challenges in defining and assessing target engagement in the context of transcranial direct current stimulation (tDCS) for psychiatric disorders. We defined the target network as the cluster of regions of interest (ROIs) that are consistently implicated in AVH based on the conjunction of multimodal meta-analytic neuroimaging data. These were prescribed in the New York Head (a population derived model) and head models of four single individuals. We appraised two potential measures of target engagement, tDCS-induced peak electric field strength and tDCS-modulated volume defined as the percentage of the volume of the AVH network exposed to electric field magnitude stronger than the postulated threshold for neuronal excitability. We examined a left unilateral (LUL) montage targeting the prefrontal cortex (PFC) and temporoparietal junction (TPJ), a bilateral (BL) prefrontal montage, and a 2 × 1 montage targeting the left PFC and the TPJ bilaterally. Using computational modeling, we estimated the peak electric field strength and modulated volume induced by each montage for current amplitudes ranging 1–4 mA. We found that the LUL montage was inferior to both other montages in terms of peak electric field strength in right-sided AVH-ROIs. The BL montage was inferior to both other montages in terms of modulated volume of the left-sided AVH-ROIs. As the modulated volume is non-linear, its variability between montages reduced for current amplitudes above 3 mA. These findings illustrate how computational target engagement for tDCS can be tailored to specific networks and provide a principled approach for future study design.

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Non-invasive Brain Stimulation and Auditory Verbal Hallucinations: New Techniques and Future Directions

Cited by 21 publications

References 125 publications

Intrinsic 40Hz-phase asymmetries predict tACS effects during conscious auditory perception

Intrinsic 40Hz-phase asymmetries predict tACS effects during conscious auditory perception

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

A Computational Assessment of Target Engagement in the Treatment of Auditory Hallucinations with Transcranial Direct Current Stimulation

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