Neurophysiology in psychosis: The quest for disease biomarkers

Wang, Baihan; Zartaloudi, Eirini; Linden, JF; Bramon, Elvira

doi:10.1038/s41398-022-01860-x

Cited by 31 publications

(13 citation statements)

References 143 publications

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“…The results of this modelling study support the view that sound intensity modulates ERP amplitudes during the latency range of MMN. This may be interpreted as loudness dependence of sensory ERP components that cause amplitude changes that spill over into the measurement window for MMN, which agrees with converging electrophysiological findings in schizophrenia patients (Hagenmuller et al, 2016; Todd et al, 2008; Wang et al, 2022). Alternatively, this could be interpreted in terms of a recent rensition of the predictive coding framework (Hertäg and Clopath, 2022; Keller and Mrsic-Flogel, 2018), whereby opposite polarity amplitude changes in response to quieter and louder deviant stimuli may be taken to reflect examples of negative (i.e.…”

Section: Discussionsupporting

confidence: 84%

“…The MMN component is conventionally measured from the difference waveform between low-probability deviant and high-probability standard stimulus ERPs evoked by a passive oddball paradigm experiment (Näätänen et al, 2004). Changes in this signal are thought to reflect perceptual processing that can become impaired by underlying neuropathology, providing significant impetus for developing MMN as a clinical biomarker (Koshiyama et al, 2020; Schuelert et al, 2018; Taylor et al, 2017; Wang et al, 2022). However, considerable impediments to these efforts stem from ambiguity concerning the underlying neurophysiology of hypothetical prediction and prediction-error signalling components, and their dissociation from overlapping obligatory components that are sensitive to physical properties of sound (Heilbron and Chait, 2018; May, 2021; May and Tiitinen, 2010; O’Reilly and Conway, 2021; O’Reilly and O’Reilly, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Recurrent neural network model of human event-related potentials in response to intensity oddball stimulation

O’Reilly

2022

Preprint

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The mismatch negativity (MMN) component of the human event-related potential (ERP) is frequently interpreted as a sensory prediction-error signal. However, there is ambiguity concerning the neurophysiology underlying hypothetical prediction and prediction-error signalling components, and whether these can be dissociated from overlapping obligatory components of the ERP that are sensitive to physical properties of sounds. In the present study, a hierarchical recurrent neural network (RNN) was fitted to ERP data from 38 subjects. After training the model to reproduce ERP waveforms evoked by 80 dB standard and 70 dB deviant stimuli, it was used to simulate a response to 90 dB deviant stimuli. Internal states of the RNN effectively combine to generate synthetic ERPs, where individual hidden units are loosely analogous to population-level sources. Model behaviour was characterised using principal component analysis of stimulus condition, layer, and individual unit responses. Hidden units were categorised according to their temporal response fields, and statistically significant differences among stimulus conditions were observed for amplitudes of units peaking in the 0 to 75 ms (P50), 75 to 125 ms (N1), and 250 to 400 ms (N3) latency ranges, surprisingly not including the measurement window of MMN. The model demonstrated opposite polarity changes in MMN amplitude produced by falling (70 dB) and rising (90 dB) intensity deviant stimuli, consistent with loudness dependence of sensory ERP components. Although perhaps less parsimoniously, these observations could be interpreted within the context of predictive coding theory, as examples of negative and positive prediction errors, respectively.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Recurrent neural network model of human event-related potentials in response to intensity oddball stimulation

O’Reilly

2022

Preprint

View full text Add to dashboard Cite

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“…Recordings are performed with a standardized set-up (BrainAmp amplifier, Brain Products, Martinsried, Germany) with 32 scalp electrodes (10/20 system). After resting-state EEG has been recorded with eyes closed for 5 min and open for 5 min, activation EEG is recorded with an auditory stimulus (P300) (76,77) for an additional 18 min.…”

Section: Electroencephalographymentioning

confidence: 99%

The multimodal Munich Clinical Deep Phenotyping study to bridge the translational gap in severe mental illness treatment research

Krčmář,

Jäger,

Boudriot

et al. 2023

Front. Psychiatry

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IntroductionTreatment of severe mental illness (SMI) symptoms, especially negative symptoms and cognitive dysfunction in schizophrenia, remains a major unmet need. There is good evidence that SMIs have a strong genetic background and are characterized by multiple biological alterations, including disturbed brain circuits and connectivity, dysregulated neuronal excitation-inhibition, disturbed dopaminergic and glutamatergic pathways, and partially dysregulated inflammatory processes. The ways in which the dysregulated signaling pathways are interconnected remains largely unknown, in part because well-characterized clinical studies on comprehensive biomaterial are lacking. Furthermore, the development of drugs to treat SMIs such as schizophrenia is limited by the use of operationalized symptom-based clusters for diagnosis.MethodsIn line with the Research Domain Criteria initiative, the Clinical Deep Phenotyping (CDP) study is using a multimodal approach to reveal the neurobiological underpinnings of clinically relevant schizophrenia subgroups by performing broad transdiagnostic clinical characterization with standardized neurocognitive assessments, multimodal neuroimaging, electrophysiological assessments, retinal investigations, and omics-based analyzes of blood and cerebrospinal fluid. Moreover, to bridge the translational gap in biological psychiatry the study includes in vitro investigations on human-induced pluripotent stem cells, which are available from a subset of participants.ResultsHere, we report on the feasibility of this multimodal approach, which has been successfully initiated in the first participants in the CDP cohort; to date, the cohort comprises over 194 individuals with SMI and 187 age and gender matched healthy controls. In addition, we describe the applied research modalities and study objectives.DiscussionThe identification of cross-diagnostic and diagnosis-specific biotype-informed subgroups of patients and the translational dissection of those subgroups may help to pave the way toward precision medicine with artificial intelligence-supported tailored interventions and treatment. This aim is particularly important in psychiatry, a field where innovation is urgently needed because specific symptom domains, such as negative symptoms and cognitive dysfunction, and treatment-resistant symptoms in general are still difficult to treat.

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“… 34 – 37 Moreover, the amplitude of the N100 component of the auditory evoked potential (AEP), an EEG signal thought to mainly reflect function in the AC, 38 – 42 is reduced in schizophrenia. 33 , 43 – 48 In addition, the N100 latency has been shown to be altered in patients with schizophrenia. 49 Studies using functional MRI and position emission tomography report activation of the temporal cortex, 50 , 51 including activation of the HG 8 , 23 , 52 – 54 and the PT 23 , 50 , 54 , 55 during active AH.…”

Section: Introductionmentioning

confidence: 99%

Auditory Cortex Thickness Is Associated With N100 Amplitude in Schizophrenia Spectrum Disorders

Slapø

Nerland

Jørgensen

et al. 2023

Schizophrenia Bulletin Open

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Background and Hypothesis The auditory cortex (AC) may play a central role in the pathophysiology of schizophrenia and auditory hallucinations (AH). Previous schizophrenia studies report thinner AC and impaired AC function, as indicated by decreased N100 amplitude of the auditory evoked potential (AEP). However, whether these structural and functional alterations link to AH in schizophrenia remain poorly understood. Study Design Patients with a schizophrenia spectrum disorder (SCZspect), including patients with a lifetime experience of AH (AH+), without (AH-) and healthy controls underwent magnetic resonance imaging (39 SCZspect, 22 AH+, 17 AH- and 146 HC) and electroencephalography (33 SCZspect, 17 AH+, 16 AH- and 144 HC). Cortical thickness of the primary (AC1, Heschl’s gyrus) and secondary (AC2, Heschl’s sulcus and the planum temporale) AC were compared between SCZspect and controls and between AH+, AH- and controls. To examine if the association between AC thickness and N100 amplitude differed between groups, we used regression models with interaction terms. Study Results N100 amplitude was nominally smaller in SCZspect (p=0.03, d=0.42) and in AH- (p=0.020, d=0.61), while AC2 was nominally thinner in AH+ (p=0.02, d=0.53) compared to controls. AC1 thickness was positively associated with N100 amplitude in SCZspect (t=2.56, p=0.016) and AH- (t=3.18, p=0.008), while AC2 thickness was positively associated with N100 amplitude in SCZspect (t=2.37, p=0.024) and in AH+ (t=2.68, p=0.019). Conclusions The novel findings of positive associations between AC thickness and N100 amplitude in SCZspect, suggest that a common neural substrate may underlie AC thickness and N100 amplitude alterations.

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Neurophysiology in psychosis: The quest for disease biomarkers

Cited by 31 publications

References 143 publications

Recurrent neural network model of human event-related potentials in response to intensity oddball stimulation

Recurrent neural network model of human event-related potentials in response to intensity oddball stimulation

The multimodal Munich Clinical Deep Phenotyping study to bridge the translational gap in severe mental illness treatment research

Auditory Cortex Thickness Is Associated With N100 Amplitude in Schizophrenia Spectrum Disorders

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