Impaired prefrontal synaptic gain in people with psychosis and their relatives during the mismatch negativity

Computational Synaptic Modeling of Pitch and Duration Mismatch Negativity in First-Episode Psychosis Reveals Selective Dysfunction of the N-Methyl-D-Aspartate Receptor

Mismatch negativity (MMN) to pitch (pMMN) and to duration (dMMN) deviant stimuli is significantly more attenuated in long-term psychotic illness compared to first-episode psychosis (FEP). It was recently shown that source-modeling of magnetically recorded MMN increases the detection of left auditory cortex MMN deficits in FEP, and that computational circuit modeling of electrically recorded MMN also reveals left-hemisphere auditory cortex abnormalities. Computational modeling using dynamic causal modeling (DCM) can also be used to infer synaptic activity from EEG-based scalp recordings. We measured pMMN and dMMN with EEG from 26 FEP and 26 matched healthy controls (HCs) and used a DCM conductance-based neural mass model including α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid, N-methyl-D-Aspartate (NMDA), and Gamma-aminobutyric acid receptors to identify any changes in effective connectivity and receptor rate constants in FEP. We modeled MMN sources in bilateral A1, superior temporal gyrus, and inferior frontal gyrus (IFG). No model parameters distinguished groups for pMMN. For dMMN, reduced NMDA receptor activity in right IFG in FEP was detected. This finding is in line with literature of prefrontal NMDA receptor hypofunction in chronic schizophrenia and suggests impaired NMDA-induced synaptic plasticity may be present at psychosis onset where scalp dMMN is only moderately reduced. To the best of our knowledge, this is the first report of impaired NMDA receptor activity in FEP found through computational modeling of dMMN and shows the potential of DCM to non-invasively reveal synaptic-level abnormalities that underly subtle functional auditory processing deficits in early psychosis.

From the Laboratory to the Real-World: The Role of Mismatch Negativity in Psychosis

Mismatch negativity (MMN) has gained attention as a biomarker for psychosis and a translational intermediate phenotype in animal models of psychosis, including rodents and non-human primates. MMN has been linked to global functioning (Global Assessment of Functioning [GAF] score) and prognosis (psychosis onset or remission), suggesting that MMN reflects activities beyond auditory processing alone. This review examines the 45-year history of MMN from the perspective of psychiatric researchers and discusses current advances in computational and translational research on MMN, summarizing the current understanding of the MMN generation mechanism. We then address the essential question, "What do we observe through MMN?" Currently, we regard the relationship between global functioning in the real world and MMN as the key to answering this question. As a preliminary investigation, we analyzed the relationship between GAF as an objective variable and MMN, diagnosis, and basic epidemiological factors (age, sex, premorbid intelligence quotient) as explanatory variables (total n = 201, healthy controls: n = 41, patients with psychiatric disorders: n = 160) without assuming diagnostic categories. The relationship between functional outcomes and MMN was confirmed without a case-control design. Finally, we propose that new neurophysiological studies should acknowledge psychophysiological responses such as emotion, intention, and autonomic responses, as well as behavioral differences among participants beyond the dichotomy between healthy controls and patients. Measurements could be conducted in various settings from the participant's perspective. We discuss the potential for research investigating psychosis based on the interaction between individuals and the environment, using MMN as an illustrative model.

Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia

Alterations of mismatch responses (ie, neural activity evoked by unexpected stimuli) are often considered a potential biomarker of schizophrenia. Going beyond establishing the type of observed alterations found in diagnosed patients and related cohorts, computational methods can yield valuable insights into the underlying disruptions of neural mechanisms and cognitive function. Here, we adopt a typology of model-based approaches from computational cognitive neuroscience, providing an overview of the study of mismatch responses and their alterations in schizophrenia from four complementary perspectives: (a) connectivity models, (b) decoding models, (c) neural network models, and (d) cognitive models. Connectivity models aim at inferring the effective connectivity patterns between brain regions that may underlie mismatch responses measured at the sensor level. Decoding models use multivariate spatiotemporal mismatch response patterns to infer the type of sensory violations or to classify participants based on their diagnosis. Neural network models such as deep convolutional neural networks can be used for improved classification performance as well as for a systematic study of various aspects of empirical data. Finally, cognitive models quantify mismatch responses in terms of signaling and updating perceptual predictions over time. In addition to describing the available methodology and reviewing the results of recent computational psychiatry studies, we offer suggestions for future work applying model-based techniques to advance the study of mismatch responses in schizophrenia.

Is auditory processing measured by the N100 an endophenotype for psychosis? A family study and a meta-analysis

BACKGROUND

The N100, an early auditory event-related potential, has been found to be altered in patients with psychosis. However, it is unclear if the N100 is a psychosis endophenotype that is also altered in the relatives of patients.

METHODS

We conducted a family study using the auditory oddball paradigm to compare the N100 amplitude and latency across 243 patients with psychosis, 86 unaffected relatives, and 194 controls. We then conducted a systematic review and a random-effects meta-analysis pooling our results and 14 previously published family studies. We compared data from a total of 999 patients, 1192 relatives, and 1253 controls in order to investigate the evidence and degree of N100 differences.

RESULTS

In our family study, patients showed reduced N100 amplitudes and prolonged N100 latencies compared to controls, but no significant differences were found between unaffected relatives and controls. The meta-analysis revealed a significant reduction of the N100 amplitude and delay of the N100 latency in both patients with psychosis (standardized mean difference [s.m.d.] = -0.48 for N100 amplitude and s.m.d. = 0.43 for N100 latency) and their relatives (s.m.d. = - 0.19 for N100 amplitude and s.m.d. = 0.33 for N100 latency). However, only the N100 latency changes in relatives remained significant when excluding studies with affected relatives.

CONCLUSIONS

N100 changes, especially prolonged N100 latencies, are present in both patients with psychosis and their relatives, making the N100 a promising endophenotype for psychosis. Such changes in the N100 may reflect changes in early auditory processing underlying the etiology of psychosis.

Mismatch negativity and polygenic risk scores for schizophrenia and bipolar disorder

OBJECTIVE

Auditory mismatch negativity (MMN) impairment is a candidate endophenotype in psychotic disorders, yet the genetic underpinnings remain to be clarified. Here, we examined the relationships between auditory MMN and polygenic risk scores (PRS) for individuals with psychotic disorders, including schizophrenia spectrum disorders (SSD) and bipolar disorder (BD) and in healthy controls (HC).

METHODS

Genotyped and clinically well-characterized individuals with psychotic disorders (n = 102), including SSD (n = 43) and BD (n = 59), and HC (n = 397) underwent a roving MMN paradigm. In addition MMN, we measured the memory traces of the repetition positivity (RP) and the deviant negativity (DN), which is believed to reflect prediction encoding and prediction error signals, respectively. SCZ and BD PRS were computed using summary statistics from the latest genome-wide association studies. The relationships between the MMN, RP, and DN and the PRSs were assessed with linear regressions.

RESULTS

We found no significant association between the SCZ or BD PRS and grand average MMN in the psychotic disorders group or in the HCs group (all p > 0.05). SCZ PRS and BD PRS were negatively associated with RP in the psychotic disorders group (β = -0.46, t = -2.86, p = 0.005 and β = -0.29, t = -0.21, p = 0.034, respectively). No significant associations were found between DN and PRS.

CONCLUSION

These findings suggest that genetic variants associated with SCZ and BD may be associated with MMN subcomponents linked to predictive coding among patients with psychotic disorders. Larger studies are needed to confirm these findings and further elucidate the genetic underpinnings of MMN impairment in psychotic disorders.

Children at Familial High risk of Schizophrenia and Bipolar Disorder Exhibit Altered Connectivity Patterns During Pre-attentive Processing of an Auditory Prediction Error

BACKGROUND AND HYPOTHESIS

Individuals with schizophrenia or bipolar disorder have attenuated auditory mismatch negativity (MMN) responses, indicating impaired sensory information processing. Computational models of effective connectivity between brain areas underlying MMN responses show reduced connectivity between fronto-temporal areas in individuals with schizophrenia. Here we ask whether children at familial high risk (FHR) of developing a serious mental disorder show similar alterations.

STUDY DESIGN

We recruited 67 children at FHR for schizophrenia, 47 children at FHR for bipolar disorder as well as 59 matched population-based controls from the Danish High Risk and Resilience study. The 11-12-year-old participants engaged in a classical auditory MMN paradigm with deviations in frequency, duration, or frequency and duration, while we recorded their EEG. We used dynamic causal modeling (DCM) to infer on the effective connectivity between brain areas underlying MMN.

STUDY RESULTS

DCM yielded strong evidence for differences in effective connectivity among groups in connections from right inferior frontal gyrus (IFG) to right superior temporal gyrus (STG), along with differences in intrinsic connectivity within primary auditory cortex (A1). Critically, the 2 high-risk groups differed in intrinsic connectivity in left STG and IFG as well as effective connectivity from right A1 to right STG. Results persisted even when controlling for past or present psychiatric diagnoses.

CONCLUSIONS

We provide novel evidence that connectivity underlying MMN responses in children at FHR for schizophrenia and bipolar disorder is altered at the age of 11-12, echoing findings that have been found in individuals with manifest schizophrenia.

Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning

Given a multitude of genetic and environmental factors, when investigating the variability in schizophrenia (SCZ) and the first-degree relatives (R-SCZ), latent disease-specific variation is usually hidden. To reliably investigate the mechanism underlying the brain deficits from the aspect of functional networks, we newly iterated a framework of contrastive variational autoencoders (cVAEs) applied in the contrasts among three groups, to disentangle the latent resting-state network patterns specified for the SCZ and R-SCZ. We demonstrated that the comparison in reconstructed resting-state networks among SCZ, R-SCZ, and healthy controls (HC) revealed network distortions of the inner-frontal hypoconnectivity and frontal-occipital hyperconnectivity, while the original ones illustrated no differences. And only the classification by adopting the reconstructed network metrics achieved satisfying performances, as the highest accuracy of 96.80% ± 2.87%, along with the precision of 95.05% ± 4.28%, recall of 98.18% ± 3.83%, and F1-score of 96.51% ± 2.83%, was obtained. These findings consistently verified the validity of the newly proposed framework for the contrasts among the three groups and provided related resting-state network evidence for illustrating the pathological mechanism underlying the brain deficits in SCZ, as well as facilitating the diagnosis of SCZ.

Why mismatch negativity continues to hold potential in probing altered brain function in schizophrenia

The brain potential known as mismatch negativity (MMN) is one of the most studied indices of altered brain function in schizophrenia. This review looks at what has been learned about MMN in schizophrenia over the last three decades and why the level of interest and activity in this field of research remains strong. A diligent consideration of available evidence suggests that MMN can serve as a biomarker in schizophrenia, but perhaps not the kind of biomarker that early research supposed. This review concludes that MMN measurement is likely to be most useful as a monitoring and response biomarker enabling tracking of an underlying pathology and efficacy of interventions, respectively. The role of, and challenges presented by, pre-clinical models is discussed as well as the merits of different methodologies that can be brought to bear in pursuing a deeper understanding of pathophysiology that might explain smaller MMN in schizophrenia.

A parahippocampal-sensory Bayesian vicious circle generates pain or tinnitus: a source-localized EEG study

Pain and tinnitus share common pathophysiological mechanisms, clinical features, and treatment approaches. A source-localized resting-state EEG study was conducted in 150 participants: 50 healthy controls, 50 pain, and 50 tinnitus patients. Resting-state activity as well as functional and effective connectivity was computed in source space. Pain and tinnitus were characterized by increased theta activity in the pregenual anterior cingulate cortex, extending to the lateral prefrontal cortex and medial anterior temporal lobe. Gamma-band activity was increased in both auditory and somatosensory cortex, irrespective of the pathology, and extended to the dorsal anterior cingulate cortex and parahippocampus. Functional and effective connectivity were largely similar in pain and tinnitus, except for a parahippocampal-sensory loop that distinguished pain from tinnitus. In tinnitus, the effective connectivity between parahippocampus and auditory cortex is bidirectional, whereas the effective connectivity between parahippocampus and somatosensory cortex is unidirectional. In pain, the parahippocampal-somatosensory cortex is bidirectional, but parahippocampal auditory cortex unidirectional. These modality-specific loops exhibited theta-gamma nesting. Applying a Bayesian brain model of brain functioning, these findings suggest that the phenomenological difference between auditory and somatosensory phantom percepts result from a vicious circle of belief updating in the context of missing sensory information. This finding may further our understanding of multisensory integration and speaks to a universal treatment for pain and tinnitus-by selectively disrupting parahippocampal-somatosensory and parahippocampal-auditory theta-gamma activity and connectivity.

The maternal brain is more flexible and responsive at rest: effective connectivity of the parental caregiving network in postpartum mothers

The field of neuroscience has largely overlooked the impact of motherhood on brain function outside the context of responses to infant stimuli. Here, we apply spectral dynamic causal modelling (spDCM) to resting-state fMRI data to investigate differences in brain function between a group of 40 first-time mothers at 1-year postpartum and 39 age- and education-matched women who have never been pregnant. Using spDCM, we investigate the directionality (top-down vs. bottom-up) and valence (inhibition vs excitation) of functional connections between six key left hemisphere brain regions implicated in motherhood: the dorsomedial prefrontal cortex, ventromedial prefrontal cortex, posterior cingulate cortex, parahippocampal gyrus, amygdala, and nucleus accumbens. We show a selective modulation of inhibitory pathways related to differences between (1) mothers and non-mothers, (2) the interactions between group and cognitive performance and (3) group and social cognition, and (4) differences related to maternal caregiving behaviour. Across analyses, we show consistent disinhibition between cognitive and affective regions suggesting more efficient, flexible, and responsive behaviour, subserving cognitive performance, social cognition, and maternal caregiving. Together our results support the interpretation of these key regions as constituting a parental caregiving network. The nucleus accumbens and the parahippocampal gyrus emerging as 'hub' regions of this network, highlighting the global importance of the affective limbic network for maternal caregiving, social cognition, and cognitive performance in the postpartum period.

Computational Modeling of Oddball Sequence Processing Exposes Common and Differential Auditory Network Changes in First-Episode Schizophrenia-Spectrum Disorders and Schizophrenia

BACKGROUND AND HYPOTHESIS

Differences in sound relevance filtering in schizophrenia are proposed to represent a key index of biological changes in brain function in the illness. This study featured a computational modeling approach to test the hypothesis that processing differences might already be evident in first-episode, becoming more pronounced in the established illness.

STUDY DESIGN

Auditory event-related potentials to a typical oddball sequence (rare pitch deviations amongst regular sounds) were recorded from 90 persons with schizophrenia-spectrum disorders (40 first-episode schizophrenia-spectrum, 50 established illness) and age-matched healthy controls. The data were analyzed using dynamic causal modeling to identify the changes in effective connectivity that best explained group differences.

STUDY RESULTS

Group differences were linked to intrinsic (within brain region) connectivity changes. In activity-dependent measures these were restricted to the left auditory cortex in first-episode schizophrenia-spectrum but were more widespread in the established illness. Modeling suggested that both established illness and first-episode schizophrenia-spectrum groups expressed significantly lower inhibition of inhibitory interneuron activity and altered gain on superficial pyramidal cells with the data indicative of differences in both putative N-methyl-d-aspartate glutamate receptor activity-dependent plasticity and classic neuromodulation.

CONCLUSIONS

The study provides further support for the notion that examining the ability to alter responsiveness to structured sound sequences in schizophrenia and first-episode schizophrenia-spectrum could be informative to uncovering the nature and progression of changes in brain function during the illness. Furthermore, modeling suggested that limited differences present at first-episode schizophrenia-spectrum may become more expansive with illness progression.

Amygdala connectivity related to subsequent stress responses during the COVID-19 outbreak

Introduction

The amygdala plays an important role in stress responses and stress-related psychiatric disorders. It is possible that amygdala connectivity may be a neurobiological vulnerability marker for stress responses or stress-related psychiatric disorders and will be useful to precisely identify the vulnerable individuals before stress happens. However, little is known about the relationship between amygdala connectivity and subsequent stress responses. The current study investigated whether amygdala connectivity measured before experiencing stress is a predisposing neural feature of subsequent stress responses while individuals face an emergent and unexpected event like the COVID-19 outbreak.

Methods

Data collected before the COVID-19 pandemic from an established fMRI cohort who lived in the pandemic center in China (Hubei) during the COVID-19 outbreak were used to investigate the relationship between amygdala connectivity and stress responses during and after the pandemic in 2020. The amygdala connectivity was measured with resting-state functional connectivity (rsFC) and effective connectivity.

Results

We found the rsFC of the right amygdala with the dorsomedial prefrontal cortex (dmPFC) was negatively correlated with the stress responses at the first survey during the COVID-19 outbreak, and the rsFC between the right amygdala and bilateral superior frontal gyri (partially overlapped with the dmPFC) was correlated with SBSC at the second survey. Dynamic causal modeling suggested that the self-connection of the right amygdala was negatively correlated with stress responses during the pandemic.

Discussion

Our findings expand our understanding about the role of amygdala in stress responses and stress-related psychiatric disorders and suggest that amygdala connectivity is a predisposing neural feature of subsequent stress responses.

Computational psychiatry: from synapses to sentience

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

Toward biophysical markers of depression vulnerability

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

Neurophysiology in psychosis: The quest for disease biomarkers

Psychotic disorders affect 3% of the population at some stage in life, are a leading cause of disability, and impose a great economic burden on society. Major breakthroughs in the genetics of psychosis have not yet been matched by an understanding of its neurobiology. Biomarkers of perception and cognition obtained through non-invasive neurophysiological tools, especially EEG, offer a unique opportunity to gain mechanistic insights. Techniques for measuring neurophysiological markers are inexpensive and ubiquitous, thus having the potential as an accessible tool for patient stratification towards early treatments leading to better outcomes. In this paper, we review the literature on neurophysiological markers for psychosis and their relevant disease mechanisms, mainly covering event-related potentials including P50/N100 sensory gating, mismatch negativity, and the N100 and P300 waveforms. While several neurophysiological deficits are well established in patients with psychosis, more research is needed to study neurophysiological markers in their unaffected relatives and individuals at clinical high risk. We need to harness EEG to investigate markers of disease risk as key steps to elucidate the aetiology of psychosis and facilitate earlier detection and treatment.

GABAA and NMDA receptor density alterations and their behavioral correlates in the gestational methylazoxymethanol acetate model for schizophrenia

Hippocampal hyperactivity driven by GABAergic interneuron deficits and NMDA receptor hypofunction is associated with the hyperdopaminergic state often observed in schizophrenia. Furthermore, previous research in the methylazoxymethanol acetate (MAM) rat model has demonstrated that repeated peripubertal diazepam administration can prevent the emergence of adult hippocampal hyperactivity, dopamine-system hyperactivity, and associated psychosis-relevant behaviors. Here, we sought to characterize hippocampal GABAA and NMDA receptors in MAM-treated rats and to elucidate the receptor mechanisms underlying the promising effects of peripubertal diazepam exposure. Quantitative receptor autoradiography was used to measure receptor density in the dorsal hippocampus CA1, ventral hippocampus CA1, and ventral subiculum. Specifically, [3H]-Ro15-4513 was used to quantify the density of α5GABAA receptors (α5GABAAR), [3H]-flumazenil to quantify α1-3;5GABAAR, and [3H]-MK801 to quantify NMDA receptors. MAM rats exhibited anxiety and schizophrenia-relevant behaviors as measured by elevated plus maze and amphetamine-induced hyperlocomotion (AIH), although diazepam only partially rescued these behaviors. α5GABAAR density was reduced in MAM-treated rats in all hippocampal sub-regions, and negatively correlated with AIH. Ventral hippocampus CA1 α5GABAAR density was positively correlated with anxiety-like behavior. Dorsal hippocampus CA1 NMDA receptor density was increased in MAM-treated rats, and positively correlated with AIH. [3H]-flumazenil revealed no significant effects. Finally, we found no significant effect of diazepam treatment on receptor densities, potentially related to the only partial rescue of schizophrenia-relevant phenotypes. Overall, our findings provide first evidence of α5GABAAR and NMDA receptor abnormalities in the MAM model, suggesting that more selective pharmacological agents may become a novel therapeutic mechanism in schizophrenia.

Computational Modeling of Electroencephalography and Functional Magnetic Resonance Imaging Paradigms Indicates a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia

BACKGROUND

Diminished synaptic gain-the sensitivity of postsynaptic responses to neural inputs-may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms.

METHODS

People with schizophrenia diagnoses (PScz) (n = 108), their relatives (n = 57), and control subjects (n = 107) underwent 3 electroencephalography (EEG) paradigms-resting, mismatch negativity, and 40-Hz auditory steady-state response-and resting functional magnetic resonance imaging. Dynamic causal modeling was used to quantify synaptic connectivity in cortical microcircuits.

RESULTS

Classic group differences in EEG features between PScz and control subjects were replicated, including increased theta and other spectral changes (resting EEG), reduced mismatch negativity, and reduced 40-Hz power. Across all 4 paradigms, characteristic PScz data features were all best explained by models with greater self-inhibition (decreased synaptic gain) in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in PScz in 3 paradigms.

CONCLUSIONS

First, characteristic EEG changes in PScz in 3 classic paradigms are all attributable to the same underlying parameter change: greater self-inhibition in pyramidal cells. Second, psychotic symptoms in PScz relate to disinhibition in neural circuits. These findings are more commensurate with the hypothesis that in PScz, a primary loss of synaptic gain on pyramidal cells is then compensated by interneuron downregulation (rather than the converse). They further suggest that psychotic symptoms relate to this secondary downregulation.

Mismatch negativity amplitude in first-degree relatives of individuals with psychotic disorders: Links with cognition and schizotypy

Mismatch negativity (MMN) amplitude is reliably reduced in psychotic disorders. While several studies have examined this effect in first-degree relatives of individuals with schizophrenia, few have sought to quantify deficits in relatives of individuals with other psychotic disorders. While some conclude that, compared to healthy subjects, first-degree relatives of schizophrenia show reduced MMN, others contradict this finding. Furthermore, though MMN is often shown to be associated with cognitive impairments and clinical symptoms in psychotic disorders, to our knowledge no studies have sought to fully examine these relationships in studies of first-degree relatives. The present study sought to clarify the extent of MMN amplitude reductions in a large sample of siblings of individuals with diverse psychotic disorders (n = 67), compared to probands with psychosis (n = 221) and never psychotic comparison subjects (n = 251). We further examined associations of MMN amplitude with cognition and schizotypal symptoms across these groups. We found that MMN amplitude was intact in siblings compared to probands. MMN amplitude was associated with cognition and schizotypal symptoms dimensionally across levels of familial risk. The present results imply that MMN reductions do not reflect genetic risk for psychotic disorders per se, and instead emerge as a result of, or in conjunction with, clinical features associated with psychosis. Such findings carry important implications for the utility of MMN amplitude as an indicator of inherited risk, and suggest that this component may be best conceptualized as an endophenotype for clinical symptoms and cognitive impairments, rather than risk for psychosis per se.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Altered Effective Connectivity within an Oculomotor Control Network in Unaffected Relatives of Individuals with Schizophrenia

The ability to rapidly stop or change a planned action is a critical cognitive process that is impaired in schizophrenia. The current study aimed to examine whether this impairment reflects familial vulnerability to schizophrenia across two experiments comparing unaffected first-degree relatives to healthy controls. First, we examined performance on a saccadic stop-signal task that required rapid inhibition of an eye movement. Then, in a different sample, we investigated behavioral and neural responses (using fMRI) during a stop-signal task variant that required rapid modification of a prepared eye movement. Here, we examined differences between relatives and healthy controls in terms of activation and effective connectivity within an oculomotor control network during task performance. Like individuals with schizophrenia, the unaffected relatives showed behavioral evidence for more inefficient inhibitory processes. Unlike previous findings in individuals with schizophrenia, however, the relatives showed evidence for a compensatory waiting strategy. Behavioral differences were accompanied by more activation among the relatives in task-relevant regions across conditions and group differences in effective connectivity across the task that were modulated differently by the instruction to exert control over a planned saccade. Effective connectivity parameters were related to behavioral measures of inhibition efficiency. The results suggest that individuals at familial risk for schizophrenia were engaging an oculomotor control network differently than controls and in a way that compromises inhibition efficiency.

Stronger Top-Down and Weaker Bottom-Up Frontotemporal Connections During Sensory Learning Are Associated With Severity of Psychotic Phenomena

Recent theories in computational psychiatry propose that unusual perceptual experiences and delusional beliefs may emerge as a consequence of aberrant inference and disruptions in sensory learning. The current study investigates these theories and examines the alterations that are specific to schizophrenia spectrum disorders vs those that occur as psychotic phenomena intensify, regardless of diagnosis. We recruited 66 participants: 22 schizophrenia spectrum inpatients, 22 nonpsychotic inpatients, and 22 nonclinical controls. Participants completed the reversal oddball task with volatility manipulated. We recorded neural responses with electroencephalography and measured behavioral errors to inferences on sound probabilities. Furthermore, we explored neural dynamics using dynamic causal modeling (DCM). Attenuated prediction errors (PEs) were specifically observed in the schizophrenia spectrum, with reductions in mismatch negativity in stable, and P300 in volatile, contexts. Conversely, aberrations in connectivity were observed across all participants as psychotic phenomena increased. DCM revealed that impaired sensory learning behavior was associated with decreased intrinsic connectivity in the left primary auditory cortex and right inferior frontal gyrus (IFG); connectivity in the latter was also reduced with greater severity of psychotic experiences. Moreover, people who experienced more hallucinations and psychotic-like symptoms had decreased bottom-up and increased top-down frontotemporal connectivity, respectively. The findings provide evidence that reduced PEs are specific to the schizophrenia spectrum, but deficits in brain connectivity are aligned on the psychosis continuum. Along the continuum, psychotic experiences were related to an aberrant interplay between top-down, bottom-up, and intrinsic connectivity in the IFG during sensory uncertainty. These findings provide novel insights into psychosis neurocomputational pathophysiology.

Cortical ensembles selective for context

Neural processing of sensory information is strongly influenced by context. For instance, cortical responses are reduced to predictable stimuli, while responses are increased to novel stimuli that deviate from contextual regularities. Such bidirectional modulation based on preceding sensory context is likely a critical component or manifestation of attention, learning, and behavior, yet how it arises in cortical circuits remains unclear. Using volumetric two-photon calcium imaging and local field potentials in primary visual cortex (V1) from awake mice presented with visual "oddball" paradigms, we identify both reductions and augmentations of stimulus-evoked responses depending, on whether the stimulus was redundant or deviant, respectively. Interestingly, deviance-augmented responses were limited to a specific subset of neurons mostly in supragranular layers. These deviance-detecting cells were spatially intermixed with other visually responsive neurons and were functionally correlated, forming a neuronal ensemble. Optogenetic suppression of prefrontal inputs to V1 reduced the contextual selectivity of deviance-detecting ensembles, demonstrating a causal role for top-down inputs. The presence of specialized context-selective ensembles in primary sensory cortex, modulated by higher cortical areas, provides a circuit substrate for the brain's construction and selection of prediction errors, computations which are key for survival and deficient in many psychiatric disorders.

Transcriptome-wide association study reveals two genes that influence mismatch negativity

Mismatch negativity (MMN) is a differential electrophysiological response measuring cortical adaptability to unpredictable stimuli. MMN is consistently attenuated in patients with psychosis. However, the genetics of MMN are uncharted, limiting the validation of MMN as a psychosis endophenotype. Here, we perform a transcriptome-wide association study of 728 individuals, which reveals 2 genes (FAM89A and ENGASE) whose expression in cortical tissues is associated with MMN. Enrichment analyses of neurodevelopmental expression signatures show that genes associated with MMN tend to be overexpressed in the frontal cortex during prenatal development but are significantly downregulated in adulthood. Endophenotype ranking value calculations comparing MMN and three other candidate psychosis endophenotypes (lateral ventricular volume and two auditory-verbal learning measures) find MMN to be considerably superior. These results yield promising insights into sensory processing in the cortex and endorse the notion of MMN as a psychosis endophenotype.

Criticality in the Healthy Brain

The excellence of the brain is its robustness under various types of noise and its flexibility under various environments. However, how the brain works is still a mystery. The critical brain hypothesis proposes a possible mechanism and states that criticality plays an important role in the healthy brain. Herein, using an electroencephalography dataset obtained from patients with psychotic disorders (PDs), ultra-high risk (UHR) individuals and healthy controls (HCs), and its dynamical network analysis, we show that the brain of HCs remains around a critical state, whereas that of patients with PD falls into more stable states. Meanwhile, the brain of UHR individuals is similar to that of PD in terms of entropy but is analogous to that of HCs in causality patterns. These results not only provide evidence for the criticality of the normal brain but also highlight the practicability of using an analytic biophysical tool to study the dynamical properties of mental diseases.

Cortical beta oscillations reflect the contextual gating of visual action feedback

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We decouple seen and felt hand postures during action via virtual reality.

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Vision of the hand is either task-relevant or a distractor.

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Task-relevance of vision is reflected by in- or decreases of occipital beta power.

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DCM suggests underlying changes in cortical (visual) excitability.

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Occipital beta may indicate the contextual gating of visual action feedback.

In sensorimotor integration, the brain needs to decide how its predictions should accommodate novel evidence by ‘gating’ sensory data depending on the current context. Here, we examined the oscillatory correlates of this process by recording magnetoencephalography (MEG) data during a new task requiring action under intersensory conflict. We used virtual reality to decouple visual (virtual) and proprioceptive (real) hand postures during a task in which the phase of grasping movements tracked a target (in either modality). Thus, we rendered visual information either task-relevant or a (to-be-ignored) distractor. Under visuo-proprioceptive incongruence, occipital beta power decreased (relative to congruence) when vision was task-relevant but increased when it had to be ignored. Dynamic causal modeling (DCM) revealed that this interaction was best explained by diametrical, task-dependent changes in visual gain. These results suggest a crucial role for beta oscillations in the contextual gating (i.e., gain or precision control) of visual vs proprioceptive action feedback, depending on current behavioral demands.

Minds and Brains, Sleep and Psychiatry

Objective

This article offers a philosophical thesis for psychiatric disorders that rests upon some simple truths about the mind and brain. Specifically, it asks whether the dual aspect monism—that emerges from sleep research and theoretical neurobiology—can be applied to pathophysiology and psychopathology in psychiatry.

Methods

Our starting point is that the mind and brain are emergent aspects of the same (neuronal) dynamics; namely, the brain–mind. Our endpoint is that synaptic dysconnection syndromes inherit the same dual aspect; namely, aberrant inference or belief updating on the one hand, and a failure of neuromodulatory synaptic gain control on the other. We start with some basic considerations from sleep research that integrate the phenomenology of dreaming with the neurophysiology of sleep.

Results

We then leverage this treatment by treating the brain as an organ of inference. Our particular focus is on the role of precision (i.e., the representation of uncertainty) in belief updating and the accompanying synaptic mechanisms.

Conclusions

Finally, we suggest a dual aspect approach—based upon belief updating (i.e., mind processes) and its neurophysiological implementation (i.e., brain processes)—has a wide explanatory compass for psychiatry and various movement disorders. This approach identifies the kind of pathophysiology that underwrites psychopathology—and points to certain psychotherapeutic and psychopharmacological targets, which may stand in mechanistic relation to each other.

The ‘mind’ emerges from Bayesian belief updating in the ‘brain’

Psychopathology can be read as aberrant belief updating.

Aberrant belief updating follows from any neuromodulatory synaptopathy

Development of Neural Response to Novel Sounds in Fragile X Syndrome: Potential Biomarkers

Auditory processing abnormalities in fragile X syndrome (FXS) may contribute to difficulties with language development, pattern identification, and contextual updating. Participants with FXS (N = 41) and controls (N = 27) underwent auditory event-related potentials during presentation of an oddball paradigm. Data was adequate for analysis for 33 participants with FXS and 27 controls (age 4-51 y, 13 females [FXS]; 4-54 y, 11 females [control]). Participants with FXS showed larger N1 and P2 amplitudes, abnormal lack of modulation of P1 and P2 amplitudes and P2 latency in response to oddball stimuli ) relative to controls: Females with FXS were more similar to controls. Participants with FXS showed a marginal speeding of the P2 latency, suggesting potentiation to oddball stimuli rather than habituation. Participants with FXS showed a heightened N1 habituation effect compared to controls. Gamma power was significantly higher for participants with FXS. Groups did not differ on mismatch negativity. Both controls and participants with FXS showed similar developmental trajectories in P1 and N1 amplitude, P2 latency, and gamma power, but not for P2 amplitude. One month retest analyses performed in 14 participants suggest strong test-retest reliability for most measures. Individuals with FXS show previously demonstrated increased response amplitude and high frequency neural activity. Despite an overall normal developmental trajectory for most measures, individuals with FXS show age-independent but gender-dependent decreases in complex processing of novel stimuli. Many markers show strong retest reliability even in children and thus are potential biomarkers for clinical trials in FXS.

Aberrant effective connectivity is associated with positive symptoms in first-episode schizophrenia

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We use DCM in patients newly diagnosed with schizophrenia.

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Patients were naïve to therapeutic antipsychotics, but not completely drug naïve.

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Patients have stronger feedforward connectivity than matched healthy controls.

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Stronger positive symptoms are associated with disinhibition in the temporal lobe.

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In active inference, this may relate to aberrant precision and prediction errors.

Schizophrenia is a neurodevelopmental psychiatric disorder thought to result from synaptic dysfunction that affects distributed brain connectivity, rather than any particular brain region. While symptomatology is traditionally divided into positive and negative symptoms, abnormal social cognition is now recognized a key component of schizophrenia. Nonetheless, we are still lacking a mechanistic understanding of effective brain connectivity in schizophrenia during social cognition and how it relates to clinical symptomatology. To address this question, we used fMRI and dynamic causal modelling (DCM) to test for abnormal brain connectivity in twenty-four patients with first-episode schizophrenia (FES) compared to twenty-five matched controls performing the Human Connectome Project (HCP) social cognition paradigm. Patients had not received regular therapeutic antipsychotics, but were not completely drug naïve. Whilst patients were less accurate than controls in judging social stimuli from non-social stimuli, our results revealed an increase in feedforward connectivity from motion-sensitive V5 to posterior superior temporal sulcus (pSTS) in patients compared to matched controls. At the same time, patients with a higher degree of positive symptoms had more disinhibition within pSTS, a region computationally involved in social cognition. We interpret these findings the framework of active inference, where increased feedforward connectivity may encode aberrant prediction errors from V5 to pSTS and local disinhibition within pSTS may reflect aberrant encoding of the precision of cortical representations about social stimuli.

Statistical decision theory and multiscale analyses of human brain data

BACKGROUND

In the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often focus on a single spatiotemporal scale only.

NEW METHOD

We discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain data at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data.

RESULTS

We provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6.

COMPARISON WITH EXISTING METHODS

This is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed.

CONCLUSIONS

We discuss a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it allows us to validate macroscale models by exploiting animal data.

A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia

Mismatch negativity (MMN) is a widely used biological marker for schizophrenia research. Previous studies reported that MMN amplitude was reduced in schizophrenia and that reduced MMN amplitude was associated with cognitive impairments and poor functional outcome in schizophrenia. However, the neurobiological mechanisms underlying the reduced MMN amplitude remain unclear. Recent studies suggest that reduced MMN amplitude may reflect altered predictive coding in schizophrenia. In this paper, we reviewed MMN studies that used new paradigms and computational modeling to investigate altered predictive coding in schizophrenia. Studies using the roving oddball paradigm and modified oddball paradigm revealed that the effects of conditional probability were impaired in schizophrenia. Studies using omission paradigms and many-standards paradigms revealed that prediction error, but not adaptation, was impaired in schizophrenia. A study using a local-global paradigm revealed that hierarchical structures were impaired at both local and global levels in schizophrenia. Furthermore, studies using dynamic causal modeling revealed that neural networks with hierarchical structures were impaired in schizophrenia. These findings indicate that altered predictive coding underlies the reduced MMN amplitude in schizophrenia. However, there are several unsolved questions about optimal procedures, association among paradigms, and heterogeneity of schizophrenia. Future studies using several paradigms and computational modeling may solve these questions, and may lead to clarifying the pathophysiology of schizophrenia and to the development of individualized treatments for schizophrenia.

Dynamic Causal Modeling of the Relationship between Cognition and Theta-alpha Oscillations in Adults with Down Syndrome

Individuals with Down syndrome (DS) show high inter-subject variability in cognitive ability and have an ultra-high risk of developing dementia (90% lifetime prevalence). Elucidating factors underlying variability in cognitive function can inform us about intellectual disability (ID) and may improve our understanding of factors associated with later cognitive decline. Increased neuronal inhibition has been posited to contribute to ID in DS. Combining electroencephalography (EEG) with dynamic causal modeling (DCM) provides a non-invasive method for investigating excitatory/inhibitory mechanisms. Resting-state EEG recordings were obtained from 36 adults with DS with no evidence of cognitive decline. Theta–alpha activity (4–13 Hz) was characterized in relation to general cognitive ability (raw Kaufmann’s Brief Intelligence Test second Edition (KBIT-2) score). Higher KBIT-2 was associated with higher frontal alpha peak amplitude and higher theta–alpha band power across distributed regions. Modeling this association with DCM revealed intrinsic self-inhibition was the key network parameter underlying observed differences in 4–13 Hz power in relation to KBIT-2 and age. In particular, intrinsic self-inhibition in right V1 was negatively correlated with KBIT-2. Results suggest intrinsic self-inhibition within the alpha network is associated with individual differences in cognitive ability in adults with DS, and may provide a potential therapeutic target for cognitive enhancement.

Working Memory Load Modulates Neuronal Coupling

There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1-3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC-FEF-LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load.

An Investigation of the Free Energy Principle for Emotion Recognition

This paper offers a prospectus of what might be achievable in the development of emotional recognition devices. It provides a conceptual overview of the free energy principle; including Markov blankets, active inference, and-in particular-a discussion of selfhood and theory of mind, followed by a brief explanation of how these concepts can explain both neural and cultural models of emotional inference. The underlying hypothesis is that emotion recognition and inference devices will evolve from state-of-the-art deep learning models into active inference schemes that go beyond marketing applications and become adjunct to psychiatric practice. Specifically, this paper proposes that a second wave of emotion recognition devices will be equipped with an emotional lexicon (or the ability to epistemically search for one), allowing the device to resolve uncertainty about emotional states by actively eliciting responses from the user and learning from these responses. Following this, a third wave of emotional devices will converge upon the user's generative model, resulting in the machine and human engaging in a reciprocal, prosocial emotional interaction, i.e., sharing a generative model of emotional states.

Making Sense of Mismatch Negativity

Evoked potentials provide valuable insight into brain processes that are integral to our ability to interact effectively and efficiently in the world. The mismatch negativity (MMN) component of the evoked potential has proven highly informative on the ways in which sensitivity to regularity contributes to perception and cognition. This review offers a compendium of research on MMN with a view to scaffolding an appreciation for its use as a tool to explore the way regularities contribute to predictions about the sensory environment over many timescales. In compiling this work, interest in MMN as an index of sensory encoding and memory are addressed, as well as attention. Perspectives on the possible underlying computational processes are reviewed as well as recent observations that invite consideration of how MMN relates to how we learn, what we learn, and why.

Distinct Time-Course of Alterations of Groups I and II Metabotropic Glutamate Receptor and GABAergic Receptor Expression Along the Dorsoventral Hippocampal Axis in an Animal Model of Psychosis

Psychosis is a clinical state that encompasses a range of abnormal conditions, including distortions in sensory information processing and the resultant delusional thinking, emotional discordance and cognitive impairments. Upon developing this condition, the rate at which cognitive and behavioral deteriorations progress steadily increases suggesting an active contribution of the first psychotic event to the progression of structural and functional abnormalities and disease establishment in diagnosed patients. Changes in GABAergic and glutamatergic function, or expression, in the hippocampus have been proposed as a key factor in the pathophysiology of psychosis. However, little is known as to the time-point of onset of putative changes, to what extent they are progressive, and their relation to disease stabilization. Here, we characterized the expression and distribution patterns of groups I and II metabotropic glutamate (mGlu) receptors and GABA receptors 1 week and 3 months after systemic treatment with an N-methyl-D-aspartate receptor (NMDAR) antagonist (MK801) that is used to model a psychosis-like state in adult rats. We found an early alteration in the expression of mGlu1, mGlu2/3, and GABA_B receptors across the hippocampal dorsoventral and transverse axes. This expanded to include an up-regulation of mGlu5 levels across the entire CA1 region and a reduction in GABA_B expression, as well as GAD67-positive interneurons particularly in the dorsal hippocampus that appeared 3 months after treatment. Our findings indicate that a reduction of excitability may occur in the hippocampus soon after first-episode psychosis. This changes, over time, into increased excitability. These hippocampus-specific alterations are likely to contribute to the pathophysiology and stabilization of psychosis.

Auditory white matter pathways are associated with effective connectivity of auditory prediction errors within a fronto-temporal network

Auditory prediction errors, i.e. the mismatch between predicted, forthcoming auditory sensations and actual sensory input, trigger the detection of surprising auditory events in the environment. Auditory mismatches engage a hierarchical functional network of cortical sources, which are also interconnected by auditory white matter pathways. Hence it is plausible that these structural and functional networks are quantitatively related. The present study set out to investigate whether structural connectivity of auditory white matter pathways enables the effective connectivity underpinning auditory mismatch responses. Participants (N = 89) underwent diffusion weighted magnetic resonance imaging (MRI) and electroencephalographic (EEG) recordings. Anatomically-constrained tractography was used to extract auditory white matter pathways, namely the bilateral arcuate fasciculi, inferior fronto-occipital fasciculi (IFOF), and the auditory interhemispheric pathway, from which Apparent Fibre Density (AFD) was calculated. EEG data were recorded in the same participants during a stochastic oddball paradigm, which was used to elicit auditory prediction error responses. Dynamic causal modelling was used to investigate the effective connectivity underlying auditory mismatch responses generated in brain regions interconnected by the above mentioned auditory white matter pathways. Our results showed that brain areas interconnected by all auditory white matter pathways best explained the dynamics of auditory mismatch responses. Furthermore, AFD in the right arcuate fasciculus was significantly associated with the effective connectivity between the cortical regions that lie within it. Taken together, these findings indicate that auditory prediction errors recruit a fronto-temporal network of brain regions that are effectively and structurally connected by auditory white matter pathways.

Alterations in Schizophrenia-Associated Genes Can Lead to Increased Power in Delta Oscillations

Genome-wide association studies have implicated many ion channels in schizophrenia pathophysiology. Although the functions of these channels are relatively well characterized by single-cell studies, the contributions of common variation in these channels to neurophysiological biomarkers and symptoms of schizophrenia remain elusive. Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta-frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a deficit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia.

Cortical Areas Associated With Mismatch Negativity: A Connectivity Study Using Propofol Anesthesia

Auditory mismatch negativity (MMN) is an event-related potential (ERP) waveform induced by rare deviant stimuli that occur in a stream of regular auditory stimuli. The generators of MMN are believed to include several different cortical regions like the bilateral temporal and the right inferior frontal gyrus (IFG). However, exact cortical regions associated with MMN remain controversial. In this study, we compared the number of long-distance connections induced by the standard and deviant stimuli during awake state and propofol anesthesia state to identify the cortical areas associated with the generation of MMN. In awake state, we find that deviant stimuli synchronize more information between the right frontal and temporal than standard stimuli. Moreover, we find that the deviant stimuli in awake state activate the bilateral frontal, central areas, the left temporal and parietal areas as compared to the anesthesia state, whereas the standard stimuli do not. These results suggest that, in addition to the bilateral temporal and the right IFG, the bilateral frontal and centro-parietal regions also contribute to the generation of MMN.

Selective Prefrontal Disinhibition in a Roving Auditory Oddball Paradigm Under N-Methyl-D-Aspartate Receptor Blockade

BACKGROUND

Disturbances in N-methyl-D-aspartate receptors (NMDARs)-as implicated in patients with schizophrenia-can cause regionally specific electrophysiological effects. Both animal models of NMDAR blockade and clinical studies in patients with schizophrenia have suggested that behavioral phenotypes are associated with reduction in inhibition within the frontal cortex.

METHODS

Here we investigate event-related potentials to a roving auditory oddball paradigm under ketamine in healthy human volunteers (N= 18; double-blind, placebo-controlled, crossover design). Using recent advances in Bayesian modeling of group effects in dynamic causal modeling, we fit biophysically plausible network models of the auditory processing hierarchy to whole-scalp event-related potential recordings. This allowed us to identify regionally specific effects of ketamine in a distributed network of interacting cortical sources.

RESULTS

We show that the effect of ketamine is best explained as a selective change in intrinsic inhibition, with a pronounced ketamine-induced reduction of inhibitory interneuron connectivity in frontal sources, compared with temporal sources. Simulations of these changes in an integrated microcircuit model shows that they are associated with a reduction in superficial pyramidal cell activity that can explain drug effects observed in the event-related potential.

CONCLUSIONS

These results are consistent with findings from invasive recordings in animal models exposed to NMDAR blockers, and provide evidence that inhibitory interneuron-specific NMDAR dysfunction may be sufficient to explain electrophysiological abnormalities induced by NMDAR blockade in human subjects.

Altered auditory processing and effective connectivity in 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11.2DS) is one of the most common copy number variants and confers a markedly increased risk for schizophrenia. As such, 22q11.2DS is a homogeneous genetic liability model which enables studies to delineate functional abnormalities that may precede disease onset. Mismatch negativity (MMN), a brain marker of change detection, is reduced in people with schizophrenia compared to healthy controls. Using dynamic causal modelling (DCM), previous studies showed that top-down effective connectivity linking the frontal and temporal cortex is reduced in schizophrenia relative to healthy controls in MMN tasks. In the search for early risk-markers for schizophrenia we investigated the neural basis of change detection in a group with 22q11.2DS. We recorded high-density EEG from 19 young non-psychotic 22q11.2 deletion carriers, as well as from 27 healthy non-carriers with comparable age distribution and sex ratio, while they listened to a sequence of sounds arranged in a roving oddball paradigm. Despite finding no significant reduction in the MMN responses, whole-scalp spatiotemporal analysis of responses to the tones revealed a greater fronto-temporal N1 component in the 22q11.2 deletion carriers. DCM showed reduced intrinsic connection within right primary auditory cortex as well as in the top-down, connection from the right inferior frontal gyrus to right superior temporal gyrus for 22q11.2 deletion carriers although not surviving correction for multiple comparison. We discuss these findings in terms of reduced adaptation and a general increased sensitivity to tones in 22q11.2DS.

Top-Down Disconnectivity in Schizophrenia During P300 Tasks

Cognitive deficits in schizophrenia are correlated with the dysfunctions of distinct brain regions including anterior cingulate cortex (ACC) and prefrontal cortex (PFC). Apart from the dysfunctions of the intrinsic connectivity of related areas, how the coupled neural populations work is also crucial in related processes. Twenty-four patients with schizophrenia (SZs) and 24 matched healthy controls (HCs) were recruited in our study. Based on the electroencephalogram (EEG) datasets recorded, the Dynamic Causal Modeling (DCM) was then adopted to estimate how the brain architecture adapts among related areas in SZs and to investigate the mechanism that accounts for their cognitive deficits. The distinct winning models in SZs and HCs consistently emphasized the importance of ACC in regulating the elicitations of P300s. Specifically, comparing to that in HCs, the winning model in SZs uncovered a compensatory pathway from dorsolateral PFC to intraparietal sulcus that promised the SZs' accomplishing P300 tasks. The findings demonstrated that the “disconnectivity hypothesis” is helpful and useful in explaining the cognitive deficits in SZs, while the brain architecture adapted with related compensatory pathway promises the limited brain cognitions in SZs. This study provides a new viewpoint that deepens our understanding of the cognitive deficits in schizophrenia.

Visual Mismatch and Predictive Coding: A Computational Single-Trial ERP Study

Predictive coding (PC) posits that the brain uses a generative model to infer the environmental causes of its sensory data and uses precision-weighted prediction errors (pwPEs) to continuously update this model. While supported by much circumstantial evidence, experimental tests grounded in formal trial-by-trial predictions are rare. One partial exception is event-related potential (ERP) studies of the auditory mismatch negativity (MMN), where computational models have found signatures of pwPEs and related model-updating processes. Here, we tested this hypothesis in the visual domain, examining possible links between visual mismatch responses and pwPEs. We used a novel visual "roving standard" paradigm to elicit mismatch responses in humans (of both sexes) by unexpected changes in either color or emotional expression of faces. Using a hierarchical Bayesian model, we simulated pwPE trajectories of a Bayes-optimal observer and used these to conduct a comprehensive trial-by-trial analysis across the time × sensor space. We found significant modulation of brain activity by both color and emotion pwPEs. The scalp distribution and timing of these single-trial pwPE responses were in agreement with visual mismatch responses obtained by traditional averaging and subtraction (deviant-minus-standard) approaches. Finally, we compared the Bayesian model to a more classical change model of MMN. Model comparison revealed that trial-wise pwPEs explained the observed mismatch responses better than categorical change detection. Our results suggest that visual mismatch responses reflect trial-wise pwPEs, as postulated by PC. These findings go beyond classical ERP analyses of visual mismatch and illustrate the utility of computational analyses for studying automatic perceptual processes.SIGNIFICANCE STATEMENT Human perception is thought to rely on a predictive model of the environment that is updated via precision-weighted prediction errors (pwPEs) when events violate expectations. This "predictive coding" view is supported by studies of the auditory mismatch negativity brain potential. However, it is less well known whether visual perception of mismatch relies on similar processes. Here we combined computational modeling and electroencephalography to test whether visual mismatch responses reflected trial-by-trial pwPEs. Applying a Bayesian model to series of face stimuli that violated expectations about color or emotional expression, we found significant modulation of brain activity by both color and emotion pwPEs. A categorical change detection model performed less convincingly. Our findings support the predictive coding interpretation of visual mismatch responses.

Sensory prediction errors in the continuum of psychosis

Sensory prediction errors are fundamental brain responses that signal a violation of expectation in either the internal or external sensory environment, and are therefore crucial for survival and adaptive behaviour. Patients with schizophrenia show deficits in these internal and external sensory prediction errors, which can be measured using electroencephalography (EEG) components such as N1 and mismatch negativity (MMN), respectively. New evidence suggests that these deficits in sensory prediction errors are more widely distributed on a continuum of psychosis, whereas psychotic experiences exist to varying degrees throughout the general population. In this paper, we review recent findings in sensory prediction errors in the auditory domain across the continuum of psychosis, and discuss these in light of the predictive coding hypothesis.

Using EEG-Guided Basket and Umbrella Trials in Psychiatry: A Precision Medicine Approach for Cognitive Impairment in Schizophrenia

Due to advances over the last several decades, many fields of medicine are moving toward a precision medicine approach where treatments are tailored to nuanced patient factors. While in some disciplines these innovations are commonplace leading to unique biomarker-guided experimental medicine trials, there are no such analogs in psychiatry. In this brief review, we will overview two unique biomarker-guided trial designs for future use in psychiatry: basket and umbrella trials. We will illustrate how such trials could be useful in psychiatry using schizophrenia as a candidate illness, the EEG measure mismatch negativity as the candidate biomarker, and cognitive impairment as the target disease dimension.

Altered intrinsic and extrinsic connectivity in schizophrenia

Schizophrenia is a disorder characterized by functional dysconnectivity among distributed brain regions. However, it is unclear how causal influences among large-scale brain networks are disrupted in schizophrenia. In this study, we used dynamic causal modeling (DCM) to assess the hypothesis that there is aberrant directed (effective) connectivity within and between three key large-scale brain networks (the dorsal attention network, the salience network and the default mode network) in schizophrenia during a working memory task. Functional MRI data during an n-back task from 40 patients with schizophrenia and 62 healthy controls were analyzed. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes, we found that intrinsic (within-region) and extrinsic (between-region) effective connectivity involving prefrontal regions were abnormal in schizophrenia. Specifically, in patients (i) inhibitory self-connections in prefrontal regions of the dorsal attention network were decreased across task conditions; (ii) extrinsic connectivity between regions of the default mode network was increased; specifically, from posterior cingulate cortex to the medial prefrontal cortex; (iii) between-network extrinsic connections involving the prefrontal cortex were altered; (iv) connections within networks and between networks were correlated with the severity of clinical symptoms and impaired cognition beyond working memory. In short, this study revealed the predominance of reduced synaptic efficacy of prefrontal efferents and afferents in the pathophysiology of schizophrenia.

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A first use of hierarchical modeling of effective connectivity to characterize large-scale networks in schizophrenia.

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Intrinsic and extrinsic effective connectivity involving prefrontal regions were abnormal in schizophrenia.

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Diagnostic connections could predict the severity of clinical symptoms and cognition in schizophrenia.

Is There an Association Between Mismatch Negativity and Cortical Thickness in Schizophrenia Patients?

INTRODUCTION

Mismatch negativity (MMN) is thought to reflect preattentive, automatic auditory processing. Reduced MMN amplitude is among the most robust findings in schizophrenia research. MMN generators have been shown to be located in the temporal and frontal cortices, which are key areas in the pathophysiology of schizophrenia. This study investigated whether frontotemporal cortical thickness was associated with reduced MMN current source density (CSD) strength in patients with schizophrenia.

METHODS

Sixteen schizophrenia patients and 18 healthy controls (HCs) were examined using magnetoencephalography while they performed a passive auditory oddball paradigm. All participants underwent a T1 structural magnetic resonance imaging scan in a separate session. We evaluated MMN CSD and cortical thickness, and their associations, in the superior and transverse temporal gyri, as well as in the inferior and middle frontal gyri.

RESULTS

Patients exhibited significantly reduced CSD strength in all temporal and frontal areas of interest relative to HCs. There was a positive correlation between CSD strength and cortical thickness in both temporal and frontal areas in HCs. However, schizophrenia patients showed negative correlations between CSD strength and cortical thickness in the bilateral inferior frontal gyri. Additionally, we found positive correlations between frontal cortical thickness and negative and total scores on the Positive and Negative Syndrome Scale (PANSS).

CONCLUSIONS

Our findings provide evidence for deficient temporal and frontal MMN generators and a disruption of normal structure-function relationship in patients with schizophrenia.

Computational Nosology and Precision Psychiatry

This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present-and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations.

Mismatch Negativity as an Indicator of Cognitive Sub-Domain Dysfunction in Amyotrophic Lateral Sclerosis

Objective

To evaluate the utility of mismatch negativity (MMN), a neurophysiologic marker of non-motor cognitive processing, in amyotrophic lateral sclerosis (ALS).

Methods

89 patients, stratified into 4 different phenotypic presentations of ALS (67 spinal-onset, 15 bulbar-onset, 7 ALS-FTD, 7 C9ORF72 gene careers), and 19 matched controls underwent 128-channel EEG data recording. Subjects were presented with standard auditory tones interleaved with pitch-deviant tones in three recording blocks. The MMN response was quantified by peak amplitude, peak delay, average amplitude, and average delay, 100–300 ms after stimuli. 64 patients underwent cognitive screening using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS), and 38 participants underwent contemporaneous cognitive assessment using the Stroop Color–Word Interference test (CWIT), which measures attention shift, inhibitory control, and error monitoring.

Results

The MMN response was observed in frontal and frontocentral regions of patient and control groups. Compared to controls, waveforms were attenuated in early onset, and the average delay was significantly increased in all of the ALS subgroups, with no significant difference between subgroups. Comparing with the control response, the ALS MMN response clustered into four new subgroups characterized by differences in response latency. The increased average delay correlated with changes in the Stroop CWIT; however, it did not show a direct relationship with age, gender, traditional phenotypes, revised ALS Functional Rating Scale, or ECAS scores.

Conclusion and significance

The MMN response in ALS patients reflects the cognitive dysfunction in specific sub-domains, as the new patient subgroups, identified by cluster analysis, do not segregate with existing clinical or cognitive classifications. Event-related potentials can provide additional quantitative neurophysiologic measures of impairment in specific cognitive sub-domains from which it may be possible to generate novel biologically relevant subgroups of ALS.

Hierarchical disruption in the Bayesian brain: Focal epilepsy and brain networks

In this opinion paper, we describe a combined view of functional and effective brain connectivity along with the free-energy principle for investigating persistent disruptions in brain networks of patients with focal epilepsy. These changes are likely reflected in effective connectivity along the cortical hierarchy and construct the basis of increased local functional connectivity in focal epilepsy. We propose a testable framework based on dynamic causal modelling and functional connectivity analysis with the capacity of explaining commonly observed connectivity changes during interictal periods. We then hypothesise their possible relation with disrupted free-energy minimisation in the Bayesian brain. This may offer a new approach for neuroimaging to specifically develop and address hypotheses regarding the network pathomechanisms underlying epileptic phenotypes.

Effects of an NMDA antagonist on the auditory mismatch negativity response to transcranial direct current stimulation

Transcranial direct current stimulation (tDCS) is a non-invasive form of brain stimulation which uses a weak constant current to alter cortical excitability and activity temporarily. tDCS-induced increases in neuronal excitability and performance improvements have been observed following anodal stimulation of brain regions associated with visual and motor functions, but relatively little research has been conducted with respect to auditory processing. Recently, pilot study results indicate that anodal tDCS can increase auditory deviance detection, whereas cathodal tDCS decreases auditory processing, as measured by a brain-based event-related potential (ERP), mismatch negativity (MMN). As evidence has shown that tDCS lasting effects may be dependent on N-methyl-D-aspartate (NMDA) receptor activity, the current study investigated the use of dextromethorphan (DMO), an NMDA antagonist, to assess possible modulation of tDCS's effects on both MMN and working memory performance. The study, conducted in 12 healthy volunteers, involved four laboratory test sessions within a randomised, placebo and sham-controlled crossover design that compared pre- and post-anodal tDCS over the auditory cortex (2 mA for 20 minutes to excite cortical activity temporarily and locally) and sham stimulation (i.e. device is turned off) during both DMO (50 mL) and placebo administration. Anodal tDCS increased MMN amplitudes with placebo administration. Significant increases were not seen with sham stimulation or with anodal stimulation during DMO administration. With sham stimulation (i.e. no stimulation), DMO decreased MMN amplitudes. Findings from this study contribute to the understanding of underlying neurobiological mechanisms mediating tDCS sensory and memory improvements.