Spontaneous Prediction Error Generation in Schizophrenia

Goal-directed human behavior is enabled by hierarchically-organized neural systems that process executive commands associated with higher brain areas in response to sensory and motor signals from lower brain areas. Psychiatric diseases and psychotic conditions are postulated to involve disturbances in these hierarchical network interactions, but the mechanism for how aberrant disease signals are generated in networks, and a systems-level framework linking disease signals to specific psychiatric symptoms remains undetermined. In this study, we show that neural networks containing schizophrenia-like deficits can spontaneously generate uncompensated error signals with properties that explain psychiatric disease symptoms, including fictive perception, altered sense of self, and unpredictable behavior. To distinguish dysfunction at the behavioral versus network level, we monitored the interactive behavior of a humanoid robot driven by the network. Mild perturbations in network connectivity resulted in the spontaneous appearance of uncompensated prediction errors and altered interactions within the network without external changes in behavior, correlating to the fictive sensations and agency experienced by episodic disease patients. In contrast, more severe deficits resulted in unstable network dynamics resulting in overt changes in behavior similar to those observed in chronic disease patients. These findings demonstrate that prediction error disequilibrium may represent an intrinsic property of schizophrenic brain networks reporting the severity and variability of disease symptoms. Moreover, these results support a systems-level model for psychiatric disease that features the spontaneous generation of maladaptive signals in hierarchical neural networks

Aberrant network connectivity during error processing in patients with schizophrenia

BACKGROUND: Neuroimaging methods have pointed to deficits in the interaction of large-scale brain networks in patients with schizophrenia. Abnormal connectivity of the right anterior insula (AI), a central hub of the salience network, is frequently reported and may underlie patients’ deficits in adaptive salience processing and cognitive control. While most previous studies used resting state approaches, we examined right AI interactions in a task-based fMRI study. METHODS: Patients with schizophrenia and healthy controls performed an adaptive version of the Eriksen Flanker task that was specifically designed to ensure a comparable number of errors between groups. RESULTS: We included 27 patients with schizophrenia and 27 healthy controls in our study. The between-groups comparison replicated the classic finding of reduced activation in the midcingulate cortex (MCC) in patients with schizophrenia during the commission of errors while controlling for confounding factors, such as task performance and error frequency, which have been neglected in many previous studies. Subsequent psychophysiological interaction analysis revealed aberrant functional connectivity (FC) between the right AI and regions in the inferior frontal gyrus and temporoparietal junction. Additionally, FC between the MCC and the dorsolateral prefrontal cortex was reduced. LIMITATIONS: As we examined a sample of medicated patients, effects of antipsychotic medication may have influenced our results. CONCLUSION: Overall, it appears that schizophrenia is associated with impairment of networks associated with detection of errors, refocusing of attention, superordinate guiding of cognitive control and their respective coordination

Anterior Cingulate Cortex Cells Identify Errors of Attentional Control Prior to Prefrontal Disengagement

The anterior cingulate cortex (ACC) is implicated in the detection of errors and the allocation of correctional adjustments. However, error detection alone is not sufficient to resolve and prevent future mistakes since errors can occur in various ways, subsequently requiring different adjustments. I therefore investigated whether the ACC tracks specific processing states that give rise to errors in order to identify which specific processing aspects need readjustment. To do this, my lab recorded from cells in the prefrontal cortex (PFC) of macaques while they were performing a selective-attention task that elicited three types of error. My study provides support for the functional role of the ACC in performance monitoring and specifying correctional adjustments through the tracking of specific sources of erroneous task outcomes

脳の計算論に基づく統合失調症の症状形成メカニズム

Tohoku University論

Neural basis of speech-gesture mismatch detection in schizophrenia spectrum disorders

Background: Patients suffering from schizophrenia spectrum disorders experience the grave effects of their illness on various facets of their daily lives. Previous investigations have shown that schizophrenia spectrum disorder patients have deficits in the perception and recognition of speech accompanied by gestures. In particular, they struggle to differentiate between related and unrelated speech-gesture combinations. Also, patients have considerable difficulties in understanding and processing abstract semantic information. A key region in the integration of speech and gesture is the inferior frontal gyrus embedded in a frontotemporal network, however, it is unclear which neural mechanisms contribute to defective mismatch and abstractness perception during the mismatch detection task. Objective: This study aimed to investigate the neural underpinnings of impaired speech-gesture mismatch detection and abstract semantic processing in schizophrenia spectrum disorder patients and to identify relevant dysfunctional brain areas. Methods: A novel mismatch-detection fMRI paradigm was implemented manipulating speech-gesture abstractness (abstract/concrete) and relatedness (related/unrelated). During fMRI data acquisition, 42 patients (schizophrenia, schizoaffective disorder or other non-organic psychotic disorder [ICD-10: F20, F25, F28; DSM-IV: 295.X]) and 36 healthy controls were presented with short video clips of an actor reciting abstract or concrete sentences accompanied by either a semantically related or unrelated gesture. Participants indicated via button press whether they perceived each gesture as matching the speech content or not. We compared task performances across groups and semantic context (abstract/concrete) using the detection rate from Signal Detection Theory by repeated-measures ANOVA. For the functional MRI data, an event-related design was chosen to measure the hemodynamic responses to each presented video. The data were loaded into a flexible-factorial analysis in a 2 x 2 x 2 design (group x abstractness x relatedness). Between-group conjunctions and group differences were respectively calculated for the contrasts unrelated ] related and abstract ] concrete in whole-brain analyses. Results: Speech-gesture mismatch detection performance was significantly impaired in patients compared to controls, irrespective of abstractness. fMRI data analysis revealed that patients exhibited reduced engagement of the right supplementary motor area and bilateral anterior cingulate cortices for unrelated ] related stimuli. A rostral part of the supplementary motor area was equally activated in both groups. In contrast, we found frontotemporal hyperactivation in patients for the same contrast. Furthermore, patients showed lower activation in bilateral frontal areas including the inferior frontal gyrus for all abstract ] concrete speech-gesture pairs. The temporal lobe, however, was engaged in both groups equally for this contrast. Discussion: In this study, we found evidence for impaired gesture-speech relatedness judgment in schizophrenia spectrum disorders. This was accompanied by dysfunctions of the supplementary motor area and the anterior cingulate cortices, possibly reflecting reduced facilitation of comprehension and defective error processing for unrelated speech-gesture combinations. The frontotemporal hyperactivation may represent an increased processing effort to compensate for the dysfunction. In addition, our data confirmed the conjecture of an inferior frontal gyrus dysfunction contributing to impaired processing of abstract semantic stimuli. Partially intact processing was discovered in a rostral part of the supplementary motor area for mismatches, and in the temporal lobes for abstract stimuli. These findings suggest that semantic processing in schizophrenia spectrum disorders is not completely dysfunctional, but that there is a functioning basis on which therapeutic measures can build on. Conclusion: We provide first evidence that impaired speech-gesture mismatch detection in schizophrenia spectrum disorders could be the result of dysfunctional activation of the supplementary motor area and anterior cingulate cortex. Failure to activate the left inferior frontal gyrus disrupts the integration of abstract speech-gesture combinations in particular. Future investigations should focus on brain-stimulation of these regions to improve communication and social functioning in schizophrenia spectrum disorders

An integrated network model of psychotic symptoms

FSW - Self-regulation models for health behavior and psychopathology - ou

Approaches For Capturing Time-Varying Functional Network Connectivity With Application to Normative Development and Mental Illness

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everything- from breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identification of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are currently diagnosed based on patients’ self-reported experiences, and observed symptoms and behaviors over the course of the illnesses. Therefore, efficient identification of biological-based markers (biomarkers) can lead to early diagnosis of these mental disorders, and provide a trajectory for disease progression. By applying advanced machine learning techniques on fMRI data, significant differences in brain function among patients with mental disorders and healthy controls can be identified. Moreover, by jointly estimating information from multiple modalities, such as, functional brain data and genetic factors, we can now investigate the relationship between brain function and genes. Functional connectivity (FC) has become a very common measure to characterize brain functions, where FC is defined as the temporal covariance of neural signals between multiple spatially distinct brain regions. Recently, researchers are studying the FC among functionally specialized brain networks which can be defined as a higher level of FC, and is termed as functional network connectivity (FNC, defined as the correlation value that summarizes the overall connection between brain ‘networks’ over time). Most functional connectivity studies have made the limiting assumption that connectivity is stationary over multiple minutes, and ignore to identify the time-varying and reoccurring patterns of FNC among brain regions (known as time-varying FNC). In this dissertation, we demonstrate the use of time-varying FNC features as potential biomarkers to differentiate between patients with mental disorders and healthy subjects. The developmental characteristics of time-varying FNC in children with typically developing brain and ASD have been extensively studies in a cross-sectional framework, and age-, sex- and disease-related FNC profiles have been proposed. Also, time-varying FNC is characterized in healthy adults and patients with severe mental disorders (SZ and BP). Moreover, an efficient classification algorithm is designed to identify patients and controls at individual level. Finally, a new framework is proposed to jointly utilize information from brain’s functional network connectivity and genetic features to find the associations between them. The frameworks that we presented here can help us understand the important role played by time-varying FNC to identify potential biomarkers for the diagnosis of severe mental disorders

Understanding mechanisms related to psychosis in Motor Neurone Disease

Psychosis is a challenging feature of the syndromes of motor neurone disease (MND), frontotemporal dementia (FTD) and their overlap (FTD-MND). Clinically evident psychosis is not common, except in those with C9orf72+ expansions. However, subthreshold psychosis or pre-psychosis processes are common and provide the opportunity to study the mechanisms of psychosis in MND and FTD-MND. My aim was to identify the prevalence and the cognitive and neural correlates of psychosis, and related processes, in MND. I used a tiered cohort study approach. Tier 1 introduced screening as standard in a regional MND clinic (N=111) using the Edinburgh Cognitive and Behavioural ALS Screen and Cambridge Behavioural Inventory-Revised (CBI-R). In Tier 2, 60 patients and 30 controls underwent neuropsychological assessment, including (i) evidence-based decision-making, to quantify jumping to conclusions (JTC), (ii) attentional control and associative learning, (iii) perceptual inference, and (iv) psychiatric screening with Neuropsychiatric Inventory (NPI), Brief Psychiatric Rating Scale (BRPS), and the Comprehensive Assessment of At-Risk Mental States (CAARMS). Tier 3 included magnetic resonance imaging of 30 patients and 20 controls. Carer reports in Tier 1 indicated that 10% of patients exhibited features suggestive of psychosis and 40% exhibited behavioural change. In Tier 2, many patients manifested abnormal behaviours (CBI-R 41%; NPI showed 19%; BPRS 24%), with 12-16% showing psychosis-specific symptoms (CBI-R and NPI psychosis index scores). In the jumping to conclusions task, patients made decisions based on less evidence than controls and were insensitive to negative feedback. Carer ratings of patient behaviour correlated with performance on the jumping to conclusions task when decisions were rewarded or costs fixed. Attentional shifting and perceptual inference were normal in MND. A principal component analysis (PCA) of questionnaires revealed two component scores, reflecting distinct patients’ and carers’ perspectives. The imaging analyses focused on the correlates of jumping to conclusions and insensitivity to negative feedback, as a potential risk profile for psychosis, with exploratory analyses of the correlates of the CBI-R psychosis index, and carers’ ratings of behaviour from the PCA. Using a Freesurfer regions-of-interest approach, grey matter volume correlated inversely with CBI-R psychosis index in the caudate, amygdala, cingulate and hippocampus. Using tract-based spatial statistics, increased mean diffusivity (MD) of diffusion weighted imaging correlated with the CBI-R psychosis responses in inferior longitudinal and uncinate fasciculi. Cost sensitivity in the JTC task correlated with cingulate and cerebellar grey matter volumes. White matter correlates of cost sensitivity included reduced FA with increasing cost sensitivity in white matter connecting the inferior frontal lobe in controls and patients. Although overt psychosis is uncommon in MND, many patients displayed abnormal behaviour or cognitive symptoms, including suboptimal reasoning biases and inferential impulsivity. Degeneration of cerebellar, cingulate and striatal grey matter, and adjacent major white matter tracts, may underlie these cognitive impairments and together represent a vulnerability to develop psychosis. Compromised reasoning and inference have implications for clinical management, including decisions around treatment options and management of well-being in MND.This research was funded by Alzheimer’s Research UK Studentship (PhD2017-26); Medical Research Council (SUAG 051/ G101400); and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care

Investigation into functional large-scale networks in individuals with schizophrenia using fMRI data and Dynamic Causal Modelling

Schizophrenia is a complex and severe psychiatric disorder with positive symptoms, negative symptoms and cognitive deficits. Preclinical neurobiological studies showed that alterations of dopaminergic and glutamatergic neurotransmitter circuits involving the prefrontal cortex resulted in cognitive impairment such as working memory. Functional activation and functional connectivity findings of functional Magnetic Resonance Imaging (fMRI) data provided support for prefrontal dysfunction during fMRI working memory tasks in individuals with schizophrenia. However, these findings do not offer a neurobiological interpretation of the fMRI data. Biophysical modelling of functional large-scale networks has been designed for the analysis of fMRI data, which can be interpreted in a mechanistic way. This approach may enable the interpretation of fMRI data in terms of altered synaptic plasticity processes found in schizophrenia. One such process is gating mechanism, which has been shown to be altered for the thalamo-cortical and meso-cortical connection in schizophrenia. The primary aim of the thesis was to investigate altered synaptic plasticity and gating mechanisms with Dynamic Causal Modelling (DCM) within functional large-scale networks during two fMRI tasks in individuals with schizophrenia. Applying nonlinear DCM to the verbal fluency fMRI task of the Edinburgh High Risk Study, we showed that the connection strengths with nonlinear modulation for the thalamo-cortical connection was reduced in subjects at high familial risk of schizophrenia when compared to healthy controls. These results suggest that nonlinear DCM enables the investigation of altered synaptic plasticity and gating mechanism from fMRI data. For the Scottish Family Mental Health Study, we reported two different optimal linear models for individuals with established schizophrenia (EST) and healthy controls during working memory function. We suggested that this result may indicate that EST and healthy controls used different functional large-scale networks. The results of nonlinear DCM analyses may suggest that gating mechanism was intact in EST and healthy controls. In conclusion, the results presented in this thesis give evidence for the role of synaptic plasticity processes as assessed in functional large-scale networks during cognitive tasks in individuals with schizophrenia