Stronger default mode network connectivity is associated with poorer clinical insight in youth at ultra high-risk for psychotic disorders

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Clinical anatomy of the precuneus and pathogenesis of the schizophrenia

Recent evidence has shown that the precuneus plays a role in the pathogenesis of schizophrenia. The precuneus is a structure of the parietal lobe's medial and posterior cortex, representing a central hub involved in multimodal integration processes. Although neglected for several years, the precuneus is highly complex and crucial for multimodal integration. It has extensive connections with different cerebral areas and is an interface between external stimuli and internal representations. In human evolution, the precuneus has increased in size and complexity, allowing the development of higher cognitive functions, such as visual-spatial ability, mental imagery, episodic memory, and other tasks involved in emotional processing and mentalization. This paper reviews the functions of the precuneus and discusses them concerning the psychopathological aspects of schizophrenia. The different neuronal circuits, such as the default mode network (DMN), in which the precuneus is involved and its alterations in the structure (grey matter) and the disconnection of pathways (white matter) are described.

Interactions between the cortical midline structures and sensorimotor network track maladaptive self-beliefs in clinical high risk for psychosis

Individuals at clinical high risk for psychosis (CHR) report a maladaptive self-concept—with more negative and less positive self-beliefs—linked to clinical symptoms and functional impairment. Alterations have also been reported in brain networks associated with intrinsic (cortical midline structures, CMS) and extrinsic (sensorimotor network, SMN) self-processing. Theoretical accounts of multiple levels of self-experience in schizophrenia suggest that interactions between these networks would be relevant for self-beliefs. This study tested whether self-beliefs related to resting-state functional connectivity within and between the CMS and SMN. Participants were 56 individuals meeting CHR criteria and 59 matched healthy community participants (HC). Pearson correlations examined potential mediators and outcomes. The CHR group reported more negative and less positive self-beliefs. Greater resting-state functional connectivity between the posterior CMS (posterior cingulate cortex) and the SMN was associated with less positive self-beliefs in CHR, but more positive self-beliefs in HC. Attenuated negative symptoms and poorer social functioning were associated with CMS-SMN connectivity (trend level after FDR-correction) and self-beliefs. Reduced connectivity between the left and right PCC was associated with lower positive self-beliefs in CHR, although this effect was specific to very low levels of positive self-beliefs. Left-right PCC connectivity did not correlate with outcomes. Dynamic interactions between intrinsic and extrinsic self-processing supported positive self-beliefs in typically developing youth while undermining positive self-beliefs in CHR youth. Implications are discussed for basic self-fragmentation, narrative self-related metacognition, and global belief updating. Interventions for self-processing may be beneficial in the CHR syndrome.

Ongoing Brain Activity and Its Role in Cognition: Dual versus Baseline Models

What is the role of the brain's ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a "dual model" of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle's original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the "baseline model." Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain's rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain's ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain's rest and task states, its DMN and non-DMN, and internally and externally oriented cognition.

Altered Resting State Functional Connectivity and Its Correlation with Cognitive Functions at Ultra High Risk for Psychosis

The aim of this study is to identify robust resting state-functional connectivity (rs-FC) alterations and their correlations with the neuropsychological characteristics of Ultra-High Risk (UHR) for psychosis subjects compared to healthy controls (HCs). Twenty individuals with UHR and sixteen HCs underwent resting-state functional magnetic resonance imaging (rs-fMRI) and a cognitive battery evaluating attention, episodic memory and executive functions. Compared to HCs, UHR individuals showed working memory and set-shifting impairments. In functional connectivity (FC) analyses, the Default Mode Network (DMN) of the UHR subjects displayed increased FC with the visual areas and decreased FC with the Dorsal Attention Network (DAN). Additionally, the salience network (SN) of the UHR subjects displayed increased connectivity with wide posterior cortical areas in the temporal, parietal and occipital lobes, corresponding to posterior nodes of the SN itself, the Somato-Motor Network (SMN) and the DAN. The SN connectivity with the left SMN and DAN was positively correlated with the Trail Making Test - B scores of the UHR subjects. These findings show that the SN and DMN, which mostly show abnormal connectivity patterns in psychosis, are also affected in UHR subjects, while the SN plays a more central role with its hyperconnectivity to the DAN and SMN.

Delineating insight-processing-related functional activations in the precuneus in first-episode psychosis patients

Poor insight is a central characteristic of psychotic disorders, and it has been suggested to result from a general dysfunction in self-reflection. However, brain processing of clinical insight and more general self-reflection has not been directly compared. We compared tasks on (1) self-reflection on psychosis-related mental functioning (clinical insight, in patients only), (2) self-reflection on mental functioning unrelated to psychosis (general metacognition), and (3) semantic control during blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging with 19 first-episode psychosis patients and 24 control participants. Arterial-spin-labeling (ASL) images were collected at rest. Clinical insight was evaluated with the Schedule for the Assessment of Insight. In patients, posterosuperior precuneus showed stronger activation during the insight task than during the semantic control task, while anteroinferior precuneus and posterior cingulate cortex (PCC) showed stronger activation during the insight task than during the general metacognition task. No significant group differences in brain activation emerged during the general metacognition task. Although the BOLD measures did not correlate with clinical insight measures, ASL-measured cerebral blood flow (CBF) values did correlate when extracted from the task-selective precuneus/PCC areas: higher CBF correlated with higher clinical insight scores. These results suggest that regions in the posteromedial cortex are selective for clinical insight.

Weaker Cerebellocortical Connectivity Within Sensorimotor and Executive Networks in Schizophrenia Compared to Healthy Controls: Relationships with Processing Speed

Background: The cognitive dysmetria theory of schizophrenia proposes that communication between the cerebellum and cerebral cortex is disrupted by structural and functional abnormalities, resulting in psychotic symptoms and cognitive deficits. Methods: Using publicly available data, resting-state functional connectivity (rsFC) was calculated from 20 hemispheric cerebellar lobules as seed regions of interest to the rest of the brain. Group differences in rsFC between individuals with schizophrenia (SZ) and healthy controls (HCs) were computed, and relationships between rsFC and symptom severity and cognitive functioning were explored. Results: HCs demonstrated stronger connectivity than SZ between several cerebellar lobules and cortical regions, most robustly between motor-related cerebellar lobules (V and VIIIa/b) and temporal and parietal cortices. In addition, seven of nine lobules in which reduced cerebellocortical connectivity was observed showed diagnosis × processing speed interactions; HC showed a positive relationship between connectivity and processing speed, whereas SZ did not show this relationship. Other cognitive domains and symptom severity did not show relationships with connectivity. Conclusions: These findings partially support the cognitive dysmetria theory, and suggest that disrupted cerebellocortical connectivity is associated with slowed processing speed in schizophrenia. Impact statement We show in this work that in chronic schizophrenia, there is weaker functional connectivity between previously unstudied inferior posterior cerebellar lobules and cortical association areas. These findings align and extend previous work showing abnormal connectivity of anterior cerebellar lobules. Further, we present a novel finding that these connectivity deficits are differentially associated with processing speed in the schizophrenia versus healthy control groups. Findings provide further evidence for cerebellocortical dysconnectivity and processing speed deficits as biomarkers of schizophrenia, which may have implications for downstream effects on higher order cognitive functions, in line with the cognitive dysmetria theory.

Social Perception Predicts Awareness of Illness in Persons With Schizophrenia

Lack of clinical insight in patients with schizophrenia is an obstacle to optimal treatment. Social cognition is one of several variables central to insight deficits in schizophrenia. The aim of this study was to investigate clinical insight in relation to one domain of social cognition, social perception, while controlling for effects of nonsocial cognition and symptom severity. Clinical insight was measured in 55 patients with schizophrenia or schizoaffective disorder, using the Birchwood Insight Scale. Relationships across domains were used to assess social perception. Social perception predicted one of three subscales of clinical insight, "awareness of illness," and was the only unique contributor to this subscale. This indicates that social perception is linked to clinical insight through awareness of illness. More research is needed to fully understand the relationship between social and nonsocial cognition and symptoms in relation to clinical insight.

Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry

Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.

Trait self-reflectiveness relates to time-varying dynamics of resting state functional connectivity and underlying structural connectomes: Role of the default mode network

BACKGROUND

Cognitive insight is defined as the ability to reflect upon oneself (i.e. self-reflectiveness), and to not be overly confident of one's own (incorrect) beliefs (i.e. self-certainty). These abilities are impaired in several disorders, while they are essential for the evaluation and regulation of one's behavior. We hypothesized that cognitive insight is a dynamic process, and therefore examined how it relates to temporal dynamics of resting state functional connectivity (FC) and underlying structural network characteristics in 58 healthy individuals.

METHODS

Cognitive insight was measured with the Beck Cognitive Insight Scale. FC characteristics were calculated after obtaining four FC states with leading eigenvector dynamics analysis. Gray matter (GM) and DTI connectomes were based on GM similarity and probabilistic tractography. Structural graph characteristics, such as path length, clustering coefficient, and small-world coefficient, were calculated with the Brain Connectivity Toolbox. FC and structural graph characteristics were correlated with cognitive insight.

RESULTS

Individuals with lower cognitive insight switched more and spent less time in a globally synchronized state. Additionally, individuals with lower self-reflectiveness spent more time in, had a higher probability of, and had a higher chance of switching to a state entailing default mode network (DMN) areas. With lower self-reflectiveness, DTI-connectomes were segregated less (i.e. lower global clustering coefficient) with lower embeddedness of the left angular gyrus specifically (i.e. lower local clustering coefficient).

CONCLUSIONS

Our results suggest less stable functional and structural networks in individuals with poorer cognitive insight, specifically self-reflectiveness. An overly present DMN appears to play a key role in poorer self-reflectiveness.

Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion

First episode psychosis (FEP), and subsequent diagnosis of schizophrenia or schizoaffective disorder, predominantly occurs during late adolescence, is accompanied by a significant decline in function and represents a traumatic experience for patients and families alike. Prior to first episode psychosis, most patients experience a prodromal period of 1-2 years, during which symptoms first appear and then progress. During that time period, subjects are referred to as being at Clinical High Risk (CHR), as a prodromal period can only be designated in hindsight in those who convert. The clinical high-risk period represents a critical window during which interventions may be targeted to slow or prevent conversion to psychosis. However, only one third of subjects at clinical high risk will convert to psychosis and receive a formal diagnosis of a primary psychotic disorder. Therefore, in order for targeted interventions to be developed and applied, predicting who among this population will convert is of critical importance. To date, a variety of neuroimaging modalities have identified numerous differences between CHR subjects and healthy controls. However, complicating attempts at predicting conversion are increasingly recognized co-morbidities, such as major depressive disorder, in a significant number of CHR subjects. The result of this is that phenotypes discovered between CHR subjects and healthy controls are likely non-specific to psychosis and generalized for major mental illness. In this paper, we selectively review evidence for neuroimaging phenotypes in CHR subjects who later converted to psychosis. We then evaluate the recent landscape of machine learning as it relates to neuroimaging phenotypes in predicting conversion to psychosis.

Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI

Although regional brain deficits have been demonstrated in schizophrenia patients by structural MRI studies, one important question that remains largely unanswered is whether the complex and subtle deficits revealed by MRI could be used as objective biomarkers to discriminate patients from healthy controls individually. To address this question, a total of 326 right-handed participants were recruited, including 163 drug-naïve first-episode schizophrenia (FES) patients and 163 demographically matched healthy controls. High-resolution anatomic data were acquired from all subjects and processed via Freesurfer software to obtain cortical thickness and surface area measurements. Subsequently, the Support Vector Machine (SVM) was used to explore the potential utility for cortical thickness and surface area measurements in the differentiation of individual patients and healthy controls. The accuracy of correct classification of patients and controls was 85.0% (specificity 87.0%, sensitivity 83.0%) for surface area and 81.8% (specificity 85.0%, sensitivity 76.9%) for cortical thickness (p<0.001 after permutation testing). Regions contributing to classification accuracy mainly included the gray matter in default mode, central executive, salience, and visual networks. Current findings, in a sample of never-treated FES patients, suggest that the patterns of illness-related gray matter changes has potential as a biomarker for identifying structural brain alterations in individuals with schizophrenia. Future prospective studies are needed to evaluate the utility of imaging biomarkers for research and potentially for clinical purpose.

Psychosis risk is associated with decreased resting-state functional connectivity between the striatum and the default mode network

Psychosis is linked to aberrant salience or to viewing neutral stimuli as self-relevant, suggesting a possible impairment in self-relevance processing. Psychosis is also associated with increased dopamine in the dorsal striatum, especially the anterior caudate (Kegeles et al., 2010). Critically, the anterior caudate is especially connected to (a) the cortical default mode network (DMN), centrally involved in self-relevance processing, and (b) to a lesser extent, the cortical frontoparietal network (FPN; Choi, Yeo, & Buckner, 2012). However, no previous study has directly examined striatal-cortical DMN connectivity in psychosis risk. In Study 1, we examined resting-state functional connectivity in psychosis risk (n = 18) and control (n = 19) groups between (a) striatal DMN and FPN subregions and (b) cortical DMN and FPN. The psychosis risk group exhibited decreased connectivity between the striatal subregions and the cortical DMN. In contrast, the psychosis risk group exhibited intact connectivity between the striatal subregions and the cortical FPN. Additionally, recent distress was also associated with decreased striatal-cortical DMN connectivity. In Study 2, to determine whether the decreased striatal-cortical DMN connectivity was specific to psychosis risk or was related to recent distress more generally, we examined the relationship between connectivity and distress in individuals diagnosed with nonpsychotic emotional distress disorders (N = 25). In contrast to Study 1, here we found that distress was associated with evidence of increased striatal-cortical DMN connectivity. Overall, the present results suggest that decreased striatal-cortical DMN connectivity is associated with psychosis risk and could contribute to aberrant salience.

Neurobiological approaches to the study of clinical and genetic high risk for developing psychosis

Research on neurobiological impairments in clinical and genetic high risk for developing psychosis individuals (CHR) has identified several brain abnormalities that impact both brain structure and function. The current review will discuss research examining brain abnormalities in clinical and genetic high risk for psychosis using magnetic resonance imaging (MRI) focusing on structural brain abnormalities, diffusion tensor imaging (DTI) focusing on the integrity of white matter tracks, functional MRI focusing on functional brain abnormalities, and EEG and event related potential (ERP) methodologies focusing on indices of cognitive dysfunction in CHR. Studies conducted across these different methodologies sought to identify brain regions and brain processes that would distinguish between those high risk individuals who converted to psychosis versus those who did not. In addition, in some of the studies, the distinction was made between individuals who converted to psychosis, those who did not, and those individuals who remained clinically symptomatic while not converting to psychosis. The brain regions most often identified as abnormal in this subject group were the brain areas often found abnormal in schizophrenia, including frontal and temporal regions. Similarly, several cognitive processes often found to be abnormal in schizophrenia have been also found impaired in CHR.

Connecting Mind, Brain, and Seizures by Default Mode

Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings From the Epilepsy Connectome Project

Cook CJ, Hwang G, Mathis J, et al. Brain Connect. 2018. doi:10.1089/brain.2018.0600. [Epub ahead of print]; PMID: 30398367

The Epilepsy Connectome Project examines the differences in connectomes between patients with temporal lobe epilepsy (TLE) and healthy controls. Using these data, the effective connectivity of the default mode network (DMN) in patients with left TLE compared to healthy controls was investigated using spectral dynamic causal modeling of resting state functional magnetic resonance imaging data. Group comparisons were made using 2 parametric empirical Bayes (PEB) models. The first level of each PEB model consisted of each participant’s spectral dynamic causal modeling. Two different second-level models were constructed: the first comparing effective connectivity of the groups directly and the second using the Rey Auditory Verbal Learning Test (RAVLT) delayed free recall index as a covariate at the second level in order to assess effective connectivity controlling for the poor memory performance of patients with left TLE. After an automated search over the nested parameter space and thresholding parameters at 95% posterior probability, both models revealed numerous connections in the DMN that lead to inhibition of the left hippocampal formation. Left hippocampal formation inhibition may be an inherent result of the left temporal epileptogenic focus as memory differences were controlled for in one model and the same connections remained. An excitatory connection from the posterior cingulate cortex to the medial prefrontal cortex was found to be concomitant with left hippocampal formation inhibition in patients with TLE when including RAVLT delayed free recall at the second level.

Distinct and opposite profiles of connectivity during self-reference task and rest in youth at clinical high risk for psychosis

Self-reference is impaired in psychotic disorders such as schizophrenia, associated with disability, and closely related to characteristic patterns of aberrant brain connectivity. However, at present, it is unclear whether self-reference is impacted in pathogenesis of the disorder. Alterations in connectivity during a self-reference task or resting-state in the psychosis risk (i.e., prodromal) period may yield important clues for biomarker development, as well as for novel treatment targets. This study examined a task-based and resting-state functional magnetic resonance imaging in individuals at clinical high risk (CHR) for psychosis (n = 22) and healthy control unaffected peers (n = 20). The self-reference task comprised three task conditions where subjects were asked if an adjective was relevant to themselves (self), a designated other individual (other), or to evaluate the word's spelling (letter). Connectivity analyses examined medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), regions commonly found in conjunction analyses of self-reference, during both the self-reference task and rest. In task connectivity analyses, CHR individuals exhibited decreased mPFC-PCC connectivity when compared to controls. In resting-state analyses, CHR participants showed greater mPFC-PCC connectivity. Taken together, results suggest that psychosis-like alterations in mPFC-PCC connectivity is present prior to psychosis onset across both task and rest.

Predictors of Outcomes in Adolescents With Clinical High Risk for Psychosis, Other Psychiatric Symptoms, and Psychosis: A Longitudinal Protocol Study

In children and adolescents, schizophrenia is one of the ten main causes of disability-adjusted life years. The identification of people at Clinical High Risk of developing Psychosis (CHR-P) is one of the most promising strategies to improve outcomes. However, in children and adolescents research on the CHR-P state is still in its infancy and the clinical validity of at-risk criteria appears understudied in this population. Furthermore, only few studies have evaluated the psychopathological, neuropsychological, neuroimaging characteristics and, especially, long-term outcomes of adolescents at high risk. We present here the protocol of an innovative longitudinal cohort study of adolescents aged 12-17. The sample will consist of patients admitted to a third level neuropsychiatric unit, belonging to one of the following three subgroups: 1) adolescents with established Diagnostic and Statistical Manual of Mental Disorder-Fifth Edition psychosis, 2) adolescents with CHR-P, and 3) adolescents with psychiatric symptoms other than established psychosis or CHR-P. The primary aim of our study is to evaluate the 2-year prognosis across the three groups. We will measure transition to psychosis (or the stability of the diagnosis of psychosis in the psychotic group), the risk of development of other psychiatric disorders, as well as socio-occupational functioning at outcome. The secondary aim will be to explore the effect of specific predictors (clinical, neuropsychological and neuroimaging factors) on the prognosis. At baseline, 1-year and 2-year follow-up participants will be assessed using standardized semi-structured interviews and instruments. Psychopathological and functioning variables, as well as neuropsychological domains will be compared across the three subgroups. Moreover, at baseline and 2-year follow-up all recruited patients will undergo a 3-Tesla magnetic resonance imaging examination and diffusion tensor imaging parameters will be analyzed. We believe that this study will advance our ability to predict outcomes in underage CHR-P samples. In particular, our data will enable a better understanding of the clinical significance of CHR-P in adolescents, and shed new light on prognostic factors that can be used to refine the prediction of clinical outcomes and the implementation of preventive interventions.

High Frequencies in QEEG Are Related to the Level of Insight in Patients With Schizophrenia

Lack of insight is a neurocognitive problem commonly encountered in patients with psychotic disorders that negatively affects treatment compliance and prognosis. Measurement of insight is based on self-report scales, which are limited due to subjectivity. This study aimed to determine the correlation between resting state beta and gamma power in 23 patients with schizophrenia and insight. It was observed that as beta and gamma power measured via qualitative electroencephalography (qEEG) increased the level of insight decreased. Negative correlation was found in F3, C3, Cz for gamma activity and in F3 and C3 for beta activity. This finding indicates that resting state qEEG could be used to evaluate the level of insight in patients with schizophrenia.

Association between catechol-O-methyltransferase genetic variation and functional connectivity in patients with first-episode schizophrenia

Dopamine in the prefrontal cortex (PFC) plays an important role in cognitive performance and regulates by catechol-O-methyltransferase (COMT) expression. To clarify the effect of COMT genotype on cognitive function in patients with schizophrenia, we performed DNA genotyping, cognitive evaluations, and functional magnetic resonance imaging (fMRI) in antipsychotic-naïve patients with first-episode schizophrenia (FES) and matched healthy control subjects. We found that all cognitive domains were impaired in patients with FES compared with healthy subjects. Moreover, COMT genotype influenced the verbal learning performance in healthy subjects, but not in patients with FES. Resting-state fMRI data revealed that patients with FES exhibited higher functional connectivity degree centrality in the medial PFC and lower degree centrality in the parietal-occipital junction than healthy subjects. Furthermore, patients with FES who were COMT Met allele carriers had higher degree centrality in the medial PFC than those with the Val/Val genotype. In contrast, in healthy controls, Met allele carriers exhibited higher degree centrality than healthy controls with the Val/Val genotype in the left hippocampus and left amygdala. There was a negative correlation between the degree centrality value in medial PFC and score of the Hopkins Verbal Learning Test-Revised (HVLT-R) in FES patients with the Met allele. Our findings suggest that COMT genotype differentially influences pathways related to cognitive performance in patients with FES versus healthy individuals, providing an important insight into schizophrenia pathophysiology.