Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers

Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia

We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.

Identifying novel biomarkers with TMS-EEG - Methodological possibilities and challenges

Biomarkers are essential for understanding the underlying pathologies in brain disorders and for developing effective treatments. Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is an emerging neurophysiological tool that can be used for biomarker development. This method can identify biomarkers associated with the function and dynamics of the inhibitory and excitatory neurotransmitter systems and effective connectivity between brain areas. In this review, we outline the current state of the TMS-EEG biomarker field by summarizing the existing protocols and the possibilities and challenges associated with this methodology.

TMS Database Registry Consortium Research Project in Japan (TReC-J) for Future Personalized Psychiatry

The registry project led by the Japanese Society for Clinical Transcranial Magnetic Stimulation (TMS) Research aims to establish a centralized database of epidemiological, clinical, and biological data on TMS therapy for refractory psychiatric disorders, including treatment-resistant depression, as well as to contribute to the elucidation of the therapeutic mechanism of TMS therapy and to the validation of its efficacy by analyzing and evaluating these data in a systematic approach. The objective of this registry project is to collect a wide range of complex data linked to patients with various neuropsychiatric disorders who received TMS therapy throughout Japan, and to make effective use of these data to promote cross-sectional and longitudinal exploratory observational studies. Research utilizing this registry project will be conducted in a multicenter, non-invasive, retrospective, and prospective observational research study design, regardless of the framework of insurance medical care, private practice, or clinical research. Through the establishment of the registry, which aims to make use of data, we will advance the elucidation of treatment mechanisms and identification of predictors of therapeutic response to TMS therapy for refractory psychiatric disorders on a more real-world research basis. Furthermore, as a future vision, we aim to develop novel neuromodulation medical devices, algorithms for predicting treatment efficacy, and digital therapeutics based on the knowledge generated from this TMS registry database.

Neocortical localization and thalamocortical modulation of neuronal hyperexcitability contribute to Fragile X Syndrome

Fragile X Syndrome (FXS) is a monogenetic form of intellectual disability and autism in which well-established knockout (KO) animal models point to neuronal hyperexcitability and abnormal gamma-frequency physiology as a basis for key disorder features. Translating these findings into patients may identify tractable treatment targets. Using source modeling of resting-state electroencephalography data, we report findings in FXS, including 1) increases in localized gamma activity, 2) pervasive changes of theta/alpha activity, indicative of disrupted thalamocortical modulation coupled with elevated gamma power, 3) stepwise moderation of low and high-frequency abnormalities based on female sex, and 4) relationship of this physiology to intellectual disability and neuropsychiatric symptoms. Our observations extend findings in Fmr1-/- KO mice to patients with FXS and raise a key role for disrupted thalamocortical modulation in local hyperexcitability. This systems-level mechanism has received limited preclinical attention but has implications for understanding fundamental disease mechanisms.

Real-time facial emotion recognition deficits across the psychosis spectrum: A B-SNIP Study

Affective and non-affective psychotic disorders are associated with variable levels of impairment in affective processing, but this domain typically has been examined via presentation of static facial images. We compared performance on a dynamic facial expression identification task across six emotions (sad, fear, surprise, disgust, anger, happy) in individuals with psychotic disorders (bipolar with psychotic features [PBD] = 113, schizoaffective [SAD] = 163, schizophrenia [SZ] = 181) and healthy controls (HC; n = 236) derived from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). These same individuals with psychotic disorders were also grouped by B-SNIP-derived Biotype (Biotype 1 [B1] = 115, Biotype 2 [B2] = 132, Biotype 3 [B3] = 158), derived from a cluster analysis applied to a large biomarker panel that did not include the current data. Irrespective of the depicted emotion, groups differed in accuracy of emotion identification (P < 0.0001). The SZ group demonstrated lower accuracy versus HC and PBD groups; the SAD group was less accurate than the HC group (Ps < 0.02). Similar overall group differences were evident in speed of identifying emotional expressions. Controlling for general cognitive ability did not eliminate most group differences on accuracy but eliminated almost all group differences on reaction time for emotion identification. Results from the Biotype groups indicated that B1 and B2 had more severe deficits in emotion recognition than HC and B3, meanwhile B3 did not show significant deficits. In sum, this characterization of facial emotion recognition deficits adds to our emerging understanding of social/emotional deficits across the psychosis spectrum.

An opportunity for primary prevention research in psychotic disorders

An opportunity has opened for research into primary prevention of psychotic disorders, based on progress in endophenotypes, genetics, and genomics. Primary prevention requires reliable prediction of susceptibility before any symptoms are present. We studied a battery of measures where published data supports abnormalities of these measurements prior to appearance of initial psychosis symptoms. These neurobiological and behavioral measurements included cognition, eye movement tracking, Event Related Potentials, and polygenic risk scores. They generated an acceptably precise separation of healthy controls from outpatients with a psychotic disorder. METHODS: The Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) measured this battery in an ancestry-diverse series of consecutively recruited adult outpatients with a psychotic disorder and healthy controls. Participants include all genders, 16 to 50 years of age, 261 with psychotic disorders (Schizophrenia (SZ) 109, Bipolar with psychosis (BPP) 92, Schizoaffective disorder (SAD) 60), 110 healthy controls. Logistic Regression, and an extension of the Linear Mixed Model to include analysis of pairwise interactions between measures (Environmental kernel Relationship Matrices (ERM)) with multiple iterations, were performed to predict case-control status. Each regression analysis was validated with four-fold cross-validation. RESULTS AND CONCLUSIONS: Sensitivity, specificity, and Area Under the Curve of Receiver Operating Characteristic of 85%, 62%, and 86%, respectively, were obtained for both analytic methods. These prediction metrics demonstrate a promising diagnostic distinction based on premorbid risk variables. There were also statistically significant pairwise interactions between measures in the ERM model. The strong prediction metrics of both types of analytic model provide proof-of-principle for biologically-based laboratory tests as a first step toward primary prevention studies. Prospective studies of adolescents at elevated risk, vs. healthy adolescent controls, would be a next step toward development of primary prevention strategies.

Heterogeneity of social cognitive performance in autism and schizophrenia

Autistic adults and those with schizophrenia (SCZ) demonstrate similar levels of reduced social cognitive performance at the group level, but it is unclear whether these patterns are relatively consistent or highly variable within and between the two conditions. Seventy-two adults with SCZ (52 male, M_age  = 28.2 years) and 94 with diagnoses on the autism spectrum (83 male, M_age  = 24.2 years) without intellectual disability completed a comprehensive social cognitive battery. Latent profile analysis identified four homogeneous subgroups that were compared on their diagnosis, independent living skills, neurocognition, and symptomatology. Two groups showed normative performance across most social cognitive tasks but were differentiated by one having significantly higher hostility and blaming biases. Autistic participants were more likely to demonstrate fully normative performance (46.8%) than participants with SCZ, whereas normative performance in SCZ was more likely to co-occur with increased hostility and blaming biases (36.1%). Approximately 43% of participants in the full sample were classified into the remaining two groups showing low or very low performance. These participants tended to perform worse on neurocognitive tests and have lower IQ and fewer independent living skills. The prevalence of low performance on social cognitive tasks was comparable across clinical groups. However, nearly half of autistic participants demonstrated normative social cognitive performance, challenging assumptions that reduced social cognitive performance is inherent to the condition. Subgrouping also revealed a meaningful distinction between the clinical groups: participants with SCZ were more likely to demonstrate hostility biases than autistic participants, even when social cognitive performance was otherwise in the typical range. LAY SUMMARY: Social cognition refers to the perception and interpretation of social information. Previous research has shown that both autistic people and those with schizophrenia demonstrate reduced performance on traditional social cognitive tasks, which we replicate here at the group level. However, we also found that almost half of autistic participants performed in the normal range. Over a third of participants with schizophrenia did as well, but for them this performance was accompanied by a hostility bias not commonly found in the autistic sample. Taken together, findings challenge assumptions that difficulties in social cognition are a uniform characteristic of these clinical conditions in those without intellectual disability.

Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins

Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is "older" than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.

From the Group to the Individual in Schizophrenia Spectrum Disorders: Biomarkers of Social Cognitive Impairments and Therapeutic Translation

People with schizophrenia spectrum disorders (SSDs) often experience persistent social cognitive impairments, associated with poor functional outcome. There are currently no approved treatment options for these debilitating symptoms, highlighting the need for novel therapeutic strategies. Work to date has elucidated differential social processes and underlying neural circuitry affected in SSDs, which may be amenable to modulation using neurostimulation. Further, advances in functional connectivity mapping and electric field modeling may be used to identify individualized treatment targets to maximize the impact of brain stimulation on social cognitive networks. Here, we review literature supporting a roadmap for translating functional connectivity biomarker discovery to individualized treatment development for social cognitive impairments in SSDs. First, we outline the relevance of social cognitive impairments in SSDs. We review machine learning approaches for dimensional brain-behavior biomarker discovery, emphasizing the importance of individual differences. We synthesize research showing that brain stimulation techniques, such as repetitive transcranial magnetic stimulation, can be used to target relevant networks. Further, functional connectivity-based individualized targeting may enhance treatment response. We then outline recent approaches to account for neuroanatomical variability and optimize coil positioning to individually maximize target engagement. Overall, the synthesized literature provides support for the utility and feasibility of this translational approach to precision treatment. The proposed roadmap to translate biomarkers of social cognitive impairments to individualized treatment is currently under evaluation in precision-guided trials. Such a translational approach may also be applicable across conditions and generalizable for the development of individualized neurostimulation targeting other behavioral deficits.

Research Domain Criteria (RDoC): Progress and Potential

The National Institute of Mental Health (NIMH) addressed in its 2008 Strategic Plan an emerging concern that the current diagnostic system was hampering translational research, as accumulating data suggested that disorder categories constituted heterogeneous syndromes rather than specific diseases. However, established practices in peer review placed high priority on extant disorders in evaluating grant applications for mental illness. To provide guidelines for alternative study designs, NIMH included a goal to develop new ways of studying psychopathology based on dimensions of measurable behavior and related neurobiological measures. The Research Domain Criteria (RDoC) project is the result, intended to build a literature that informs new conceptions of mental illness and future revisions to diagnostic manuals. The framework calls for the study of empirically-derived fundamental dimensions as characterized by related behavioral/psychological and neurobiological data (e.g., reward valuation, working memory). RDoC also emphasizes full-range dimensional approaches (from typical to increasingly abnormal), neurodevelopment and environmental effects, and research designs that integrate data across behavioral, biological, and self-report measures. This commentary provides an overview of the project's first decade and its potential future directions. RDoC remains grounded in experimental psychopathology perspectives, and its progress is strongly linked to psychological measurement and integrative approaches to brain-behavior relationships.

Characterising cognitive heterogeneity in individuals at clinical high-risk for psychosis: a cluster analysis with clinical and functional outcome prediction

Schizophrenia is characterised by cognitive impairments that are already present during early stages, including in the clinical high-risk for psychosis (CHR-P) state and first-episode psychosis (FEP). Moreover, data suggest the presence of distinct cognitive subtypes during early-stage psychosis, with evidence for spared vs. impaired cognitive profiles that may be differentially associated with symptomatic and functional outcomes. Using cluster analysis, we sought to determine whether cognitive subgroups were associated with clinical and functional outcomes in CHR-P individuals. Data were available for 146 CHR-P participants of whom 122 completed a 6- and/or 12-month follow-up; 15 FEP participants; 47 participants not fulfilling CHR-P criteria (CHR-Ns); and 53 healthy controls (HCs). We performed hierarchical cluster analysis on principal components derived from neurocognitive and social cognitive measures. Within the CHR-P group, clusters were compared on clinical and functional variables and examined for associations with global functioning, persistent attenuated psychotic symptoms and transition to psychosis. Two discrete cognitive subgroups emerged across all participants: 45.9% of CHR-P individuals were cognitively impaired compared to 93.3% of FEP, 29.8% of CHR-N and 30.2% of HC participants. Cognitively impaired CHR-P participants also had significantly poorer functioning at baseline and follow-up than their cognitively spared counterparts. Specifically, cluster membership predicted functional but not clinical outcome. Our findings support the existence of distinct cognitive subgroups in CHR-P individuals that are associated with functional outcomes, with implications for early intervention and the understanding of underlying developmental processes.

Multimodal Correlates of Cannabis Use among U.S. Veterans with Bipolar Disorder: An Integrated Study of Clinical, Cognitive, and Functional Outcomes

Objective: Cannabis use (CU) is common among persons with bipolar disorder (BD). Evidence suggests that CU is associated with poorer outcomes among persons with BD; however, these findings remain inconsistent. The present exploratory study aims to examine clinical, functional, and cognitive correlates of CU among persons with BD. Methods: U.S. veterans with BD type I who participated in a large-scale, nationwide study were categorized into four groups: current CU, past CU, past other drug use, and no drug use. Bivariate analyses, univariate analyses of covariance, and Levene's Test for Equality of Variance were used to compare groups on clinical, cognitive, and functional measures. Results: Of 254 (84.6% male) veterans with BD type I included in the analyses, 13 (5.1%) had current CU, 37 (14.5%) past CU, 77 (30.3%) past other drug use, and 127 (50%) reported no drug use. BD with CU was associated with post-traumatic stress disorder (PTSD) and experiencing lifetime suicidal ideation. Notably, current CU was associated with higher working memory performance, compared to both past CU and no drug use. Likewise, current CU was associated with higher functional capacity, compared to past CU as well as no drug use. Conclusions: These findings contribute to the growing literature on the complex effects of cannabis on BD. As the commercialization and legalization of cannabis increases, further research in this area is warranted to quantify posed risks to this population, and thereby guide clinical decision-making.

Using psychosis biotypes and the Framingham model for parsing psychosis biology

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has invested in the collection and use of multiple biomarkers in individuals with psychosis. We expect psychosis biology and its distinctive types to be reflected in the biomarkers, as they are the 'behaviors' of the brain. Like infectious diseases, we expect the etiologies of these biomarker-driven entities to be multiple and complex. Biomarkers have not yet been annotated with disease characteristics and need to be. As a model, we seek to adopt aspects of the Framingham Heart Study (FHS) to guide and organize these observations.

Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia

Brain morphology differs markedly between individuals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each individual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes; (2) endothelial cells; (3) oligodendrocyte progenitor cells (OPCs); (4) excitatory neurons; and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same individuals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells (r = -0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes (r = -0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-individual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.

Toward a Multidimensional Understanding of Misophonia Using Cluster-Based Phenotyping

Misophonia is a condition characterized by hypersensitivity and strong emotional reactivity to specific auditory stimuli. Misophonia clinical presentations are relatively complex and reflect individualized experiences across clinical populations. Like some overlapping neurodevelopmental and neuropsychiatric disorders, misophonia is potentially syndromic where symptom patterns rather than any one symptom contribute to diagnosis. The current study conducted an exploratory k-means cluster analysis to evaluate symptom presentation in a non-clinical sample of young adult undergraduate students (N = 343). Individuals participated in a self-report spectrum characteristics survey indexing misophonia, tinnitus severity, sensory hypersensitivity, and social and psychiatric symptoms. Results supported a three-cluster solution that split participants on symptom presentation: cluster 1 presented with more severe misophonia symptoms but few overlapping formally diagnosed psychiatric co-occurring conditions; cluster 3 was characterized by a more nuanced clinical presentation of misophonia with broad-band sensory hypersensitivities, tinnitus, and increased incidence of social processing and psychiatric symptoms, and cluster 2 was relatively unaffected by misophonia or other sensitivities. Clustering results illustrate the spectrum characteristics of misophonia where symptom patterns range from more “pure” form misophonia to presentations that involve more broad-range sensory-related and psychiatric symptoms. Subgroups of individuals with misophonia may characterize differential neuropsychiatric risk patterns and stem from potentially different causative factors, highlighting the importance of exploring misophonia as a multidimensional condition of complex etiology.

Impact of polygenic risk for coronary artery disease and cardiovascular medication burden on cognitive impairment in psychotic disorders

BACKGROUND

Cognitive impairment is a core deficit across psychotic disorders, the causes and therapeutics of which remain unclear. Epidemiological observations have suggested associations between cognitive dysfunction in psychotic disorders and cardiovascular risk factors, but an underlying etiology has not been established.

METHODS

Neuropsychological performance using the Brief Assessment of Cognition in Schizophrenia (BACS) was assessed in 616 individuals of European ancestry (403 psychosis, 213 controls). Polygenic risk scores for coronary artery disease (PRSCAD) were quantified for each participant across 13 p-value thresholds (PT 0.5-5e-8). Cardiovascular and psychotropic medications were categorized for association analyses. Each PRSCAD was examined in relation to the BACS and the optimized PT was confirmed with five-fold cross-validation and independent validation. Functional enrichment analyses were used to identify biological mechanisms linked to PRSCAD-cognition associations. Multiple regression analyses examined PRSCAD under the optimal PT and medication burden in relation to the BACS composite and subtest scores.

RESULTS

Higher PRSCAD was associated with lower BACS composite scores (p = 0.001) in the psychosis group, primarily driven by the Verbal Memory subtest (p < 0.001). Genes linked to multiple nervous system related processes and pathways were significantly enriched in PRSCAD. After controlling for PRSCAD, a greater number of cardiovascular medications was also correlated with worse BACS performance in patients with psychotic disorders (p = 0.029).

CONCLUSIONS

Higher PRSCAD and taking more cardiovascular medications were both significantly associated with cognitive impairment in psychosis. These findings indicate that cardiovascular factors may increase the risk for cognitive dysfunction and related functional outcomes among individuals with psychotic disorders.

Ten-year course of cognition in first-episode non-affective psychosis patients: PAFIP cohort

BACKGROUND

A large body of research states that cognitive impairment in schizophrenia is static. Nevertheless, most previous studies lack a control group or have small study samples or short follow-up periods.

METHOD

We aimed to address these limitations by studying a large epidemiological cohort of patients with first-episode schizophrenia spectrum disorders and a comparable control sample for a 10-year period.

RESULTS

Our results support the generalized stability of cognitive functions in schizophrenia spectrum disorders considering the entire group. However, the existence of a subgroup of patients characterized by deteriorating cognition and worse long-term clinical outcomes must be noted. Nevertheless, it was not possible to identify concomitant factors or predictors of deterioration (all Ps > 0.05).

CONCLUSIONS

Cognitive functions in schizophrenia spectrum disorder are stable; however, a subgroup of subjects that deteriorate can be characterized.

Clinical insight and cognitive functioning as mediators in the relationships between symptoms of psychosis, depression, and suicide ideation in first-episode psychosis

First-episode psychosis (FEP) is a particularly high-risk period for suicide. Literature suggests poor cognitive functioning may serve as a protective factor, while investigations of clinical insight reveal a complex relationship with suicide outcomes. This study examined the mediating role of cognition and clinical insight in the relationships between positive and negative symptoms, depression, and subsequent suicide ideation among individuals in FEP. Data were obtained from the Recovery After an Initial Schizophrenia Episode project. Participants (n = 404) included adolescents and adults in FEP between the ages of 15 and 40. Measurement utilized the Calgary Depression Rating Scale, Positive and Negative Syndrome Scale, and Brief Assessment of Cognition in Schizophrenia. Structural equation modeling was used to examine the mediation model. The likelihood of experiencing suicide ideation was significantly decreased when working memory was stronger (b = -0.034, SE = 0.02, OR = 0.967, p < .05), and significantly increased when clinical insight was stronger (b = 0.191, SE = 0.08, OR = 1.21, p < .01), positive symptoms were greater (b = 0.422, SE = 0.20, OR = 1.52, p < .05) and depressive symptoms were greater (b = 0.545, SE = 0.15, OR = 1.70, p < .001). Clinical insight and working memory functioned as mediators in the relationships between depression, positive symptoms, negative symptoms, and suicide ideation. Findings suggest it is essential that clinicians have awareness of insight being a risk factor for suicide ideation and balance therapeutic efforts to strengthen clinical insight and cognition in psychosocial treatments with suicide risk assessment and prevention methods.

Toward personalized medicine in connectomic deep brain stimulation

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.

Thalamocortical dysrhythmia in patients with schizophrenia spectrum disorder and individuals at clinical high risk for psychosis

Thalamocortical dysrhythmia (TCD) is a model characterized by abnormal resting-state thalamic oscillatory patterns where the alpha rhythm is replaced by cross-frequency coupling of low- and high-frequency rhythms. Although disrupted thalamic function is a suggested important pathophysiological mechanism underlying schizophrenia, knowledge regarding the TCD model in schizophrenia spectrum disorder (SSD) patients and individuals at clinical high risk (CHR) for psychosis is limited. A total of 169 SSD patients, 106 individuals at CHR for psychosis, and 105 healthy controls (HCs) underwent resting-state electroencephalography recordings. We performed mean global field power (MGFP) spectral analysis between 1 and 49 Hz as well as source-level theta phase-gamma amplitude coupling (TGC) analysis and compared resting-state oscillatory patterns across groups. Correlations between altered TGC values and psychotic symptom severity in the patient group were investigated. Spectral MGFP of low- and high-frequencies was larger in the SSD and CHR groups than in the HC group. The TGC of SSD patients was greater than that of HCs in the right frontal, right parietal, and left and right limbic lobes. Greater TGC in the right frontal and limbic lobes was associated with positive symptom severity in SSD patients. However, TGC in the CHR group was comparable to that in the HCs and was smaller than that in the SSD group in widespread cortical regions. The TCD pattern may be apparent after frank psychotic disorder onset in tandem with overt positive symptoms. A psychosis-risk state without overt psychotic symptoms could be characterized by abnormally increased low- and high-frequency activities with relatively preserved TGC.

Auditory mismatch negativity in bipolar disorder: a focused review

The auditory mismatch negativity, a component of the event-related potential elicited by an unexpected stimulus in a sequence of acoustic stimuli, provides an objective measure of the accuracy of the echoic information processing of the human brain in vivo. Auditory mismatch negativity is also a useful probe of cortical glutamatergic N-methyl-d-aspartate receptor activity and disturbance. Notably, auditory mismatch negativity is consistently impaired in schizophrenia. Because of the wide spectrum extending from bipolar affective illness and schizoaffective psychosis to typical schizophrenia, we examined the literature on auditory mismatch negativity in bipolar disorder with the aim to find any neurophysiological dysfunction concerning pre-attentive information processing shared by these clinical conditions. This focused review includes 26 original articles published in peer-reviewed journals and indexed in the National Institutes of Health National Library of Medicine (PubMed) search system. Overall, evidence is consistent with the finding that auditory mismatch negativity is impaired in bipolar disorder with psychotic features, even though to a lesser extent than in schizophrenia. It must be acknowledged that, in a few twin and family studies, mismatch negativity abnormalities were not specifically associated with bipolar disorder. In conclusion, auditory mismatch negativity research supports the involvement of the N-methyl-d-aspartate system in the pathophysiology of bipolar disorder, as previously assessed for schizophrenia, thus creating an intriguing trait d'union between these two mental illnesses and stimulating the development of novel therapeutic agents. With additional replication and validation, auditory mismatch negativity may be further considered as a correlate of a common psychopathology of schizophrenia and bipolar spectrum illnesses.

Psychosis Biotypes: Replication and Validation from the B-SNIP Consortium

Current clinical phenomenological diagnosis in psychiatry neither captures biologically homologous disease entities nor allows for individualized treatment prescriptions based on neurobiology. In this report, we studied two large samples of cases with schizophrenia, schizoaffective, and bipolar I disorder with psychosis, presentations with clinical features of hallucinations, delusions, thought disorder, affective, or negative symptoms. A biomarker approach to subtyping psychosis cases (called psychosis Biotypes) captured neurobiological homology that was missed by conventional clinical diagnoses. Two samples (called "B-SNIP1" with 711 psychosis and 274 healthy persons, and the "replication sample" with 717 psychosis and 198 healthy persons) showed that 44 individual biomarkers, drawn from general cognition (BACS), motor inhibitory (stop signal), saccadic system (pro- and anti-saccades), and auditory EEG/ERP (paired-stimuli and oddball) tasks of psychosis-relevant brain functions were replicable (r's from .96-.99) and temporally stable (r's from .76-.95). Using numerical taxonomy (k-means clustering) with nine groups of integrated biomarker characteristics (called bio-factors) yielded three Biotypes that were virtually identical between the two samples and showed highly similar case assignments to subgroups based on cross-validations (88.5%-89%). Biotypes-1 and -2 shared poor cognition. Biotype-1 was further characterized by low neural response magnitudes, while Biotype-2 was further characterized by overactive neural responses and poor sensory motor inhibition. Biotype-3 was nearly normal on all bio-factors. Construct validation of Biotype EEG/ERP neurophysiology using measures of intrinsic neural activity and auditory steady state stimulation highlighted the robustness of these outcomes. Psychosis Biotypes may yield meaningful neurobiological targets for treatments and etiological investigations.

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.

Transdiagnostic phenotypes of compulsive behavior and associations with psychological, cognitive, and neurobiological affective processing

Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18-45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled "Compulsive Non-Avoidant", "Compulsive Reactive" and "Compulsive Stressed". They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.

Application of evoked response audiometry for specifying aberrant gamma oscillations in schizophrenia

Gamma oscillations probed using auditory steady-state response (ASSR) are promising clinical biomarkers that may give rise to novel therapeutic interventions for schizophrenia. Optimizing clinical settings for these biomarker-driven interventions will require a quick and easy assessment system for gamma oscillations in psychiatry. ASSR has been used in clinical otolaryngology for evoked response audiometry (ERA) in order to judge hearing loss by focusing on the phase-locked response detectability via an automated analysis system. Herein, a standard ERA system with 40- and 46-Hz ASSRs was applied to evaluate the brain pathophysiology of patients with schizophrenia. Both ASSRs in the ERA system showed excellent detectability regarding the phase-locked response in healthy subjects and sharply captured the deficits of the phase-locked response caused by aberrant gamma oscillations in individuals with schizophrenia. These findings demonstrate the capability of the ERA system to specify patients who have aberrant gamma oscillations. The ERA system may have a potential to serve as a real-world clinical medium for upcoming biomarker-driven therapeutics in psychiatry.

The Utility of Research Domain Criteria in Diagnosis and Management of Dual Disorders: A Mini-Review

The Research Domain Criteria (RDoC) initiative has been considered a comprehensive alternative classification framework for understanding neuropsychiatric ailments, as opposed to the longstanding, traditional DSM framework. Where the DSM categorizes neuropsychiatric disorders as each being distinct and diagnostically defined by the presence of specified symptoms, RDoC provides a multidimensional conceptualization of psychiatric disorders with neurobiological roots. By taking a multidimensional approach, RDoC overcomes two major constraints of the DSM framework: that is, that the DSM is categorical in its approach to psychiatric disorders to the point of understating the intersectionality between concomitant disorders, and that the DSM focuses mainly on clinical features. RDoC seems to better account for the intersection between dual disorders and considers a range of factors, from the more microscopic (e.g., genetics or molecular functions) to the more macroscopic (e.g., environmental influences). The multidimensional approach of RDoC is particularly appealing in the context of dual disorders. Dual disorders refers to a concurrent psychiatric disorder with an addiction disorder. RDoC accounts for the fact that there is often overlap in symptoms across and bidirectional influence between various disorders. However, to date, there is limited research into the clinical utility of RDoC, and less so in the context of the clinical management of dual disorders. In this Mini-Review, we discuss how RDoC differs from the DSM, what outcomes have been reported in utilizing RDoC clinically, the utility of RDoC for the diagnosis, management, and monitoring of psychopathology, and the limitations of RDoC as well as avenues for future research.

Inflammation Subtypes and Translating Inflammation-Related Genetic Findings in Schizophrenia and Related Psychoses: A Perspective on Pathways for Treatment Stratification and Novel Therapies

Dysregulation of immunological and inflammatory processes is frequently observed in psychotic disorders. Numerous studies have examined the complex components of innate and adaptive immune processes in schizophrenia and related psychoses. Elevated inflammation in these conditions is related to neurobiological phenotypes and associated with both genetics and environmental exposures. Recent studies have utilized multivariate cytokine approaches to identify what appears to be a subset of individuals with elevated inflammation. The degree to which these findings represent a general process of dysregulated inflammation or whether there are more refined subtypes remains unclear. Brain-imaging studies have attempted to establish the link between peripheral inflammation and gray matter disruption, white matter abnormalities, and neuropsychological phenotypes. However, the interplay between peripheral inflammation and neuroinflammation, as well as the consequences of this interplay, in the context of psychosis remains unclear and requires further investigation. This Perspectives article reviews the following elements of immune dysregulation and its clinical and therapeutic implications: (1) evidence supporting inflammation and immune dysregulation in schizophrenia and related psychoses; (2) recent advances in approaches to characterizing subgroups of patients with elevated inflammation; (3) relationships between peripheral inflammation and brain-imaging indicators of neuroinflammation; (4) convergence of large-scale genetic findings and peripheral inflammation findings; and (5) therapeutic implications: anti-inflammation interventions leveraging genetic findings for drug discovery and repurposing. We offer perspectives and examples of how multiomics technologies may be useful for constructing and studying immunogenetic signatures. Advancing research in this area will facilitate biomarker discovery, disease subtyping, and the development of etiological treatments for immune dysregulation in psychosis.

Trends in big data analyses by multicenter collaborative translational research in psychiatry

The underlying pathologies of psychiatric disorders, which cause substantial personal and social losses, remain unknown, and their elucidation is an urgent issue. To clarify the core pathological mechanisms underlying psychiatric disorders, in addition to laboratory-based research that incorporates the latest findings, it is necessary to conduct large-sample-size research and verify reproducibility. For this purpose, it is critical to conduct multicenter collaborative research across various fields, such as psychiatry, neuroscience, molecular biology, genomics, neuroimaging, cognitive science, neurophysiology, psychology, and pharmacology. Moreover, collaborative research plays an important role in the development of young researchers. In this respect, the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and Cognitive Genetics Collaborative Research Organization (COCORO) have played important roles. In this review, we first overview the importance of multicenter collaborative research and our target psychiatric disorders. Then, we introduce research findings on the pathophysiology of psychiatric disorders from neurocognitive, neurophysiological, neuroimaging, genetic, and basic neuroscience perspectives, focusing mainly on the findings obtained by COCORO. It is our hope that multicenter collaborative research will contribute to the elucidation of the pathological basis of psychiatric disorders.

Interactive effects of polygenic risk and cognitive subtype on brain morphology in schizophrenia spectrum and bipolar disorders

Grey matter volume (GMV) may be associated with polygenic risk for schizophrenia (PRS-SZ) and severe cognitive deficits in people with schizophrenia, schizoaffective disorder (collectively SSD), and bipolar disorder (BD). This study examined the interactive effects of PRS-SZ and cognitive subtypes of SSD and BD in relation to GMV. Two-step cluster analysis was performed on 146 clinical cases (69 SSD and 77 BD) assessed on eight cognitive domains (verbal and visual memory, executive function, processing speed, visual processing, language ability, working memory, and planning). Among them, 55 BD, 51 SSD, and 58 healthy controls (HC), contributed to focal analyses of the relationships between cognitive subtypes, PRS-SZ and their interaction on GMV. Two distinct cognitive subtypes were evident among the combined sample of cases: a 'cognitive deficit' group (CD; N = 31, 20SSD/11BD) showed severe impairment across all cognitive indices, and a 'cognitively spared' (CS; N = 75; 31SSD/44BD) group showed intermediate cognitive performance that was significantly worse than the HC group but better than the CD subgroup. A cognitive subgroup-by-PRS-SZ interaction was significantly associated with GMV in the left precentral gyrus. Moderation analyses revealed a significant negative relationship between PRS-SZ and GMV in the CD group only. At low and average (but not high) PRS-SZ, larger precentral GMV was evident in the CD group compared to both CS and HC groups, and in the CS group compared to HCs. This study provides evidence for a relationship between regional GMV changes and PRS-SZ in psychosis spectrum cases with cognitive deficits, but not in cases cognitively spared.

Brick by Brick: Building a Transdiagnostic Understanding of Inflammation in Psychiatry

ABSTRACT

Inflammatory phenomena are found in many psychiatric disorders-notably, depression, schizophrenia, and posttraumatic stress disorder. Inflammation has been linked to severity and treatment resistance, and may both contribute to, and result from, the pathophysiology of some psychiatric illnesses. Emerging research suggests that inflammation may contribute to symptom domains of reward, motor processing, and threat reactivity across different psychiatric diagnoses. Reward-processing deficits contribute to motivational impairments in depression and schizophrenia, and motor-processing deficits contribute to psychomotor slowing in both depression and schizophrenia. A number of experimental models and clinical trials suggest that inflammation produces deficits in reward and motor processing through common pathways connecting the cortex and the striatum, which includes the nucleus accumbens, caudate nucleus, and putamen.The observed effects of inflammation on psychiatric disorders may cut across traditional conceptualizations of psychiatric diagnoses. Further study may lead to targeted immunomodulating treatments that address difficult-to-treat symptoms in a number of psychiatric disorders. In this review, we use a Research Domain Criteria framework to discuss proposed mechanisms for inflammation and its effects on the domains of reward processing, psychomotor slowing, and threat reactivity. We also discuss data that support contributing roles of metabolic dysregulation and sex differences on the behavioral outcomes of inflammation. Finally, we discuss ways that future studies can help disentangle this complex topic to yield fruitful results that will help advance the field of psychoneuroimmunology.

The Quest for Psychiatric Advancement through Theory, beyond Serendipity

Over the past century, advancements in psychiatric treatments have freed countless individuals from the burden of life-long, incapacitating mental illness. These treatments have largely been discovered by chance. Theory has driven advancement in the natural sciences and other branches of medicine, but psychiatry remains a field in its "infancy". The targets for healing in psychiatry lie within the realm of the mind's subjective experience and thought, which we cannot yet describe in terms of their biological underpinnings in the brain. Our technology is sufficiently advanced to study brain neurons and their interactions on an electrophysiological and molecular level, but we cannot say how these form a single feeling or thought. While psychiatry waits for its "Copernican Revolution", we continue the work in developing theories and associated experiments based on our existing diagnostic systems, for example, the Diagnostic and Statistical Manual of Mental Disorders (DSM), International Classification of Diseases (ICD), or the more newly introduced Research Domain Criteria (RDoC) framework. Understanding the subjective reality of the mind in biological terms would doubtless lead to huge advances in psychiatry, as well as to ethical dilemmas, from which we are spared for the time being.

Towards the understanding of state-independent neural traits underlying psychiatric disorders

Hampered by the symptom complexity and diversity, the understanding of fundamental mechanisms underlying psychiatric disorders remains elusive. Traditional neuroscience research focusing on each behavioral domain separately may lack an overarching view of the pathogenesis of an entire disorder, offering limited power to identify core neuropathology that could possibly account for the disorder's various symptoms. The search for neural traits that are robustly present across different brain functional states and disease stages may provide insights into the rudimentary changes beneath manifest clinical phenotypes and thus help penetrate the causal mechanisms underlying a complex disorder. In this review, I briefly summarize previous research on this topic, emphasize how neural traits may help boost the understanding of biological mechanisms underlying psychiatric disorders, and exemplify how the observed traits may aid individualized predictions for diagnosis and prognosis in precision psychiatry, in particular related to schizophrenia. I also discuss a proposed research framework that can be leveraged for future studies on neural traits, as well as considerations for future applications of this nascent research strategy.

Testing the Stability and Validity of an Executive Dysfunction Classification Using Task-Based Assessment in Children and Adolescents

OBJECTIVE

It is unclear if pediatric executive dysfunction assessed only with cognitive tasks predicts clinically relevant outcomes independently of psychiatric diagnoses. This study tested the stability and validity of a task-based classification of executive function.

METHOD

A total of 2,207 individuals (6-17 years old) from the Brazilian High-Risk Cohort Study participated in this study (1,930 at baseline, 1,532 at follow-up). Executive function was measured using tests of working memory and inhibitory control. Dichotomized age- and sex-standardized performances were used as input in latent class analysis and receiver operating curves to create an executive dysfunction classification (EDC). The study tested EDC's stability over time, association with symptoms, functional impairment, a polymorphism in the CADM2 gene, polygenic risk scores (PRS), and brain structure. Analyses covaried for age, sex, social class, IQ, and psychiatric diagnoses.

RESULTS

EDC at baseline predicted itself at follow-up (odds ratio [OR] = 5.11; 95% CI 3.41-7.64). Participants in the EDC reported symptoms spanning several domains of psychopathology and exhibited impairment in multiple settings, including more adverse school events (OR = 2.530; 95% CI 1.838-3.483). Children in the EDC presented higher attention-deficit/hyperactivity disorder and lower educational attainment PRS at baseline; higher schizophrenia PRS at follow-up; and lower chances of presenting a polymorphism in a gene previously linked to high performance in executive function (CADM2 gene). They also exhibited smaller intracranial volumes and smaller bilateral cortical surface areas in several brain regions.

CONCLUSION

Task-based executive dysfunction is associated with several validators, independently of psychiatric diagnoses and intelligence. Further refinement of task-based assessments might generate clinically useful tools.

Searching for Imaging Biomarkers of Psychotic Dysconnectivity

BACKGROUND

Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.

METHODS

We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.

RESULTS

Although we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results.

CONCLUSIONS

Connectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

Neuroimaging Association Scores: reliability and validity of aggregate measures of brain structural features linked to mental disorders in youth

In genetics, aggregation of many loci with small effect sizes into a single score improved prediction. Nevertheless, studies applying easily replicable weighted scores to neuroimaging data are lacking. Our aim was to assess the reliability and validity of the Neuroimaging Association Score (NAS), which combines information from structural brain features previously linked to mental disorders. Participants were 726 youth (aged 6-14) from two cities in Brazil who underwent MRI and psychopathology assessment at baseline and 387 at 3-year follow-up. Results were replicated in two samples: IMAGEN (n = 1627) and the Healthy Brain Network (n = 843). NAS were derived by summing the product of each standardized brain feature by the effect size of the association of that brain feature with seven psychiatric disorders documented by previous meta-analyses. NAS were calculated for surface area, cortical thickness and subcortical volumes using T1-weighted scans. NAS reliability, temporal stability and psychopathology and cognition prediction were analyzed. NAS for surface area showed high internal consistency and 3-year stability and predicted general psychopathology and cognition with higher replicability than specific symptomatic domains for all samples. They also predicted general psychopathology with higher replicability than single structures alone, accounting for 1-3% of the variance, but without directionality. The NAS for cortical thickness and subcortical volumes showed lower internal consistency and less replicable associations with behavioural phenotypes. These findings indicate the NAS based on surface area might be replicable markers of general psychopathology, but these links are unlikely to be causal or clinically useful yet.

Search for schizophrenia and bipolar biotypes using functional network properties

INTRODUCTION

Recent studies support the identification of valid subtypes within schizophrenia and bipolar disorder using cluster analysis. Our aim was to identify meaningful biotypes of psychosis based on network properties of the electroencephalogram. We hypothesized that these parameters would be more altered in a subgroup of patients also characterized by more severe deficits in other clinical, cognitive, and biological measurements.

METHODS

A clustering analysis was performed using the electroencephalogram-based network parameters derived from graph-theory obtained during a P300 task of 137 schizophrenia (of them, 35 first episodes) and 46 bipolar patients. Both prestimulus and modulation of the electroencephalogram were included in the analysis. Demographic, clinical, cognitive, structural cerebral data, and the modulation of the spectral entropy of the electroencephalogram were compared between clusters. Data from 158 healthy controls were included for further comparisons.

RESULTS

We identified two clusters of patients. One cluster presented higher prestimulus connectivity strength, clustering coefficient, path-length, and lower small-world index compared to controls. The modulation of clustering coefficient and path-length parameters was smaller in the former cluster, which also showed an altered structural connectivity network and a widespread cortical thinning. The other cluster of patients did not show significant differences with controls in the functional network properties. No significant differences were found between patients´ clusters in first episodes and bipolar proportions, symptoms scores, cognitive performance, or spectral entropy modulation.

CONCLUSION

These data support the existence of a subgroup within psychosis with altered global properties of functional and structural connectivity.

Social brain network predicts real-world social network in individuals with social anhedonia

Social anhedonia (SA) impairs social functioning in schizophrenia. Previous evidence suggested that certain brain regions predict longitudinal change of real-world social outcomes, yet previous study designs have failed to capture the corresponding functional connectivity among the brain regions involved. This study measured the real-world social network in 22 pairs of individuals with high and low levels of SA, and followed up them for 21 months. We further explored whether resting-state social brain network characteristics could predict the longitudinal variations of real-world social network. Our results showed that social brain network characteristics could predict the change of real-world social networks in both the high SA and low SA groups. However, the results differed between the two groups, i.e., the topological characteristics of the social brain network predicted real-world social network change in the high SA group; whereas the functional connectivity within the social brain network predicted real-world social network change in the low SA group. Principal component analysis and linear regression analysis on the entire sample showed that the functional connectivity component centered at the right orbital inferior frontal gyrus could best predict social network change. Our findings support the notion that social brain network characteristics could predict social network development.

Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification

In drug-resistant temporal lobe epilepsy (TLE), precise predictions of drug response, surgical outcome, and cognitive dysfunction at an individual level remain challenging. A possible explanation may lie in the dominant "one-size-fits-all" group-level analytical approaches that does not allow parsing inter-individual variations along the disease spectrum. Conversely, analyzing inter-patient heterogeneity is increasingly recognized as a step towards person-centered care. Here, we utilized unsupervised machine learning to estimate latent relations (or disease factors) from 3 T multimodal MRI features (cortical thickness, hippocampal volume, FLAIR, T1/FLAIR, diffusion parameters) representing whole-brain patterns of structural pathology in 82 TLE patients. We assessed the specificity of our approach against age- and sex-matched healthy individuals and a cohort of frontal lobe epilepsy patients with histologically-verified focal cortical dysplasia. We identified four latent disease factors variably co-expressed within each patient and characterized by ipsilateral hippocampal microstructural alterations, loss of myelin and atrophy (Factor-1), bilateral paralimbic and hippocampal gliosis (Factor-2), bilateral neocortical atrophy (Factor-3), bilateral white matter microstructural alterations (Factor-4). Bootstrap analysis and parameter variations supported high stability and robustness of these factors. Moreover, they were not expressed in healthy controls and only negligibly in disease controls, supporting specificity. Supervised classifiers trained on latent disease factors could predict patient-specific drug-response in 76 ± 3% and postsurgical seizure outcome in 88 ± 2%, outperforming classifiers that did not operate on latent factor information. Latent factor models predicted inter-patient variability in cognitive dysfunction (verbal IQ: r = 0.40 ± 0.03; memory: r = 0.35 ± 0.03; sequential motor tapping: r = 0.36 ± 0.04), again outperforming baseline learners. Data-driven analysis of disease factors provides a novel appraisal of the continuum of interindividual variability, which is likely determined by multiple interacting pathological processes. Incorporating interindividual variability is likely to improve clinical prognostics.

A Transdiagnostic Biotype Detection Method for Schizophrenia and Autism Spectrum Disorder Based on Graph Kernel

Psychiatric diagnoses based on clinical manifestations are prone to be inaccurate. Schizophrenia (SZ) and autism spectrum disorder (ASD) were historically considered as the same disorder, and they still have many overlaps of clinical symptoms in the current standard. Therefore, there is an urgent need to explore the potential biotypes for them using neuroimaging measures such as brain functional connectivity (FC). However, previous studies have not effectively leveraged FC in detecting biotypes. Considering that graph theory helps reveal the topological information in FC, in this paper, we propose a graph kernel-based clustering method to explore transdiagnostic biotypes using FC estimated from functional magnetic resonance imaging (fMRI) data. In our method, frequent subnetworks are identified from the whole-brain FCs of all subjects, and then the graph kernel similarity is computed to measure the relationship between subjects for clustering. Based on fMRI data of 137 SZ and 150 ASD subjects, we obtained meaningful biotypes using our method, which shows significant differences between the identified biotypes in FC. In brief, our graph kernel-based clustering method is promising for transdiagnostic biotype detection.

Resting state functional connectivity subtypes predict discrete patterns of cognitive-affective functioning across levels of analysis among patients with treatment-resistant depression

Resting state functional connectivity (RSFC) in ventral affective (VAN), default mode (DMN) and cognitive control (CCN) networks may partially underlie heterogeneity in depression. The current study used data-driven parsing of RSFC to identify subgroups of patients with treatment-resistant depression (TRD; n = 70) and determine if subgroups generalized to transdiagnostic measures of cognitive-affective functioning relevant to depression (indexed across self-report, behavioral, and molecular levels of analysis). RSFC paths within key networks were characterized using Subgroup-Group Iterative Multiple Model Estimation. Three connectivity-based subgroups emerged: Subgroup A, the largest subset and containing the fewest pathways; Subgroup B, containing unique bidirectional VAN/DMN negative feedback; and Subgroup C, containing the most pathways. Compared to other subgroups, subgroup B was characterized by lower self-reported positive affect and subgroup C by higher self-reported positive affect, greater variability in induced positive affect, worse response inhibition, and reduced striatal tissue iron concentration. RSFC-based categorization revealed three TRD subtypes associated with discrete aberrations in transdiagnostic cognitive-affective functioning that were largely unified across levels of analysis and were maintained after accounting for the variability captured by a disorder-specific measure of depressive symptoms. Findings advance understanding of transdiagnostic brain-behavior heterogeneity in TRD and may inform novel treatment targets for this population.

Subtypes of schizophrenia identified by multi-omic measures associated with dysregulated immune function

Epigenetic modifications are plausible molecular sources of phenotypic heterogeneity across schizophrenia patients. The current study investigated biological heterogeneity in schizophrenia using peripheral epigenetic profiles to delineate illness subtypes independent of their phenomenological manifestations. We applied epigenome-wide profiling with a DNA methylation array from blood samples of 63 schizophrenia patients and 59 healthy controls. Non-negative matrix factorization (NMF) and k-means clustering were performed to identify DNA methylation-related patient subtypes. The validity of the partition was tested by assessing the profile of the T cell receptor (TCR) repertoires. The uniqueness of the identified subtypes in relation to brain structural and clinical measures were evaluated. Two distinct patterns of DNA methylation profiles were identified in patients. One subtype (60.3% of patients) showed relatively limited changes in methylation levels and cell composition compared to controls, while a second subtype (39.7% of patients) exhibited widespread methylation level alterations among genes enriched in immune cell activity, as well as a higher proportion of neutrophils and lower proportion of lymphocytes. Differentiation of the two patient subtypes was validated by TCR repertoires, which paralleled the partition based on DNA methylation profiles. The subtype with widespread methylation modifications had higher symptom severity, performed worse on cognitive measures, and displayed greater reductions in fractional anisotropy of white matter tracts and evidence of gray matter thickening compared to the other subtype. Identification of a distinct subtype of schizophrenia with unique molecular, cerebral, and clinical features provide a novel parcellation of the schizophrenia syndrome with potential to guide development of individualized therapeutics.

Brain-Based Biotypes of Psychiatric Vulnerability in the Acute Aftermath of Trauma

OBJECTIVE

Major negative life events, such as trauma exposure, can play a key role in igniting or exacerbating psychopathology. However, few disorders are diagnosed with respect to precipitating events, and the role of these events in the unfolding of new psychopathology is not well understood. The authors conducted a multisite transdiagnostic longitudinal study of trauma exposure and related mental health outcomes to identify neurobiological predictors of risk, resilience, and different symptom presentations.

METHODS

A total of 146 participants (discovery cohort: N=69; internal replication cohort: N=77) were recruited from emergency departments within 72 hours of a trauma and followed for the next 6 months with a survey, MRI, and physiological assessments.

RESULTS

Task-based functional MRI 2 weeks after a motor vehicle collision identified four clusters of individuals based on profiles of neural activity reflecting threat reactivity, reward reactivity, and inhibitory engagement. Three clusters were replicated in an independent sample with a variety of trauma types. The clusters showed different longitudinal patterns of posttrauma symptoms.

CONCLUSIONS

These findings provide a novel characterization of heterogeneous stress responses shortly after trauma exposure, identifying potential neuroimaging-based biotypes of trauma resilience and psychopathology.

Characterising Demographic, Clinical and Functional Features of Cognitive Subgroups in Schizophrenia Spectrum Disorders: A Systematic Review

Considerable cognitive heterogeneity is present within the schizophrenia spectrum disorder (SSD) population. Several subgroups characterised by more homogenous cognitive profiles have been identified. It is not yet clear however, whether these subgroups represent different points along a continuum of cognitive symptom severity, or whether they reflect unique profiles of the disorder. One way to determine this is by comparing subgroups on their non-cognitive characteristics. The aim of the present review was to systematically summarise our current understanding of the non-cognitive features of the cognitive subgroups of schizophrenia spectrum disorder (SSD). Thirty-five relevant studies were identified from January 1980 to March 2020. Cognitive subgroups were consistently compared on age, sex, education, age of illness onset, illness duration, positive, negative and disorganised symptoms, depression and psychosocial functioning. It was revealed that subgroups were consistently distinguished by education, negative symptom severity and degree of functional impairment; with subgroups characterised by worse cognitive functioning performing/rated worse on these characteristics. The lack of consistent subgroup differences for the majority of the non-cognitive characteristics provides partial support for the notion that cognitive subgrouping in SSD is not simply reflecting a rehash of previously identified clinical subtypes. However, as subgroups were consistently distinguished by three characteristics known to be associated with cognition, our understanding of the extent to which the cognitive subgrouping approach is representing separate subtypes versus subdivisions along a continuum of symptom severity is still not definitive.

A Diagnosis and Biotype Comparison Across the Psychosis Spectrum: Investigating Volume and Shape Amygdala-Hippocampal Differences from the B-SNIP Study

OBJECTIVE

Brain-based Biotypes for psychotic disorders have been developed as part of the B-SNIP consortium to create neurobiologically distinct subgroups within idiopathic psychosis, independent from traditional phenomenological diagnostic methods. In the current study, we aimed to validate the Biotype model by assessing differences in volume and shape of the amygdala and hippocampus contrasting traditional clinical diagnoses with Biotype classification.

METHODS

A total of 811 participants from 6 sites were included: probands with schizophrenia (n = 199), schizoaffective disorder (n = 122), psychotic bipolar disorder with psychosis (n = 160), and healthy controls (n = 330). Biotype classification, previously developed using cognitive and electrophysiological data and K-means clustering, was used to categorize psychosis probands into 3 Biotypes, with Biotype-1 (B-1) showing reduced neural salience and severe cognitive impairment. MAGeT-Brain segmentation was used to determine amygdala and hippocampal volumetric data and shape deformations.

RESULTS

When using Biotype classification, B-1 showed the strongest reductions in amygdala-hippocampal volume and the most widespread shape abnormalities. Using clinical diagnosis, probands with schizophrenia and schizoaffective disorder showed the most significant reductions of amygdala and hippocampal volumes and the most abnormal hippocampal shape compared with healthy controls. Biotype classification provided the strongest neuroanatomical differences compared with conventional DSM diagnoses, with the best discrimination seen using bilateral amygdala and right hippocampal volumes in B-1.

CONCLUSION

These findings characterize amygdala and hippocampal volumetric and shape abnormalities across the psychosis spectrum. Grouping individuals by Biotype showed greater between-group discrimination, suggesting a promising approach and a favorable target for characterizing biological heterogeneity across the psychosis spectrum.

Oculomotor deficits in attention deficit hyperactivity disorder (ADHD): A systematic review and comprehensive meta-analysis

Atypical motor coordination and cognitive processes, such as response inhibition and working memory, have been extensively researched in individuals with attention deficit hyperactivity disorder (ADHD). Oculomotor neural circuits overlap extensively with regions involved in motor planning and cognition, therefore studies of oculomotor function may offer unique insights into motor and cognitive control in ADHD. We performed a series of pairwise meta-analyses based on data from 26 oculomotor studies in ADHD to examine whether there were differences in performance on visually-guided saccade, gap, antisaccade, memory-guided, pursuit eye movements and fixation tasks. These analyses revealed oculomotor disturbances in ADHD, particularly for difficulties relating to saccade inhibition, memorizing visual target locations and initiating antisaccades. There was no evidence for pursuit eye movement disturbances or saccade dysmetria. Investigating oculomotor abnormalities in ADHD may provide insight into top-down cognitive control processes and motor control, and may serve as a promising biomarker in ADHD research and clinical practice.

Auditory Oddball Responses Across the Schizophrenia-Bipolar Spectrum and Their Relationship to Cognitive and Clinical Features

OBJECTIVE

Neural activations during auditory oddball tasks may be endophenotypes for psychosis and bipolar disorder. The authors investigated oddball neural deviations that discriminate multiple diagnostic groups across the schizophrenia-bipolar spectrum (schizophrenia, schizoaffective disorder, psychotic bipolar disorder, and nonpsychotic bipolar disorder) and clarified their relationship to clinical and cognitive features.

METHODS

Auditory oddball responses to standard and target tones from 64 sensor EEG recordings were compared across patients with psychosis (total N=597; schizophrenia, N=225; schizoaffective disorder, N=201; bipolar disorder with psychosis, N=171), patients with bipolar disorder without psychosis (N=66), and healthy comparison subjects (N=415) from the second iteration of the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP2) study. EEG activity was analyzed in voltage and in the time-frequency domain (low, beta, and gamma bands). Event-related potentials (ERPs) were compared with those from an independent sample collected during the first iteration of B-SNIP (B-SNIP1; healthy subjects, N=211; psychosis group, N=526) to establish the repeatability of complex oddball ERPs across multiple psychosis syndromes (r values >0.94 between B-SNIP1 and B-SNIP2).

RESULTS

Twenty-six EEG features differentiated the groups; they were used in discriminant and correlational analyses. EEG variables from the N100, P300, and low-frequency ranges separated the groups along a diagnostic continuum from healthy to bipolar disorder with psychosis/bipolar disorder without psychosis to schizoaffective disorder/schizophrenia and were strongly related to general cognitive function (r=0.91). P50 responses to standard trials and early beta/gamma frequency responses separated the bipolar disorder without psychosis group from the bipolar disorder with psychosis group. P200, N200, and late beta/gamma frequency responses separated the two bipolar disorder groups from the other groups.

CONCLUSIONS

Neural deviations during auditory processing are related to psychosis history and bipolar disorder. There is a powerful transdiagnostic relationship between severity of these neural deviations and general cognitive performance. These results have implications for understanding the neurobiology of clinical syndromes across the schizophrenia-bipolar spectrum that may have an impact on future biomarker research.