ABSTRACT
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.
Subject(s)
Brain , Machine Learning , Magnetic Resonance Imaging , Neuroimaging , Psychotic Disorders , Humans , Psychotic Disorders/pathology , Psychotic Disorders/diagnostic imaging , Magnetic Resonance Imaging/methods , Male , Female , Brain/pathology , Brain/diagnostic imaging , Neuroimaging/methods , Adult , Young Adult , Adolescent , Prodromal SymptomsABSTRACT
INTRODUCTION: Negative symptoms impact the quality of life of individuals with psychosis and current treatment options for negative symptoms have limited effectiveness. Previous studies have demonstrated that complement and coagulation pathway protein levels are related to later psychotic experiences, psychotic disorder, and functioning. However, the prognostic relationship between complement and coagulation proteins and negative symptoms is poorly characterised. METHODS: In the North American Prodrome Longitudinal Studies 2 and 3, negative symptoms in 431 individuals at clinical high-risk for psychosis (mean age: 18.2, SD 3.6; 42.5 % female) were measured at multiple visits over 2 years using the Scale of Psychosis-Risk Symptoms. Plasma proteins were quantified at baseline using mass spectrometry. Four factors were derived to represent levels of proteins involved in the activation or regulation of the complement or coagulation systems. The relationships between standardised protein group factors and serial measurements of negative symptoms over time were modelled using generalised least squares regression. Analyses were adjusted for baseline candidate prognostic factors: negative symptoms, positive symptoms, functioning, depressive symptoms, suicidal ideation, cannabis use, tobacco use, antipsychotic use, antidepressant use, age, and sex. RESULTS: Clinical and demographic prognostic factors of follow-up negative symptoms included negative, positive, and depressive symptoms, functioning, and age. Adjusting for all candidate prognostic factors, the complement regulators group and the coagulation regulators group were identified as prognostic factors of follow-up negative symptoms (ß: 0.501, 95 % CI: 0.160, 0.842; ß: 0.430, 95 % CI: 0.080, 0.780 respectively. The relationship between complement regulator levels and negative symptoms was also observed in NAPLS2 alone (ß: 0.501, 95 % CI: -0.037, 1.039) and NAPLS3 alone, additionally adjusting for BMI (ß: 0.442, 95 % CI: 0.127, 0.757). CONCLUSION: The results indicate that plasma complement and coagulation regulator levels are prognostic factors of negative symptoms, independent of clinical and demographic prognostic factors. These results suggest complement and coagulation regulator levels could have potential utility in informing treatment decisions for negative symptoms in individuals at risk.
Subject(s)
Complement System Proteins , Psychotic Disorders , Humans , Female , Male , Prognosis , Adolescent , Young Adult , Complement System Proteins/metabolism , Complement System Proteins/analysis , Psychotic Disorders/blood , Adult , Blood Coagulation Factors/metabolism , Blood Coagulation Factors/analysis , Longitudinal StudiesABSTRACT
BACKGROUND: Choroid plexus (ChP) enlargement exists in first-episode and chronic psychosis, but whether enlargement occurs before psychosis onset is unknown. This study investigated whether ChP volume is enlarged in individuals with clinical high-risk (CHR) for psychosis and whether these changes are related to clinical, neuroanatomical, and plasma analytes. METHODS: Clinical and neuroimaging data from the North American Prodrome Longitudinal Study 2 (NAPLS2) was used for analysis. 509 participants (169 controls, 340 CHR) were recruited. Conversion status was determined after 2-years of follow-up, with 36 psychosis converters. The lateral ventricle ChP was manually segmented from baseline scans. A subsample of 31 controls and 53 CHR had plasma analyte and neuroimaging data. RESULTS: Compared to controls, CHR (d = 0.23, p = 0.017) and non-converters (d = 0.22, p = 0.03) demonstrated higher ChP volumes, but not in converters. In CHR, greater ChP volume correlated with lower cortical (r = -0.22, p < 0.001), subcortical gray matter (r = -0.21, p < 0.001), and total white matter volume (r = -0.28,p < 0.001), as well as larger lateral ventricle volume (r = 0.63,p < 0.001). Greater ChP volume correlated with makers functionally associated with the lateral ventricle ChP in CHR [CCL1 (r = -0.30, p = 0.035), ICAM1 (r = 0.33, p = 0.02)], converters [IL1ß (r = 0.66, p = 0.004)], and non-converters [BMP6 (r = -0.96, p < 0.001), CALB1 (r = -0.98, p < 0.001), ICAM1 (r = 0.80, p = 0.003), SELE (r = 0.59, p = 0.026), SHBG (r = 0.99, p < 0.001), TNFRSF10C (r = 0.78, p = 0.001)]. CONCLUSIONS: CHR and non-converters demonstrated significantly larger ChP volumes compared to controls. Enlarged ChP was associated with neuroanatomical alterations and analyte markers functionally associated with the ChP. These findings suggest that the ChP may be a key an important biomarker in CHR.
Subject(s)
Choroid Plexus , Psychotic Disorders , Humans , Choroid Plexus/diagnostic imaging , Longitudinal Studies , Phenotype , Psychotic Disorders/diagnostic imaging , NeuroimagingABSTRACT
The 1965 discovery of the P300 component of the electroencephalography (EEG)-based event-related potential (ERP), along with the subsequent identification of its alteration in people with schizophrenia, initiated over 50 years of P300 research in schizophrenia. Here, we review what we now know about P300 in schizophrenia after nearly six decades of research. We describe recent efforts to expand our understanding of P300 beyond its sensitivity to schizophrenia itself to its potential role as a biomarker of risk for psychosis or a heritable endophenotype that bridges genetic risk and psychosis phenomenology. We also highlight efforts to move beyond a syndrome-based approach to understand P300 within the context of the clinical, cognitive, and presumed pathophysiological heterogeneity among people diagnosed with schizophrenia. Finally, we describe several recent approaches that extend beyond measuring the traditional P300 ERP component in people with schizophrenia, including time-frequency analyses and pharmacological challenge studies, that may help to clarify specific cognitive mechanisms that are disrupted in schizophrenia. Moreover, we discuss several promising areas for future research, including studies of animal models that can be used for treatment development.
Subject(s)
Psychotic Disorders , Schizophrenia , Humans , Electroencephalography , Event-Related Potentials, P300/physiology , Evoked PotentialsABSTRACT
Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.
ABSTRACT
Recent neuroimaging studies and publicly disseminated analytic tools suggest that regional morphometric analyses covary for global thickness. We empirically demonstrated that this statistical approach severely underestimates regional thickness dysmorphology in psychiatric disorders. Study 1 included 90 healthy control participants, 51 participants at clinical high risk for psychosis, and 78 participants with early-illness schizophrenia. Study 2 included 56 healthy control participants, 83 participants with nonaffective psychosis, and 30 participants with affective psychosis. We examined global and regional thickness correlations, global thickness group differences, and regional thickness group differences with and without global thickness covariation. Global and regional thickness were strongly correlated across groups. Global thickness was lower in the schizophrenia spectrum groups than the other groups. Regional thickness deficits in schizophrenia spectrum groups were attenuated or eliminated with global thickness covariation. Eliminating the variation that regional thickness shares with global thickness eliminated disease-related effects. This statistical approach results in erroneous conclusions that regional thickness is normal in disorders like schizophrenia or clinical high risk syndrome.
ABSTRACT
Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 schizophrenia patients and demographically matched 160 healthy controls. Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available brain networks between SZ patients and healthy controls (HC). These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN) and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to healthy control group. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. k-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that it appears healthy for a brain to primarily circulate through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. However, individuals with SZ seem to struggle with transiently attaining these more focused and structured connectivity patterns. Proposed ICE measure presents a novel framework for gaining deeper insights into understanding mechanisms of healthy and disease brain states and a substantial step forward in the developing advanced methods of diagnostics of mental health conditions.
ABSTRACT
BACKGROUND: Sensory prediction allows the brain to anticipate and parse incoming self-generated sensory information from externally generated signals. Sensory prediction breakdowns may contribute to perceptual and agency abnormalities in psychosis (hallucinations, delusions). The pons, a central node in a cortico-ponto-cerebellar-thalamo-cortical circuit, is thought to support sensory prediction. Examination of pons connectivity in schizophrenia and its role in sensory prediction abnormalities is lacking. METHODS: We examined these relationships using resting-state functional magnetic resonance imaging and the electroencephalography-based auditory N1 event-related potential in 143 participants with psychotic spectrum disorders (PSPs) (with schizophrenia, schizoaffective disorder, or bipolar disorder); 63 first-degree relatives of individuals with psychosis; 45 people at clinical high risk for psychosis; and 124 unaffected comparison participants. This unique sample allowed examination across the psychosis spectrum and illness trajectory. Seeding from the pons, we extracted average connectivity values from thalamic and cerebellar clusters showing differences between PSPs and unaffected comparison participants. We predicted N1 amplitude attenuation during a vocalization task from pons connectivity and group membership. We correlated participant-level connectivity in PSPs and people at clinical high risk for psychosis with hallucination and delusion severity. RESULTS: Compared to unaffected comparison participants, PSPs showed pons hypoconnectivity to 2 cerebellar clusters, and first-degree relatives of individuals with psychosis showed hypoconnectivity to 1 of these clusters. Pons-to-cerebellum connectivity was positively correlated with N1 attenuation; only PSPs with heightened pons-to-postcentral gyrus connectivity showed this pattern, suggesting a possible compensatory mechanism. Pons-to-cerebellum hypoconnectivity was correlated with greater hallucination severity specifically among PSPs with schizophrenia. CONCLUSIONS: Deficient pons-to-cerebellum connectivity linked sensory prediction network breakdowns with perceptual abnormalities in schizophrenia. Findings highlight shared features and clinical heterogeneity across the psychosis spectrum.
Subject(s)
Cerebellum , Electroencephalography , Hallucinations , Magnetic Resonance Imaging , Pons , Psychotic Disorders , Schizophrenia , Humans , Hallucinations/physiopathology , Schizophrenia/physiopathology , Schizophrenia/complications , Male , Female , Adult , Psychotic Disorders/physiopathology , Psychotic Disorders/diagnostic imaging , Psychotic Disorders/complications , Pons/physiopathology , Cerebellum/physiopathology , Cerebellum/diagnostic imaging , Neural Pathways/physiopathology , Young AdultABSTRACT
BACKGROUND AND HYPOTHESIS: Brain development/aging is not uniform across individuals,spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological ageâ >â chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or agingmap onto specific symptom facets. STUDY DESIGN: Using structural MRI, we compared the brain age gap among CHR-P (nâ =â 51), ESZ (nâ =â 78), and unaffected comparison participants (UCP; nâ =â 90), and examined associations with CHR-P psychosis conversion (CHR-P converters nâ =â 10; CHR-P non-converters; nâ =â 23) and positive and negative symptoms. STUDY RESULTS: ESZ showed a greater brain age gap relative to UCP and CHR-P (Psâ <â .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (Pâ =â .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (Pâ =â .008), but not expressive negative symptom severity. CONCLUSIONS: Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.
ABSTRACT
BACKGROUND AND HYPOTHESIS: Social and academic adjustment deteriorate in the years preceding a psychotic disorder diagnosis. Analyses of premorbid adjustment have recently been extended into the clinical high risk for psychosis (CHR) syndrome to identify risk factors and developmental pathways toward psychotic disorders. Work so far has been at the between-person level, which has constrained analyses of premorbid adjustment, clinical covariates, and conversion to psychosis. STUDY DESIGN: Growth-curve models examined longitudinal trajectories in retrospective reports of premorbid social and academic adjustment from youth at CHR (nâ =â 498). Interaction models tested whether known covariates of premorbid adjustment problems (attenuated negative symptoms, cognition, and childhood trauma) were associated with different premorbid adjustment trajectories in converters vs non-converters (ie, participants who did/did not develop psychotic disorders within 2-year follow-up). STUDY RESULTS: Converters reported poorer social adjustment throughout the premorbid period. Converters who developed psychosis with an affective component reported poorer academic adjustment throughout the premorbid period than those who developed non-affective psychosis. Tentatively, baseline attenuated negative symptoms may have been associated with worsening social adjustment in the premorbid period for non-converters only. Childhood trauma impact was associated with fewer academic functioning problems among converters. Cognition effects did not differ based on conversion status. CONCLUSIONS: Premorbid social function is an important factor in risk for conversion to psychosis. Negative symptoms and childhood trauma had different relationships to premorbid functioning in converters vs non-converters. Mechanisms linking symptoms and trauma to functional impairment may be different in converters vs non-converters, suggesting possible new avenues for risk assessment.
ABSTRACT
BACKGROUND AND HYPOTHESIS: The Structured Interview for Psychosis-Risk Syndromes (SIPS) and other assessments of psychosis risk define clinical high risk for psychosis (CHR) by the presence of attenuated psychotic symptoms. Despite extensive research on attenuated psychotic symptoms, substantial questions remain about their internal psychometric structure and relationships to comorbid non-psychotic symptoms. STUDY DESIGN: Hierarchical and bifactor models were developed for the SIPS in a large CHR sample (NAPLS-3, Nâ =â 787) and confirmed through preregistered replication in an independent sample (NAPLS-2, Nâ =â 1043). Criterion validity was tested through relationships with CHR status, comorbid symptoms/diagnoses, functional impairment, demographics, neurocognition, and conversion to psychotic disorders. STUDY RESULTS: Most variance in SIPS items (75%-77%) was attributable to a general factor. Hierarchical and bifactor models included a general factor and five specific/lower-order factors (positive symptoms, eccentricity, avolition, lack of emotion, and deteriorated thought process). CHR participants were elevated on the general factor and the positive symptoms factor. The general factor was associated with depressive symptoms; functional impairment; and mood, anxiety, and schizotypal personality diagnoses. The general factor was the best predictor of psychotic disorders (dâ ≥â 0.50). Positive symptoms and eccentricity had specific effects on conversion outcomes. The deteriorated thought process was least meaningful/replicable. CONCLUSIONS: Attenuated psychotic symptoms, measured by the SIPS, have a complex hierarchical structure with a strong general factor. The general factor relates to internalizing symptoms and functional impairment, emphasizing the roles of general psychopathological distress/impairment in psychosis risk. Shared symptom variance complicates the interpretation of raw symptom scores. Broad transdiagnostic assessment is warranted to model psychosis risk accurately.
ABSTRACT
Schizophrenia is associated with robust white matter (WM) abnormalities but influences of potentially confounding variables and relationships with cognitive performance and symptom severity remain to be fully determined. This study was designed to evaluate WM abnormalities based on diffusion tensor imaging (DTI) in individuals with schizophrenia, and their relationships with cognitive performance and symptom severity. Data from individuals with schizophrenia (SZ; n=138, mean age±SD=39.02±11.82; 105 males) and healthy controls (HC; n=143, mean age±SD=37.07±10.84; 102 males) were collected as part of the Function Biomedical Informatics Research Network Phase 3 study. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were compared between individuals with schizophrenia and healthy controls, and their relationships with neurocognitive performance and symptomatology assessed. Individuals with SZ had significantly lower FA in forceps minor and the left inferior fronto-occipital fasciculus compared to HC. FA in several tracts were associated with speed of processing and attention/vigilance and the severity of the negative symptom alogia. This study suggests that regional WM abnormalities are fundamentally involved in the pathophysiology of schizophrenia and may contribute to cognitive performance deficits and symptom expression observed in schizophrenia.
Subject(s)
Diffusion Tensor Imaging , Schizophrenia , White Matter , Humans , Male , Schizophrenia/diagnostic imaging , Schizophrenia/pathology , White Matter/diagnostic imaging , White Matter/pathology , Female , Adult , Middle Aged , Severity of Illness Index , Cognitive Dysfunction/diagnostic imaging , Cognitive Dysfunction/pathology , Cognitive Dysfunction/psychology , Cognitive Dysfunction/physiopathologyABSTRACT
Sex differences have been observed in individuals with schizophrenia and for those at clinical high risk (CHR) for psychosis. However, specific differences in CHR individuals who transition to psychosis remain inconsistent and understudied. This study aimed to investigate sex differences in 156 CHR individuals who made the transition to psychosis. A wide range of demographics, positive and negative symptoms, depression, anxiety, social and role functioning, trauma, and substance use were assessed at baseline and symptoms and diagnoses at the time of transition. Fluctuations in positive and negative symptoms and different medications were also assessed. No sex differences were observed at baseline for those who later transitioned to psychosis. At transition, males were significantly more likely to be diagnosed as having schizophrenia or schizophreniform disorder and through the course of the study, males were more likely to be taking stimulants. Limitations in this study was the lack of longitudinal follow-up post transition. The study highlights the need for further research on sex differences in individuals who transition to psychosis. Understanding these differences can have implications for treatment and monitoring of CHR individuals.
ABSTRACT
The prodromal phase of schizophrenia provides an optimal opportunity to mitigate the profound functional disability that is often associated with fully expressed psychosis. Considerable evidence supports the importance of neurocognition in the development of interpersonal (social) and academic (role) skills. Further findings from adolescents and young adults at clinical high risk for developing psychosis (CHRP) suggest that treatment for functioning might be most effective when targeting early and specific neurocognitive deficits. The current study addresses this critical intervention issue by examining the potential of neurocognitive deficits at intake for predicting social and role functioning over time in CHR-P youth. The study included 345 CHR-P participants from the second phase of the North American Prodrome Longitudinal Study (NAPLS2) with baseline neurocognition and 2-year follow-up data on social and role functioning. Slower baseline processing speed consistently predicted poor social functioning over time, while attention deficits predicted poor role functioning at baseline and follow-up. In addition, the impact of processing speed and attention impairments on social and role functioning, respectively, persisted even when adjusting the regression models for attenuated positive, negative, and disorganized symptoms, and transition status. The current study demonstrates for, arguably the first time, that processing speed and attention are strongly predictive of social and role functioning over time, respectively, above and beyond the impact of symptoms and those CHR-P individuals that develop psychosis over the course of the study. These findings imply that early neurocognition is a critical treatment target linked to the developmental trajectory of social and role functioning.
ABSTRACT
The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium's transdiagnostic dimensional model of psychopathology has considerable support; however, this model has been underresearched in individuals at clinical high risk for psychosis (CHR-P), a population that may advance the model. CHR-P individuals not only have attenuated psychotic symptoms that vary in severity, but also have many comorbid diagnoses and varied clinical outcomes, including disorders with uncertain relations to HiTOP (e.g., obsessive-compulsive disorder). The present study used self-report and interview data from North American Prodrome Longitudinal Study-3 (710 CHR, 96 controls) to replicate the HiTOP model and test specific hypotheses regarding disorders with uncertain relations to its dimensions. Additionally, the present study examined the HiTOP model in relation to childhood trauma, declines in social functioning, and development of full psychosis. Confirmatory factor analysis indicated that the HiTOP model's fit was nearly adequate (e.g., comparative fit index = .89), though several theory-relevant modifications were indicated. Additionally, specific tests were conducted to gain a more fine-grained perspective on how disorders with less clear prior evidence were related to the HiTOP model. Notable findings from these analyses include bipolar spectrum disorders relating to the psychosis super spectrum (i.e., .39 loading), and obsessive-compulsive disorder showing a complex pattern of loadings (e.g., internalizing and psychosis). The final model parsimoniously accounted for childhood trauma (e.g., super spectra rs = .22-.32), associations with current functioning, and predicted future conversion to a psychotic disorder (e.g., super spectra R² = .13). Overall, these results inform the HiTOP model and suggest its promise for CHR-P research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Subject(s)
Bipolar Disorder , Mental Disorders , Psychotic Disorders , Humans , Mental Disorders/diagnosis , Longitudinal Studies , Psychotic Disorders/diagnosis , Psychotic Disorders/epidemiology , PsychopathologyABSTRACT
Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total nâ =â 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (nâ =â 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.
Subject(s)
Biomarkers , Prodromal Symptoms , Proteomics , Psychotic Disorders , Humans , Psychotic Disorders/blood , Female , Male , Biomarkers/blood , Young Adult , Adolescent , Adult , Disease Progression , Longitudinal Studies , RiskABSTRACT
Research has found strong evidence for common and distinct morphometric brain abnormality profiles in nonaffective psychosis (NAff-P) and affective psychosis (Aff-P). Due to chronicity and prolonged medication exposure confounds, it is crucial to examine structural morphometry early in the course of psychosis. Using Human Connectome Project-Early Psychosis data, multivariate profile analyses were implemented to examine regional profiles for cortical thickness, cortical surface area, subcortical volume, and ventricular volume in healthy control (HC; nâ =â 56), early illness NAff-P (nâ =â 83), and Aff-P (nâ =â 30) groups after accounting for normal aging. Associations with symptom severity, functioning, and cognition were also examined. Group regional profiles were significantly nonparallel and differed in level for cortical thickness (Pâ <â .001), with NAff-P having widespread cortical thinning relative to HC and Aff-P and some regions showing greater deficits than others. Significant nonparallelism of group regional profiles was also evident for cortical surface area (Pâ <â .006), with Aff-P and N-Aff-P differing from HC and from each other (Pâ <â .001). For subcortical volume, there was significant profile nonparallelism with NAff-P having an enlarged left pallidum and smaller accumbens and hippocampus (Pâ <â .028), and Aff-P having a smaller accumbens and amygdala (Pâ <â .006), relative to HC. NAff-P also had larger basal ganglia compared to Aff-P. Furthermore, NAff-P had enlarged ventricles (Pâ <â .055) compared to HC and Aff-P. Additionally, greater ventricular volume was associated with increased manic symptoms in NAff-P and Aff-P. Overall, this study found common and distinct regional morphometric profile abnormalities in early illness NAff-P and Aff-P, providing evidence for both shared and disease-specific pathophysiological processes.