Psychosis Biotypes: Replication and Validation from the B-SNIP Consortium.
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ABSTRACT:
SUBMITTER: Clementz BA
PROVIDER: S-EPMC8781330 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWe quantified frequency-specific, absolute, and fractional amplitude of low-frequency fluctuations (ALFF/fALFF) across the schizophrenia (SZ)-psychotic bipolar disorder (PBP) psychosis spectrum using resting functional magnetic resonance imaging data from the large BSNIP family study.MethodsWe assessed 242 healthy controls (HC), 547 probands (180 PBP, 220 SZ, and 147 schizoaffective disorder-SAD), and 410 of their first-degree relatives (134 PBPR, 150SZR, and 126 SADR). Following standard preprocessing in statistical parametric mapping (SPM8), we computed absolute and fractional power (ALFF/fALFF) in 2 low-frequency bands: slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz). We evaluated voxelwise post hoc differences across traditional Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnostic categories.ResultsAcross ALFF/fALFF, in contrast to HC, BP/SAD showed hypoactivation in frontal/anterior brain regions in the slow-5 band and hypoactivation in posterior brain regions in the slow-4 band. SZ showed consistent hypoactivation in precuneus/cuneus and posterior cingulate across both bands and indices. Increased ALFF/fALFF was noted predominantly in deep subcortical and temporal structures across probands in both bands and indices. Across probands, spatial ALFF/fALFF differences in SAD resembled PBP more than SZ. None of these ALFF/fALFF differences were detected in relatives.ConclusionsResults suggest ALFF/fALFF is a putative biomarker rather than a familial endophenotype. Overall sensitivity to discriminate proband brain alteration was stronger for fALFF than ALFF. Patterns of differences noted in SAD were more similar to those observed in PBP. Differential effects were noted across the 2 frequency bands, more prominently for BP/SAD compared with SZ, suggesting frequency-sensitive physiologic mechanisms for the former.
| S-EPMC4601713 | biostudies-literature
Project description:Clinical phenomenology remains the primary means for classifying psychoses despite considerable evidence that this method incompletely captures biologically meaningful differentiations. Rather than relying on clinical diagnoses as the gold standard, this project drew on neurobiological heterogeneity among psychosis cases to delineate subgroups independent of their phenomenological manifestations.A large biomarker panel (neuropsychological, stop signal, saccadic control, and auditory stimulation paradigms) characterizing diverse aspects of brain function was collected on individuals with schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis (N=711), their first-degree relatives (N=883), and demographically comparable healthy subjects (N=278). Biomarker variance across paradigms was exploited to create nine integrated variables that were used to capture neurobiological variance among the psychosis cases. Data on external validating measures (social functioning, structural magnetic resonance imaging, family biomarkers, and clinical information) were collected.Multivariate taxometric analyses identified three neurobiologically distinct psychosis biotypes that did not respect clinical diagnosis boundaries. The same analysis procedure using clinical DSM diagnoses as the criteria was best described by a single severity continuum (schizophrenia worse than schizoaffective disorder worse than bipolar psychosis); this was not the case for biotypes. The external validating measures supported the distinctiveness of these subgroups compared with clinical diagnosis, highlighting a possible advantage of neurobiological versus clinical categorization schemes for differentiating psychotic disorders.These data illustrate how multiple pathways may lead to clinically similar psychosis manifestations, and they provide explanations for the marked heterogeneity observed across laboratories on the same biomarker variables when DSM diagnoses are used as the gold standard.
| S-EPMC5314432 | biostudies-literature
Project description: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.
| S-EPMC9236198 | biostudies-literature
Project description:ObjectiveThe study examined gray matter volume across psychosis diagnoses organized by dimensional and DSM-IV categories from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) sample.MethodIn total, 351 probands with psychosis (146 with schizophrenia, 90 with schizoaffective disorder, and 115 with psychotic bipolar I disorder), 369 of their first-degree relatives (134 were relatives of individuals with schizophrenia, 106 of individuals with schizoaffective disorder, and 129 of individuals with psychotic bipolar I disorder), and 200 healthy comparison subjects were assessed. Gray matter volumes from 3-T T1-weighted images were analyzed using the VBM8 toolbox for SPM8, and outcomes were determined at a false discovery rate-corrected threshold of p<0.005.ResultsAcross the psychosis dimension, probands (N=351) and relatives with psychosis spectrum disorders (N=34) showed substantial overlapping gray matter reductions throughout the neocortex, whereas relatives without psychosis spectrum (N=332) had normal gray matter volumes relative to comparison subjects. Across DSM-IV diagnoses, schizophrenia and schizoaffective probands showed overlapping gray matter reductions in numerous cortical and subcortical regions, whereas psychotic bipolar probands showed limited gray matter reductions localized to the frontotemporal cortex relative to comparison subjects. All relative groups had gray matter volumes that did not differ from comparison subjects.ConclusionsAcross the dimensional psychosis categories, these findings indicate extensive neocortical gray matter reductions in psychosis probands and relatives with psychosis spectrum disorders, possibly reflecting lifetime psychosis burden, but normal gray matter in nonpsychotic relatives. Traditional DSM-IV psychosis grouping revealed partially divergent gray matter phenotypes for probands with schizophrenia or schizoaffective disorder (extensive neocortical or subcortical gray matter reductions) relative to those with psychotic bipolar disorder (smaller reductions were limited to frontotemporal regions). The dimensional conceptualization of psychosis appears useful in defining more homogenous disease categories that may help identify underlying psychosis biomarkers and develop a biologically driven diagnostic system and targeted treatments.
| S-EPMC6487663 | biostudies-literature
Project description:BACKGROUND:Psychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechanisms. A possible approach to dissect biological heterogeneity is to look for biologically meaningful subtypes. A recent study Drysdale et al. (2017) showed promising results along this line by simultaneously using resting state fMRI and clinical data and identified four distinct subtypes of depression with different clinical profiles and abnormal resting state fMRI connectivity. These subtypes were predictive of treatment response to transcranial magnetic stimulation therapy. OBJECTIVE:Here, we attempted to replicate the procedure followed in the Drysdale et al. study and their findings in a different clinical population and a more heterogeneous sample of 187 participants with depression and anxiety. We aimed to answer the following questions: 1) Using the same procedure, can we find a statistically significant and reliable relationship between brain connectivity and clinical symptoms? 2) Is the observed relationship similar to the one found in the original study? 3) Can we identify distinct and reliable subtypes? 4) Do they have similar clinical profiles as the subtypes identified in the original study? METHODS:We followed the original procedure as closely as possible, including a canonical correlation analysis to find a low dimensional representation of clinically relevant resting state fMRI features, followed by hierarchical clustering to identify subtypes. We extended the original procedure using additional statistical tests, to test the statistical significance of the relationship between resting state fMRI and clinical data, and the existence of distinct subtypes. Furthermore, we examined the stability of the whole procedure using resampling. RESULTS AND CONCLUSION:As in the original study, we found extremely high canonical correlations between functional connectivity and clinical symptoms, and an optimal three-cluster solution. However, neither canonical correlations nor clusters were statistically significant. On the basis of our extensive evaluations of the analysis methodology used and within the limits of comparison of our sample relative to the sample used in Drysdale et al., we argue that the evidence for the existence of the distinct resting state connectivity-based subtypes of depression should be interpreted with caution.
| S-EPMC6543446 | biostudies-literature
Project description:BackgroundThe current definitions of psychotic illness lack biological validity, motivating alternative biomarker-driven disease entities. Building on experimental constructs-Biotypes-that were previously developed from cognitive and neurophysiologic measures, we contrast brain anatomy characteristics across Biotypes alongside conventional diagnoses, examining gray matter density (GMD) as an independent validator for the Biotypes.MethodsWhole brain GMD measures were examined in probands, their relatives, and healthy subjects organized by Biotype and then by DSM-IV-TR diagnosis (n = 1409) using voxel-based morphometry with subsequent subject-level regional characterization and distribution analyses.ResultsProbands grouped by Biotype versus healthy controls showed a stepwise pattern of GMD reductions as follows: Biotype1, extensive and diffusely distributed GMD loss, with the largest effects in frontal, anterior/middle cingulate cortex, and temporal regions; Biotype2, intermediate and more localized reductions, with the largest effects in insula and frontotemporal regions; and Biotype3, small reductions localized to anterior limbic regions. Relatives showed regionally distinct GMD reductions versus healthy controls, with primarily anterior (frontotemporal) effects in Biotype1; posterior (temporo-parieto-cerebellar) in Biotype2; and normal GMD in Biotype3. Schizophrenia and schizoaffective probands versus healthy controls showed overlapping GMD reductions, with the largest effects in frontotemporal and parietal regions; psychotic bipolar probands had small reductions, primarily in frontal regions. GMD changes in relatives followed regional patterns observed in probands, albeit less extensive. Biotypes showed stronger between-group separation based on GMD than the conventional diagnoses and were the strongest predictor of GMD change.ConclusionsGMD biomarkers depicted unique brain structure characteristics within Biotypes, consistent with their cognitive and sensorimotor profiles, and provided stronger discrimination for biologically driven biotypes than symptom-based diagnoses.
| S-EPMC6501573 | biostudies-literature
Project description:Biological risk factors underlying psychosis are poorly understood. Biological underpinnings of the dimension of psychosis can be derived using genetic associations with intermediate phenotypes such as subcomponents of auditory event-related potentials (ERPs). Various ERP subcomponent abnormalities in schizophrenia and psychotic bipolar disorder are heritable and are expressed in unaffected relatives, although studies investigating genetic contributions to ERP abnormalities are limited. The authors used a novel parallel independent component analysis (para-ICA) to determine which empirically derived gene clusters are associated with data-driven ERP subcomponents, assuming a complex etiology underlying psychosis.The authors examined the multivariate polygenic association of ERP subcomponents from 64-channel auditory oddball data in 144 individuals with schizophrenia, 210 psychotic bipolar disorder probands, and 95 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Data were reduced by principal components analysis to two target and one standard ERP waveforms. Multivariate association of compressed ERP waveforms with a set of 20,329 single-nucleotide polymorphisms (SNPs) (reduced from a 1-million-SNP array) was examined using para-ICA. Genes associated with SNPs were further examined using pathway analysis tools.Para-ICA identified four ERP components that were significantly correlated with three genetic components. Enrichment analysis revealed complement immune response pathway and multiple processes that significantly mediate ERP abnormalities in psychosis, including synaptic cell adhesion, axon guidance, and neurogenesis.This study identified three genetic components comprising multiple genes mediating ERP subcomponent abnormalities in schizophrenia and psychotic bipolar disorder. The data suggest a possible polygenic structure comprising genes influencing key neurodevelopmental processes, neural circuitry, and brain function mediating biological pathways plausibly associated with psychosis.
| S-EPMC4455958 | biostudies-literature
Project description:BackgroundSensorimotor abnormalities precede and predict the onset of psychosis. Despite the practical utility of sensorimotor abnormalities for early identification, prediction, and individualized medicine applications, there is currently no dedicated self-report instrument designed to capture these important behaviors. The current study assessed and validated a questionnaire designed for use in individuals at clinical high-risk for psychosis (CHR).MethodsThe current study included both exploratory (n = 3009) and validation (n = 439) analytic datasets-that included individuals identified as meeting criteria for a CHR syndrome (n = 84)-who completed the novel Sensorimotor Abnormalities and Psychosis-Risk (SMAP-R) Scale, clinical interviews and a finger-tapping task. The structure of the scale and reliability of items were consistent across 2 analytic datasets. The resulting scales were assessed for discriminant validity across CHR, community sample non-psychiatric volunteer, and clinical groups.ResultsThe scale showed a consistent structure across 2 analytic datasets subscale structure. The resultant subscale structure was consistent with conceptual models of sensorimotor pathology in psychosis (coordination and dyskinesia) in both the exploratory and the validation analytic dataset. Further, these subscales showed discriminant, predictive, and convergent validity. The sensorimotor abnormality scales discriminated CHR from community sample non-psychiatric controls and clinical samples. Finally, these subscales predicted to risk calculator scores and showed convergent validity with sensorimotor performance on a finger-tapping task.ConclusionThe SMAP-R scale demonstrated good internal, discriminant, predictive, and convergent validity, and subscales mapped on to conceptually relevant sensorimotor circuits. Features of the scale may facilitate widespread incorporation of sensorimotor screening into psychosis-risk research and practice.
| S-EPMC7965079 | biostudies-literature
Project description:ObjectiveBrain-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.MethodsA 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.ResultsWhen 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.ConclusionThese 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.
| S-EPMC8530385 | biostudies-literature
Project description:Early detection and prospective evaluation of clinical high-risk (CHR) individuals who may develop schizophrenia or other psychotic disorders is critical for predicting psychosis onset and for testing preventive interventions.To elucidate the neuropsychology of the CHR syndrome, to determine the association of neuropsychological function with conversion to psychosis and family history of psychosis, and to examine whether baseline neuropsychological functioning predicts subsequent psychosis.Longitudinal study with 2(1/2) years of follow-up.Eight centers participating in the North American Prodrome Longitudinal Study.Three hundred four prospectively identified CHR individuals meeting Structured Interview for Prodromal Syndromes criteria, 52 non-CHR persons with a family history of psychosis in first- or second-degree relatives (family high-risk group), and 193 normal controls with neither a family history of psychosis nor a CHR syndrome, all of whom underwent baseline neuropsychological evaluations.A neurocognitive composite score, 8 individual neuropsychological measures, an IQ estimate, and high-risk status.Global ("composite") neuropsychological functioning was comparably impaired in the CHR and family high-risk groups compared with controls, but profiles differed significantly between groups. Neuropsychological functioning in the CHR group was significantly lower in persons who progressed to psychosis than in those who did not and was worst in the subgroup with a family history of psychosis. Tests of processing speed and verbal learning and memory were most sensitive in discriminating CHR individuals from controls, although reductions were less severe than in established schizophrenia. Neuropsychological functioning did not contribute uniquely to the prediction of psychosis beyond clinical criteria, but worse verbal memory predicted more rapid conversion.These findings document that CHR individuals have significant neuropsychological difficulties, particularly those who later develop psychosis. This dysfunction is generally of moderate severity but less than in first-episode schizophrenia, suggesting that further decline may occur after baseline CHR assessment.
| S-EPMC3332118 | biostudies-literature