Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia.

The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

Deficits in generalized cognitive ability, visual sensorimotor function, and inhibitory control represent discrete domains of neurobehavioral deficit in psychotic disorders.

Psychotic disorders are characterized by impaired cognition, yet some reports indicate specific deficits extend beyond reduced general cognitive ability. This study utilized exploratory and confirmatory factor analytic methods to evaluate the latent structure of a broad neurocognitive battery used in the Bipolar-Schizophrenia Network of Intermediate Phenotypes (B-SNIP) study, which included neuropsychological and neurophysiological measures in psychotic disorder probands and their unaffected first-degree relatives. Findings indicate that the factor structure of data from this set of assessments is more complex than the unitary factor of global cognitive ability underlying the Brief Assessment of Cognition in Schizophrenia (BACS). In addition to assessing generalized cognitive ability, two other factors were identified: visual sensorimotor function and inhibitory behavioral control. This complex cognitive architecture, derived in controls, generalized to patients across the psychosis spectrum and to their unaffected relatives. These findings highlight the need for a more differentiated assessment of neurobehavioral functions in studies designed to test for diagnostically specific biomarkers, endophenotypes for gene discovery and beneficial effects of therapeutics on cognitive function.

Catechol-O-methyltransferase genotype differentially contributes to the flexibility and stability of cognitive sets in patients with psychotic disorders and their first-degree relatives.

Dopaminergic activity in prefrontal cortex is modulated by the low (Met) and high (Val) activity of the rs4680 Val158Met single nucleotide polymorphism (SNP) in the Catechol-O-Methyltransferase (COMT) gene. While this has been related to working memory maintenance in patients with schizophrenia, the familial pattern, impact across the psychosis spectrum, and the role of this genotype on other aspects of behavior, such as cognitive flexibility, remains unclear. The relationship between COMT Val158Met genotype and both cognitive stability and flexibility were assessed using the Penn Conditional Exclusion Test (PCET) in healthy controls (n = 241), patients with psychotic disorders (n = 542), and their first-degree relatives (n = 613) from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Higher rates of perseverative errors (poor flexibility) were associated with the low-activity COMT genotype (Met allele carriers) in probands compared to their first-degree relatives with the same genotype. Probands and first-degree relatives homozygous for the high-activity COMT enzyme (Val/Val) showed elevated rates of regressive errors (poor stability) compared to controls. Conversely, heterozygous relatives had comparable regressive error rates to controls, with probands showing elevated errors in comparison. These findings suggest that impaired suppression of learned response patterns and reduced stability of mental sets may be a familial intermediate cognitive phenotype related to Val COMT allele genotype.

Intrinsic neural activity differences in psychosis biotypes: Findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium.

Intro: The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) proposed "Biotypes," subgroups of psychosis cases with neuro-cognitive homology. Neural activity unbound to stimulus processing (nonspecific or intrinsic activity) was important for differentiating Biotypes, with Biotype-2 characterized by high nonspecific neural activity. A precise estimate of intrinsic activity (IA) was not included in the initial Biotypes characterization. This report hypothesizes intrinsic activity is a critical differentiating feature for psychosis Biotypes. Method: Participants were recruited at B-SNIP sites and included probands with psychosis (schizophrenia, schizoaffective disorder, bipolar I disorder), their first-degree biological relatives, and healthy persons (N = 1338). Probands were also sub-grouped by psychosis Biotype. 10-sec inter-stimulus intervals during an auditory paired-stimuli task were used to quantify intrinsic activity from 64 EEG sensors. Single-trial power and connectivity measures at empirically derived frequency bands were quantified. Multivariate discriminant and correlational analyses were used to summarize variables that efficiently and maximally differentiated groups by conventional diagnoses and Biotypes and to determine their relationship to clinical and social functioning. Results: Biotype-1 consistently exhibited low IA, and Biotype 2 exhibited high IA relative to healthy persons across power frequency bands (delta/theta, alpha, beta, gamma) and alpha band connectivity estimates. DSM groups did not differ from healthy persons on any IA measure. Discussion: Psychosis Biotypes, but not DSM syndromes, were differentiated by intrinsic activity; Biotype-2 was uniquely characterized by an accentuation of this measure. Neurobiologically defined psychosis subgroups may facilitate the use of intrinsic activity in translation models aimed at developing effective treatments for psychosisrelevant deviations in neural modulation.

Regressing to Prior Response Preference After Set Switching Implicates Striatal Dysfunction Across Psychotic Disorders: Findings From the B-SNIP Study.

Difficulty switching behavioral response sets is established in psychotic disorders. In rodent models, prefrontal lesions cause difficulty initially switching to new response sets (perseverative errors) while striatal lesions cause difficulty suppressing responses to previous choice preferences (regressive errors). Studies of psychotic disorders have not previously assessed these 2 error types. Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) participants included probands with schizophrenia (N = 212), psychotic bipolar (N = 192), and schizoaffective disorder (N = 131), their first-degree relatives (N = 267,226,165 respectively), and healthy controls (N = 258). Participants completed the Penn Conditional Exclusion Test (PCET) to assess cognitive set switching and the Brief Assessment of Cognition in Schizophrenia (BACS) to assess generalized neuropsychological dysfunction. All proband groups displayed elevated rates of perseverative and regressive errors compared to controls. After correcting for generalized cognitive deficits to identify specific deficits in set shifting and maintenance, there were no significant group differences for perseverative errors, while the increased rate of regressive errors remained significant. Level of regressive errors was similar across proband groups with minimal correlations with antipsychotic medication dose, clinical ratings, and demographic characteristics. Relatives of schizophrenia patients showed increased rates of regressive errors, but familiality of this trait was significant only in bipolar pedigrees. Regressive errors were partially independent of generalized cognitive deficits, suggesting a potentially informative and specific cognitive deficit across psychotic disorders. Preclinical data indicate that this deficit could be related to altered function in a neural system that may include the dorsal striatum or other elements of frontostriatal systems.

Working memory impairment in probands with schizoaffective disorder and first degree relatives of schizophrenia probands extend beyond deficits predicted by generalized neuropsychological impairment.

OBJECTIVE: Working memory impairment is well established in psychotic disorders. However, the relative magnitude, diagnostic specificity, familiality pattern, and degree of independence from generalized cognitive deficits across psychotic disorders remain unclear. METHOD: Participants from the Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study included probands with schizophrenia (N=289), psychotic bipolar disorder (N=227), schizoaffective disorder (N=165), their first-degree relatives (N=315, N=259, N=193, respectively), and healthy controls (N=289). All were administered the WMS-III Spatial Span working memory test and the Brief Assessment of Cognition in Schizophrenia (BACS) battery. RESULTS: All proband groups displayed significant deficits for both forward and backward span compared to controls. However, after covarying for generalized cognitive impairments (BACS composite), all proband groups showed a 74% or greater effect size reduction with only schizoaffective probands showing residual backward span deficits compared to controls. Significant familiality was seen in schizophrenia and bipolar pedigrees. In relatives, both forward and backward span deficits were again attenuated after covarying BACS scores and residual backward span deficits were seen in relatives of schizophrenia patients. CONCLUSIONS: Overall, both probands and relatives showed a similar pattern of robust working memory deficits that were largely attenuated when controlling for generalized cognitive deficits.