Publications by authors named "Godfrey D Pearlson"

327 Publications

Subtyping Schizophrenia Patients Based on Patterns of Structural Brain Alterations.

Schizophr Bull 2021 Sep 11. Epub 2021 Sep 11.

Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.

Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.
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http://dx.doi.org/10.1093/schbul/sbab110DOI Listing
September 2021

Effects of the Fyn kinase inhibitor saracatinib on ventral striatal activity during performance of an fMRI monetary incentive delay task in individuals family history positive or negative for alcohol use disorder. A pilot randomised trial.

Neuropsychopharmacology 2021 Sep 2. Epub 2021 Sep 2.

Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA.

Altered striatal regulation of the GluN2B subunit of N-methyl-D-aspartate (NMDA) glutamate receptors by the Fyn/Src family of protein tyrosine kinases has been implicated in animal alcohol consumption. Previously, we have described differences between individuals positive (FHP) and negative (FHN) for familial alcohol use disorder (AUD) in the ventral striatal (VS) activation associated with monetary incentive delay task (MIDT) performance during functional magnetic resonance imaging (fMRI). Here, we used AZD0530 (saracatinib), a centrally active Fyn/Src inhibitor to probe the role of Fyn/Src regulation of NMDA receptors (NMDAR) in VS activation differences between FHP and FHN individuals during fMRI MIDT performance. We studied 21 FHN and 22 FHP individuals, all without AUD. In two sessions, spaced 1 week apart, we administered 125 mg of saracatinib or placebo in a double-blind manner, prior to measuring VS signal during fMRI MIDT performance. MIDT comprises reward prospect, anticipation, and outcome phases. During the initial (prospect of reward) task phase, there was a significant group-by-condition interaction such that, relative to placebo, saracatinib reduced VS BOLD signal in FHP and increased it in FHN individuals. This study provides the first human evidence that elevated signaling in striatal protein kinase A-dependent pathways may contribute to familial AUD risk via amplifying the neural response to the prospect of reward. As Fyn kinase is responsible for NMDAR upregulation, these data are consistent with previous evidence for upregulated NMDAR function within reward circuitry in AUD risk. These findings also suggest a possible therapeutic role for Src/Fyn kinase inhibitors in AUD risk.
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http://dx.doi.org/10.1038/s41386-021-01157-5DOI Listing
September 2021

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

Schizophr Bull 2021 Aug 19. Epub 2021 Aug 19.

Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA.

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.
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http://dx.doi.org/10.1093/schbul/sbab090DOI Listing
August 2021

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

Am J Psychiatry 2021 Aug 19:appiajp202120071043. Epub 2021 Aug 19.

Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens (Parker, Trotti, McDowell, Clementz); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago (Hill); Department of Psychiatry, UT Southwestern Medical Center, Dallas (Ivleva, Tamminga); Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, Conn. (Pearlson); Olin Center, Institute of Living, Hartford Healthcare Corporation, Hartford, Conn. (Pearlson); and Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Cambridge, Mass. (Keshavan).

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.
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http://dx.doi.org/10.1176/appi.ajp.2021.20071043DOI Listing
August 2021

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

Schizophr Res 2021 Aug 13;236:54-60. Epub 2021 Aug 13.

Rosalind Franklin University of Medicine and Science, Department of Psychology, North Chicago, IL, United States.

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.
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http://dx.doi.org/10.1016/j.schres.2021.07.036DOI Listing
August 2021

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Hum Brain Mapp 2021 Oct 29;42(14):4658-4670. Epub 2021 Jul 29.

Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.
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http://dx.doi.org/10.1002/hbm.25574DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8410550PMC
October 2021

Genome-wide association study accounting for anticholinergic burden to examine cognitive dysfunction in psychotic disorders.

Neuropsychopharmacology 2021 09 18;46(10):1802-1810. Epub 2021 Jun 18.

Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA.

Identifying genetic contributors to cognitive impairments in psychosis-spectrum disorders can advance understanding of disease pathophysiology. Although CNS medications are known to affect cognitive performance, they are often not accounted for in genetic association studies. In this study, we performed a genome-wide association study (GWAS) of global cognitive performance, measured as composite z-scores from the Brief Assessment of Cognition in Schizophrenia (BACS), in persons with psychotic disorders and controls (N = 817; 682 cases and 135 controls) from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study. Analyses accounting for anticholinergic exposures from both psychiatric and non-psychiatric medications revealed five significantly associated variants located at the chromosome 3p21.1 locus, with the top SNP rs1076425 in the inter-alpha-trypsin inhibitor heavy chain 1 (ITIH1) gene (P = 3.25×E-9). The inclusion of anticholinergic burden improved association models (P < 0.001) and the number of significant SNPs identified. The effect sizes and direction of effect of the top variants remained consistent when investigating findings within individuals receiving specific antipsychotic drugs and after accounting for antipsychotic dose. These associations were replicated in a separate study sample of untreated first-episode psychosis. The chromosome 3p21.1 locus was previously reported to have association with the risk for psychotic disorders and cognitive performance in healthy individuals. Our findings suggest that this region may be a psychosis risk locus that is associated with cognitive mechanisms. Our data highlight the general point that the inclusion of medication exposure information may improve the detection of gene-cognition associations in psychiatric genetic research.
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http://dx.doi.org/10.1038/s41386-021-01057-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358015PMC
September 2021

Individual differences in the associations between risk factors for alcohol use disorder and alcohol use-related outcomes.

Psychol Addict Behav 2021 Aug 10;35(5):501-513. Epub 2021 Jun 10.

Department of Psychiatry.

Background: Family history of alcohol use disorder; AUD (FH +) and impulsivity-related traits are known risk factors for problem drinking that have been investigated in predominately White samples. This cross-sectional study examined whether these risk factors vary by sex in the overall, majority White sample and in a Black subsample.

Method: A model building regression procedure was used to investigate the combined effect of FH + and impulsivity-related traits on alcohol quantity, frequency, and problems by sex (overall sample: = 757, 50% female, 73% White, age = 33.74, = 11.60; Black subsample: = 138, 47% female, age = 33.60, = 9.87).

Results: No sex differences were found in the compounding effects of FH + and impulsivity-related traits on alcohol outcomes. Males reported more physical, social, and overall alcohol-related problems than females. FH + was positively associated with all alcohol-related consequences. Poor self-regulation was the only trait associated with all alcohol outcomes. : A three-way interaction suggested a negative association between inhibition and frequency of alcohol use among FH + males only. A two-way interaction also suggested impulse control was associated with more interpersonal alcohol-related problems among males only. Main effects were also found in the expected direction such that higher impulsivity and FH + were associated with poorer alcohol outcomes.

Conclusion: These findings suggest no sex differences in the overall sample in the interactive effects of established risk factors for AUD on alcohol outcomes, and that poor self-regulation may be key for personality-targeted alcohol prevention and intervention programs. Preliminary findings of sex differences in the Black subsample should be replicated. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/adb0000733DOI Listing
August 2021

Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples.

Curr Opin Neurol 2021 08;34(4):469-479

Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.

Purpose Of Review: The 'holy grail' of clinical applications of neuroimaging to neurological and psychiatric disorders via personalized biomarkers has remained mostly elusive, despite considerable effort. However, there are many reasons to continue to be hopeful, as the field has made remarkable advances over the past few years, fueled by a variety of converging technical and data developments.

Recent Findings: We discuss a number of advances that are accelerating the push for neuroimaging biomarkers including the advent of the 'neuroscience big data' era, biomarker data competitions, the development of more sophisticated algorithms including 'guided' data-driven approaches that facilitate automation of network-based analyses, dynamic connectivity, and deep learning. Another key advance includes multimodal data fusion approaches which can provide convergent and complementary evidence pointing to possible mechanisms as well as increase predictive accuracy.

Summary: The search for clinically relevant neuroimaging biomarkers for neurological and psychiatric disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent examples from studies in our group, and link to other ongoing work in the field. It is critical that access and use of these advanced approaches becomes mainstream, this will help propel the community forward and facilitate the production of robust and replicable neuroimaging biomarkers.
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http://dx.doi.org/10.1097/WCO.0000000000000967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263510PMC
August 2021

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity.

Front Neural Circuits 2021 18;15:649417. Epub 2021 Mar 18.

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.

Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.
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http://dx.doi.org/10.3389/fncir.2021.649417DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8013735PMC
March 2021

Effects of weather and season on human brain volume.

PLoS One 2021 24;16(3):e0236303. Epub 2021 Mar 24.

Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, United States of America.

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p = 0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right- cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen's d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236303PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990212PMC
August 2021

Biomarker Profiles in Psychosis Risk Groups Within Unaffected Relatives Based on Familiality and Age.

Schizophr Bull 2021 07;47(4):1058-1067

Department of Psychiatry, the University of Texas Southwestern Medical Center, Dallas, TX.

Investigating biomarkers in unaffected relatives (UR) of individuals with psychotic disorders has already proven productive in research on psychosis neurobiology. However, there is considerable heterogeneity among UR based on features linked to psychosis vulnerability. Here, using the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) dataset, we examined cognitive and neurophysiologic biomarkers in first-degree UR of psychosis probands, stratified by 2 widely used risk factors: familiality status of the respective proband (the presence or absence of a first- or second-degree relative with a history of psychotic disorder) and age (within or older than the common age range for developing psychosis). We investigated biomarkers that best differentiate the above specific risk subgroups. Additionally, we examined the relationship of biomarkers with Polygenic Risk Scores for Schizophrenia (PRSSCZ) in a subsample of Caucasian probands and healthy controls (HC). Our results demonstrate that the Brief Assessment of Cognition in Schizophrenia (BACS) score, antisaccade error (ASE) factor, and stop-signal task (SST) factor best differentiate UR (n = 169) from HC (n = 137) (P = .013). Biomarker profiles of UR of familial (n = 82) and non-familial (n = 83) probands were not significantly different. Furthermore, ASE and SST factors best differentiated younger UR (age ≤ 30) (n = 59) from older UR (n = 110) and HC from both age groups (age ≤ 30 years, n=49; age > 30 years, n = 88) (P < .001). In addition, BACS (r = -0.175, P = .006) and ASE factor (r = 0.188, P = .006) showed associations with PRSSCZ. Taken together, our findings indicate that cognitive biomarkers-"top-down inhibition" impairments in particular-may be of critical importance as indicators of psychosis vulnerability.
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http://dx.doi.org/10.1093/schbul/sbab013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266584PMC
July 2021

Neural Processing of Repeated Emotional Scenes in Schizophrenia, Schizoaffective Disorder, and Bipolar Disorder.

Schizophr Bull 2021 Aug;47(5):1473-1481

Department of Psychology, University of Georgia, 613 Psychology Building, 125 Baldwin St., Athens, GA 30602, USA.

Impaired emotional processing and cognitive functioning are common in schizophrenia, schizoaffective disorder, and bipolar disorders, causing significant socioemotional disability. While a large body of research demonstrates abnormal cognition/emotion interactions in these disorders, previous studies investigating abnormalities in the emotional scene response using event-related potentials (ERPs) have yielded mixed findings, and few studies compare findings across psychiatric diagnoses. The current study investigates the effects of emotion and repetition on ERPs in a large, well-characterized sample of participants with schizophrenia-bipolar syndromes. Two ERP components that are modulated by emotional content and scene repetition, the early posterior negativity (EPN) and late positive potential (LPP), were recorded in healthy controls and participants with schizophrenia, schizoaffective disorder, bipolar disorder with psychosis, and bipolar disorder without psychosis. Effects of emotion and repetition were compared across groups. Results displayed significant but small effects in schizophrenia and schizoaffective disorder, with diminished EPN amplitudes to neutral and novel scenes, reduced LPP amplitudes to emotional scenes, and attenuated effects of scene repetition. Despite significant findings, small effect sizes indicate that emotional scene processing is predominantly intact in these disorders. Multivariate analyses indicate that these mild ERP abnormalities are related to cognition, psychosocial functioning, and psychosis severity. This relationship suggests that impaired cognition, rather than diagnosis or mood disturbance, may underlie disrupted neural scene processing in schizophrenia-bipolar syndromes.
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http://dx.doi.org/10.1093/schbul/sbab018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379546PMC
August 2021

Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series.

Schizophr Res 2021 Mar 3. Epub 2021 Mar 3.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta 30303, GA, USA. Electronic address:

Background: Psychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging.

Methods: In this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data. A new multi-scale recurrent neural network (MsRNN) model was developed and applied to fMRI time courses (TCs) for multi-class classification. The high-level representations of the original TCs were then submitted to a tSNE clustering model for visualizing the group differences between disorders. A leave-one-feature-out approach was used for disorder-related biomarker identification.

Results: When studying fMRI from schizophrenia, psychotic bipolar disorder, schizoaffective disorder, and healthy individuals, the accuracy of a 4-class classification reached 46%, significantly above chance. The hippocampus, supplementary motor area and paracentral lobule were discovered as the most contributing regional TCs in the multi-class classification. Beyond this, visualization of the tSNE clustering suggested that the disease severity can be captured and schizoaffective disorder (SAD) may be separated into two subtypes. SAD cluster1 has significantly higher Positive And Negative Syndrome Scale (PANSS) scores than SAD cluster2 in PANSS negative2 (emotional withdrawal), general2 (anxiety), general3 (guilt feelings), general4 (tension).

Conclusions: The proposed deep classification and clustering framework is not only able to identify psychiatric disorders with high accuracy, but also interpret the correlation between brain networks and specific psychiatric disorders, and reveal the relationship between them. This work provides a promising way to investigate a spectrum of similar disorders using neuroimaging-based measures.
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http://dx.doi.org/10.1016/j.schres.2021.02.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8413409PMC
March 2021

Searching for Imaging Biomarkers of Psychotic Dysconnectivity.

Biol Psychiatry Cogn Neurosci Neuroimaging 2020 Dec 16. Epub 2020 Dec 16.

Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut.

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.
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http://dx.doi.org/10.1016/j.bpsc.2020.12.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206251PMC
December 2020

Antisaccade error rates and gap effects in psychosis syndromes from bipolar-schizophrenia network for intermediate phenotypes 2 (B-SNIP2).

Psychol Med 2021 Feb 24:1-10. Epub 2021 Feb 24.

Departments of Psychology & Neuroscience, University of Georgia, Athens, GA, USA.

Background: Antisaccade tasks can be used to index cognitive control processes, e.g. attention, behavioral inhibition, working memory, and goal maintenance in people with brain disorders. Though diagnoses of schizophrenia (SZ), schizoaffective (SAD), and bipolar I with psychosis (BDP) are typically considered to be distinct entities, previous work shows patterns of cognitive deficits differing in degree, rather than in kind, across these syndromes.

Methods: Large samples of individuals with psychotic disorders were recruited through the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (B-SNIP2) study. Anti- and pro-saccade task performances were evaluated in 189 people with SZ, 185 people with SAD, 96 people with BDP, and 279 healthy comparison participants. Logistic functions were fitted to each group's antisaccade speed-performance tradeoff patterns.

Results: Psychosis groups had higher antisaccade error rates than the healthy group, with SZ and SAD participants committing 2 times as many errors, and BDP participants committing 1.5 times as many errors. Latencies on correctly performed antisaccade trials in SZ and SAD were longer than in healthy participants, although error trial latencies were preserved. Parameters of speed-performance tradeoff functions indicated that compared to the healthy group, SZ and SAD groups had optimal performance characterized by more errors, as well as less benefit from prolonged response latencies. Prosaccade metrics did not differ between groups.

Conclusions: With basic prosaccade mechanisms intact, the higher speed-performance tradeoff cost for antisaccade performance in psychosis cases indicates a deficit that is specific to the higher-order cognitive aspects of saccade generation.
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http://dx.doi.org/10.1017/S003329172000478XDOI Listing
February 2021

Reduced white matter microstructure in bipolar disorder with and without psychosis.

Bipolar Disord 2021 Feb 7. Epub 2021 Feb 7.

Departments of Psychology and Neuroscience, University of Georgia, Athens, GA, USA.

Objectives: Affective and psychotic features overlap considerably in bipolar I disorder, complicating efforts to determine its etiology and develop targeted treatments. In order to clarify whether mechanisms are similar or divergent for bipolar disorder with psychosis (BDP) and bipolar disorder with no psychosis (BDNP), neurobiological profiles for both the groups must first be established. This study examines white matter structure in the BDP and BDNP groups, in an effort to identify portions of white matter that may differ between the bipolar and healthy groups or between the bipolar subgroups themselves.

Methods: Diffusion-weighted imaging data were acquired from participants with BDP (n = 45), BDNP (n = 40), and healthy comparisons (HC) (n = 66). Fractional anisotropy (FA), radial diffusivity (RD), and spin distribution function (SDF) values indexing white matter diffusivity or spin density were calculated and compared between the groups.

Results: In comparisons between both the bipolar groups and HC, FA (FDR < 0.00001) and RD (FDR = 0.0037) differed minimally, in localized portions of the left cingulum and corpus callosum, while reductions in SDF (FDR = 0.0002) were more widespread. The bipolar subgroups did not differ from each other on FA, RD, or SDF metrics.

Conclusions: Together, these results demonstrate a novel profile of white matter differences in bipolar disorder and suggest that this white matter pathology is associated with the affective disturbance common to those with bipolar disorder rather than the psychotic features unique to some. The white matter alterations identified in this study may provide substrates for future studies examining specific mechanisms that target affective domains of illness.
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http://dx.doi.org/10.1111/bdi.13055DOI Listing
February 2021

Regression dynamic causal modeling for resting-state fMRI.

Hum Brain Mapp 2021 May 4;42(7):2159-2180. Epub 2021 Feb 4.

Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.

"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI-regression dynamic causal modeling (rDCM)-extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.
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http://dx.doi.org/10.1002/hbm.25357DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046067PMC
May 2021

Multiple overlapping dynamic patterns of the visual sensory network in schizophrenia.

Schizophr Res 2021 02 9;228:103-111. Epub 2021 Jan 9.

Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States of America; Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States of America; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America. Electronic address:

Although visual processing impairments have been explored in schizophrenia (SZ), their underlying neurobiology of the visual processing impairments has not been widely studied. Also, while some research has hinted at differences in information transfer and flow in SZ, there are few investigations of the dynamics of functional connectivity within visual networks. In this study, we analyzed resting-state fMRI data of the visual sensory network (VSN) in 160 healthy control (HC) subjects and 151 SZ subjects. We estimated 9 independent components within the VSN. Then, we calculated the dynamic functional network connectivity (dFNC) using the Pearson correlation. Next, using k-means clustering, we partitioned the dFNCs into five distinct states, and then we calculated the portion of time each subject spent in each state, which we termed the occupancy rate (OCR). Using OCR, we compared HC with SZ subjects and investigated the link between OCR and visual learning in SZ subjects. Besides, we compared the VSN functional connectivity of SZ and HC subjects in each state. We found that this network is indeed highly dynamic. Each state represents a unique connectivity pattern of fluctuations in VSN FNC, and all states showed significant disruption in SZ. Overall, HC showed stronger connectivity within the VSN in states. SZ subjects spent more time in a state in which the connectivity between the middle temporal gyrus and other regions of VNS is highly negative. Besides, OCR in a state with strong positive connectivity between the middle temporal gyrus and other regions correlated significantly with visual learning scores in SZ.
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http://dx.doi.org/10.1016/j.schres.2020.11.055DOI Listing
February 2021

Associations of cannabis use disorder with cognition, brain structure, and brain function in African Americans.

Hum Brain Mapp 2021 04 19;42(6):1727-1741. Epub 2020 Dec 19.

Department of Psychiatry, School of Medicine, Yale University, New Haven, Connecticut, USA.

Although previous studies have highlighted associations of cannabis use with cognition and brain morphometry, critical questions remain with regard to the association between cannabis use and brain structural and functional connectivity. In a cross-sectional community sample of 205 African Americans (age 18-70) we tested for associations of cannabis use disorder (CUD, n = 57) with multi-domain cognitive measures and structural, diffusion, and resting state brain-imaging phenotypes. Post hoc model evidence was computed with Bayes factors (BF) and posterior probabilities of association (PPA) to account for multiple testing. General cognitive functioning, verbal intelligence, verbal memory, working memory, and motor speed were lower in the CUD group compared with non-users (p < .011; 1.9 < BF < 3,217). CUD was associated with altered functional connectivity in a network comprising the motor-hand region in the superior parietal gyri and the anterior insula (p < .04). These differences were not explained by alcohol, other drug use, or education. No associations with CUD were observed in cortical thickness, cortical surface area, subcortical or cerebellar volumes (0.12 < BF < 1.5), or graph-theoretical metrics of resting state connectivity (PPA < 0.01). In a large sample collected irrespective of cannabis used to minimize recruitment bias, we confirm the literature on poorer cognitive functioning in CUD, and an absence of volumetric brain differences between CUD and non-CUD. We did not find evidence for or against a disruption of structural connectivity, whereas we did find localized resting state functional dysconnectivity in CUD. There was sufficient proof, however, that organization of functional connectivity as determined via graph metrics does not differ between CUD and non-user group.
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http://dx.doi.org/10.1002/hbm.25324DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978126PMC
April 2021

Multivariate relationships between peripheral inflammatory marker subtypes and cognitive and brain structural measures in psychosis.

Mol Psychiatry 2020 Oct 15. Epub 2020 Oct 15.

Department of Experimental and Clinical Pharmacology and Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA.

Elevations in peripheral inflammatory markers have been reported in patients with psychosis. Whether this represents an inflammatory process defined by individual or subgroups of markers is unclear. Further, relationships between peripheral inflammatory marker elevations and brain structure, cognition, and clinical features of psychosis remain unclear. We hypothesized that a pattern of plasma inflammatory markers, and an inflammatory subtype established from this pattern, would be elevated across the psychosis spectrum and associated with cognition and brain structural alterations. Clinically stable psychosis probands (Schizophrenia spectrum, n = 79; Psychotic Bipolar disorder, n = 61) and matched healthy controls (HC, n = 60) were assessed for 15 peripheral inflammatory markers, cortical thickness, subcortical volume, cognition, and symptoms. A combination of unsupervised exploratory factor analysis and hierarchical clustering was used to identify inflammation subtypes. Levels of IL6, TNFα, VEGF, and CRP were significantly higher in psychosis probands compared to HCs, and there were marker-specific differences when comparing diagnostic groups. Individual and/or inflammatory marker patterns were associated with neuroimaging, cognition, and symptom measures. A higher inflammation subgroup was defined by elevations in a group of 7 markers in 36% of Probands and 20% of HCs. Probands in the elevated inflammatory marker group performed significantly worse on cognitive measures of visuo-spatial working memory and response inhibition, displayed elevated hippocampal, amygdala, putamen and thalamus volumes, and evidence of gray matter thickening compared to the proband group with low inflammatory marker levels. These findings specify the nature of peripheral inflammatory marker alterations in psychotic disorders and establish clinical, neurocognitive and neuroanatomic associations with increased inflammatory activation in psychosis. The identification of a specific subgroup of patients with inflammatory alteration provides a potential means for targeting treatment with anti-inflammatory medications.
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http://dx.doi.org/10.1038/s41380-020-00914-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046847PMC
October 2020

Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder.

Hum Brain Mapp 2021 01 23;42(1):80-94. Epub 2020 Sep 23.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.

The dynamics of the human brain span multiple spatial scales, from connectivity associated with a specific region/network to the global organization, each representing different brain mechanisms. Yet brain reconfigurations at different spatial scales are seldom explored and whether they are associated with the neural aspects of brain disorders is far from understood. In this study, we introduced a dynamic measure called step-wise functional network reconfiguration (sFNR) to characterize how brain configuration rewires at different spatial scales. We applied sFNR to two independent datasets, one includes 160 healthy controls (HCs) and 151 patients with schizophrenia (SZ) and the other one includes 314 HCs and 255 individuals with autism spectrum disorder (ASD). We found that both SZ and ASD have increased whole-brain sFNR and sFNR between cerebellar and subcortical/sensorimotor domains. At the ICN level, the abnormalities in SZ are mainly located in ICNs within subcortical, sensory, and cerebellar domains, while the abnormalities in ASD are more widespread across domains. Interestingly, the overlap SZ-ASD abnormality in sFNR between cerebellar and sensorimotor domains was correlated with the reasoning-problem-solving performance in SZ (r = -.1652, p = .0058) as well as the Autism Diagnostic Observation Schedule in ASD (r = .1853, p = .0077). Our findings suggest that dynamic reconfiguration deficits may represent a key intersecting point for SZ and ASD. The investigation of brain dynamics at different spatial scales can provide comprehensive insights into the functional reconfiguration, which might advance our knowledge of cognitive decline and other pathophysiology in brain disorders.
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http://dx.doi.org/10.1002/hbm.25205DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721229PMC
January 2021

Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia.

Neuroimage 2021 01 17;224:117385. Epub 2020 Sep 17.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.

The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as "brain states", have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the "strong" states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117385DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781150PMC
January 2021

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

Schizophr Res 2020 09 21;223:236-241. Epub 2020 Aug 21.

Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States. Electronic address:

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.
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http://dx.doi.org/10.1016/j.schres.2020.08.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704884PMC
September 2020

Resting state auditory-language cortex connectivity is associated with hallucinations in clinical and biological subtypes of psychotic disorders.

Neuroimage Clin 2020 22;27:102358. Epub 2020 Jul 22.

Department of Psychiatry and Behavioral Neuroscience, University of Chicago, IL, USA. Electronic address:

Background: Auditory hallucinations are prevalent across the major psychotic disorders, but their underlying mechanism is poorly understood. Limited prior work supports a hypothesis of altered auditory/language brain systems. To more definitively assess this, we examined whether alterations in resting state connectivity of auditory and language cortices are associated with hallucination severity in a large sample of individuals in the schizo-bipolar spectrum.

Methods: Whole brain resting state connectivity of auditory and language cortex (primary auditory cortex, unimodal auditory association cortex, Wernicke's area [speech and heteromodal association cortex] and Broca's area [speech production motor]) was evaluated for 243 subjects with schizophrenia, schizoaffective, or bipolar disorder with psychosis and 186 healthy controls from the Bipolar Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study. Regression analyses were conducted to evaluate whether resting state connectivity of auditory and language cortex was a significant predictor of current overall hallucination severity (information about specific modality of hallucinations experienced was not available).

Results: Increased connectivity between lower and higher order regions of left temporal-parietal auditory/language processing cortex was associated with worse hallucination severity for all psychosis patients. Additionally, within bipolar subjects, increased interhemispheric connectivity between higher order temporal-parietal auditory/language regions was related to greater hallucination severity. When patients were categorized by B-SNIP biomarker-based Biotype groups, interhemispheric connectivity between left auditory association cortex and right core auditory cortex was related to greater hallucination severity for Biotype 1 patients. Exploratory analyses resulted in different patterns of connectivity of auditory/language cortex in patients and controls, unrelated to current hallucination severity.

Conclusions: Although the findings cannot be precisely attributed to auditory hallucination severity or possible differences in such experiences between groups, increased connectivity among the left hemisphere auditory and receptive language cortex may represent a significant factor contributing to hallucination severity across psychotic disorders, and additional subgroup specific connectivity alterations may also be present.
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http://dx.doi.org/10.1016/j.nicl.2020.102358DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398970PMC
June 2021

Default mode network modulation by mentalizing in young adults with autism spectrum disorder or schizophrenia.

Neuroimage Clin 2020 8;27:102343. Epub 2020 Jul 8.

Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA. Electronic address:

Schizophrenia and autism spectrum disorder (ASD) are nosologically distinct neurodevelopmental disorders with similar deficits in social cognition, including the ability to form mental representations of others (i.e., mentalizing). However, the extent of patient deficit overlap in underlying neural mechanisms is unclear. Our goal was to examine deficits in mentalizing task-related (MTR) activity modulation in schizophrenia and ASD and the relationship of such deficits with social functioning and psychotic symptoms in patients. Adults, ages 18-34, diagnosed with either ASD or schizophrenia, and typically developed controls (n = 30/group), performed an interactive functional MRI Domino task. Using independent component analysis, we analyzed game intervals known to stimulate mentalizing in the default mode network (DMN), i.e., medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), precuneus, and temporoparietal junction (TPJ), for group differences in MTR activity and associations between MTR activity and social and psychosis measures. Compared to controls, both schizophrenia and ASD groups showed MTR activity deficits in PCC and TPJ. In TPJ and MPFC, MTR activity modulation was associated with social communication impairments only in ASD. In precuneus, MTR activity was associated with increased self-reported fantasizing only in schizophrenia. In schizophrenia, we found no indication of over-mentalizing activity or an association between MTR activity and psychotic symptoms. Results suggest shared neural deficits between ASD and schizophrenia in mentalizing-associated DMN regions; however, neural organization might correspond to different dimensional social deficits. Our results therefore indicate the importance of examining both categorical-clinical diagnosis and social functioning dimensional constructs when examining neural deficits in schizophrenia and ASD.
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http://dx.doi.org/10.1016/j.nicl.2020.102343DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381691PMC
June 2021

Distinguishing patterns of impairment on inhibitory control and general cognitive ability among bipolar with and without psychosis, schizophrenia, and schizoaffective disorder.

Schizophr Res 2020 09 14;223:148-157. Epub 2020 Jul 14.

Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States. Electronic address:

Background: Deficits in inhibitory control on a Stop Signal Task (SST) were previously observed to be of similar magnitude across schizophrenia, schizoaffective, and bipolar disorder with psychosis, despite variation in general cognitive ability. Understanding different patterns of performance on the SST may elucidate different pathways to the impaired inhibitory control each group displayed. Comparing nonpsychotic bipolar disorder to the psychosis groups on SST may also expand our understanding of the shared neurobiology of this illness spectrum.

Methods: We tested schizophrenia (n = 220), schizoaffective (n = 216), bipolar disorder with (n = 192) and without psychosis (n = 67), and 280 healthy comparison participants with a SST and the Brief Assessment of Cognition in Schizophrenia (BACS), a measure of general cognitive ability.

Results: All patient groups had a similar degree of impaired inhibitory control over prepotent responses. However, bipolar groups differed from schizophrenia and schizoaffective groups in showing speeded responses and inhibition errors that were not accounted for by general cognitive ability. Schizophrenia and schizoaffective groups had a broader set of deficits on inhibition and greater general cognitive deficit, which fully accounted for the inhibition deficits. No differences were found between the clinically well-matched bipolar with and without psychosis groups, including for inhibitory control or general cognitive ability.

Conclusions: We conclude that 1) while impaired inhibitory control on a SST is of similar magnitude across the schizo-bipolar spectrum, including nonpsychotic bipolar, different mechanisms may underlie the impairments, and 2) history of psychosis in bipolar disorder does not differentially impact inhibitory behavioral control or general cognitive abilities.
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http://dx.doi.org/10.1016/j.schres.2020.06.033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704797PMC
September 2020

Testing Psychosis Phenotypes From Bipolar-Schizophrenia Network for Intermediate Phenotypes for Clinical Application: Biotype Characteristics and Targets.

Biol Psychiatry Cogn Neurosci Neuroimaging 2020 08 28;5(8):808-818. Epub 2020 Apr 28.

Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address:

Background: Psychiatry aspires to the molecular understanding of its disorders and, with that knowledge, to precision medicine. Research supporting such goals in the dimension of psychosis has been compromised, in part, by using phenomenology alone to estimate disease entities. To this end, we are proponents of a deep phenotyping approach in psychosis, using computational strategies to discover the most informative phenotypic fingerprint as a promising strategy to uncover mechanisms in psychosis.

Methods: Doing this, the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has used biomarkers to identify distinct subtypes of psychosis with replicable biomarker characteristics. While we have presented these entities as relevant, their potential utility in clinical practice has not yet been demonstrated.

Results: Here we carried out an analysis of clinical features that characterize biotypes. We found that biotypes have unique and defining clinical characteristics that could be used as initial screens in the clinical and research settings. Differences in these clinical features appear to be consistent with biotype biomarker profiles, indicating a link between biological features and clinical presentation. Clinical features associated with biotypes differ from those associated with DSM diagnoses, indicating that biotypes and DSM syndromes are not redundant and are likely to yield different treatment predictions. We highlight 3 predictions based on biotype that are derived from individual biomarker features and cannot be obtained from DSM psychosis syndromes.

Conclusions: In the future, biotypes may prove to be useful for targeting distinct molecular, circuit, cognitive, and psychosocial therapies for improved functional outcomes.
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http://dx.doi.org/10.1016/j.bpsc.2020.03.011DOI Listing
August 2020

Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis.

Neuroimage Clin 2020 26;27:102284. Epub 2020 May 26.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.

It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.
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http://dx.doi.org/10.1016/j.nicl.2020.102284DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306624PMC
March 2021
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