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Background: Disorganization, presenting as impairment in thought, language and goal-directed behavior, is a core multidimensional syndrome of psychotic disorders. This study examined whether scalable computational measures of spoken language, and smartphone usage pattern, could serve as digital biomarkers of clinical disorganization symptoms., Methods: We examined in a longitudinal cohort of adults with a psychotic disorder, the associations between clinical measures of disorganization and computational measures of 1) spoken language derived from monthly, semi-structured, recorded clinical interviews; and 2) smartphone usage pattern derived via passive sensing technologies over the month prior to the interview. The language features included speech quantity, rate, fluency, and semantic regularity. The smartphone features included data missingness and phone usage during sleep time. The clinical measures consisted of the Positive and Negative Symptom Scale (PANSS) conceptual disorganization, difficulty in abstract thinking, and poor attention, items. Mixed linear regression analyses were used to estimate both fixed and random effects., Results: Greater severity of clinical symptoms of conceptual disorganization was associated with greater verbosity and more disfluent speech. Greater severity of conceptual disorganization was also associated with greater missingness of smartphone data, and greater smartphone usage during sleep time. While the observed associations were significant across the group, there was also significant variation between individuals., Conclusions: The findings suggest that digital measures of speech disfluency may serve as scalable markers of conceptual disorganization. The findings warrant further investigation into the use of recorded interviews and passive sensing technologies to assist in the characterization and tracking of psychotic illness., Competing Interests: Declaration of competing interest Justin T Baker has received consulting fees and equity from Mindstrong, Inc., as well as consultant fees from Verily Life Sciences, unrelated to the present work., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Objectives: This study aimed to (1) determine the feasibility of collecting behavioral data from participants hospitalized with acute psychosis and (2) begin to evaluate the clinical information that can be computationally derived from such data., Methods: Behavioral data was collected across 99 sessions from 38 participants recruited from an inpatient psychiatric unit. Each session started with a semi-structured interview modeled on a typical "clinical rounds" encounter and included administration of the Positive and Negative Syndrome Scale (PANSS)., Analysis: We quantified aspects of participants' verbal behavior during the interview using lexical, coherence, and disfluency features. We then used two complementary approaches to explore our second objective. The first approach used predictive models to estimate participants' PANSS scores from their language features. Our second approach used inferential models to quantify the relationships between individual language features and symptom measures., Results: Our predictive models showed promise but lacked sufficient data to achieve clinically useful accuracy. Our inferential models identified statistically significant relationships between numerous language features and symptom domains., Conclusion: Our interview recording procedures were well-tolerated and produced adequate data for transcription and analysis. The results of our inferential modeling suggest that automatic measurements of expressive language contain signals highly relevant to the assessment of psychosis. These findings establish the potential of measuring language during a clinical interview in a naturalistic setting and generate specific hypotheses that can be tested in future studies. This, in turn, will lead to more accurate modeling and better understanding of the relationships between expressive language and psychosis., (Copyright © 2021. Published by Elsevier B.V.)

Background: Symptoms and functioning are critical dimensions in those with schizophrenia and are typically measured using validated conventional clinical assessments. Researchers and clinicians have begun to use real-time digital methods, such as ecological momentary assessment (EMA), to assess symptoms and functioning in the moment and outside of traditional hospital and laboratory settings, which may yield more naturalistic data. Although digital methods have advantages, it is unclear whether these momentary assessments capture core aspects of symptoms and functioning., Objective: This systematic literature review aimed to evaluate the association between conventional clinical and momentary-based assessments of functioning and symptoms in individuals with schizophrenia., Methods: Studies were included if they met the following criteria: (1) written or translated into English; (2) peer-reviewed; (3) included primary quantitative data; (4) 60% of the clinical sample included persons with schizophrenia spectrum disorders; (5) included a clinical assessment of functioning and/or symptoms; (6) included active momentary assessment and/or passive data; and (7) assessed the relationship between the momentary and conventional clinical assessments., Results: A total of 49 studies (87 analyses) were included. Conventional clinical assessments of functioning and positive, negative, and depressive symptoms were related to momentary assessments of these symptom domains. Passive data was beneficial for assessing negative symptoms, but research is warranted for other domains., Conclusions: The reviewed studies highlight the utility of EMA methodologies to collect detailed data on symptoms and functioning. Such data is being used to develop more sophisticated models of schizophrenia to enhance our understanding of important mechanisms and develop targeted interventions., Competing Interests: Declaration of competing interest Dr. Browne is funded by the Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship in Geriatrics. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the United States Government or Department of Veterans Affairs. Dr. Baker currently receives consulting fees and equity from Mindstrong, Inc. and has in the past 12 months received consulting fees from Verily Life Sciences., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Cognitive dysfunction is common in psychotic disorders, and may reflect underlying pathophysiology. However, substantial cognitive heterogeneity exists both within and between diagnostic categories, creating challenges for studying the neurobiology of cognitive dysfunction in patients. The aim of this study was to identify patients with psychosis with intact versus impaired cognitive profiles, and to examine resting state functional connectivity between patient groups and compared to healthy controls to determine the extent to which patterns of connectivity are overlapping or distinct., Methods: Participants with affective or non-affective psychosis (n=120) and healthy controls (n=31) were administered the MATRICS Consensus Cognitive Battery, clinical and community functioning assessments, and an fMRI scan to measure resting state functional connectivity (RSFC). Cognitive composite scores were used to identify groups of patients with and without cognitive dysfunction. RSFC was compared between groups of patients and healthy controls, controlling for demographic and clinical variables., Results: Both cognitively intact and cognitively impaired patients showed decreased intrinsic connectivity compared to controls in frontoparietal control (FPN) and motor networks. Patients with cognitive impairment showed additional reductions in FPN connectivity compared to patients with intact cognition, particularly in subnetwork A., Conclusions: We leveraged the heterogeneity in cognitive ability among patients with psychosis to disentangle the relative contributions of cognitive dysfunction and presence of an underlying psychotic illness using resting state functional connectivity. These findings suggest at least partially separable effects of presence of a psychotic disorder and neurocognitive impairment contributing to network dysconnectivity in psychosis., (Copyright © 2018. Published by Elsevier B.V.)

Background: Schizophrenia is a heterogeneous disorder that may consist of multiple etiologies and disease processes. Auditory hallucinations (AH), which are common and often disabling, represent a narrower and more basic dimension of psychosis than schizophrenia. Previous studies suggest that abnormal primary auditory cortex activity is associated with AH pathogenesis. We thus investigated functional connectivity, using a seed in primary auditory cortex, in schizophrenia patients with and without AH and healthy controls, to examine neural circuit abnormalities associated more specifically with AH than the myriad other symptoms that comprise schizophrenia., Methods: Using resting-state fMRI (rsfMRI), we investigated functional connectivity of the primary auditory cortex, located on Heschl's gyrus, in schizophrenia spectrum patients with AH. Participants were patients with schizophrenia, schizoaffective disorder, or schizophreniform disorder with lifetime AH (n=27); patients with the same diagnoses but no lifetime AH (n=14); and healthy controls (n=28)., Results: Patients with AH vulnerability showed increased left Heschl's gyrus functional connectivity with left frontoparietal regions and decreased functional connectivity with right hippocampal formation and mediodorsal thalamus compared to patients without lifetime AH. Furthermore, among AH patients, left Heschl's gyrus functional connectivity covaried positively with AH severity in left inferior frontal gyrus (Broca's area), left lateral STG, right pre- and postcentral gyri, cingulate cortex, and orbitofrontal cortex. There were no differences between patients with and without lifetime AH in right Heschl's gyrus seeded functional connectivity., Conclusions: Abnormal interactions between left Heschl's gyrus and regions involved in speech/language, memory, and the monitoring of self-generated events may contribute to AH vulnerability., (Copyright © 2012 Elsevier B.V. All rights reserved.)

There is growing evidence that schizophrenia (SZ) and bipolar disorder (BD) overlap significantly in risk factors, neurobiological features, clinical presentations, and outcomes. SZ is characterized by well documented gray matter (GM) abnormalities in multiple frontal, temporal and subcortical structures. Recent voxel-based morphometry (VBM) studies and meta-analyses in BD also report GM reductions in overlapping, albeit less widespread, brain regions. Psychosis, a hallmark of SZ, is also experienced by a significant proportion of BD patients and there is evidence that psychotic BD may be characterized by specific clinical and pathophysiological features. However, there are few studies comparing GM between SZ and psychotic BD. In this study we compared GM volumes in a sample of 58 SZ patients, 28 BD patients experiencing psychotic symptoms and 43 healthy controls using whole-brain voxel-based morphometry. SZ patients had GM reductions in multiple frontal and temporal regions compared to healthy controls and in the subgenual cortex compared to psychotic BD patients. GM volume was increased in the right posterior cerebellum in SZ patients compared to controls. However, psychotic BD patients did not show significant GM deficits compared to healthy controls or SZ patients. We conclude that GM abnormality as measured by VBM analysis is less pronounced in psychotic BD compared to SZ. This may be due to disease-specific factors or medications used more commonly in BD., (Copyright © 2012 Elsevier B.V. All rights reserved.)