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  1. Article ; Online: From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights.

    Hänsel, Katrin / Dudgeon, Sarah N / Cheung, Kei-Hoi / Durant, Thomas J S / Schulz, Wade L

    Journal of medical systems

    2023  Volume 47, Issue 1, Page(s) 65

    Abstract: Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. ... ...

    Abstract Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.
    MeSH term(s) Humans ; Algorithms ; Pattern Recognition, Automated ; Biomedical Research ; Phenotype ; Precision Medicine
    Language English
    Publishing date 2023-05-17
    Publishing country United States
    Document type Letter
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-023-01951-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Latent Factors of Language Disturbance and Relationships to Quantitative Speech Features.

    Tang, Sunny X / Hänsel, Katrin / Cong, Yan / Nikzad, Amir H / Mehta, Aarush / Cho, Sunghye / Berretta, Sarah / Behbehani, Leily / Pradhan, Sameer / John, Majnu / Liberman, Mark Y

    Schizophrenia bulletin

    2023  Volume 49, Issue Suppl_2, Page(s) S93–S103

    Abstract: Background and hypothesis: Quantitative acoustic and textual measures derived from speech ("speech features") may provide valuable biomarkers for psychiatric disorders, particularly schizophrenia spectrum disorders (SSD). We sought to identify cross- ... ...

    Abstract Background and hypothesis: Quantitative acoustic and textual measures derived from speech ("speech features") may provide valuable biomarkers for psychiatric disorders, particularly schizophrenia spectrum disorders (SSD). We sought to identify cross-diagnostic latent factors for speech disturbance with relevance for SSD and computational modeling.
    Study design: Clinical ratings for speech disturbance were generated across 14 items for a cross-diagnostic sample (N = 334), including SSD (n = 90). Speech features were quantified using an automated pipeline for brief recorded samples of free speech. Factor models for the clinical ratings were generated using exploratory factor analysis, then tested with confirmatory factor analysis in the cross-diagnostic and SSD groups. The relationships between factor scores and computational speech features were examined for 202 of the participants.
    Study results: We found a 3-factor model with a good fit in the cross-diagnostic group and an acceptable fit for the SSD subsample. The model identifies an impaired expressivity factor and 2 interrelated disorganized factors for inefficient and incoherent speech. Incoherent speech was specific to psychosis groups, while inefficient speech and impaired expressivity showed intermediate effects in people with nonpsychotic disorders. Each of the 3 factors had significant and distinct relationships with speech features, which differed for the cross-diagnostic vs SSD groups.
    Conclusions: We report a cross-diagnostic 3-factor model for speech disturbance which is supported by good statistical measures, intuitive, applicable to SSD, and relatable to linguistic theories. It provides a valuable framework for understanding speech disturbance and appropriate targets for modeling with quantitative speech features.
    MeSH term(s) Humans ; Speech ; Language ; Schizophrenia/complications ; Psychotic Disorders/complications ; Factor Analysis, Statistical
    Language English
    Publishing date 2023-03-22
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 439173-1
    ISSN 1745-1701 ; 0586-7614
    ISSN (online) 1745-1701
    ISSN 0586-7614
    DOI 10.1093/schbul/sbac145
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  3. Article: Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders.

    Hänsel, Katrin / Lin, Inna Wanyin / Sobolev, Michael / Muscat, Whitney / Yum-Chan, Sabrina / De Choudhury, Munmun / Kane, John M / Birnbaum, Michael L

    Frontiers in psychiatry

    2021  Volume 12, Page(s) 691327

    Abstract: Background and Objectives: ...

    Abstract Background and Objectives:
    Language English
    Publishing date 2021-08-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2564218-2
    ISSN 1664-0640
    ISSN 1664-0640
    DOI 10.3389/fpsyt.2021.691327
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  4. Article ; Online: Who does what to whom? graph representations of action-predication in speech relate to psychopathological dimensions of psychosis.

    Nikzad, Amir H / Cong, Yan / Berretta, Sarah / Hänsel, Katrin / Cho, Sunghye / Pradhan, Sameer / Behbehani, Leily / DeSouza, Danielle D / Liberman, Mark Y / Tang, Sunny X

    Schizophrenia (Heidelberg, Germany)

    2022  Volume 8, Issue 1, Page(s) 58

    Abstract: Graphical representations of speech generate powerful computational measures related to psychosis. Previous studies have mostly relied on structural relations between words as the basis of graph formation, i.e., connecting each word to the next in a ... ...

    Abstract Graphical representations of speech generate powerful computational measures related to psychosis. Previous studies have mostly relied on structural relations between words as the basis of graph formation, i.e., connecting each word to the next in a sequence of words. Here, we introduced a method of graph formation grounded in semantic relationships by identifying elements that act upon each other (action relation) and the contents of those actions (predication relation). Speech from picture descriptions and open-ended narrative tasks were collected from a cross-diagnostic group of healthy volunteers and people with psychotic or non-psychotic disorders. Recordings were transcribed and underwent automated language processing, including semantic role labeling to identify action and predication relations. Structural and semantic graph features were computed using static and dynamic (moving-window) techniques. Compared to structural graphs, semantic graphs were more strongly correlated with dimensional psychosis symptoms. Dynamic features also outperformed static features, and samples from picture descriptions yielded larger effect sizes than narrative responses for psychosis diagnoses and symptom dimensions. Overall, semantic graphs captured unique and clinically meaningful information about psychosis and related symptom dimensions. These features, particularly when derived from semi-structured tasks using dynamic measurement, are meaningful additions to the repertoire of computational linguistic methods in psychiatry.
    Language English
    Publishing date 2022-07-05
    Publishing country Germany
    Document type Journal Article
    ISSN 2754-6993
    ISSN (online) 2754-6993
    DOI 10.1038/s41537-022-00263-7
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  5. Article ; Online: Metabolic disturbances, hemoglobin A1c, and social cognition impairment in Schizophrenia spectrum disorders.

    Tang, Sunny X / Oliver, Lindsay D / Hänsel, Katrin / DeRosse, Pamela / John, Majnu / Khairullah, Ammar / Gold, James M / Buchanan, Robert W / Voineskos, Aristotle / Malhotra, Anil K

    Translational psychiatry

    2022  Volume 12, Issue 1, Page(s) 233

    Abstract: Social cognitive impairments are core features of schizophrenia spectrum disorders (SSD) and are associated with greater functional impairment and decreased quality of life. Metabolic disturbances have been related to greater impairment in general ... ...

    Abstract Social cognitive impairments are core features of schizophrenia spectrum disorders (SSD) and are associated with greater functional impairment and decreased quality of life. Metabolic disturbances have been related to greater impairment in general neurocognition, but their relationship to social cognition has not been previously reported. In this study, metabolic measures and social cognition were assessed in 245 participants with SSD and 165 healthy comparison subjects (HC), excluding those with hemoglobin A1c (HbA1c) > 6.5%. Tasks assessed emotion processing, theory of mind, and social perception. Functional connectivity within and between social cognitive networks was measured during a naturalistic social task. Among SSD, a significant inverse relationship was found between social cognition and cumulative metabolic burden (β = -0.38, p < 0.001) and HbA1c (β = -0.37, p < 0.001). The relationship between social cognition and HbA1c was robust across domains and measures of social cognition and after accounting for age, sex, race, non-social neurocognition, hospitalization, and treatment with different antipsychotic medications. Negative connectivity between affect sharing and motor resonance networks was a partial mediator of this relationship across SSD and HC groups (β = -0.05, p = 0.008). There was a group x HbA1c effect indicating that SSD participants were more adversely affected by increasing HbA1c. Thus, we provide the first report of a robust relationship in SSD between social cognition and abnormal glucose metabolism. If replicated and found to be causal, insulin sensitivity and blood glucose may present as promising targets for improving social cognition, functional outcomes, and quality of life in SSD.
    MeSH term(s) Cognition ; Glycated Hemoglobin A ; Humans ; Quality of Life ; Schizophrenia/complications ; Social Cognition ; Social Perception
    Chemical Substances Glycated Hemoglobin A
    Language English
    Publishing date 2022-06-06
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2609311-X
    ISSN 2158-3188 ; 2158-3188
    ISSN (online) 2158-3188
    ISSN 2158-3188
    DOI 10.1038/s41398-022-02002-z
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  6. Article ; Online: Clinical and computational speech measures are associated with social cognition in schizophrenia spectrum disorders.

    Tang, Sunny X / Cong, Yan / Nikzad, Amir H / Mehta, Aarush / Cho, Sunghye / Hänsel, Katrin / Berretta, Sarah / Dhar, Aamina A / Kane, John M / Malhotra, Anil K

    Schizophrenia research

    2022  Volume 259, Page(s) 28–37

    Abstract: In this study, we compared three domains of social cognition (emotion processing, mentalizing, and attribution bias) to clinical and computational language measures in 63 participants with schizophrenia spectrum disorders. Based on the active inference ... ...

    Abstract In this study, we compared three domains of social cognition (emotion processing, mentalizing, and attribution bias) to clinical and computational language measures in 63 participants with schizophrenia spectrum disorders. Based on the active inference model for discourse, we hypothesized that emotion processing and mentalizing, but not attribution bias, would be related to language disturbances. Clinical ratings for speech disturbance assessed disorganized and underproductive dimensions. Computational features included speech graph metrics, use of modal verbs, use of first-person pronouns, cosine similarity of adjacent utterances, and measures of sentiment; these were represented by four principal components. We found that higher clinical ratings for disorganized speech were predicted by greater impairments in both emotion processing and mentalizing, and that these relationships remained significant when accounting for demographic variables, overall psychosis symptoms, and verbal ability. Similarly, a computational speech component reflecting insular speech was consistently predicted by impairment in emotion processing. There were notable trends for computational speech components reflecting underproductive speech and decreased content-rich speech predicting mentalizing ability. Exploratory longitudinal analyses in a small subset of participants (n = 17) found that improvements in both emotion processing and mentalizing predicted improvements in disorganized speech. Attribution bias did not demonstrate strong relationships with language measures. Altogether, our findings are consistent with the active inference model of discourse and suggest greater emphasis on treatments that target social cognitive and language systems.
    MeSH term(s) Humans ; Schizophrenia/complications ; Social Cognition ; Speech ; Schizophrenic Psychology ; Psychotic Disorders/complications ; Communication Disorders
    Language English
    Publishing date 2022-07-11
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 639422-x
    ISSN 1573-2509 ; 0920-9964
    ISSN (online) 1573-2509
    ISSN 0920-9964
    DOI 10.1016/j.schres.2022.06.012
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  7. Article ; Online: High burden of viruses and bacterial pathobionts drives heightened nasal innate immunity in children with and without SARS-CoV-2

    Watkins, Timothy A. / Cheemarla, Nagarjuna R. / Hansel, Katrin / Green, Alex B. / Amat, Julien A.R. / Lozano, Richard / Dudgeon, Sarah N. / Landry, Marie L. / Schulz, Wade L / Foxman, Ellen F.

    medRxiv

    Abstract: Recent work indicates that heightened nasal innate immunity in children may impact SARS-CoV-2 pathogenesis. Here, we identified drivers of nasal innate immune activation in children using cytokine profiling and multiplex pathogen detection in 291 ... ...

    Abstract Recent work indicates that heightened nasal innate immunity in children may impact SARS-CoV-2 pathogenesis. Here, we identified drivers of nasal innate immune activation in children using cytokine profiling and multiplex pathogen detection in 291 pediatric nasopharyngeal samples from the 2022 Omicron surge. Nasal viruses and bacterial pathobionts were highly prevalent, especially in younger children (81% of symptomatic and 37% asymptomatic children overall; 91% and 62% in subjects <5 yrs). For SARS-CoV-2, viral load was highest in young children, and viral load in single infections or combined viral loads in coinfections best predicted nasal CXCL10, a biomarker of the mucosal interferon response. Bacterial pathobionts correlated with high nasal IL-1 beta and TNF, but not CXCL10, and viral-bacterial coinfections showed a combined immunophenotype. These findings reveal virus and bacteria as drivers of heightened nasal innate immunity in children and suggest that frequent host-pathogen interactions shape responses to respiratory viruses in this age group
    Keywords covid19
    Language English
    Publishing date 2023-06-20
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2023.06.17.23291498
    Database COVID19

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  8. Article ; Online: High burden of viruses and bacterial pathobionts drives heightened nasal innate immunity in children with and without SARS-CoV-2

    Watkins, Timothy A. / Cheemarla, Nagarjuna R. / Hänsel, Katrin / Green, Alex B. / Amat, Julien A.R. / Lozano, Richard / Dudgeon, Sarah N. / Landry, Marie L. / Schulz, Wade L. / Foxman, Ellen F.

    medRxiv

    Abstract: Recent work indicates that heightened nasal innate immunity in children may impact SARS-CoV-2 pathogenesis. Here, we identified drivers of nasal innate immune activation in children using cytokine profiling and multiplex pathogen detection in 291 ... ...

    Abstract Recent work indicates that heightened nasal innate immunity in children may impact SARS-CoV-2 pathogenesis. Here, we identified drivers of nasal innate immune activation in children using cytokine profiling and multiplex pathogen detection in 291 pediatric nasopharyngeal samples from the 2022 Omicron surge. Nasal viruses and bacterial pathobionts were highly prevalent, especially in younger children (81% of symptomatic and 37% asymptomatic children overall; 91% and 62% in subjects <5 yrs). For SARS-CoV-2, viral load was highest in young children, and viral load in single infections or combined viral loads in coinfections best predicted nasal CXCL10, a biomarker of the mucosal interferon response. Bacterial pathobionts correlated with high nasal IL-1 beta and TNF, but not CXCL10, and viral-bacterial coinfections showed a combined immunophenotype. These findings reveal virus and bacteria as drivers of heightened nasal innate immunity in children and suggest that frequent host-pathogen interactions shape responses to respiratory viruses in this age group
    Keywords covid19
    Language English
    Publishing date 2023-06-20
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2023.06.17.23291498
    Database COVID19

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