Raquel E. Gur
VerifiedUniversity of Pennsylvania · Rehabilitation Medicine
Active 1973–2024
About
Raquel E. Gur, MD, PhD, is the Karl and Linda Rickels Professor of Psychiatry at the University of Pennsylvania School of Medicine. She serves as the Director of the Neurodevelopment and Psychosis Section in the Department of Psychiatry, as well as the Director of the Lifespan Brain Institute (LiBI) and Co-Director of the Penn Translational Neuroscience Center at the same institution. Her research expertise focuses on the study of brain and behavior in normative and neuropsychiatric populations, with particular attention to the neuropsychiatric presentation and course of psychosis and neurodevelopmental disorders associated with rare copy number variants. Dr. Gur's work involves understanding the pathways to psychosis and factors associated with risk for psychosis spectrum disorders, utilizing neuroimaging and genetic approaches to investigate brain development and its links to mental health. Her clinical expertise includes the neuropsychiatric presentation of complex disorders, contributing to the advancement of knowledge in neurodevelopmental and psychiatric conditions.
Research topics
- Computer Science
- Psychology
- Psychiatry
- Artificial Intelligence
- Neuroscience
- Medicine
- Biology
- Data Mining
- Cognitive psychology
- Genetics
- Natural Language Processing
- Physics
- Database
- Machine Learning
- Clinical psychology
- Computer vision
- Computational biology
- Audiology
- Data science
- Statistics
- Demography
- Developmental psychology
- Social psychology
- Mathematics
Selected publications
Intrinsic activity development unfolds along a sensorimotor–association cortical axis in youth
Nature Neuroscience · 2023 · 185 citations
- Neuroscience
- Psychology
- Biology
Mapping genomic loci implicates genes and synaptic biology in schizophrenia
Nature · 2022 · 2693 citations
- Computer Science
- Biology
- Computational biology
, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.
Brain charts for the human lifespan
Nature · 2022 · 1720 citations
- Computer Science
- Biology
- Neuroscience
, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
Psychological Medicine · 2021 · 15 citations
Senior authorCorresponding- Computer Science
- Machine Learning
- Psychology
BACKGROUND: Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of 'risk calculators' for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples. METHODS: We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting 'psychosis risk' enriched community samples. We assessed 632 participants age 8-21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines. RESULTS: Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70. CONCLUSIONS: Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.
Schizophrenia · 2021 · 124 citations
- Natural Language Processing
- Computer Science
- Artificial Intelligence
Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., "the," "a,"). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.
Disruption of the blood–brain barrier in 22q11.2 deletion syndrome
Brain · 2021 · 64 citations
- Neuroscience
- Immunology
- Medicine
Neuroimmune dysregulation is implicated in neuropsychiatric disorders including schizophrenia. As the blood-brain barrier is the immunological interface between the brain and the periphery, we investigated whether this vascular phenotype is intrinsically compromised in the most common genetic risk factor for schizophrenia, the 22q11.2 deletion syndrome (22qDS). Blood-brain barrier like endothelium differentiated from human 22qDS+schizophrenia-induced pluripotent stem cells exhibited impaired barrier integrity, a phenotype substantiated in a mouse model of 22qDS. The proinflammatory intercellular adhesion molecule-1 was upregulated in 22qDS+schizophrenia-induced blood-brain barrier and in 22qDS mice, indicating compromise of the blood-brain barrier immune privilege. This immune imbalance resulted in increased migration/activation of leucocytes crossing the 22qDS+schizophrenia blood-brain barrier. We also found heightened astrocyte activation in murine 22qDS, suggesting that the blood-brain barrier promotes astrocyte-mediated neuroinflammation. Finally, we substantiated these findings in post-mortem 22qDS brain tissue. Overall, the barrier-promoting and immune privilege properties of the 22qDS blood-brain barrier are compromised, and this might increase the risk for neuropsychiatric disease.
Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years
Human Brain Mapping · 2021 · 289 citations
- Psychology
- Neuroscience
- Developmental psychology
Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
Neuropsychopharmacology · 2021 · 21 citations
- Psychology
- Neuroscience
- Developmental psychology
Translational Psychiatry · 2021 · 68 citations
- Psychology
- Clinical psychology
- Psychiatry
The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5-90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.
QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data
Nature Methods · 2021 · 315 citations
- Computer Science
- Computer Science
- Data Mining
Recent grants
NIH · $720k · 2017
NIH · $6.5M · 2017
NIH · $8.2M · 2019
NIH · $2.9M · 2009
Psychosis: A Convergent Neuroscience Perspective
NIH · $7.6M · 1993–2025
Frequent coauthors
- 1662 shared
Ruben C. Gur
Children's Hospital of Philadelphia
- 816 shared
Tyler M. Moore
California University of Pennsylvania
- 773 shared
Theodore D. Satterthwaite
Children's Hospital of Philadelphia
- 727 shared
Monica E. Calkins
University of Pennsylvania
- 557 shared
Ran Barzilay
University of Pennsylvania
- 472 shared
David R. Roalf
- 400 shared
Kosha Ruparel
- 364 shared
Daniel H. Wolf
University of Pennsylvania
Awards & honors
- Predoctoral Fellowship, Stanford University (1974)
- Postdoctoral Fellowship, University of Pennsylvania (1975)
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