Ruben C. Gur
· Professor of Psychology in PsychiatryVerifiedUniversity of Pennsylvania · Rehabilitation Medicine
Active 1973–2025
About
Ruben C. Gur, PhD, is a Professor of Psychology in Psychiatry and the Director of Neuropsychology and the Brain Behavior Laboratory at the Department of Psychiatry, Hospital of the University of Pennsylvania. His educational background includes a B.A. from Hebrew University of Jerusalem, an M.A. and Ph.D. in Clinical Psychology from Michigan State University. His research expertise encompasses cognition, schizophrenia, and neuroimaging. His work involves studying brain development, mental health, neuroanatomical correlates of psychiatric symptoms, and the genetic and cellular pathways underlying psychiatric disorders. Dr. Gur has contributed to understanding brain structure alterations in youth with internalizing or externalizing disorders, mapping individual differences in neurodevelopment, and exploring genetic influences on psychiatric traits.
Research topics
- Computer Science
- Psychology
- Neuroscience
- Medicine
- Psychiatry
- Cognitive psychology
- Data Mining
- Artificial Intelligence
- Developmental psychology
- Biology
- Physics
- Computational biology
- Database
- Clinical psychology
- Gerontology
- Computer vision
- Audiology
- Machine Learning
- Endocrinology
- Data science
- Statistics
- Radiology
- Physiology
- Cognitive science
Selected publications
Schizophrenia · 2025-04-03 · 7 citations
articleOpen accessModern research management, particularly for publicly funded studies, assumes a data governance model in which grantees are considered stewards rather than owners of important data sets. Thus, there is an expectation that collected data are shared as widely as possible with the general research community. This presents problems in complex studies that involve sensitive health information. The latter requires balancing participant privacy with the needs of the research community. Here, we report on the data operation ecosystem crafted for the Accelerating Medicines Partnership® Schizophrenia project, an international observational study of young individuals at clinical high risk for developing a psychotic disorder. We review data capture systems, data dictionaries, organization principles, data flow, security, quality control protocols, data visualization, monitoring, and dissemination through the NIMH Data Archive platform. We focus on the interconnectedness of these steps, where our goal is to design a seamless data flow and an alignment with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles while balancing local regulatory and ethical considerations. This process-oriented approach leverages automated pipelines for data flow to enhance data quality, speed, and collaboration, underscoring the project's contribution to advancing research practices involving multisite studies of sensitive mental health conditions. An important feature is the data's close-to-real-time quality assessment (QA) and quality control (QC). The focus on close-to-real-time QA/QC makes it possible for a subject to redo a testing session, as well as facilitate course corrections to prevent repeating errors in future data acquisition. Watch Dr. Sylvain Bouix discuss his work and this article: https://vimeo.com/1025555648 .
Optimizing Biophysical Large-Scale Brain Circuit Models With Deep Neural Networks
bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-07
preprintOpen accessBiophysical modeling provides mechanistic insights into brain function, from single-neuron dynamics to large-scale circuit models bridging macro-scale brain activity with microscale measurements. Biophysical models are governed by biologically meaningful parameters, many of which can be experimentally measured. Some parameters are unknown, and optimizing their values can dramatically improve adherence to experimental data, significantly enhancing biological plausibility. Previous optimization methods - such as exhaustive search, gradient descent, evolutionary strategies and Bayesian optimization - require repeated, computationally expensive numerical integration of biophysical differential equations, limiting scalability to population-level datasets. Here, we introduce DELSSOME (DEep Learning for Surrogate Statistics Optimization in MEan field modeling), a framework that bypasses numerical integration by directly predicting whether model parameters produce realistic brain dynamics. When applied to the widely used feedback inhibition control (FIC) mean field model, DELSSOME achieves a 2000× speedup over Euler integration. By embedding DELSSOME within an evolutionary optimization strategy, trained models generalize to new datasets without additional tuning, enabling a 50× speedup in FIC model estimation while preserving neurobiological insights. The massive acceleration facilitates large-scale mechanistic modeling in population-level neuroscience, unlocking new opportunities for understanding brain function.
UNC Libraries · 2025-12-05
articleOpen accessNeuroanatomical Correlates of Negative Symptoms in Schizophrenia
bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-23
preprintOpen accessBackground: Schizophrenia is characterized by widespread structural brain abnormalities, but associations between structural abnormalities and negative symptom severity are not well understood. Negative symptoms have been conceptualized in a hierarchical structure of two second-order dimensions-motivation and pleasure (MAP) and expression (EXP)-and five first-order domains: anhedonia, avolition, and asociality (MAP), and blunted affect and alogia (EXP). A better understanding of the neural circuitry underlying negative symptom dimensions and domains is important given their reported association with poor functional outcome and lack of available treatments. Study Design: The meta-analysis included 1,591 individuals with schizophrenia across 16 samples with structural imaging and Scale for Assessment of Negative Symptoms data. The study generated correlations of cortical thickness and subcortical volumes with the negative symptom dimensions and domains. Study results: Negative symptoms showed mainly negative associations with cortical thickness and subcortical volumes. The effect sizes were small but there was a pattern of associations in predominantly frontal lobe cortical thickness and limbic subcortical volumes. The regional correlation patterns of cortical thickness and subcortical volumes with symptom domains support the conceptualized hierarchical structure of negative symptoms: correlations of MAP domains were stronger with the MAP than EXP dimension, and vice versa. Exploratory analyses with receptor densities further supported the hierarchy. Conclusion: Our findings reveal small but consistent associations between negative symptom dimensions and predominantly prefrontal region cortical thickness, and limbic region volumes. These findings advance our understanding of the network of anatomical regions that may contribute to the severity of negative symptoms in schizophrenia.
Two Axes of White Matter Development
bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-20 · 3 citations
preprintOpen accessDespite decades of neuroimaging research, how white matter develops along the length of major tracts in humans remains unknown. Here, we identify fundamental patterns of white matter maturation by examining developmental variation along major, long-range cortico-cortical tracts in youth ages 5-23 years using diffusion MRI from three large-scale, cross-sectional datasets (total N = 2,716). Across datasets, we delineate two replicable axes of human white matter development. First, we find a deep-to-superficial axis, in which superficial tract regions near the cortical surface exhibit greater age-related change than deep tract regions. Second, we demonstrate that the development of superficial tract regions aligns with the cortical hierarchy defined by the sensorimotor-association axis, with tract ends adjacent to sensorimotor cortices maturing earlier than those adjacent to association cortices. These results reveal developmental variation along tracts that conventional tract-average analyses have previously obscured, challenging the implicit assumption that white matter tracts mature uniformly along their length. Such developmental variation along tracts may have functional implications, including mitigating ephaptic coupling in densely packed deep tract regions and tuning neural synchrony through hierarchical development in superficial tract regions - ultimately refining neural transmission in youth.
Biological Psychiatry · 2025-04-09
articleSchizophrenia · 2025-03-24 · 8 citations
articleOpen accessCognitive impairment occurs at higher rates in individuals at clinical high risk (CHR) for psychosis relative to healthy peers, and it contributes unique variance to multivariate prediction models of transition to psychosis. Such impairment is considered a core biomarker of schizophrenia. Thus, cognition is a key domain measured in the Accelerating Medicines Partnership® program for Schizophrenia (AMP SCZ initiative). The aim of this paper is to describe the rationale, processes, considerations, and final harmonization of the cognitive battery used in AMP SCZ across the two data collection networks. This battery comprises tests of general intellect and specific cognitive domains. We estimate premorbid intelligence at baseline and measure current intelligence at baseline and 2 years. Eight tests from the Penn Computerized Neurocognitive Battery (PennCNB), which measure verbal learning and memory, sensorimotor ability, attention, emotion recognition, working memory, processing speed, verbal memory, visual memory, and motor speed are administered repeatedly at baseline, and four follow-up timepoints over 2 years.
Biological Psychiatry · 2025-04-09
articleHuman Brain Mapping · 2025-12-01
articleOpen accessDeriving white matter (WM) bundles in vivo has thus far mainly been applied in research settings, leveraging high angular resolution, multi-shell diffusion MRI (dMRI) acquisitions that enable modern reconstruction methods. However, these advanced acquisitions are both time-consuming and costly to acquire. The ability to reconstruct WM bundles in the massive amounts of existing single-shelled, lower angular resolution data from legacy research studies and healthcare systems would offer much broader clinical applications and population-level generalizability. While legacy scans may offer a valuable, large-scale complement to contemporary research datasets, the reliability of white matter bundles derived from these scans remains unclear. Here, we leverage a large research dataset where each 64-direction dMRI scan was acquired as two independent 32-direction runs per subject. To investigate how a state-of-the-art bundle-specific reconstruction method generalizes to this data, we evaluated the test-retest reliability of WM bundles reconstructed from the two 32-direction scans across three orientation distribution function (ODF) reconstruction methods: generalized q-sampling imaging (GQI), constrained spherical deconvolution (CSD), and single-shell three-tissue CSD (SS3T). We found that the majority of WM bundles could be reliably extracted from dMRI scans that were acquired using the 32-direction, single-shell acquisition scheme. The mean Dice coefficient of reconstructed WM bundles was consistently higher within subject than between subject for all WM bundles and ODF reconstruction methods, illustrating preservation of person-specific anatomy. Further, when using features of the bundles to predict complex reasoning assessed using a computerized cognitive battery, we observed stable prediction accuracies (r: 0.15-0.36) across the test-retest data. Among the three ODF reconstruction methods, SS3T had a good balance between sensitivity and specificity when comparing the reconstructed bundles to atlas bundles, a high intra-class correlation of extracted features, more plausible bundles, and strong predictive performance. More broadly, these results demonstrate that bundle-specific reconstruction can achieve robust performance even on lower angular resolution, single-shell dMRI, with particular advantages for ODF methods optimized for single-shell data. This highlights the considerable potential for dMRI collected in healthcare settings and legacy research datasets to accelerate and expand the scope of WM research.
bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-12
preprintOpen accessAbstract Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource – the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) – that combines longitudinal multimodal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, -back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.
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
Raquel E. Gur
Children's Hospital of Philadelphia
- 776 shared
Tyler M. Moore
California University of Pennsylvania
- 775 shared
Theodore D. Satterthwaite
Children's Hospital of Philadelphia
- 681 shared
Monica E. Calkins
University of Pennsylvania
- 444 shared
David R. Roalf
- 383 shared
Daniel H. Wolf
University of Pennsylvania
- 363 shared
Kosha Ruparel
- 358 shared
Ran Barzilay
University of Pennsylvania
Education
- 1970
B.A.
Hebrew University of Jerusalem
- 1971
M.A.
Michigan State University
- 1973
Ph.D., Clinical Psychology
Michigan State University
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