Jocelyn Ricard

All Publications

• Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness SCIENCE ADVANCES Chopra, S., Dhamala, E., Lawhead, C., Ricard, J. A., Orchard, E. R., An, L., Chen, P., Wulan, N., Kumar, P., Rubenstein, A., Moses, J., Chen, L., Levi, P., Holmes, A., Aquino, K., Fornito, A., Harpaz-Rotem, I., Germine, L. T., Baker, J. T., Yeo, B., Holmes, A. J. 2024; 10 (45): eadn1862

  Abstract

  A primary aim of computational psychiatry is to establish predictive models linking individual differences in brain functioning with symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and associated with poor outcomes. Recent work suggests that thousands of participants may be necessary for the accurate and reliable prediction of cognition, questioning the utility of most patient collection efforts. Here, using a transfer learning framework, we train a model on functional neuroimaging data from the UK Biobank to predict cognitive functioning in three transdiagnostic samples (ns = 101 to 224). We demonstrate prediction performance in all three samples comparable to that reported in larger prediction studies and a boost of up to 116% relative to classical models trained directly in the smaller samples. Critically, the model generalizes across datasets, maintaining performance when trained and tested across independent samples. This work establishes that predictive models derived in large population-level datasets can boost the prediction of cognition across clinical studies.

  View details for DOI 10.1126/sciadv.adn1862

  View details for Web of Science ID 001348273700004

  View details for PubMedID 39504381

  View details for PubMedCentralID PMC11540040

• A shared spatial topography links the functional connectome correlates of cocaine use disorder and dopamine D2/3 receptor densities. Communications biology Ricard, J. A., Labache, L., Segal, A., Dhamala, E., Cocuzza, C. V., Jones, G., Yip, S. W., Chopra, S., Holmes, A. J. 2024; 7 (1): 1178

  Abstract

  The biological mechanisms that contribute to cocaine and other substance use disorders involve an array of cortical and subcortical systems. Prior work on the development and maintenance of substance use has largely focused on cortico-striatal circuits, with relatively less attention on alterations within and across large-scale functional brain networks, and associated aspects of the dopamine system. Here, we characterize patterns of functional connectivity in cocaine use disorder and their spatial association with neurotransmitter receptor densities and transporter bindings assessed through PET. Profiles of functional connectivity in cocaine use disorder reliably linked with spatial densities of dopamine D2/3 receptors across independent datasets. These findings demonstrate that the topography of dopamine receptor densities may underlie patterns of functional connectivity in cocaine use disorder, as assessed through fMRI.

  View details for DOI 10.1038/s42003-024-06836-9

  View details for PubMedID 39300138

  View details for PubMedCentralID PMC11413242

• The Transdiagnostic Connectome Project: a richly phenotyped open dataset for advancing the study of brain-behavior relationships in psychiatry. medRxiv : the preprint server for health sciences Chopra, S., Cocuzza, C. V., Lawhead, C., Ricard, J. A., Labache, L., Patrick, L. M., Kumar, P., Rubenstein, A., Moses, J., Chen, L., Blankenbaker, C., Gillis, B., Germine, L. T., Harpaz-Rote, I., Yeo, B. T., Baker, J. T., Holmes, A. J. 2024

  Abstract

  An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs. Additionally, participants completed over 50 psychological and cognitive assessments. Here, we detail available behavioral data as well as raw and processed MRI derivatives. Associations between data processing and quality metrics, such as head motion, are reported. Processed data exhibit classic task activation effects and canonical functional network organization. Overall, we provide a comprehensive and analysis-ready transdiagnostic dataset, which we hope will accelerate the identification of illness-relevant features of brain functioning, enabling future discoveries in basic and clinical neuroscience.

  View details for DOI 10.1101/2024.06.18.24309054

  View details for PubMedID 38946958

  View details for PubMedCentralID PMC11213088

• Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data. Nature neuroscience Ricard, J. A., Parker, T. C., Dhamala, E., Kwasa, J., Allsop, A., Holmes, A. J. 2023; 26 (1): 4-11

  Abstract

  Across the brain sciences, institutions and individuals have begun to actively acknowledge and address the presence of racism, bias, and associated barriers to inclusivity within our community. However, even with these recent calls to action, limited attention has been directed to inequities in the research methods and analytic approaches we use. The very process of science, including how we recruit, the methodologies we utilize and the analyses we conduct, can have marked downstream effects on the equity and generalizability of scientific discoveries across the global population. Despite our best intentions, the use of field-standard approaches can inadvertently exclude participants from engaging in research and yield biased brain-behavior relationships. To address these pressing issues, we discuss actionable ways and important questions to move the fields of neuroscience and psychology forward in designing better studies to address the history of exclusionary practices in human brain mapping.

  View details for DOI 10.1038/s41593-022-01218-y

  View details for PubMedID 36564545

  View details for PubMedCentralID 8528500