The mission of the Machine Learning Core is to support researchers in the NIMH intramural research program who want to address research problems in psychology, psychiatry, and neuroscience, using statistics and machine learning approaches. We do this by consulting with individual researchers and guiding them in the use of the appropriate tools and methods, or by taking on the analysis process ourselves, if this is more expedient. In parallel, we are a machine learning research group and, as such, develop new methods and analysis approaches, motivated by the needs of researchers or by the practical possibilities arising from advances in the field.
contact: francisco.pereira@nih.gov
Members
The Machine Learning Core supports researchers in the NIMH intramural research program who want to address research problems in clinical and cognitive neuroscience using machine learning approaches.
Al Xin
Software and Resources
- VICE toolbox for creating interpretable item embeddings from odd-one-out triplet task judgments
(the primary developer is Lukas Muttenthaler, please see VICE paper for the method we developed jointly) - R and python packages for fitting functional linear mixed effects models to fiber photometry data
(the primary developer is Gabriel Loewinger, please see fLMM paper for details) - Interpretability-Constrained Questionnaire Factorization, for generating interpretable factors and loadings from questionnaire data
(the primary developer is Ka Chun Lam, please see ICQF preprint for details) - One-step functional GEE algorithm for longitudinal functional regression"
(the primary developer is Gabriel Loewinger, please see fast GEE paper for details)
Preprints (under review)
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Harnessing Natural Language Processing for Automated Exposure Therapy Coding in Youth with OCD
Lossio-Ventura J.A., Frank S., Ringlein G., Bonson K., Olszko A., Knobel A., Pine D.S., Freeman J., Benito K., Jangraw D.C, -
Fast Penalized Generalized Estimating Equations for Large Longitudinal Functional Datasets
Loewinger G., Levis A., Cui E., Pereira F. -
Persistent representation of a prior schema in the orbitofrontal cortex facilitates learning of a conflicting schema
Maor I., Atwell J., Ascher I., Zhao Y., Takahashi Y.K., Hart E., Pereira F., Schoenbaum G. -
Nonparametric causal inference for optogenetics: sequential excursion effects for dynamic regimes
Loewinger G., Levis A., Pereira F. -
Detecting Cry in Daylong Audio Recordings using Machine Learning: The Development and Evaluation of Binary Classifiers
Henry L.M., Lee K., Hansen E., Tandilashvili E., Rozsypal J., Erjo T., Raven J. G., Reynolds H. M., Curtis P., Haller S.,Pine D., Norton E., Wakschlag K.S., Pereira F., Brotman M.A. -
The encoding of interoceptive-based predictions by the paraventricular nucleus of the thalamus D2+ neurons
Machen B., Miller S., Xin A., Lampert C., Assaf L., Tucker J., Pereira F., Loewinger G., Beas S. -
In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants “rest” matters
Gonzalez-Castillo J., Spurney M., Lam K.C., Gephart I. S., Pereira F., Handwerker D. A., Kam J. W. Y., Bandettini P. -
Interpretable factorization of clinical questionnaires to identify latent factors of psychopathology
Lam K.C., Mahony B.W., Raznahan A., Pereira F.
Selected publications
(click to jump to each section: Methods and Applications in Neuroscience, Psychiatry, and Cognitive Psychology/Neuroscience)
Methods
- A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments
Loewinger G., Cui E., Lovinger D., Pereira F.
in press at eLife, 2025 - More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed Routing
Shaier S., Pereira F., K von der Wense, LE Hunter, M Jones
in International Conference on Learning Representations, 2025 - "Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects"
Levis A., Loewinger G., Pereira
in Neural Information Processing Systems, 2024 - A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data
Lossio-Ventura J. A., Weger R., Lee A., Guinee E., Chung J. Y., Atlas L. Y., Linos E., Pereira F.
JMIR Mental Health Vol 11, 2024 - "Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness"
McClure P., Moraczewski D., Lam K. C., Thomas A., Pereira F.
Aperture Neuro, 2023 -
"Real-time variational method for learning neural trajectory and its dynamics"
Matthew Dowling, Yuan Zhao, Il Memming Park
in International Conference on Learning Representations, 2023 -
Linear time GPs for inferring latent trajectories from neural spike trains"
Matthew Dowling, Yuan Zhao, Il Memming Park
in International Conference on Machine Learning, 2023 - "VICE: Variational Interpretable Concept Embeddings"
Muttenthaler L., Zheng C., McClure P., Vandermeulen R., Hebart M., Pereira F.
in Neural Information Processing Systems, 2022 - Semantic Projection: Recovering Human Knowledge of Multiple, Distinct, Object Features from Word Embeddings
Grand G., Blank I., Pereira F., Fedorenko E.
Nature Human Behaviour, 2022 - "Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks"
McClure, P., Rho, N., Lee, J., Kaczmarzyk, J., Zheng, C., Ghosh, S., Nielson, D., Thomas, A., Bandettini, P., Pereira, F.
Frontiers in Neuroinformatics, 2019 - "Revealing interpretable object representations from human behavior"
Zheng, C., Pereira, F., Baker, C., Hebart, M.
International Conference on Learning Representations, 2019 - "Validating the Representational Space of Deep Reinforcement Learning Models of Behavior with Neural Data"
Bruch S. N. , McClure P., Zhou J., Schoenbaum G., Pereira F.
bioRxiv preprint - A Deep Neural Network Tool for Automatic Segmentation of Human Body Parts in Natural Scenes
McClure P. , Reimann G., Ramot M., Pereira F.
arXiv preprint - "Deep Neural Networks in Computational Neuroscience"
Kietzmann, T., McClure, P., Kriegeskorte, N.
Oxford Research Encyclopedia of Neuroscience, 2019 - "Distributed Weight Consolidation: A Brain Segmentation Case Study"
McClure P., Zheng C., Kaczmarzyk J., Rogers-Lee J., Ghosh S., Nielson D., Bandettini P., Pereira F.
Neural Information Processing Systems, 2018 - "Extrapolating Expected Accuracies for Large Multi-Class Problems"
Zheng, C., Achanta R., Benjamini. Y.
Journal of Machine Learning Research vol. 19. 2018.
Applications (Cognitive Psychology/Neuroscience)
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Neural and behavioral reinstatement jointly reflect retrieval of narrative events
Nau M., Greene A., Tarder-Stoll H., Lossio-Ventura J.A., Pereira F., Chen J., Baldassano C., Baker C.I. in press at Nature Communications, 2025 - "Manifold learning for fMRI time-varying functional connectivity"
Gonzalez-Castillo J., Fernandez I., Lam K., Handwerker D., Pereira F., Bandettini P.
Front Hum Neurosci. 2023; 17: 1134012 - "The temporal representation of experience in subjective mood"
Keren H., Zheng C., Jangraw D. C., Chang K., Vitale A., Nielson D., Rutledge R. B., Pereira F., Stringaris A.
eLife 2021 - "Mental representations of objects reflect the ways in which we interact with them"
Lam K. C., Pereira F., Vaziri-Pashkam M., Woodard K., McMahon E.
Proceedings of the Cognitive Science Society Conference, 2021 [selected for oral presentation] - "Revealing the multidimensional mental representations of natural objects underlying human similarity judgments"
Hebart, M., Zheng, C., Pereira, F., Baker, C.
Nature Human Behaviour, 2020 - "Subtle predictive movements reveal actions regardless of social context"
McMahon, E.G., Zheng, C.Y., Pereira, F., Gonzalez, R., Ungerleider, L.G. and Vaziri-Pashkam, M.
Journal of vision 19 (7), 16-16, 2019 - Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest
Gonzalez-Castillo J., Caballero-Gaudes C., Topolski N., Handwerker D., Pereira F. , Bandettini P.
Neuroimage 15; 202: 116129. 2019 - "Toward a universal decoder of linguistic meaning from brain activation"
Pereira F., Lou B., Pritchett B., Ritter S., Gershman S., Kanwisher N., Botvinick M., Fedorenko E.
Nature Communications 9 (963), 2018
Applications (Neuroscience)
- Brain-wide presynaptic networks of functionally distinct cortical neurons
Inacio A. R., Lam K. C., Zhao Y., Pereira F., Gerfen C. R., Lee S.
in Nature, 2025 - Distinct prelimbic cortex ensembles encode response execution and inhibition
Madangopal R., Zhao Y., Heins C., Zhou U., Liang B., Barbera G., Lam K.C., Komer L.E., Weber S.J., Thompson D. J., Gera Y., Pham D.Q., Savell K.E., Warren B.L., Caprioli D., Venniro M., Bossert J.M., Ramsey L.A., Jedema H.P., Schoenbaum G., Lin D.T., Shaham Y., Pereira F., Hope B.T.
in press at Proceedings of the National Academy of Sciences, 2025 - Dissociable encoding of motivated behavior by parallel thalamo-striatal projections
Beas S., Khan I., Gao C., Loewinger G., Macdonald E., Bashford A., Rodriguez-Gonzalez S., Pereira F., Penzo M.A.
in Current Biology, 2024 - "Sensory and Choice Responses in MT Distinct from Motion Encoding"
Aaron J. Levi, Yuan Zhao, Il Memming Park and Alexander C. Huk
Journal of Neuroscience 22 March 2023, 43 (12) 2090-2103 - "Working memory and reward increase the accuracy of animal location encoding in the medial prefrontal cortex"
Ma X., Zheng C., Chen Y., Pereira F., Zheng L.
Cerebral Cortex, 2022, 1-15 - Cell-type-specific recruitment of GABAergic interneurons in the primary somatosensory cortex by long-range inputs
Naskar S., Qi J., Pereira F., Gerfen, C., Lee, S.
Cell Reports 34, 108774, 2021
Applications (Psychiatry)
- Multivariate prediction of temper outbursts in a sample of youth enriched for irritability using ecological momentary assessment data: A registered report
Saha D., Naim R., Pereira F., Brotman M.A., Zheng C.Y.
in PLoS one, vol. 30 (3), 2025 - Dynamic effects of psychiatric vulnerability, loneliness, and social distancing on distress during the first year of the COVID-19 pandemic: Insights from a large-scale longitudinal study
Atlas L. Y., Farmer C., Shaw J., Gibbon A., Guinee E.P., Lossio-Ventura J. A., Ballard E., Ernst M., Japee S., Pereira F., Chung J. Y.
in Nature Mental Health, 2024 - Outcomes that matter to depressed adolescents can be identified with large language models
Xin A., Lossio-Ventura J. A., Krause, K. R., Fiorini, G., Midgley N., Pereira F., and Nielson D. M.
in Journal of the American Medical Informatics Association, 2024 - Subjective Affective Experience under threat is shaped by environmental affordances
Qi S., Nielson D.M., Marcotulli D., Pine D.S., Stringaris A.
in Public Library of Science One, 2024 - Test-retest reliability of functional connectivity in adolescents with depression
Camp C., Noble S., Scheinost D., Stringaris A., Nielson D.M.
in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2024 - "A Highly Replicable Decline in Mood During Rest and Simple Tasks"
Jangraw D., Keren H., Sun H., Bedder R., Rutledge R., Pereira F., Thomas A., Pine D., Zheng C., Nielson D., Stringaris A.
Nature Human Behaviour 7 (4), 596-610, 2023 - Trends in Language Use During the COVID-19 Pandemic and Relationship Between Language Use and Mental Health: Text Analysis Based on Free Responses From a Longitudinal Study
Weger R., Lossio-Ventura J. A., Rose-McCandlish M., Shaw J., Sinclair S., Pereira F., Chung J., Atlas L.
JMIR Mental Health 10 (1), e40899, 2023 - Validation of CBCL depression scores of adolescents in three independent datasets
Zelenina M., Pine D.S., Stringaris A., Nielson D.M.
JCPP Advances, 2023 - "Mood and Behaviors of Adolescents With Depression in a Longitudinal Study Before and During the COVID-19 Pandemic"
Sadeghi N., Fors P.Q., Eisner L., Taigman J., Qi K., Gorham L.S., Camp C.C., O'Callaghan G., Rodriguez D., McGuire J., Garth E.M., Engel C., Davis M., Towbin K.E., Stringaris A., Nielson D.M. - "Origins of Anhedonia in Childhood and Adolescence"
Prabhakar J., Nielson D.M., Stringaris A. - "The temporal representation of experience in subjective mood"
Keren H., Zheng C., Jangraw D. C., Chang K., Vitale A., Nielson D., Rutledge R. B., Pereira F., Stringaris A.
eLife 2021 - Gauging facial feature viewing preference as a stable individual trait in autism spectrum disorder.
Reimann G., Walsh C., Csumitta K., McClure P., Pereira F., Martin A., Ramot M.
Autism Research 14:1670–1683, 2021 - "Magnetoencephalographic Correlates of Mood and Reward Dynamics in Human Adolescents"
Liuzzi, L., Chang, K.K., Zheng, C., Keren, H., Saha, D., Nielson, D.M. and Stringaris, A.
Cerebral Cortex, 2021 - Data‐driven identification of subtypes of executive function across typical development, attention deficit hyperactivity disorder, and autism spectrum disorders
Vaidya C., You X., Mostofsky S., Pereira F., Berl M., Kenworthy L.
J Child Psychol Psychiatry 61(1): 51–61. 2019
Nicole Kuznetsov
Patrick McClure
Yenho Chen
