The mission of the Machine Learning Team is to support researchers in the NIMH intramural research program who want to address research problems in clinical and cognitive 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
Team members
The Machine Learning Team supports researchers in the NIMH intramural research program who want to address research problems in clinical and cognitive neuroscience using machine learning approaches.
Team Members
Dylan Nielson
Job title
research scientist
Gabriel Loewinger
Job title
research scientist
Preprints (under review)
- "'Is there anything else you would like to tell us?': An analysis of language features in text responses to a study on mental health during the COVID-19 pandemic"
Weger R., Lossio-Ventura J. A., Rose-McCandlish M., Shaw J., Sinclair S., Pereira F., Chung J., Atlas L.
Selected publications by team members
Methods
- "VICE: Variational Interpretable Concept Embeddings"
Muttenthaler L., Zheng C., McClure P., Vandermeulen R., Hebart M., Pereira F.
to appear in Neural Information Processing Systems, 2022 - "Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness"
McClure P., Moraczewski D., Lam K. C., Thomas A., Pereira F.
in press at Aperture Neuro - Semantic Projection: Recovering Human Knowledge of Multiple, Distinct, Object Features from Word Embeddings
Grand G., Blank I., Pereira F., Fedorenko E.
Nature Human Behaviour, 2021 - "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 - "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 - "Deep Neural Networks in Computational Neuroscience"
Kietzmann, T., McClure, P., Kriegeskorte, N.
Oxford Research Encyclopedia of Neuroscience, 2019 - "Extrapolating Expected Accuracies for Large Multi-Class Problems"
Zheng, C., Achanta R., Benjamini. Y.
Journal of Machine Learning Research vol. 19. 2018. - "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
Applications
- "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.
in press at Nature Human Behaviour - "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 - "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 [talk] - 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 - 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 - "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 - "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 - 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 - "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
Former Team Members
Patrick McClure
Job title
Assistant Professor, Computer Science Dept., Naval Postgraduate School
Links
Yenho Chen
Job title
Machine Learning Ph.D. student
Nicole Kuznetsov
Job title
postbac IRTA / M.S. student
