Neuroinformatics lab
We develop computational models and signal processing tools to decode brain connectivity and function using genomics and imaging. We are particularly interested in constructing a bridge between genetics and behavior through interpretable models that operate on multi-modal neural data at molecular, circuit and whole-brain resolutions.
Apply for Ph.D. student and postdoctoral positions now (see below).
Diversity, Equity and Inclusion
We believe that better ideas emerge in science and engineering by a diverse ensemble of creators. We acknowledge that socioeconomic status, race, gender, and culture can be serious barriers of entry and we are committed to making an active effort to overcome these obstacles. One such way we aim to make an impact is in hiring. To make the lab diverse and more equitable, we highly encourage candidates from underrepresented groups to apply to both Ph.D. student and postdoctoral positions. See the link here to discover additional NYU wide initiatives.
Erdem Varol (PI)
I’m an assistant professor at the Department of Computer Science & Engineering at Tandon School of Engineering, NYU. I’m also a part of the VIDA center. Previously, I was a postdoc at the Department of Statistics, Zuckerman Institute, Columbia University with Liam Paninski. Before that, I did my Ph.D. at the Department of Electrical & Systems Engineering at the University of Pennsylvania under Christos Davatzikos. Whilst at Penn, I also received an A.M degree in Statistics from the Wharton School. I recently received the NIH/NIMH K99/R00 award in 2022. I’m originally from Ankara, Turkey and grew up in New York City. I have two basset hounds.
Email: ev2240@nyu.edu
CV: [Link]
Google Scholar: [Link]
Twitter: [Link]
Team
Shuonan Chen
Visiting PostDoc (2024 - present)
Columbia University
Website: TBD
Michael Middleton
(co-advised w/ Claudio Silva)
Ph.D. student (2023 - present)
Previously @ University of Colorado
Margaret Conde Paredes
(co-advised w/ Attila Losonczy)
Ph.D. student (2023 - present)
Previously @ UCL
Website: TBD
Daniela Shoham
Research assistant (2023 - present)
Barnard College
Projects
The overarching research theme of this lab is investigating the genetic underpinnings of brain connectivity at the circuit level and the behavioral and pathological phenotypes they present in neuropsychiatric disorders. We will investigate the structural and functional connectivity of the brain on a macro-scale using clinical neuroimaging datasets and on a micro-scale using single-neuron resolution imaging and genomics datasets collected from small animal model systems. Deciphering the genetic correlates of brain connectivity in health and disease will have a broader impact in basic neuroscience and clinical research to better understand neuropathogenesis and enable the design of precision medicine treatments. PDF document with project details can be found here. Slide deck that covers broad lab policies is found here.
In this project, we aim to disentangle the heterogeneity of structural and functional brain patterns in clinical and normative populations using novel machine learning methods and MRI and fMRI data.
In this project, we use state-of-the-art electrophysiology probes to map circuit connectivity in mammalian brains and account for signal corruption and motion artefacts.
In this project, we integrate single cell resolution EM connectomics data with single cell resolution genomics to discover the relationship between gene expression and circuit connectivity.
Publications
2023
Zhang, Y., He, T., Boussard, J., Windolf, C., Winter, O., Trautmann, E., ... & Paninski, L. (2023). Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. NeurIPS 2023 (Spotlight)
Coughlin, B., Muñoz, W., Kfir, Y., Young, M. J., Meszéna, D., Jamali, M., ... & Paulk, A. C. (2023). Modified Neuropixels probes for recording human neurophysiology in the operating room. Nature Protocols, 1-27.
Wen, J., Skampardoni, I., Tian, Y. E., Yang, Z., Cui, Y., Erus, G., ... & Davatzikos, C. (2023). Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability. medRxiv, 2023-08.
Wen, J., Varol, E., Yang, Z., Hwang, G., Dwyer, D., Kazerooni, A. F., ... & Davatzikos, C. (2023). Subtyping brain diseases from imaging data. Machine Learning for Brain Disorders (pp. 491-510). New York, NY: Springer US.
Hwang, G., Wen, J., Sotardi, S., Brodkin, E. S., Chand, G. B., Dwyer, D. B., ... & Davatzikos, C. (2023). Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population. JAMA psychiatry.
Dwyer, D. B., Dazzan, P., Chand, G. B., Pigoni, A., Khuntia, A., Wen, J., ... & Koutsouleris, N. (2023). Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium. Molecular Psychiatry.
Nejatbaksh, A., Dey, N., Venkatachalam, V., Yemini, E., Paninski, L., Varol, E. (2023). Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. IPMI 2023. (Oral presentation)
Windolf, C., Paulk, A. C., Kfir, Y., Trautmann, E., Garcia, S., Meszéna, D., ... & Varol, E. (2023). Robust online multiband drift estimation in electrophysiology data. ICASSP 2023. (Top 3% of papers)
Chen, S., Rao, B. Y., Herrlinger, S., Losonczy, A., Paninski, L., Varol, E. (2023). Multimodal microscopy image alignment using spatial and shape information and a branch-and-bound algorithm. ICASSP 2023.
2022
Windolf, C., Paulk, A. C., Kfir, Y., Trautmann, E., Garcia, S., Meszéna, D., ... & Varol, E. (2022). Robust online multiband drift estimation in electrophysiology data. bioRxiv.
Wen, J., Fu, C. H., Tosun, D., Veturi, Y., Yang, Z., Abdulkadir, A., ... & Jicha, G. (2022). Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression. JAMA psychiatry, 79(5), 464-474.
Chand, G. B., Singhal, P., Dwyer, D. B., Wen, J., Erus, G., Doshi, J., ... & Davatzikos, C. (2022). Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population. American Journal of Psychiatry, appi-ajp.
Wen, J., Varol, E., Yang, Z., Hwang, G., Dwyer, D., Kazerooni, A. F., ... & Davatzikos, C. (2022). Subtyping brain diseases from imaging data. arXiv preprint arXiv:2202.10945.
Hwang, G., Wen, J., Sotardi, S., Brodkin, E. S., Chand, G. B., Dwyer, D. B., ... & Davatzikos, C. (2022). Three Imaging Endophenotypes Characterize Neuroanatomical Heterogeneity of Autism Spectrum Disorder. medRxiv.
Barrett, A., Varol, E., Weinreb, A., Taylor, S. R., McWhirter, R., Cros, C., ... & Hammarlund, M. (2022). Integrating bulk and single cell RNA-seq refines transcriptomic profiles of specific C. elegans neurons. bioRxiv.
Wen, J., Varol, E., Sotiras, A., Yang, Z., Chand, G. B., Erus, G., ... & Alzheimer's Disease Neuroimaging Initiative. (2022). Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes. Medical Image Analysis, 75, 102304.
2021
Boussard, J., Varol, E., Lee, H. D., Dethe, N., & Paninski, L. (2021). Three-dimensional spike localization and improved motion correction for Neuropixels recordings. Advances in Neural Information Processing Systems, 34, 22095-22105.
Rao, B. Y., Peterson, A. M., Kandror, E. K., Herrlinger, S., Losonczy, A., Paninski, L., ... & Varol, E. (2021, September). Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 466-475). Springer, Cham.
Tekieli, T., Yemini, E., Nejatbakhsh, A., Wang, C., Varol, E., Fernandez, R. W., ... & Hobert, O. (2021). Visualizing the organization and differentiation of the male-specific nervous system of C. elegans. Development, 148(18), dev199687.
Taylor, S. R., Santpere, G., Weinreb, A., Barrett, A., Reilly, M. B., Xu, C., ... & Miller III, D. M. (2021). Molecular topography of an entire nervous system. Cell, 184(16), 4329-4347.
Varol, E., Boussard, J., Dethe, N., Winter, O., Urai, A., Laboratory, T. I. B., ... & Paninski, L. (2021, June). Decentralized motion inference and registration of neuropixel data. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1085-1089). IEEE.
Baller, E. B., Kaczkurkin, A. N., Sotiras, A., Adebimpe, A., Bassett, D. S., Calkins, M. E., ... & Satterthwaite, T. D. (2021). Neurocognitive and functional heterogeneity in depressed youth. Neuropsychopharmacology, 46(4), 783-790.
Gross, P., Johnson, J., Romero, C. M., Eaton, D. M., Poulet, C., Sanchez-Alonso, J., ... & Houser, S. R. (2021). Interaction of the joining region in junctophilin-2 with the L-type Ca2+ channel is pivotal for cardiac dyad assembly and intracellular Ca2+ dynamics. Circulation research, 128(1), 92-114.
Yemini, E., Lin, A., Nejatbakhsh, A., Varol, E., Sun, R., Mena, G. E., ... & Hobert, O. (2021). NeuroPAL: a multicolor atlas for whole-brain neuronal identification in C. elegans. Cell, 184(1), 272-288.
Berghoff, E. G., Glenwinkel, L., Bhattacharya, A., Sun, H., Varol, E., Mohammadi, N., ... & Hobert, O. (2021). The Prop1-like homeobox gene unc-42 specifies the identity of synaptically connected neurons. Elife, 10.
Nejatbakhsh A, Varol E. Neuron matching in C. elegans with robust approximate linear regression without correspondence. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2021 (pp. 2837-2846).
2020
Varol, E., Nejatbakhsh, A., Sun, R., Mena, G., Yemini, E., Hobert, O., & Paninski, L. (2020, October). Statistical atlas of c. elegans neurons. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 119-129). Springer, Cham.
Nejatbakhsh, A., Varol, E., Yemini, E., Venkatachalam, V., Lin, A., Samuel, A. D., ... & Paninski, L. (2020, October). Demixing calcium imaging data in C. elegans via deformable non-negative matrix factorization. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 14-24). Springer, Cham.
Nejatbakhsh, A., Varol, E., Yemini, E., Hobert, O., & Paninski, L. (2020, October). Probabilistic joint segmentation and labeling of c. elegans neurons. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 130-140). Springer, Cham.
Wen, J., Varol, E., Chand, G., Sotiras, A., & Davatzikos, C. (2020, October). MAGIC: multi-scale heterogeneity analysis and clustering for brain diseases. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 678-687). Springer, Cham.
Reilly, M. B., Cros, C., Varol, E., Yemini, E., & Hobert, O. (2020). Unique homeobox codes delineate all the neuron classes of C. elegans. Nature, 584(7822), 595-601.
Mena, G., Nejatbakhsh, A., Varol, E., & Niles-Weed, J. (2020). Sinkhorn EM: an expectation-maximization algorithm based on entropic optimal transport. arXiv preprint arXiv:2006.16548.
Chand, G. B., Dwyer, D. B., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., ... & Davatzikos, C. (2020). Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain, 143(3), 1027-1038.
Kaczkurkin, A. N., Sotiras, A., Baller, E. B., Barzilay, R., Calkins, M. E., Chand, G. B., ... & Satterthwaite, T. D. (2020). Neurostructural heterogeneity in youths with internalizing symptoms. Biological psychiatry, 87(5), 473-482.
Truelove-Hill, M., Erus, G., Bashyam, V., Varol, E., Sako, C., Gur, R. C., ... & Davatzikos, C. (2020). A multidimensional neural maturation index reveals reproducible developmental patterns in children and adolescents. Journal of Neuroscience, 40(6), 1265-1275.
Mena, G., Varol, E., Nejatbakhsh, A., Yemini, E., & Paninski, L. (2020, February). Sinkhorn permutation variational marginal inference. In Symposium on Advances in Approximate Bayesian Inference (pp. 1-9). PMLR.
2019
Varol, E., Nejatbakhsh, A., & McGrory, C. (2019). Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution. arXiv preprint arXiv:1912.03463.
Varol, E., & Nejatbakhsh, A. (2019). Wasserstein total variation filtering. arXiv preprint arXiv:1910.10822.
2018
Varol, E., Sotiras, A., Zeng, K., & Davatzikos, C. (2018, September). Generative discriminative models for multivariate inference and statistical mapping in medical imaging. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 540-548). Springer, Cham.
Varol, E., Sotiras, A., & Davatzikos, C. (2018). MIDAS: regionally linear multivariate discriminative statistical mapping. NeuroImage, 174, 111-126.
Varol, E., Sotiras, A., & Davatzikos, C. (2018, April). Regionally discriminative multivariate statistical mapping. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 1560-1563). IEEE.
2017
Dong, A., Toledo, J. B., Honnorat, N., Doshi, J., Varol, E., Sotiras, A., ... & Alzheimer’s Disease Neuroimaging Initiative. (2017). Heterogeneity of neuroanatomical patterns in prodromal Alzheimer’s disease: links to cognition, progression and biomarkers. Brain, 140(3), 735-747.
Varol, E., Sotiras, A., Davatzikos, C., & Alzheimer's Disease Neuroimaging Initiative. (2017). HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage, 145, 346-364.
2016 and older
Varol, E., Sotiras, A., & Davatzikos, C. (2016, October). Structured outlier detection in neuroimaging studies with minimal convex polytopes. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 300-307). Springer, Cham.
Allen, G. I., Amoroso, N., Anghel, C., Balagurusamy, V., Bare, C. J., Beaton, D., ... & Alzheimer's Disease Neuroimaging Initiative. (2016). Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease. Alzheimer's & Dementia, 12(6), 645-653.
Gross, P., Honnorat, N., Varol, E., Wallner, M., Trappanese, D. M., Sharp, T. E., ... & Houser, S. R. (2016). Nuquantus: Machine learning software for the characterization and quantification of cell nuclei in complex immunofluorescent tissue images. Scientific reports, 6(1), 1-12.
Sotiras, A., Gaonkar, B., Eavani, H., Honnorat, N., Varol, E., Dong, A., & Davatzikos, C. (2016). Machine learning as a means toward precision diagnostics and prognostics. In Machine learning and medical imaging (pp. 299-334). Academic Press.
Varol, E., Sotiras, A., & Davatzikos, C. (2015, October). Disentangling disease heterogeneity with max-margin multiple hyperplane classifier. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 702-709). Springer, Cham.
Varol, E., & Davatzikos, C. (2014, September). Supervised block sparse dictionary learning for simultaneous clustering and classification in computational anatomy. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 446-453). Springer, Cham.
Varol, E., Gaonkar, B., & Davatzikos, C. (2013, April). Classifying medical images using morphological appearance manifolds. In 2013 IEEE 10th International Symposium on Biomedical Imaging (pp. 744-747). IEEE.
Varol, E., Gaonkar, B., Erus, G., Schultz, R., & Davatzikos, C. (2012, May). Feature ranking based nested support vector machine ensemble for medical image classification. In 2012 9Th IEEE international symposium on biomedical imaging (ISBI) (pp. 146-149). IEEE.
Funding sources
K99/R00 Award: Transcriptional basis of neural architectures (1K99MH128772-01A1) (2022-2027)
Job openings
Ph.D. student openings
Requirements: Qualified Ph.D. student candidates should have strong computational skills and a Bachelor of Science degree (or equivalent) from backgrounds such as computer science, electrical engineering, statistics and biomedical engineering as well as an interest in applied neuroscience problems.
Application process: Applications will need to be submitted to NYU Tandon School of Engineering CS&E Ph.D. program online portal, denoting the preference to work with Dr. Varol’s group. Ph.D. program application deadline for Fall matriculation is usually Dec. 1 of the previous academic year.
Postdoctoral fellow openings
Project area 1: Bioinformatics, connectomics, statistics and machine learning
Project area 2: Brain computer interface, neural data science, signal processing
Requirements: Qualifications include a strong research portfolio in computational neuroscience, statistical neuroscience, machine learning, or a related field. Backgrounds in analyzing large neural and genomics datasets, modeling complex neural systems, and state-of-the-art machine learning and computer vision are preferred. These positions are highly interdisciplinary with the opportunity to work with collaborators at NYU Langone medical school, Center for Neural Science and the New York Genome Center. Applicants should have a PhD in Computer Science, Electrical Engineering, Statistics, Bioinformatics, Machine Learning, Physics, Applied Mathematics, or Computational Neuroscience.
Appointment: The initial appointments will be for one year, and are renewable. Salaries will be set based on experience and skills.
Application process: Applicants should apply on interfolio (apply.interfolio.com/143893) and provide a package that includes the following documents in pdf form:
1. a one-page description of past research experience
2. a one-page description of future research interests and goals
3. CV
4. two letters of reference