About

Erdem Varol is an assistant professor at the Department of Computer Science & Engineering at NYU Tandon School of Engineering. He heads the Neuroinformatics Lab, which focuses on developing next-generation signal processing and analysis tools for neuroscience data. His research interests lie at the intersection of computer science, neuroscience, and genomics, with particular emphasis on building causal models that explain neural network connectivity in human and animal brains as a function of genetic signatures. Varol's work involves integrating gene expression data with connectomics to understand how neurons wire themselves into functional circuits, exemplified by his development of the computational tool ConnectionMiner. This tool combines gene expression and wiring data to infer neuronal identities and predict synaptic connectivity, providing insights into the molecular mechanisms underlying neural circuit assembly. His research aims to decode brain connectivity, understand neural development, and inform studies of neurodevelopmental disorders, leveraging artificial intelligence and biological data to advance neuroscience and related fields.

Research topics

• Neuroscience
• Biology
• Genetics
• Computational biology
• Psychiatry
• Psychology
• Cognitive science
• Cognitive psychology

Selected publications

• A transcriptomic axis aligns with in vivo functional dynamics in hippocampal inhibitory circuits

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-08 · 1 citations

  articleOpen access

  Abstract Linking molecular identity to function in vivo at single-cell resolution remains an outstanding challenge in neuroscience. Here, we bridge this gap in the mouse hippocampus with an end-to-end pipeline of cell-resolved two-photon imaging and spatial transcriptomics. CA1 interneurons exhibiting heterogeneous physiological responses during a virtual-reality navigation task were post hoc clustered by gene expression into 5 GABAergic subclasses and 14 types. Physiological responses of individual cells aligned with a transcriptomic axis, and a classifier trained on physiological features alone recovered the same ordered organization. Our approach establishes a direct, scalable framework for linking in vivo circuit dynamics to constituent cell identity, revealing a transcriptomic axis that encompasses the structural and functional diversity of hippocampal inhibitory neurons. One-Sentence Summary Tracking neurons from behavior to spatial transcriptomics links in vivo function to molecular identity in the hippocampus.

  PublisherOA PDFDOI

• Integrating bulk and single cell RNA-seq refines transcriptomic profiles of individual C. elegans neurons

  eLife · 2026-01-05 · 1 citations

  articleOpen access

  Neuron-specific morphology and function are fundamentally tied to differences in gene expression across the nervous system. We previously generated a single cell RNA-seq (scRNA-Seq) dataset for every anatomical neuron class in the C. elegans hermaphrodite. Here we present a complementary set of bulk RNA-seq samples for 52 of the 118 canonical neuron classes in C. elegans. We show that the bulk RNA-seq dataset captures both lowly expressed and noncoding RNAs that are not detected in the scRNA-Seq profile, but also includes false positives due to contamination by other cell types. We present an analytical strategy that integrates the two datasets, preserving both the specificity of scRNA-Seq data and the sensitivity of bulk RNA-Seq. We show that this integrated dataset enhances the sensitivity and accuracy of transcript detection and differential gene analysis. In addition, we show that the bulk RNA-Seq data set detects differentially expressed non-coding RNAs across neuron types, including multiple families of non-polyadenylated transcripts. We propose that our approach provides a new strategy for interrogating gene expression by bridging the gap between bulk and single cell methodologies for transcriptomic studies. We suggest that these datasets advance the goal of delineating the mechanisms that define morphology and connectivity in the nervous system.

  PublisherDOI

• 2P-NucTag: On-demand phototagging for molecular analysis of functionally identified cortical neurons

  Neuron · 2026-04-01

  article
  PublisherDOI

• Sex‐specific patterns of a machine learning‐derived Alzheimer's brain atrophy imaging signature in participants without diagnosed cognitive impairment: A multi‐cohort study

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  Abstract Background We investigated sex differences in a machine learning‐derived imaging signature of AD brain atrophy (i.e., SPARE‐AD 5 ), in relation to age, genetic factors ( APOE ε4 allele), and multi‐organ biological age gap (BAG 2,3 ). Methods Data from the iSTAGING and MULTI consortia included 53,622 participants without diagnosed cognitive impairment (mean age: 61.8 ± 12.6 years; 54% women). The SPARE‐AD model uses a support vector machine with a linear kernel to distinguish between cognitively normal individuals and those with AD 5 . Generalized linear models assessed sex differences and nine BAG associations with SPARE‐AD, adjusting for age, sex, APOE ε4, and interactions, and analysis of covariance (ANCOVA) with Tukey's test to assess differences in SPARE‐AD scores between APOE ε4 allele carrier groups. Results Overall, SPARE‐AD increased with age (β = 0.018, p < 2e‐16). Women had higher SPARE‐AD scores than men (β = ‐0.393, p < 2e‐16). Women had higher SPARE‐AD scores at younger ages but lower values at older ages (β = 0.006, p < 2e‐16 for the age‐sex interaction term) when compared to males (Figure 1a). Furthermore, SPARE‐AD was positively associated with the number of APOE ε4 alleles (β = 0.018, p = 1.06e‐6). Non‐carriers and heterozygous carriers of the APOE ε4 allele exhibited lower SPARE‐AD scores compared to homozygous carriers in analyses of both combined sexes and in men alone; this pattern was not observed in women (Figure 1b‐d). Among the nine BAGs, the brain BAG was most strongly associated with SPARE‐AD in both sexes combined (β = 0.018, p = 1.09e‐302) (Figure 2a) and separately (women: β = 0.017, p = 5.08e‐128; men: β = 0.019, p = 4.24e‐175) (Figure 2b‐c). Other significant BAG associations were observed in men and not in women, including musculoskeletal (β = 0.004, p = 0.02), immune (β = 0.004, p = 0.02), and metabolic BAGs (β = 0.005, p = 0.02) (Figure 2b‐d). Conclusion SPARE‐AD scores increased with age and were higher in women at younger ages but lower than men at older ages, with a significant age*sex interaction, and were positively associated with the number of the APOE ε4 allele, particularly in men.

  PublisherOA PDFDOI

• Integrating bulk and single cell RNA-seq refines transcriptomic profiles of individual <i>C. elegans</i> neurons

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-27

  preprintOpen access

  Neuron-specific morphology and function are fundamentally tied to differences in gene expression across the nervous system. We previously generated a single cell RNA-seq (scRNA-Seq) dataset for every anatomical neuron class in the C. elegans hermaphrodite. Here we present a complementary set of bulk RNA-seq samples for 52 of the 118 canonical neuron classes in C. elegans . We show that the bulk RNA-seq dataset captures both lowly expressed and noncoding RNAs that are not detected in the scRNA-Seq profile, but also includes false positives due to contamination by other cell types. We present an analytical strategy that integrates the two datasets, preserving both the specificity of scRNA-Seq data and the sensitivity of bulk RNA-Seq. We show that this integrated dataset enhances the sensitivity and accuracy of transcript detection and differential gene analysis. In addition, we show that the bulk RNA-Seq data set detects differentially expressed non-coding RNAs across neuron types, including multiple families of non-polyadenylated transcripts. We propose that our approach provides a new strategy for interrogating gene expression by bridging the gap between bulk and single cell methodologies for transcriptomic studies. We suggest that these datasets advance the goal of delineating the mechanisms that define morphology and connectivity in the nervous system.

  PublisherDOI

• Integrating bulk and single cell RNA-seq refines transcriptomic profiles of individual C. elegans neurons

  eLife · 2025-04-11 · 2 citations

  preprintOpen access

  Neuron-specific morphology and function are fundamentally tied to differences in gene expression across the nervous system. We previously generated a single cell RNA-seq (scRNA-Seq) dataset for every anatomical neuron class in the C. elegans hermaphrodite. Here we present a complementary set of bulk RNA-seq samples for 52 of the 118 canonical neuron classes in C. elegans. We show that the bulk RNA-seq dataset captures both lowly expressed and noncoding RNAs that are not detected in the scRNA-Seq profile, but also includes false positives due to contamination by other cell types. We present an analytical strategy that integrates the two datasets, preserving both the specificity of scRNA-Seq data and the sensitivity of bulk RNA-Seq. We show that this integrated dataset enhances the sensitivity and accuracy of transcript detection and differential gene analysis. In addition, we show that the bulk RNA-Seq data set detects differentially expressed non-coding RNAs across neuron types, including multiple families of non-polyadenylated transcripts. We propose that our approach provides a new strategy for interrogating gene expression by bridging the gap between bulk and single cell methodologies for transcriptomic studies. We suggest that these datasets advance the goal of delineating the mechanisms that define morphology and connectivity in the nervous system.

  PublisherOA PDFDOI

• Human single neural and columnar dynamics recorded by intraoperative high-density penetrating electrodes

  Neuroscience Applied · 2025-01-01

  articleOpen access
  PublisherDOI

• Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders

  Nature Biomedical Engineering · 2025-06-06 · 10 citations

  articleOpen access
  PublisherOA PDFDOI

• Integrating bulk and single cell RNA-seq refines transcriptomic profiles of individual C. elegans neurons

  eLife · 2025-04-11 · 3 citations

  preprintOpen access

  Abstract Neuron-specific morphology and function are fundamentally tied to differences in gene expression across the nervous system. We previously generated a single cell RNA-seq (scRNA-Seq) dataset for every anatomical neuron class in the C. elegans hermaphrodite. Here we present a complementary set of bulk RNA-seq samples for 52 of the 118 canonical neuron classes in C. elegans. We show that the bulk RNA-seq dataset captures both lowly expressed and noncoding RNAs that are not detected in the scRNA-Seq profile, but also includes false positives due to contamination by other cell types. We present an analytical strategy that integrates the two datasets, preserving both the specificity of scRNA-Seq data and the sensitivity of bulk RNA-Seq. We show that this integrated dataset enhances the sensitivity and accuracy of transcript detection and differential gene analysis. In addition, we show that the bulk RNA-Seq data set detects differentially expressed non-coding RNAs across neuron types, including multiple families of non-polyadenylated transcripts. We propose that our approach provides a new strategy for interrogating gene expression by bridging the gap between bulk and single cell methodologies for transcriptomic studies. We suggest that these datasets advance the goal of delineating the mechanisms that define morphology and connectivity in the nervous system.

  PublisherDOI

• WormID-Bench: A Benchmark for Whole-Brain Activity Extraction in <i>C. elegans</i>

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-07 · 2 citations

  preprintOpen access

  Abstract The nematode C. elegans is a premier model organism for studying neural circuit function due to its fully mapped connectome and genetically identifiable neurons. Recent advances in 3D light microscopy and fluorescent protein tagging have enabled whole-brain imaging at single-neuron resolution. However, extracting meaningful neural dynamics from these high-resolution recordings requires addressing three fundamental challenges: (i) accurate detection of individual neurons in fluorescence images, (ii) precise identification of neuron classes based on anatomical and colorimetric cues, and (iii) robust tracking of neurons over time in calcium imaging videos. To systematically evaluate these challenges, we introduce WormID-Bench, a large-scale, multi-laboratory dataset comprising 118 worms from five distinct research groups, along with standardized evaluation metrics for detection, identification, and tracking. Our benchmark reveals that existing computational approaches show substantial room for improvement in sensitivity, specificity, and generalization across diverse experimental conditions. By providing an open and reproducible benchmarking framework 1 , WormID-Bench aims to accelerate the development of high-throughput and scalable computational tools for whole-brain neural dynamics extraction in C. elegans , setting the stage for broader advancements in functional connectomics.

  PublisherOA PDFDOI

Frequent coauthors

• Aristeidis Sotiras

  67 shared

• Christos Davatzikos

  University of Pennsylvania

  63 shared

• Chuanjun Zhuo

  Nankai University

  61 shared

• Benedicto Crespo‐Facorro

  Centro de Investigación Biomédica en Red de Salud Mental

  57 shared

• Theodore D. Satterthwaite

  Children's Hospital of Philadelphia

  43 shared

• Stephen J. Wood

  43 shared

• Ganesh B. Chand

  California University of Pennsylvania

  43 shared

• Ruben C. Gur

  Children's Hospital of Philadelphia

  41 shared

Awards & honors

• NIH/NIMH K99/R00 award (2022)