Erdem Varol

The paper links in vivo functional dynamics to molecular identity in hippocampal inhibitory circuits using single-cell transcriptomics and two-photon imaging.

The paper introduces 2P-NucTag, a method to label and profile functionally distinct neurons in vivo, revealing gene expression differences between place and silent hippocampal pyramidal neurons.

This paper improves neural decoding by exploiting correlations across trials and sessions, showing better accuracy for four behaviors in mice using interpretable models.

This study creates a gene expression atlas of C. elegans L1 neurons, identifying differentially expressed genes and regulatory networks involved in postembryonic neurogenesis and maturation.

CellSpliceNet predicts alternative splicing outcomes in C. elegans neurons by integrating multiple data modalities and achieving high predictive accuracy.

The paper identifies nine neuroimaging endophenotypes linked to genetic loci and shows they improve prediction of systemic diseases and mortality.

DREDge improves motion correction in high-density extracellular recordings across species, enhancing reliability and reducing errors in electrophysiological data.

The paper develops ConnectionMiner to integrate scRNAseq and connectome data, revealing gene expression patterns linked to synaptic connectivity in Drosophila leg motor circuits.

The paper integrates bulk and single-cell RNA-seq data from C. elegans neurons to improve transcript detection and differential gene analysis.

WormID-Bench evaluates methods for neuron detection, identification, and tracking in C. elegans using a multi-laboratory dataset, highlighting the need for improved computational approaches.

The paper presents an atlas of alternative splicing in C. elegans neurons, identifies novel splicing events, and describes regulatory patterns and networks.

The paper examines inhibitory interneuron dynamics in 22q11.2 deletion mutant mice during spatial tasks, finding subtype-specific disruptions in activity and microcircuit organization.

This paper introduces a spike sorting-free decoding method using density-based modeling to improve neural decoding performance without explicitly clustering spikes.

This paper introduces DREDge, an algorithm for robust online drift estimation in electrophysiology data, improving scalability and temporal resolution for human and mouse recordings.

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

The paper integrates bulk and single-cell RNA-seq data from C. elegans neurons to improve transcript detection and differential gene analysis.

This paper introduces MAGIC, a multi-scale semi-supervised clustering method for brain imaging data to identify disease subtypes, and evaluates its performance on simulated and clinical datasets.

This paper introduces methods for 3D spike localization and motion correction in Neuropixels recordings, improving spike sorting and neural activity tracking.

The paper uses NeuroPAL to visualize and analyze the differentiation of male-specific neurons in C. elegans, showing coordinated terminal differentiation in the fourth larval stage.

The paper provides a comprehensive gene expression profile of all 302 neurons in the C. elegans nervous system and identifies cis-regulatory elements and adhesion proteins involved in synaptic specificity.

The paper introduces NeuroPAL, a multicolor transgene for neuronal identification in C. elegans, and demonstrates its use in mapping neurotransmitter receptors, cell fate changes, and brain activity.

This study identifies three neurocognitive subtypes of depression in youth using machine learning, showing distinct neural signatures despite similar clinical symptoms.

This paper introduces a deformable non-negative matrix factorization method to extract neural signals from moving animals, outperforming traditional methods on simulated and real data.

This study identifies two neurobiologically distinct subtypes of youth with internalizing symptoms using structural brain imaging and machine learning.