Carey E. Priebe

Measuring the functional complexity of nanoscale connectomes: polarity matters

The paper develops a statistic called XORness to measure functional complexity in nanoscale connectomes and shows it is high in both real and generated connectomes with similar network statistics.

Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome

The paper uses generative network models to analyze bilateral symmetry in a Drosophila larval brain connectome, identifying significant differences in connection probabilities and proposing adjusted definitions of symmetry.

The connectome of an insect brain

The paper maps the synaptic-resolution connectome of a Drosophila larva brain, identifying neuron types, circuit motifs, and structural features.

Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes

This paper introduces a modified graph matching algorithm for better pairing of bilaterally homologous neurons in connectomes by leveraging contralateral connections.

A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis

This paper introduces a low-resource pipeline for estimating connectomes from MRI data, demonstrating its reliability across diverse datasets without manual intervention.

Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics

This paper introduces a new statistic called discriminability to improve replicability by distinguishing accidental from systematic deviations in data, showing its effectiveness in both simulated and real datasets.

Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding

This paper introduces a method to classify neurons based on their connectivity using a stochastic block model and spectral embedding, achieving accurate clustering even with noise and edge swaps.

Modern Machine Learning: Partition & Vote

This paper explains decision forests and deep networks as partition and vote schemes, linking them to statistical pattern recognition and brain functioning.

Heritability of the Mouse Brain Connectome

This study examines the heritability of mouse brain connectomes using high-resolution MRI and finds high heritability in brain volumes and diffusion metrics.

Connectal Coding: Discovering the Structures Linking Cognitive Phenotypes to Individual Histories

The paper introduces a formal statistical framework for connectal coding to link cognitive phenotypes to individual histories across different experimental modalities and phylogeny.

A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability

The paper develops a pipeline to estimate connectomes from neuroimaging data, showing high consistency within individuals but significant variability across studies and sites.

The Complete Connectome Of A Learning And Memory Center In An Insect Brain

The paper reconstructs the complete synaptic connectome of the Drosophila larval mushroom body, revealing novel circuit structures and input integration patterns in Kenyon cells.