Xilin Shen

The paper develops a data-driven method to identify individualized disordered connectivity patterns from fMRI data, revealing diverse neurobiological mechanisms underlying transdiagnostic psychiatric symptoms.

This paper introduces a feedback approach to connectome-based predictive modeling to identify which brain systems standardized tests and clinical symptoms reflect, enabling better-targeted test development.

The paper investigates the neural origins of quasi-periodic patterns in resting-state fMRI using combined calcium imaging and fMRI, finding a time-locked correlation between neural activity and BOLD signals.

This paper introduces a biphasic acclimation protocol for simultaneous wide-field fluorescent Ca2+ imaging and fMRI in awake mice, showing convergent and divergent patterns in brain function between modalities.

This paper improves brain connectome analysis by incorporating global and local features, enhancing individual identification and phenotype prediction.

The paper uses connectome-based predictive modeling to show that circuits related to clinical symptoms and cognitive deficits are localized in specific brain networks.

The paper uses simultaneous calcium imaging and fMRI in mice to identify cell-type specific signals in the brain with high spatiotemporal resolution.

This paper develops a geometrically constrained framework to map brain regions across species and shows geometry influences regional organization through patterning molecules.

The paper uses whole-brain functional connectivity to predict regional tau PET in preclinical Alzheimer's disease, finding it outperforms amyloid PET models and generalizes to a clinical dataset in tau-elevated individuals.

This study investigates the effects of repetitive mild closed-head injury on synaptic density and BOLD-fMRI signal homogeneity in mice, finding decreased synapse density and increased regional homogeneity.

The paper uses Ca2+ imaging and fMRI-BOLD in mice to compare functional network organization, finding overlapping but distinct patterns between modalities.

The paper uses fMRI to track functional connectivity changes in a mouse model of Alzheimer's disease over time and shows that mGluR5 modulator treatment reverses these deficits.

The paper uses connectome-based predictive modeling to show sex differences in brain network predictors of memory performance, with women relying more on the default mode network and men on visual networks.

The paper examines sex differences in default mode network connectivity across aging adults, finding age-related changes in women that resemble preclinical Alzheimer's disease patterns.

The paper introduces a pipeline for analyzing widefield calcium imaging data and shows how functional network properties differ with wakefulness and across cell types.

This study develops a whole-brain functional connectivity model using fMRI data to predict individual differences in long-term memory, showing it is distinct from working memory networks.

The paper shows that functional brain atlases change depending on brain state, using fMRI data from one subject and two replication datasets.

The paper develops a method to simultaneously image local microcircuits and whole cortical activity in awake mice, and uses it to study neuron associations with network motifs.

This paper combines mesoscopic Ca2+ imaging and fMRI to study brain activity across spatiotemporal scales, finding correspondence between signals and stable connectivity matrices.

This study uses connectome-based predictive modeling on fMRI data to identify functional brain networks that predict social ability measures in autism spectrum disorder.

The paper shows that individual functional connectomes, derived from resting-state fMRI, are unique and stable over months to years across different age groups.