Reza Abbasi-Asl

The paper identifies individualized brain-body signatures of positive emotions using multimodal neurophysiological data and finds that emotions are represented by person-specific multimodal ensembles.

The paper constructs a large-scale directed functional circuit map in mouse visual cortex using calcium imaging and causal inference, showing alignment with synaptic connectivity across multiple scales.

This study investigates the neurophysiological correlates of awe using EEG, finding increased complexity in neural signals during awe experiences, though with limited generalization to other datasets.

This paper introduces CellTransformer, a self-supervised framework for detecting fine-grained spatial domains in mouse brain spatial transcriptomics data, achieving high consistency across large datasets.

This study investigates how motor learning in Parkinson's Disease patients involves coordinated low-frequency cortico-basal ganglia activity to optimize preplanning of motor sequences.

The paper identifies neural assemblies consistent with Hebb's theory using a novel dataset linking optical physiology to connectivity, finding they are structurally integrated but not defined by strong monosynaptic connections.

This study shows that model compression improves the interpretability and stability of voxel-wise CNN models for brain activity prediction without sacrificing accuracy.

This paper identifies stable spatial gene expression patterns in the mouse brain using staNMF, revealing new regions and outperforming PCA and clustering methods.

The paper develops a multi-scale model of mouse primary visual cortex integrating structural and functional data to identify cell-class specific connectivity rules.

The paper determines the depth limit of 2-photon imaging in mouse brain tissue by comparing 2- and 3-photon excitation, finding accuracy ceases at 450 m where in- and out-of-focus fluorescence balance.

The paper develops a biophysically detailed in silico model of layer 4 of the mouse primary visual cortex, showing how functional synaptic connectivity reproduces experimental observations of neuronal responses to visual stimuli.

The paper introduces DeepTune, a framework to interpret neural network models of V4 neurons, revealing diverse shape/texture tuning and suppressive responses in primate visual cortex.