BrainSpan Atlas of Developing Human Brain

Related lectures (30)

Covers the data available from the Allen human brain atlases, including gene expression analysis and the BrainSpan Atlas.

Covers the tutorial on performing a search in the Allen Brain Atlas, exploring gene expression data.

Offers a tutorial on interpreting gene expression heat maps from the Allen Institute.

Explains transcriptomics, RNA-seq workflow, and gene expression analysis for disease biomarkers.

Discusses the diversity of neocortical neurons, challenges in dissociating human brain cells, and the evolution of expanded neocortex across mammals.

Covers methods to identify transcriptomic types and classify bipolar cells using computational techniques and molecular profiling.

Covers the Allen Mouse Brain Atlas, brain regions, gene expression data, and exploration tools.

Explores the application of neuroanatomy to autism, focusing on Purkinje cells and intercellular gaps.

Explores single-cell RNA-seq, tissue dissociation, capture methods, and differential gene expression analysis.

Introduces the Anatomic Gene Expression Atlas (AGEA) for exploring gene expression patterns in the mouse brain.

Explores a mathematical model of the brain, discussing dynamics, equilibria, orientation selectivity, and cortical network models.

Explores ribosome profiling techniques, chemical applications, and translational insights in gene-specific features and behavioral responses.

Covers gene expression, protein life cycle, and genetic code in neuroscience.

Explores the overview, rationale, and strategies of simulation neuroscience, emphasizing the challenges of reconstructing and simulating the brain.

Explores the Allen Institute Mouse Brain data collection and development processes.

Covers combining electrical, morphological, and molecular profiles to study single neurons.

Covers gene expression data quantification, genome analysis, and large-scale studies.

Provides an overview of neuroanatomy, focusing on the central nervous system structures and neuron types.

On Single-cell Temporal-Omics explores inferring transcriptional dynamics from snapshot data and interpreting RNA velocity in RNA metabolism.

Covers the structure and functions of nucleic acids, including genetic information storage and gene expression.

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