Morphological heterogeneity of layer VI neurons in mouse barrel cortex

• Published in: Journal of comparative neurology (1911) Vol. 512; no. 6; pp. 726 - 746
• Main Authors: Chen, Chia-Chien, Abrams, Svetlana, Pinhas, Alex, Brumberg, Joshua C.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 20.02.2009
• Subjects: Animals; barrel cortex; Cell Shape; Cluster Analysis; Dendrites - ultrastructure; Female; Golgi; Male; Mice; Microscopy - instrumentation; Microscopy - methods; neocortex layer VI; neuronal morphology; Neurons - classification; Neurons - cytology; Principal Component Analysis; Somatosensory Cortex - cytology
• ISSN: 0021-9967; 1096-9861
• DOI: 10.1002/cne.21926

Understanding the basic neuronal building blocks of the neocortex is a necessary first step toward comprehending the composition of cortical circuits. Neocortical layer VI is the most morphologically diverse layer and plays a pivotal role in gating information to the cortex via its feedback connection to the thalamus and other ipsilateral and callosal corticocortical connections. The heterogeneity of function within this layer is presumably linked to its varied morphological composition. However, so far, very few studies have attempted to define cell classes in this layer using unbiased quantitative methodologies. Utilizing the Golgi staining technique along with the Neurolucida software, we recontructed 222 cortical neurons from layer VI of mouse barrel cortex. Morphological analyses were performed by quantifying somatic and dendritic parameters, and, by using principal component and cluster analyses, we quantitatively categorized neurons into six distinct morphological groups. Additional systematic replication on a separate population of neurons yielded similar results, demonstrating the consistency and reliability of our categorization methodology. Subsequent post hoc analyses of dendritic parameters supported our neuronal classification scheme. Characterizing neuronal elements with unbiased quantitative techniques provides a framework for better understanding structure–function relationships within neocortical circuits in general. J. Comp. Neurol. 512:726–746, 2009. © 2008 Wiley‐Liss, Inc.