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Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis.

Gillette TA, Ascoli GA - BMC Bioinformatics (2015)

Bottom Line: Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees.In addition, pyramidal apical dendrites reveal a distinct motif profile.The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. todd.gillette@gmail.com.

ABSTRACT

Background: The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns.

Results: Using all possible branching topologies for comparison we show that the relative abundance of short patterns of up to three bifurcations, together with overall tree size, effectively capture the local branching patterns of neurons. Dendrites and axons display broadly similar topological motifs (over-represented patterns) and anti-motifs (under-represented patterns), differing most in their proportions of bifurcations with one terminal branch and in select sub-sequences of three bifurcations. In addition, pyramidal apical dendrites reveal a distinct motif profile.

Conclusions: The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.

No MeSH data available.


Related in: MedlinePlus

Converting tree to sequence. a. Bifurcation nodes are encoded as characters based on whether their child branches bifurcate or terminate. Arborization (A) nodes have two bifurcating children. Continuation (C) nodes have one bifurcating and one terminating child. Termination (T) nodes have two terminating children. b. Nodes are traversed depth-first starting from the smaller side which optimally preserves locality. c. Hippocampal pyramidal cell apical (green) and basal (blue) dendrograms and morphologies are shown (NMO_00191 from [62]), with enlargement of a portion of the apical dendrite (right) and coloring in the sequence. Node types are colored and numbered by their order in the sequence starting with the first node in the subtree. d. The entire pyramidal cell morphology is shown (top), with dendrogram (bottom) and sequence representations (background) of the axonal arbor (magenta) (NMO_07897 from [63]).
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Fig1: Converting tree to sequence. a. Bifurcation nodes are encoded as characters based on whether their child branches bifurcate or terminate. Arborization (A) nodes have two bifurcating children. Continuation (C) nodes have one bifurcating and one terminating child. Termination (T) nodes have two terminating children. b. Nodes are traversed depth-first starting from the smaller side which optimally preserves locality. c. Hippocampal pyramidal cell apical (green) and basal (blue) dendrograms and morphologies are shown (NMO_00191 from [62]), with enlargement of a portion of the apical dendrite (right) and coloring in the sequence. Node types are colored and numbered by their order in the sequence starting with the first node in the subtree. d. The entire pyramidal cell morphology is shown (top), with dendrogram (bottom) and sequence representations (background) of the axonal arbor (magenta) (NMO_07897 from [63]).

Mentions: The possible ways to encode a neurite as a sequence are numerous. As the first and simplest approach, we used local topology alone for the encoding of bifurcation nodes. Specifically, bifurcations are encoded on the basis of whether their child branches lead to bifurcations or terminations. Bifurcations in which both child branches themselves bifurcate are encoded with the letter ‘A’ (for “arborizing”). Bifurcations with one bifurcating child and one terminating child are encoded as ‘C’ as the tree “continues” without adding a new subtree. Bifurcations with two terminating children are encoded as ‘T’ (Figure 1a). These definitions are equivalent to those used in vertex analysis [33] with the A, C, and T bifurcation types referred to as tertiary, secondary, and primary nodes. Note that terminal branches, though not explicitly encoded, are fully accounted for in this method.Figure 1


Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis.

Gillette TA, Ascoli GA - BMC Bioinformatics (2015)

Converting tree to sequence. a. Bifurcation nodes are encoded as characters based on whether their child branches bifurcate or terminate. Arborization (A) nodes have two bifurcating children. Continuation (C) nodes have one bifurcating and one terminating child. Termination (T) nodes have two terminating children. b. Nodes are traversed depth-first starting from the smaller side which optimally preserves locality. c. Hippocampal pyramidal cell apical (green) and basal (blue) dendrograms and morphologies are shown (NMO_00191 from [62]), with enlargement of a portion of the apical dendrite (right) and coloring in the sequence. Node types are colored and numbered by their order in the sequence starting with the first node in the subtree. d. The entire pyramidal cell morphology is shown (top), with dendrogram (bottom) and sequence representations (background) of the axonal arbor (magenta) (NMO_07897 from [63]).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4496917&req=5

Fig1: Converting tree to sequence. a. Bifurcation nodes are encoded as characters based on whether their child branches bifurcate or terminate. Arborization (A) nodes have two bifurcating children. Continuation (C) nodes have one bifurcating and one terminating child. Termination (T) nodes have two terminating children. b. Nodes are traversed depth-first starting from the smaller side which optimally preserves locality. c. Hippocampal pyramidal cell apical (green) and basal (blue) dendrograms and morphologies are shown (NMO_00191 from [62]), with enlargement of a portion of the apical dendrite (right) and coloring in the sequence. Node types are colored and numbered by their order in the sequence starting with the first node in the subtree. d. The entire pyramidal cell morphology is shown (top), with dendrogram (bottom) and sequence representations (background) of the axonal arbor (magenta) (NMO_07897 from [63]).
Mentions: The possible ways to encode a neurite as a sequence are numerous. As the first and simplest approach, we used local topology alone for the encoding of bifurcation nodes. Specifically, bifurcations are encoded on the basis of whether their child branches lead to bifurcations or terminations. Bifurcations in which both child branches themselves bifurcate are encoded with the letter ‘A’ (for “arborizing”). Bifurcations with one bifurcating child and one terminating child are encoded as ‘C’ as the tree “continues” without adding a new subtree. Bifurcations with two terminating children are encoded as ‘T’ (Figure 1a). These definitions are equivalent to those used in vertex analysis [33] with the A, C, and T bifurcation types referred to as tertiary, secondary, and primary nodes. Note that terminal branches, though not explicitly encoded, are fully accounted for in this method.Figure 1

Bottom Line: Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees.In addition, pyramidal apical dendrites reveal a distinct motif profile.The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. todd.gillette@gmail.com.

ABSTRACT

Background: The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns.

Results: Using all possible branching topologies for comparison we show that the relative abundance of short patterns of up to three bifurcations, together with overall tree size, effectively capture the local branching patterns of neurons. Dendrites and axons display broadly similar topological motifs (over-represented patterns) and anti-motifs (under-represented patterns), differing most in their proportions of bifurcations with one terminal branch and in select sub-sequences of three bifurcations. In addition, pyramidal apical dendrites reveal a distinct motif profile.

Conclusions: The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.

No MeSH data available.


Related in: MedlinePlus