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Directed network motifs in Alzheimer's disease and mild cognitive impairment.

Friedman EJ, Young K, Tremper G, Liang J, Landsberg AS, Schuff N, Alzheimer's Disease Neuroimaging Initiati - PLoS ONE (2015)

Bottom Line: This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains.Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV).Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD.

View Article: PubMed Central - PubMed

Affiliation: International Computer Science Institute, Berkeley, CA, United States of America; Department of Computer Science, University of California, Berkeley, Berkeley, CA, United States of America.

ABSTRACT
Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer's disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer's disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer's disease.

No MeSH data available.


Related in: MedlinePlus

Representative motifs.Representative 3-node motifs (and one 4-node directed motifs). The motif ID numbers are those used by the Kavosh software (Kashani et al. 2009). Note that the motif ID numbers for different values of k can be duplicates, i.e. the same motif number can refer to both a 2-motif and a 3-motif.
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pone.0124453.g004: Representative motifs.Representative 3-node motifs (and one 4-node directed motifs). The motif ID numbers are those used by the Kavosh software (Kashani et al. 2009). Note that the motif ID numbers for different values of k can be duplicates, i.e. the same motif number can refer to both a 2-motif and a 3-motif.

Mentions: Our analysis focuses on directed motifs, a sensitive indicator of the local structure in directed networks [8,9,22]. A k-motif is a small, connected, k-node sub-network of the original network, as shown in Fig 3. For example, in a directed network there can be at most 13 distinct directed 3-motifs and 199 distinct directed 4-motifs, several of which are shown in Fig 4. To find the motifs in a network we used the Kavosh software package [23], which exhaustively computes the numbers of each type of k-motif in a network (here we employ the same motif labeling scheme used in that paper). At the top level, this algorithm is quite simple. It finds all subnetworks of size k and then puts them into groups with the same network structure. The details are much more complex, as Kavosh uses sophisticated tree-based data structures to find all subnetworks efficiently and then applies a state of the art algorithm (NAUTY) based on canonical labeling to compare subnetworks.


Directed network motifs in Alzheimer's disease and mild cognitive impairment.

Friedman EJ, Young K, Tremper G, Liang J, Landsberg AS, Schuff N, Alzheimer's Disease Neuroimaging Initiati - PLoS ONE (2015)

Representative motifs.Representative 3-node motifs (and one 4-node directed motifs). The motif ID numbers are those used by the Kavosh software (Kashani et al. 2009). Note that the motif ID numbers for different values of k can be duplicates, i.e. the same motif number can refer to both a 2-motif and a 3-motif.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4400037&req=5

pone.0124453.g004: Representative motifs.Representative 3-node motifs (and one 4-node directed motifs). The motif ID numbers are those used by the Kavosh software (Kashani et al. 2009). Note that the motif ID numbers for different values of k can be duplicates, i.e. the same motif number can refer to both a 2-motif and a 3-motif.
Mentions: Our analysis focuses on directed motifs, a sensitive indicator of the local structure in directed networks [8,9,22]. A k-motif is a small, connected, k-node sub-network of the original network, as shown in Fig 3. For example, in a directed network there can be at most 13 distinct directed 3-motifs and 199 distinct directed 4-motifs, several of which are shown in Fig 4. To find the motifs in a network we used the Kavosh software package [23], which exhaustively computes the numbers of each type of k-motif in a network (here we employ the same motif labeling scheme used in that paper). At the top level, this algorithm is quite simple. It finds all subnetworks of size k and then puts them into groups with the same network structure. The details are much more complex, as Kavosh uses sophisticated tree-based data structures to find all subnetworks efficiently and then applies a state of the art algorithm (NAUTY) based on canonical labeling to compare subnetworks.

Bottom Line: This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains.Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV).Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD.

View Article: PubMed Central - PubMed

Affiliation: International Computer Science Institute, Berkeley, CA, United States of America; Department of Computer Science, University of California, Berkeley, Berkeley, CA, United States of America.

ABSTRACT
Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer's disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer's disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer's disease.

No MeSH data available.


Related in: MedlinePlus