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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

DPNet matrices.(a) Connectivity heat map for sample NC patient DPNet matrix. Note that it is not symmetric, as the connections are directed and has horizontal bands that reflect nodes that might be infected in the early period. (b) Matrix representation of thresholded DPNet network for NC patient. (c) Connectivity heat map for sample AD patient DPNet matrix. (d) Matrix representation of thresholded DPNet network for NC patient.
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pone.0124453.g002: DPNet matrices.(a) Connectivity heat map for sample NC patient DPNet matrix. Note that it is not symmetric, as the connections are directed and has horizontal bands that reflect nodes that might be infected in the early period. (b) Matrix representation of thresholded DPNet network for NC patient. (c) Connectivity heat map for sample AD patient DPNet matrix. (d) Matrix representation of thresholded DPNet network for NC patient.

Mentions: Since each node in our network construction is a standard anatomical ROI, the directed networks we work with all have 88 nodes, which include subcortical regions. The number of edges can vary depending upon the choice of a threshold (as is typical in brain network constructions [2]); however in this paper we choose individual thresholds for each subject such that the directed networks of all subjects have exactly 880 edges (yielding an average outdegree of 10). (As is standard in most network analyses [2], we do not allow for self edges (edges from a node to itself).) This procedure whereby we individually select the threshold for each subject so as to maintain a constant average degree is significant, since motif distributions are highly dependent on the number of edges, and thus this procedure controls for the exogenous source of noise in the comparison of different subjects’ networks. The exact choice of the average outdegree, 10, was chosen to be consistent with other studies and provide maximum information as discussed in more detail in [3]. Analyses with other average outdegrees yield substantially similar results and are discussed in the results section. Fig 2 shows heatmaps for one NC and one AD patient both before and after thresholding.


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)

DPNet matrices.(a) Connectivity heat map for sample NC patient DPNet matrix. Note that it is not symmetric, as the connections are directed and has horizontal bands that reflect nodes that might be infected in the early period. (b) Matrix representation of thresholded DPNet network for NC patient. (c) Connectivity heat map for sample AD patient DPNet matrix. (d) Matrix representation of thresholded DPNet network for NC patient.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0124453.g002: DPNet matrices.(a) Connectivity heat map for sample NC patient DPNet matrix. Note that it is not symmetric, as the connections are directed and has horizontal bands that reflect nodes that might be infected in the early period. (b) Matrix representation of thresholded DPNet network for NC patient. (c) Connectivity heat map for sample AD patient DPNet matrix. (d) Matrix representation of thresholded DPNet network for NC patient.
Mentions: Since each node in our network construction is a standard anatomical ROI, the directed networks we work with all have 88 nodes, which include subcortical regions. The number of edges can vary depending upon the choice of a threshold (as is typical in brain network constructions [2]); however in this paper we choose individual thresholds for each subject such that the directed networks of all subjects have exactly 880 edges (yielding an average outdegree of 10). (As is standard in most network analyses [2], we do not allow for self edges (edges from a node to itself).) This procedure whereby we individually select the threshold for each subject so as to maintain a constant average degree is significant, since motif distributions are highly dependent on the number of edges, and thus this procedure controls for the exogenous source of noise in the comparison of different subjects’ networks. The exact choice of the average outdegree, 10, was chosen to be consistent with other studies and provide maximum information as discussed in more detail in [3]. Analyses with other average outdegrees yield substantially similar results and are discussed in the results section. Fig 2 shows heatmaps for one NC and one AD patient both before and after thresholding.

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