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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 construction.Schematic DPNet Construction for three regions-of-interest (ROIs) based on three yearly (T = 1,2,3) MRIs. ROI 1 thins in the early period (large positive thinning rate) and remains unchanged (0 rate) in the progressed period. ROI 2 thickens in the early period and thins in the progressed period. ROI 3 thins in the early period and thickens in the progressed period. Thus, the DPNet has edges from 1→2 and 3→2 showing the thinning progression and possible disease progression. However, even though the rate in the early period for ROI 2 is similar to the rate for ROI 3 in the progressed period, there is no edge from 2 to 3, since a negative thinning rate is not evidence of a diseased ROI. Intuitively, we see that ROIs 1 and 3 are thinning in the early period, which implies that they are diseased early, while ROI 2 appears diseased in the progressed period and may have been infected by ROI 1 or 3 or perhaps both, which is represented by the directed edges.
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pone.0124453.g001: DPNet construction.Schematic DPNet Construction for three regions-of-interest (ROIs) based on three yearly (T = 1,2,3) MRIs. ROI 1 thins in the early period (large positive thinning rate) and remains unchanged (0 rate) in the progressed period. ROI 2 thickens in the early period and thins in the progressed period. ROI 3 thins in the early period and thickens in the progressed period. Thus, the DPNet has edges from 1→2 and 3→2 showing the thinning progression and possible disease progression. However, even though the rate in the early period for ROI 2 is similar to the rate for ROI 3 in the progressed period, there is no edge from 2 to 3, since a negative thinning rate is not evidence of a diseased ROI. Intuitively, we see that ROIs 1 and 3 are thinning in the early period, which implies that they are diseased early, while ROI 2 appears diseased in the progressed period and may have been infected by ROI 1 or 3 or perhaps both, which is represented by the directed edges.

Mentions: DPNets are constructed from the rates of cortical thickness changes in brain regions, attempting to find signals of disease progression which is indicated by thinning rates. See Fig 1 for an overview of their construction and Friedman et al. [3] for more details. This study used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The Freesurfer automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest as well as labeling subcortical regions was used for extracting 88 regions-of-interests (ROI).


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 construction.Schematic DPNet Construction for three regions-of-interest (ROIs) based on three yearly (T = 1,2,3) MRIs. ROI 1 thins in the early period (large positive thinning rate) and remains unchanged (0 rate) in the progressed period. ROI 2 thickens in the early period and thins in the progressed period. ROI 3 thins in the early period and thickens in the progressed period. Thus, the DPNet has edges from 1→2 and 3→2 showing the thinning progression and possible disease progression. However, even though the rate in the early period for ROI 2 is similar to the rate for ROI 3 in the progressed period, there is no edge from 2 to 3, since a negative thinning rate is not evidence of a diseased ROI. Intuitively, we see that ROIs 1 and 3 are thinning in the early period, which implies that they are diseased early, while ROI 2 appears diseased in the progressed period and may have been infected by ROI 1 or 3 or perhaps both, which is represented by the directed edges.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0124453.g001: DPNet construction.Schematic DPNet Construction for three regions-of-interest (ROIs) based on three yearly (T = 1,2,3) MRIs. ROI 1 thins in the early period (large positive thinning rate) and remains unchanged (0 rate) in the progressed period. ROI 2 thickens in the early period and thins in the progressed period. ROI 3 thins in the early period and thickens in the progressed period. Thus, the DPNet has edges from 1→2 and 3→2 showing the thinning progression and possible disease progression. However, even though the rate in the early period for ROI 2 is similar to the rate for ROI 3 in the progressed period, there is no edge from 2 to 3, since a negative thinning rate is not evidence of a diseased ROI. Intuitively, we see that ROIs 1 and 3 are thinning in the early period, which implies that they are diseased early, while ROI 2 appears diseased in the progressed period and may have been infected by ROI 1 or 3 or perhaps both, which is represented by the directed edges.
Mentions: DPNets are constructed from the rates of cortical thickness changes in brain regions, attempting to find signals of disease progression which is indicated by thinning rates. See Fig 1 for an overview of their construction and Friedman et al. [3] for more details. This study used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The Freesurfer automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest as well as labeling subcortical regions was used for extracting 88 regions-of-interests (ROI).

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