Detecting Alzheimer’s disease using directed graphs

Jianjia Wang; Richard C. Wilson; Edwin R. Hancock

doi:10.1007/978-3-319-58961-9_9

Detecting Alzheimer’s disease using directed graphs

Jianjia Wang^*, Richard C. Wilson, Edwin R. Hancock

^*Corresponding author for this work

University of York

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

9 Citations (Scopus)

Abstract

The neurobiology of Alzheimer’s disease (AD) has been extensively studied by applying network analysis techniques to activation patterns in fMRI images. However, the structure of the directed networks representing the activation patterns, and their differences in healthy and Alzheimer’s people remain poorly understood. In this paper, we aim to identify the differences in fMRI activation network structure for patients with AD, late mild cognitive impairment (LMCI) and early mild cognitive impairment (EMCI). We use a directed graph theoretical approach combined with entropic measurements to distinguish subjects falling into these three categories and the normal healthy control (HC) group. We explore three methods. The first is based on applying linear discriminant analysis to vectors representing the in and out degree statistics of different anatomical regions. The second uses an entropic measure of node assortativity to gauge the asymmetries in the node with in and out degree. The final approach selects the most salient anatomical brain regions and uses the degree statistics of the connecting directed edges.

Original language	English
Title of host publication	Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings
Editors	Pasquale Foggia, Mario Vento, Cheng-Lin Liu
Publisher	Springer Verlag
Pages	94-104
Number of pages	11
ISBN (Print)	9783319589602
DOIs	https://doi.org/10.1007/978-3-319-58961-9_9
Publication status	Published - 2017
Externally published	Yes
Event	11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017 - Anacapri, Italy Duration: 16 May 2017 → 18 May 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10310 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017
Country/Territory	Italy
City	Anacapri
Period	16/05/17 → 18/05/17

Keywords

Alzheimer’s disease (AD)
Directed graphs entropy
FMRI Networks

Access to Document

10.1007/978-3-319-58961-9_9

Cite this

Wang, J., Wilson, R. C., & Hancock, E. R. (2017). Detecting Alzheimer’s disease using directed graphs. In P. Foggia, M. Vento, & C.-L. Liu (Eds.), Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings (pp. 94-104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10310 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-58961-9_9

Wang, Jianjia ; Wilson, Richard C. ; Hancock, Edwin R. / Detecting Alzheimer’s disease using directed graphs. Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings. editor / Pasquale Foggia ; Mario Vento ; Cheng-Lin Liu. Springer Verlag, 2017. pp. 94-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{04af71d5b2b3428881356b4e26bb6fdc,

title = "Detecting Alzheimer{\textquoteright}s disease using directed graphs",

abstract = "The neurobiology of Alzheimer{\textquoteright}s disease (AD) has been extensively studied by applying network analysis techniques to activation patterns in fMRI images. However, the structure of the directed networks representing the activation patterns, and their differences in healthy and Alzheimer{\textquoteright}s people remain poorly understood. In this paper, we aim to identify the differences in fMRI activation network structure for patients with AD, late mild cognitive impairment (LMCI) and early mild cognitive impairment (EMCI). We use a directed graph theoretical approach combined with entropic measurements to distinguish subjects falling into these three categories and the normal healthy control (HC) group. We explore three methods. The first is based on applying linear discriminant analysis to vectors representing the in and out degree statistics of different anatomical regions. The second uses an entropic measure of node assortativity to gauge the asymmetries in the node with in and out degree. The final approach selects the most salient anatomical brain regions and uses the degree statistics of the connecting directed edges.",

keywords = "Alzheimer{\textquoteright}s disease (AD), Directed graphs entropy, FMRI Networks",

author = "Jianjia Wang and Wilson, {Richard C.} and Hancock, {Edwin R.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017 ; Conference date: 16-05-2017 Through 18-05-2017",

year = "2017",

doi = "10.1007/978-3-319-58961-9_9",

language = "English",

isbn = "9783319589602",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "94--104",

editor = "Pasquale Foggia and Mario Vento and Cheng-Lin Liu",

booktitle = "Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings",

}

Wang, J, Wilson, RC & Hancock, ER 2017, Detecting Alzheimer’s disease using directed graphs. in P Foggia, M Vento & C-L Liu (eds), Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10310 LNCS, Springer Verlag, pp. 94-104, 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017 , Anacapri, Italy, 16/05/17. https://doi.org/10.1007/978-3-319-58961-9_9

Detecting Alzheimer’s disease using directed graphs. / Wang, Jianjia; Wilson, Richard C.; Hancock, Edwin R.
Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings. ed. / Pasquale Foggia; Mario Vento; Cheng-Lin Liu. Springer Verlag, 2017. p. 94-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10310 LNCS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Detecting Alzheimer’s disease using directed graphs

AU - Wang, Jianjia

AU - Wilson, Richard C.

AU - Hancock, Edwin R.

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - The neurobiology of Alzheimer’s disease (AD) has been extensively studied by applying network analysis techniques to activation patterns in fMRI images. However, the structure of the directed networks representing the activation patterns, and their differences in healthy and Alzheimer’s people remain poorly understood. In this paper, we aim to identify the differences in fMRI activation network structure for patients with AD, late mild cognitive impairment (LMCI) and early mild cognitive impairment (EMCI). We use a directed graph theoretical approach combined with entropic measurements to distinguish subjects falling into these three categories and the normal healthy control (HC) group. We explore three methods. The first is based on applying linear discriminant analysis to vectors representing the in and out degree statistics of different anatomical regions. The second uses an entropic measure of node assortativity to gauge the asymmetries in the node with in and out degree. The final approach selects the most salient anatomical brain regions and uses the degree statistics of the connecting directed edges.

AB - The neurobiology of Alzheimer’s disease (AD) has been extensively studied by applying network analysis techniques to activation patterns in fMRI images. However, the structure of the directed networks representing the activation patterns, and their differences in healthy and Alzheimer’s people remain poorly understood. In this paper, we aim to identify the differences in fMRI activation network structure for patients with AD, late mild cognitive impairment (LMCI) and early mild cognitive impairment (EMCI). We use a directed graph theoretical approach combined with entropic measurements to distinguish subjects falling into these three categories and the normal healthy control (HC) group. We explore three methods. The first is based on applying linear discriminant analysis to vectors representing the in and out degree statistics of different anatomical regions. The second uses an entropic measure of node assortativity to gauge the asymmetries in the node with in and out degree. The final approach selects the most salient anatomical brain regions and uses the degree statistics of the connecting directed edges.

KW - Alzheimer’s disease (AD)

KW - Directed graphs entropy

KW - FMRI Networks

UR - http://www.scopus.com/inward/record.url?scp=85019615888&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-58961-9_9

DO - 10.1007/978-3-319-58961-9_9

M3 - Conference Proceeding

AN - SCOPUS:85019615888

SN - 9783319589602

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 94

EP - 104

BT - Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings

A2 - Foggia, Pasquale

A2 - Vento, Mario

A2 - Liu, Cheng-Lin

PB - Springer Verlag

T2 - 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017

Y2 - 16 May 2017 through 18 May 2017

ER -

Wang J, Wilson RC, Hancock ER. Detecting Alzheimer’s disease using directed graphs. In Foggia P, Vento M, Liu CL, editors, Graph-Based Representations in Pattern Recognition - 11th IAPR-TC-15 International Workshop, GbRPR 2017, Proceedings. Springer Verlag. 2017. p. 94-104. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-58961-9_9

Detecting Alzheimer’s disease using directed graphs

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