Scholars@Duke publication: Accurate identification of MCI patients via enriched white-matter connectivity network

Accurate identification of MCI patients via enriched white-matter connectivity network

Publication , Journal Article

Wee, CY; Yap, PT; Brownyke, JN; Potter, GG; Steffens, DC; Welsh-Bohmer, K; Wang, L; Shen, D

Published in: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

October 25, 2010

Published version (DOI)

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD), is frequently considered to be a good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques have made understanding neurological disorders at a whole brain connectivity level possible. Accordingly, we propose a network-based multivariate classification algorithm, using a collection of measures derived from white-matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber penetration count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities (λ 1, λ 2, λ 3), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), the average statistics of each ROI in relation to the remaining ROIs are extracted as features for classification. These features are then sieved to select the most discriminant subset of features for building an MCI classifier via support vector machines (SVMs). Cross-validation results indicate better diagnostic power of the proposed enriched WM connection description than simple description with any single physiological parameter. © 2010 Springer-Verlag Berlin Heidelberg.

Duke Scholars

Author Jeffrey Nicholas Browndyke Psychiatry & Behavioral Sciences, Behavioral Medicine & Neur ...

Author Guy Glenn Potter Psychiatry & Behavioral Sciences, Adult Psychiatry & Psychol ...

Author David Carl Steffens Psychiatry & Behavioral Sciences, Adult Psychiatry & Psychol ...

Author Kathleen Anne Welsh-Bohmer Psychiatry & Behavioral Sciences, Behavioral Medicine & Neur ...

Published In

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

DOI

10.1007/978-3-642-15948-0_18

EISSN

1611-3349

ISSN

0302-9743

Publication Date

October 25, 2010

Volume

6357 LNCS

Start / End Page

140 / 147

Related Subject Headings

Artificial Intelligence & Image Processing
46 Information and computing sciences

Citation

APA

Chicago

ICMJE

MLA

NLM

Wee, C. Y., Yap, P. T., Brownyke, J. N., Potter, G. G., Steffens, D. C., Welsh-Bohmer, K., … Shen, D. (2010). Accurate identification of MCI patients via enriched white-matter connectivity network. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6357 LNCS, 140–147. https://doi.org/10.1007/978-3-642-15948-0_18

Wee, C. Y., P. T. Yap, J. N. Brownyke, G. G. Potter, D. C. Steffens, K. Welsh-Bohmer, L. Wang, and D. Shen. “Accurate identification of MCI patients via enriched white-matter connectivity network.” Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 6357 LNCS (October 25, 2010): 140–47. https://doi.org/10.1007/978-3-642-15948-0_18.

Wee CY, Yap PT, Brownyke JN, Potter GG, Steffens DC, Welsh-Bohmer K, et al. Accurate identification of MCI patients via enriched white-matter connectivity network. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2010 Oct 25;6357 LNCS:140–7.

Wee, C. Y., et al. “Accurate identification of MCI patients via enriched white-matter connectivity network.” Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 6357 LNCS, Oct. 2010, pp. 140–47. Scopus, doi:10.1007/978-3-642-15948-0_18.

Wee CY, Yap PT, Brownyke JN, Potter GG, Steffens DC, Welsh-Bohmer K, Wang L, Shen D. Accurate identification of MCI patients via enriched white-matter connectivity network. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2010 Oct 25;6357 LNCS:140–147.

Published In

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

DOI

10.1007/978-3-642-15948-0_18

EISSN

1611-3349

ISSN

0302-9743

Publication Date

October 25, 2010

Volume

6357 LNCS

Start / End Page

140 / 147

Related Subject Headings

Artificial Intelligence & Image Processing
46 Information and computing sciences