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Proceedings Paper

Discriminative analysis of non-linear brain connectivity for leukoaraiosis with resting-state fMRI
Author(s): Youzhi Lai; Lele Xu; Li Yao; Xia Wu
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Paper Abstract

Leukoaraiosis (LA) describes diffuse white matter abnormalities on CT or MR brain scans, often seen in the normal elderly and in association with vascular risk factors such as hypertension, or in the context of cognitive impairment. The mechanism of cognitive dysfunction is still unclear. The recent clinical studies have revealed that the severity of LA was not corresponding to the cognitive level, and functional connectivity analysis is an appropriate method to detect the relation between LA and cognitive decline. However, existing functional connectivity analyses of LA have been mostly limited to linear associations. In this investigation, a novel measure utilizing the extended maximal information coefficient (eMIC) was applied to construct non-linear functional connectivity in 44 LA subjects (9 dementia, 25 mild cognitive impairment (MCI) and 10 cognitively normal (CN)). The strength of non-linear functional connections for the first 1% of discriminative power increased in MCI compared with CN and dementia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the non-linear and linear connectivity have similar but not completely the same spatial distribution in human brain. In the multivariate pattern analysis with multiple classifiers, the non-linear functional connectivity mostly identified dementia, MCI and CN from LA with a relatively higher accuracy rate than the linear measure. Our findings revealed the non-linear functional connectivity provided useful discriminative power in classification of LA, and the spatial distributed changes between the non-linear and linear measure may indicate the underlying mechanism of cognitive dysfunction in LA.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142I (20 March 2015); doi: 10.1117/12.2081378
Show Author Affiliations
Youzhi Lai, Beijing Normal Univ. (China)
Lele Xu, Beijing Normal Univ. (China)
Li Yao, Beijing Normal Univ. (China)
Xia Wu, Beijing Normal Univ. (China)
State Key Lab. of Transducer Technology (China)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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