Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease

Javier Escudero; Keith Smith; Hamed Azami; Daniel Abásolo

doi:10.1109/EMBC.2016.7591314

Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease

Javier Escudero^*, Keith Smith, Hamed Azami, Daniel Abásolo

^*Corresponding author for this work

Computer And Information Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

5 Citations (Scopus)

Abstract

Functional connectivity has proven useful to characterise electroencephalogram (EEG) activity in Alzheimer's disease (AD). However, most current functional connectivity analyses have been static, disregarding any potential variability of the connectivity with time. In this pilot study, we compute short-time resting state EEG functional connectivity based on the imaginary part of coherency for 12 AD patients and 11 controls. We derive binary unweighted graphs using the cluster-span threshold, an objective binary threshold. For each short-time binary graph, we calculate its local clustering coefficient (C_loc), degree (K), and efficiency (E). The distribution of these graph metrics for each participant is then characterised with four statistical moments: mean, variance, skewness, and kurtosis. The results show significant differences between groups in the mean of K and E, and the kurtosis of C_loc and K. Although not significant when considered alone, the skewness of C_loc is the most frequently selected feature for the discrimination of subject groups. These results suggest that the variability of EEG functional connectivity may convey useful information about AD.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	2810-2813
Number of pages	4
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7591314
Publication status	Published - 18 Oct 2016
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: 16 Aug 2016 → 20 Aug 2016

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2016-October
ISSN (Print)	1557-170X

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/Territory	United States
City	Orlando
Period	16/08/16 → 20/08/16

Funding

This work was partially supported by the Engineering and Physical Sciences Research Council (EPSRC) via a DTP studentship to K. Smith and the research project EP/N014421/1 to J. Escudero

Keywords

Alzheimer's disease
electroencephalogram (EEG) activity
EEG functional connectivity

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10.1109/EMBC.2016.7591314

Cite this

Escudero, J., Smith, K., Azami, H., & Abásolo, D. (2016). Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2810-2813). Article 7591314 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). IEEE. https://doi.org/10.1109/EMBC.2016.7591314

Escudero, Javier ; Smith, Keith ; Azami, Hamed et al. / Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway, NJ : IEEE, 2016. pp. 2810-2813 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease",

abstract = "Functional connectivity has proven useful to characterise electroencephalogram (EEG) activity in Alzheimer's disease (AD). However, most current functional connectivity analyses have been static, disregarding any potential variability of the connectivity with time. In this pilot study, we compute short-time resting state EEG functional connectivity based on the imaginary part of coherency for 12 AD patients and 11 controls. We derive binary unweighted graphs using the cluster-span threshold, an objective binary threshold. For each short-time binary graph, we calculate its local clustering coefficient (Cloc), degree (K), and efficiency (E). The distribution of these graph metrics for each participant is then characterised with four statistical moments: mean, variance, skewness, and kurtosis. The results show significant differences between groups in the mean of K and E, and the kurtosis of Cloc and K. Although not significant when considered alone, the skewness of Cloc is the most frequently selected feature for the discrimination of subject groups. These results suggest that the variability of EEG functional connectivity may convey useful information about AD.",

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author = "Javier Escudero and Keith Smith and Hamed Azami and Daniel Ab{\'a}solo",

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Escudero, J, Smith, K, Azami, H & Abásolo, D 2016, Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)., 7591314, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, IEEE, Piscataway, NJ, pp. 2810-2813, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 16/08/16. https://doi.org/10.1109/EMBC.2016.7591314

Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease. / Escudero, Javier; Smith, Keith; Azami, Hamed et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway, NJ: IEEE, 2016. p. 2810-2813 7591314 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AU - Azami, Hamed

AU - Abásolo, Daniel

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Y1 - 2016/10/18

N2 - Functional connectivity has proven useful to characterise electroencephalogram (EEG) activity in Alzheimer's disease (AD). However, most current functional connectivity analyses have been static, disregarding any potential variability of the connectivity with time. In this pilot study, we compute short-time resting state EEG functional connectivity based on the imaginary part of coherency for 12 AD patients and 11 controls. We derive binary unweighted graphs using the cluster-span threshold, an objective binary threshold. For each short-time binary graph, we calculate its local clustering coefficient (Cloc), degree (K), and efficiency (E). The distribution of these graph metrics for each participant is then characterised with four statistical moments: mean, variance, skewness, and kurtosis. The results show significant differences between groups in the mean of K and E, and the kurtosis of Cloc and K. Although not significant when considered alone, the skewness of Cloc is the most frequently selected feature for the discrimination of subject groups. These results suggest that the variability of EEG functional connectivity may convey useful information about AD.

AB - Functional connectivity has proven useful to characterise electroencephalogram (EEG) activity in Alzheimer's disease (AD). However, most current functional connectivity analyses have been static, disregarding any potential variability of the connectivity with time. In this pilot study, we compute short-time resting state EEG functional connectivity based on the imaginary part of coherency for 12 AD patients and 11 controls. We derive binary unweighted graphs using the cluster-span threshold, an objective binary threshold. For each short-time binary graph, we calculate its local clustering coefficient (Cloc), degree (K), and efficiency (E). The distribution of these graph metrics for each participant is then characterised with four statistical moments: mean, variance, skewness, and kurtosis. The results show significant differences between groups in the mean of K and E, and the kurtosis of Cloc and K. Although not significant when considered alone, the skewness of Cloc is the most frequently selected feature for the discrimination of subject groups. These results suggest that the variability of EEG functional connectivity may convey useful information about AD.

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Y2 - 16 August 2016 through 20 August 2016

ER -

Escudero J, Smith K, Azami H, Abásolo D. Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway, NJ: IEEE. 2016. p. 2810-2813. 7591314. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7591314

Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease

Abstract

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Keywords

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