Network influence based classification and comparison of neurological conditions

Research output: Contribution to conferencePaper

Abstract

Variations in the influence of brain regions are used to classify neurological conditions by identifying eigenvector-based communities in connectivity matrices, generated from resting state functional magnetic resonance imaging scans. These communities capture the network influence of each brain region, revealing that the subjects with Alzheimer’s disease (AD) have a significantly lower degree of variation in their most influential brain regions when compared with healthy control (HC) and amnestic mild cognitive impairment (aMCI) subjects. Classification of subjects based on their pattern of influential regions is demonstrated with neural networks identifying HC, aMCI and AD subjects. The difference between these conditions are investigated by altering brain region influence so that a neural network changes a subject’s classification. This conversion is performed on healthy subjects changing to aMCI or AD, and for aMCI subjects changing to AD. The results highlight potential compensatory mechanisms that increase functional activity in certain regions for those with aMCI, such as in the right parahippocampal gyrus and regions in the default mode network, but these same regions experience significant decline in those that convert from aMCI to AD.

Conference

ConferenceComplex Networks 2019
CountryPortugal
CityLisbon
Period10/12/1912/12/19
Internet address

Fingerprint

Alzheimer Disease
Brain
Community Networks
Parahippocampal Gyrus
Cognitive Dysfunction
Healthy Volunteers
Magnetic Resonance Imaging

Keywords

  • functional connectivity
  • community detection
  • eigenvector
  • dementia

Cite this

@conference{775e986037294189b0faae837028cd44,
title = "Network influence based classification and comparison of neurological conditions",
abstract = "Variations in the influence of brain regions are used to classify neurological conditions by identifying eigenvector-based communities in connectivity matrices, generated from resting state functional magnetic resonance imaging scans. These communities capture the network influence of each brain region, revealing that the subjects with Alzheimer’s disease (AD) have a significantly lower degree of variation in their most influential brain regions when compared with healthy control (HC) and amnestic mild cognitive impairment (aMCI) subjects. Classification of subjects based on their pattern of influential regions is demonstrated with neural networks identifying HC, aMCI and AD subjects. The difference between these conditions are investigated by altering brain region influence so that a neural network changes a subject’s classification. This conversion is performed on healthy subjects changing to aMCI or AD, and for aMCI subjects changing to AD. The results highlight potential compensatory mechanisms that increase functional activity in certain regions for those with aMCI, such as in the right parahippocampal gyrus and regions in the default mode network, but these same regions experience significant decline in those that convert from aMCI to AD.",
keywords = "functional connectivity, community detection, eigenvector, dementia",
author = "Ruaridh Clark and Niia Nikolova and Malcolm Macdonald and McGeown, {William J.}",
year = "2019",
month = "12",
day = "10",
language = "English",
note = "Complex Networks 2019 : The 8th International Conference on Complex Networks and their Applications ; Conference date: 10-12-2019 Through 12-12-2019",
url = "https://www.complexnetworks.org/",

}

Clark, R, Nikolova, N, Macdonald, M & McGeown, WJ 2019, 'Network influence based classification and comparison of neurological conditions' Paper presented at Complex Networks 2019, Lisbon, Portugal, 10/12/19 - 12/12/19, .

Network influence based classification and comparison of neurological conditions. / Clark, Ruaridh; Nikolova, Niia; Macdonald, Malcolm; McGeown, William J.

2019. Paper presented at Complex Networks 2019, Lisbon, Portugal.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Network influence based classification and comparison of neurological conditions

AU - Clark, Ruaridh

AU - Nikolova, Niia

AU - Macdonald, Malcolm

AU - McGeown, William J.

PY - 2019/12/10

Y1 - 2019/12/10

N2 - Variations in the influence of brain regions are used to classify neurological conditions by identifying eigenvector-based communities in connectivity matrices, generated from resting state functional magnetic resonance imaging scans. These communities capture the network influence of each brain region, revealing that the subjects with Alzheimer’s disease (AD) have a significantly lower degree of variation in their most influential brain regions when compared with healthy control (HC) and amnestic mild cognitive impairment (aMCI) subjects. Classification of subjects based on their pattern of influential regions is demonstrated with neural networks identifying HC, aMCI and AD subjects. The difference between these conditions are investigated by altering brain region influence so that a neural network changes a subject’s classification. This conversion is performed on healthy subjects changing to aMCI or AD, and for aMCI subjects changing to AD. The results highlight potential compensatory mechanisms that increase functional activity in certain regions for those with aMCI, such as in the right parahippocampal gyrus and regions in the default mode network, but these same regions experience significant decline in those that convert from aMCI to AD.

AB - Variations in the influence of brain regions are used to classify neurological conditions by identifying eigenvector-based communities in connectivity matrices, generated from resting state functional magnetic resonance imaging scans. These communities capture the network influence of each brain region, revealing that the subjects with Alzheimer’s disease (AD) have a significantly lower degree of variation in their most influential brain regions when compared with healthy control (HC) and amnestic mild cognitive impairment (aMCI) subjects. Classification of subjects based on their pattern of influential regions is demonstrated with neural networks identifying HC, aMCI and AD subjects. The difference between these conditions are investigated by altering brain region influence so that a neural network changes a subject’s classification. This conversion is performed on healthy subjects changing to aMCI or AD, and for aMCI subjects changing to AD. The results highlight potential compensatory mechanisms that increase functional activity in certain regions for those with aMCI, such as in the right parahippocampal gyrus and regions in the default mode network, but these same regions experience significant decline in those that convert from aMCI to AD.

KW - functional connectivity

KW - community detection

KW - eigenvector

KW - dementia

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