Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease
Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use net...
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doaj-db4345dec8a84626ab793348f4c061f72020-11-24T22:46:09ZengFrontiers Media S.A.Frontiers in Neurology1664-22952017-11-01810.3389/fneur.2017.00580299554Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s DiseaseNeil P. Oxtoby0Sara Garbarino1Nicholas C. Firth2Nicholas C. Firth3Jason D. Warren4Jonathan M. Schott5Daniel C. Alexander6For the Alzheimer’s Disease Neuroimaging InitiativeProgression of Neurodegenerative Disease Group (POND), Centre for Medical Image Computing, Department of Computer Science, University College London, London, United KingdomProgression of Neurodegenerative Disease Group (POND), Centre for Medical Image Computing, Department of Computer Science, University College London, London, United KingdomProgression of Neurodegenerative Disease Group (POND), Centre for Medical Image Computing, Department of Computer Science, University College London, London, United KingdomDementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, United KingdomDementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, United KingdomDementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, United KingdomProgression of Neurodegenerative Disease Group (POND), Centre for Medical Image Computing, Department of Computer Science, University College London, London, United KingdomModel-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases.http://journal.frontiersin.org/article/10.3389/fneur.2017.00580/fullbrain connectivity analysisdata-drivenAlzheimer’s diseasedisease progression modelinggraph theory analysiscomputational model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Neil P. Oxtoby Sara Garbarino Nicholas C. Firth Nicholas C. Firth Jason D. Warren Jonathan M. Schott Daniel C. Alexander For the Alzheimer’s Disease Neuroimaging Initiative |
spellingShingle |
Neil P. Oxtoby Sara Garbarino Nicholas C. Firth Nicholas C. Firth Jason D. Warren Jonathan M. Schott Daniel C. Alexander For the Alzheimer’s Disease Neuroimaging Initiative Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease Frontiers in Neurology brain connectivity analysis data-driven Alzheimer’s disease disease progression modeling graph theory analysis computational model |
author_facet |
Neil P. Oxtoby Sara Garbarino Nicholas C. Firth Nicholas C. Firth Jason D. Warren Jonathan M. Schott Daniel C. Alexander For the Alzheimer’s Disease Neuroimaging Initiative |
author_sort |
Neil P. Oxtoby |
title |
Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease |
title_short |
Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease |
title_full |
Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease |
title_fullStr |
Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease |
title_full_unstemmed |
Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease |
title_sort |
data-driven sequence of changes to anatomical brain connectivity in sporadic alzheimer’s disease |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neurology |
issn |
1664-2295 |
publishDate |
2017-11-01 |
description |
Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases. |
topic |
brain connectivity analysis data-driven Alzheimer’s disease disease progression modeling graph theory analysis computational model |
url |
http://journal.frontiersin.org/article/10.3389/fneur.2017.00580/full |
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