Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease
The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer’s disease (...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-05-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpsyt.2020.00360/full |
| _version_ | 1828842708752400384 |
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| author | Fabian Wagner Marco Duering Benno G. Gesierich Christian Enzinger Stefan Ropele Peter Dal-Bianco Florian Mayer Reinhold Schmidt Marisa Koini |
| author_facet | Fabian Wagner Marco Duering Benno G. Gesierich Christian Enzinger Stefan Ropele Peter Dal-Bianco Florian Mayer Reinhold Schmidt Marisa Koini |
| author_sort | Fabian Wagner |
| collection | DOAJ |
| description | The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer’s disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited. |
| first_indexed | 2024-12-12T20:30:06Z |
| format | Article |
| id | doaj.art-72b4079769504fc79bb316b3fc90e4f5 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-12-12T20:30:06Z |
| publishDate | 2020-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-72b4079769504fc79bb316b3fc90e4f52022-12-22T00:13:03ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402020-05-011110.3389/fpsyt.2020.00360506370Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s DiseaseFabian Wagner0Marco Duering1Benno G. Gesierich2Christian Enzinger3Stefan Ropele4Peter Dal-Bianco5Florian Mayer6Reinhold Schmidt7Marisa Koini8Department of Neurology, Medical University of Graz, Graz, AustriaInstitute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, GermanyInstitute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, GermanyDepartment of Neurology, Medical University of Graz, Graz, AustriaDepartment of Neurology, Medical University of Graz, Graz, AustriaDepartment of Neurology, Medical University of Vienna, Vienna, AustriaDepartment of Neurology, Medical University of Vienna, Vienna, AustriaDepartment of Neurology, Medical University of Graz, Graz, AustriaDepartment of Neurology, Medical University of Graz, Graz, AustriaThe study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer’s disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.https://www.frontiersin.org/article/10.3389/fpsyt.2020.00360/fullstructural covariance networklongitudinalAlzheimercognitionrandom forest |
| spellingShingle | Fabian Wagner Marco Duering Benno G. Gesierich Christian Enzinger Stefan Ropele Peter Dal-Bianco Florian Mayer Reinhold Schmidt Marisa Koini Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease Frontiers in Psychiatry structural covariance network longitudinal Alzheimer cognition random forest |
| title | Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease |
| title_full | Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease |
| title_fullStr | Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease |
| title_full_unstemmed | Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease |
| title_short | Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease |
| title_sort | gray matter covariance networks as classifiers and predictors of cognitive function in alzheimer s disease |
| topic | structural covariance network longitudinal Alzheimer cognition random forest |
| url | https://www.frontiersin.org/article/10.3389/fpsyt.2020.00360/full |
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