Support vector based autoregressive mixed models of longitudinal brain changes and corresponding genetics in alzheimer’s disease

Alzheimer’s Disease Neuroimaging Initiative

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Longitudinal data used as repeat measures may capture the proportion of total variance due to genetic factors with greater sensitivity. However, for brain imaging in studies of older adults, there is a steady decline of brain tissue. It is important to establish such estimation methods using longitudinal data, while properly modeling within-subject variation and rate of tissue atrophy. However, to date, neuroimaging studies have been limited to using only two timepoints, and have not considered diagnostic-specific trends in clinically heterogeneous samples. Modeling temporal patterns of brain structure specific to neurodegenerative disease, while simultaneously assessing the contribution of genetic and environmental risk factors, is essential to understanding and predicting disease progression. We use data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to model the genetic effects on brain cortical measurements from three repeated measures across two years. We refine our model for specific diagnostic groups, including cognitively normal elderly individuals, individuals with mild cognitive impairment and AD, and then distinguish between those who remain stable or convert to AD. We propose a support vector based, longitudinal autoregressive linear mixed model (ARLMM) for long-term repeated measurements, offering greater sensitivity than cross-sectional analyses in baseline scans alone.

Original languageEnglish
Title of host publicationPredictive Intelligence in Medicine - 2nd International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsIslem Rekik, Ehsan Adeli, Sang Hyun Park
PublisherSpringer
Pages160-167
Number of pages8
ISBN (Print)9783030322809
DOIs
StatePublished - 2019
Externally publishedYes
Event2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201913 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11843 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period13/10/1913/10/19

Keywords

  • Alzheimer’s Disease
  • Longitudinal mixed model
  • Support vector machine

Fingerprint

Dive into the research topics of 'Support vector based autoregressive mixed models of longitudinal brain changes and corresponding genetics in alzheimer’s disease'. Together they form a unique fingerprint.

Cite this