STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data

Hyun, Jung Won; Li, Yimei; Huang, Chao; Styner, Martin; Lin, Weili; Zhu, Hongtu

doi:10.1016/j.neuroimage.2016.04.023

Cited by 28 publications

(27 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advances in machine learning, e.g. Gaussian process modelling, have also introduced novel opportunities for personalized healthcare, shifting from “one-size-fits-all” population modeling towards personalized models 45 – 48 .…”

Section: Introductionmentioning

confidence: 99%

Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database

Ledig

Schuh

Guerrero

et al. 2018

Sci Rep

131

View full text Add to dashboard Cite

Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.

show abstract

Section: Introductionmentioning

confidence: 99%

Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database

Ledig

Schuh

Guerrero

et al. 2018

Sci Rep

131

View full text Add to dashboard Cite

show abstract

“…Another issue in longitudinal studies that must be accounted for is the within-subject correlation among repeated measurements of each phenotype (Verbeke and Molenberghs, 2009; Hyun et al, 2016). Various correlation structures can be formulated through z i ( t ) T b ik and Σ b .…”

Section: Methods: L2r2 Model Formulationmentioning

confidence: 99%

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

Khondker²,

Ibrahim³

et al. 2017

NeuroImage

Self Cite

View full text Add to dashboard Cite

To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chainMonte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients’ brain images obtained from the Alzheimer’s Disease Neuroimaging Initiative.

show abstract

“…Recently, several longitudinal neuroimaging studies have collected much longitudinal data to better understand the progress of neuropsychiatric and neurodegenerative diseases . Therefore, the longitudinal parameter change in MRI may be a crucial factor in the prediction of future conversion from MCI to AD .…”

Section: Introductionmentioning

confidence: 99%

Longitudinal structural MRI analysis and classification in Alzheimer's disease and mild cognitive impairment

Zhang

Liu

2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Early diagnosis or detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is crucial so as to intervene in advance and to better understand the neurodegenerative process. Gray matter volume (GMV) plays an important role in demonstrating unique anatomical characteristics of the brain regions and further differentiates AD, MCI and normal control (NC). In this study, 317 subjects (100 NC, 58 stable MCI (sMCI), 53 converted MCI (cMCI) and 106 AD) are selected from the Alzheimer's Disease Neuroimaging Initiative database. First, the differences of GMV patterns among the between-group comparisons at different time points and the development of longitudinal pattern within the same group are compared. Next, the longitudinal feature combination strategy is applied to construct the classification model by using a support vector machine (SVM) combined with the nested leave-one-out crossvalidation (LOOCV) method. The brain structure experiences a gradual change in the process of developing from NC to AD. In addition, the baseline GMV combined with the longitudinal measurements for 2 years of follow-up data yielded optimal classification results. Specifically, the AD-NC comparison achieves the best classification performance with 98.06% accuracy, 97.17% sensitivity, 99.00% specificity, 99.04% positive predictive value (PPV) and 97.06% negative predictive value (NPV). The comparison of the two subtypes of MCI (ie, sMCI and cMCI) also achieves high accuracy. Other between-group comparisons also receive high classification performance. According to statistics, caudate nucleus, hippocampus, temporal pole and lenticular putamen are the most important contribution areas to the between-group comparisons. Our research has the potential to improve the clinical diagnosis of subtypes of MCI and predict the risk of its conversion to AD. K E Y W O R D SAlzheimer's disease, gray matter volume, longitudinal analysis, longitudinal classification, mild cognitive impairment

show abstract

STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data

Cited by 28 publications

References 38 publications

Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database

Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

Longitudinal structural MRI analysis and classification in Alzheimer's disease and mild cognitive impairment

Contact Info

Product

Resources

About