Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments

Lu, Lyujian; Elbeleidy, Saad; Baker, Lauren Zoe; Wang, Hua

doi:10.1609/aaai.v34i01.5426

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…As a result, it is difficult to directly use the dynamic imaging data to build machine learning models. To address this difficulty, we propose to learn a novel temporally augmented representation with a fixed length for every participant learned from their dynamic imaging data (Lu et al 2019(Lu et al , 2020a.…”

Section: Learning Temporally Augmented Representations For Dynamic Im...mentioning

confidence: 99%

“…Because multiple types of imaging biomarkers can be extracted from brain scans, the phenotypic measurements can be naturally formulated as multi-modal data (Wang et al 2012b;Brand et al 2018Brand et al , 2019Lu et al 2020a;Brand et al 2020b). Suppose a total of K types of biomarkers are extracted from the brain scans, we have…”

Section: Integrating Static and Multi-modal Dynamic Data Using Genoty...mentioning

confidence: 99%

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Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations

Seo

Brand

Wang

et al. 2021

AAAI

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Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes severe problems in patients’ thinking, memory, and behavior. An early diagnosis is crucial to prevent AD progression; to this end, many algorithmic approaches have recently been proposed to predict cognitive decline. However, these predictive models often fail to integrate heterogeneous genetic and neuroimaging biomarkers and struggle to handle missing data. In this work we propose a novel objective function and an associated optimization algorithm to identify cognitive decline related to AD. Our approach is designed to incorporate dynamic neuroimaging data by way of a participant-specific augmentation combined with multimodal data integration aligned via a regression task. Our approach, in order to incorporate additional side-information, utilizes structured regularization techniques popularized in recent AD literature. Armed with the fixed-length vector representation learned from the multimodal dynamic and static modalities, conventional machine learning methods can be used to predict the clinical outcomes associated with AD. Our experimental results show that the proposed augmentation model improves the prediction performance on cognitive assessment scores for a collection of popular machine learning algorithms. The results of our approach are interpreted to validate existing genetic and neuroimaging biomarkers that have been shown to be predictive of cognitive decline.

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Section: Learning Temporally Augmented Representations For Dynamic Im...mentioning

confidence: 99%

Section: Integrating Static and Multi-modal Dynamic Data Using Genoty...mentioning

confidence: 99%

Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations

Seo

Brand

Wang

et al. 2021

AAAI

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SOM2LM: Self-Organized Multi-Modal Longitudinal Maps

Ouyang,

Zhao,

Adeli

et al. 2024

Lecture Notes in Computer Science

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Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline

Seo,

Brand,

Wang

2024

BMC Med Inform Decis Mak

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Background Alzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Given that there is currently no cure, it is critical to detect AD in its early stage during the disease progression. Recently, many statistical learning methods have been presented to identify cognitive decline with temporal data, but few of these methods integrate heterogeneous phenotype and genetic information together to improve the accuracy of prediction. In addition, many of these models are often unable to handle incomplete temporal data; this often manifests itself in the removal of records to ensure consistency in the number of records across participants. Results To address these issues, in this work we propose a novel approach to integrate the genetic data and the longitudinal phenotype data to learn a fixed-length “enriched” biomarker representation derived from the temporal heterogeneous neuroimaging records. Armed with this enriched representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD. Conclusion The proposed method shows improved prediction performance when applied to data derived from Alzheimer’s Disease Neruoimaging Initiative cohort. In addition, our approach can be easily interpreted to allow for the identification and validation of biomarkers associated with cognitive decline.

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Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments

Cited by 3 publications

References 15 publications

Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations

Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations

SOM2LM: Self-Organized Multi-Modal Longitudinal Maps

Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline

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