Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer’s disease to Parkinson’s disease View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-11-25

AUTHORS

Hongyoon Choi, Yu Kyeong Kim, Eun Jin Yoon, Jee-Young Lee, Dong Soo Lee

ABSTRACT

PurposeAlthough functional brain imaging has been used for the early and objective assessment of cognitive dysfunction, there is a lack of generalized image-based biomarker which can evaluate individual’s cognitive dysfunction in various disorders. To this end, we developed a deep learning-based cognitive signature of FDG brain PET adaptable for Parkinson’s disease (PD) as well as Alzheimer’s disease (AD).MethodsA deep learning model for discriminating AD from normal controls (NCs) was built by a training set consisting of 636 FDG PET obtained from Alzheimer’s Disease Neuroimaging Initiative database. The model was directly transferred to images of mild cognitive impairment (MCI) patients (n = 666) for identifying who would rapidly convert to AD and another independent cohort consisting of 62 PD patients to differentiate PD patients with dementia. The model accuracy was measured by area under curve (AUC) of receiver operating characteristic (ROC) analysis. The relationship between all images was visualized by two-dimensional projection of the deep learning-based features. The model was also designed to predict cognitive score of the subjects and validated in PD patients. Cognitive dysfunction-related regions were visualized by feature maps of the deep CNN model.ResultsAUC of ROC for differentiating AD from NC was 0.94 (95% CI 0.89–0.98). The transfer of the model could differentiate MCI patients who would convert to AD (AUC = 0.82) and PD with dementia (AUC = 0.81). The two-dimensional projection mapping visualized the degree of cognitive dysfunction compared with normal brains regardless of different disease cohorts. Predicted cognitive score, an output of the model, was highly correlated with the mini-mental status exam scores. Individual cognitive dysfunction-related regions included cingulate and high frontoparietal cortices, while they showed individual variability.ConclusionThe deep learning-based cognitive function evaluation model could be successfully transferred to multiple disease domains. We suggest that this approach might be extended to an objective cognitive signature that provides quantitative biomarker for cognitive dysfunction across various neurodegenerative disorders. More... »

PAGES

403-412

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00259-019-04538-7

DOI

http://dx.doi.org/10.1007/s00259-019-04538-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1122867961

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31768599


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