Publications

@article{chang2021skin,

title = {Unsupervised Skin Feature Tracking with Deep Neural Networks},

author = {Jose Ramon Chang and Torbjörn E. M. Nordling},

url = {https://arxiv.org/abs/2405.04943},

year  = {2024},

date = {2024-05-08},

journal = {arXiv preprint},

publisher = {Cornell University},

abstract = {Facial feature tracking is essential in imaging ballistocardiography for accurate heart rate estimation and enables motor degradation quantification in Parkinson's disease through skin feature tracking. While deep convolutional neural networks have shown remarkable accuracy in tracking tasks, they typically require extensive labeled data for supervised training. Our proposed pipeline employs a convolutional stacked autoencoder to match image crops with a reference crop containing the target feature, learning deep feature encodings specific to the object category in an unsupervised manner, thus reducing data requirements. To overcome edge effects making the performance dependent on crop size, we introduced a Gaussian weight on the residual errors of the pixels when calculating the loss function. Training the autoencoder on facial images and validating its performance on manually labeled face and hand videos, our Deep Feature Encodings (DFE) method demonstrated superior tracking accuracy with a mean error ranging from 0.6 to 3.3 pixels, outperforming traditional methods like SIFT, SURF, Lucas Kanade, and the latest transformers like PIPs++ and CoTracker. Overall, our unsupervised learning approach excels in tracking various skin features under significant motion conditions, providing superior feature descriptors for tracking, matching, and image registration compared to both traditional and state-of-the-art supervised learning methods.},

keywords = {autoencoder, Cotracker, feature matching, feature tracking, image registration, Lucas-Kanade method, PIPs, SIFT, SURF},

pubstate = {published},

tppubtype = {article}

}

@article{Chang2024AgePrediction,

title = {Age prediction using resting-state functional MRI},

author = {Jose Ramon Chang Irias and Zai-Fu Yao and Shulan Hsieh and Torbjörn E. M. Nordling},

year  = {2024},

date = {2024-01-01},

journal = {Neuroinformatics, Accepted},

publisher = {Springer},

abstract = {The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. 

Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. 

In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. 

We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). 

Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.},

keywords = {Abnormal Brain Aging, Brain Aging, Default Mode Network, feature matching, Least Absolute Shrinkage and Selection Operator, Resting-State Functional MRI},

pubstate = {published},

tppubtype = {article}

}

@article{Chang2023AgePredictionMedRxiv,

title = {Age prediction using resting-state functional MRI},

author = {Jose Ramon Chang Irias and Zai-Fu Yao and Shulan Hsieh and Torbjörn E. M. Nordling},

url = {https://www.medrxiv.org/content/early/2023/12/28/2023.12.26.23300530},

doi = {10.1101/2023.12.26.23300530},

year  = {2023},

date = {2023-12-01},

journal = {medRxiv},

publisher = {Cold Spring Harbor Laboratory Press},

abstract = {The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain’s health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further. 

	Competing Interest Statement 

	The authors have declared no competing interest. 

	Funding Statement 

	This study was funded by the Ministry of Science and Technology (MOST) of Taiwan (grant number MOST 104-2410-H-006-021-MY2; MOST 106-2410-H-006-031-MY2; MOST 107-2634-F-006-009; MOST 111-2221-E-006-186), and by the National Science and Technology Council (NSTC) of Taiwan (grant number NSTC 112-2321-B-006-013; NSTC 112-2314-B-006-079). 

	Author Declarations 

	I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes 

	The details of the IRB/oversight body that provided approval or exemption for the research described are given below: 

	Ethics committee/IRB of National Cheng Kung University Research Ethics Committee gave ethical approval for this workI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes 

	I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes 

	The data used in this study is not publicly available due to ethical and privacy considerations. The data utilized for our research is the property of the Mind Research Imaging Center at National Cheng Kung University. Access to the data may be granted in accordance with the ethical and legal guidelines established by the institution. For inquiries or requests regarding data access, interested parties should contact the Mind Research Imaging Center at National Cheng Kung University for further information and permissions.},

keywords = {Abnormal Brain Aging, Brain Aging, Default Mode Network, feature matching, Least Absolute Shrinkage and Selection Operator, Resting-State Functional MRI},

pubstate = {published},

tppubtype = {article}

}