Poster No:
695
Submission Type:
Abstract Submission
Authors:
Tzung-Chien Hsieh1, Shriya Jaddu2, Hannah Weiland1, Merle ten Hagen1, Jing-Mei Li1, Chi-Chia Chang3, Sun-Yuan Hsieh3, Hsin-Hung Chou4, William Dobyns5, Wei-Liang Chen6
Institutions:
1Institute for Genomic Statistics and Bioinformatics, University Hospital of Bonn, Bonn, Germany, 2Center for Neuroscience and behavioral Medicine, Children’s National Hospital, Washington, Washington, DC, 3Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, 4Department of Computer Science and Information Engineering, National Chi Nan University, Nantou, Taiwan, 5Department of Genetics, George Washington University, Washington, Washington, DC, 6University of Minnesota, Pediatric Genetics & Metabolsim, Minneapolis, Minneapolis, MN
First Author:
Tzung-Chien Hsieh
Institute for Genomic Statistics and Bioinformatics, University Hospital of Bonn
Bonn, Germany
Co-Author(s):
Shriya Jaddu
Center for Neuroscience and behavioral Medicine, Children’s National Hospital, Washington
Washington, DC
Hannah Weiland
Institute for Genomic Statistics and Bioinformatics, University Hospital of Bonn
Bonn, Germany
Merle ten Hagen
Institute for Genomic Statistics and Bioinformatics, University Hospital of Bonn
Bonn, Germany
Jing-Mei Li
Institute for Genomic Statistics and Bioinformatics, University Hospital of Bonn
Bonn, Germany
Chi-Chia Chang
Department of Computer Science and Information Engineering, National Cheng Kung University
Tainan, Taiwan
Sun-Yuan Hsieh
Department of Computer Science and Information Engineering, National Cheng Kung University
Tainan, Taiwan
Hsin-Hung Chou
Department of Computer Science and Information Engineering, National Chi Nan University
Nantou, Taiwan
William Dobyns
Department of Genetics, George Washington University, Washington
Washington, DC
Wei-Liang Chen
University of Minnesota, Pediatric Genetics & Metabolsim, Minneapolis
Minneapolis, MN
Introduction:
Many rare disorders, particularly neurodevelopmental conditions, manifest structural brain malformations. Just as dysmorphologists rely on facial gestalt recognition to identify syndromes, radiologists and neurologists face similar challenges in identifying the "brain gestalt" of rare disorders-especially when encountering rare conditions or those they have not previously seen. Next-generation phenotyping (NGP) has been proven capable of supporting clinicians in recognizing facial dysmorphic patterns associated with the underlying syndrome through training on thousands of patient photographs. Beyond facial image analysis, NGP can also be applied to brain MRI data to identify structural malformations, such as Dandy-Walker malformation and lissencephaly, by learning patterns from large datasets of brain MRI images. In this work, we propose a deep learning-based NGP approach to detect brain malformations and their associated disorders, providing clinicians with diagnostic support and enabling integration into variant prioritization pipelines.
Methods:
We curated a dataset of 413 brain MRI images from publications and clinicians, covering 56 different disorders, and stored it in the GestaltMatcher Database (GMDB) (Hsieh et al., 2022). To learn the brain structures from MRI, we applied transfer learning using ResNet-50, pre-trained on the fastMRI dataset from NYU School of Medicine, comprising 6,970 MRIs for age prediction (Knoll et al., 2020). This model was then used to encode each MRI into a high-dimensional feature vector, creating the "Clinical Brain Phenotype Space (CBPS)." In CBPS, each MRI is represented as a point, where proximity indicates phenotypic similarity between brains. To refine our focus on pediatric brains, we encoded 883 MRIs from a public dataset and 396 from the Preschool MRI dataset. We used feature-space distances to measure the probability of associated disorders.
Results:
We evaluated our approach on two conditions: Dandy-Walker malformation and Ogden syndrome. In CBPS, we successfully distinguished both conditions from healthy controls by leave-one-out cross-validation. When visualized using t-SNE, patients with Dandy-Walker malformation formed a distinct cluster. At the same time, patients with Ogden syndrome also demonstrated clear separation from controls, validating the potential of CBPS for phenotypic clustering and disease prediction.
Conclusions:
This study demonstrates the application of NGP to structural brain malformations in rare disorders. While our initial analysis focused on two specific conditions, the results highlight the feasibility of extending this approach to a broader spectrum of genetic disorders. With ongoing data curation and patient recruitment through the GMDB consortium, we envision scaling this work to encompass hundreds of disorders, thereby advancing the diagnosis and understanding of rare conditions on a global scale.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Genetics:
Neurogenetic Syndromes 1
Modeling and Analysis Methods:
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Neuroanatomy Other
Keywords:
Computing
Cortex
Development
DISORDERS
Machine Learning
MRI
Pediatric Disorders
STRUCTURAL MRI
Other - Next-Generation Phenotyping
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
Computational modeling
Other, Please specify
-
Machine Learning
For human MRI, what field strength scanner do you use?
1.5T
3.0T
Provide references using APA citation style.
Hsieh, T.-C. (2022). GestaltMatcher facilitates rare disease matching using facial phenotype descriptors. Nature Genetics, 54(3), 349–357. https://doi.org/10.1038/s41588-021-01010-x
Knoll, F. (2020). fastMRI: A publicly available raw k-space and DICOM dataset for accelerated MR image reconstruction using machine learning. Radiology: Artificial Intelligence, 2(1), e190007. https://doi.org/10.1148/ryai.2020190007
No