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Stoebner ZA, Hett K, Lyu I, Johnson H, Paulsen JS, Long JD, Oguz I. Comprehensive shape analysis of the cortex in Huntington's disease. Hum Brain Mapp 2023; 44:1417-1431. [PMID: 36409662 PMCID: PMC9921229 DOI: 10.1002/hbm.26125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/15/2022] [Accepted: 09/28/2022] [Indexed: 11/22/2022] Open
Abstract
The striatum has traditionally been the focus of Huntington's disease research due to the primary insult to this region and its central role in motor symptoms. Beyond the striatum, evidence of cortical alterations caused by Huntington's disease has surfaced. However, findings are not coherent between studies which have used cortical thickness for Huntington's disease since it is the well-established cortical metric of interest in other diseases. In this study, we propose a more comprehensive approach to cortical morphology in Huntington's disease using cortical thickness, sulcal depth, and local gyrification index. Our results show consistency with prior findings in cortical thickness, including its limitations. Our comparison between cortical thickness and local gyrification index underscores the complementary nature of these two measures-cortical thickness detects changes in the sensorimotor and posterior areas while local gyrification index identifies insular differences. Since local gyrification index and cortical thickness measures detect changes in different regions, the two used in tandem could provide a clinically relevant measure of disease progression. Our findings suggest that differences in insular regions may correspond to earlier neurodegeneration and may provide a complementary cortical measure for detection of subtle early cortical changes due to Huntington's disease.
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Affiliation(s)
- Zachary A Stoebner
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA.,University of Texas at Austin, Austin, Texas, USA
| | - Kilian Hett
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Ilwoo Lyu
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Computer Science and Engineering, UNIST, Ulsan, South Korea
| | - Hans Johnson
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Jane S Paulsen
- Department of Neurology, University of Wisconsin, Madison, Wisconsin, USA
| | - Jeffrey D Long
- Department of Psychiatry, University of Iowa, Iowa City, Iowa, USA.,Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA
| | - Ipek Oguz
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
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Hett K, Giraud R, Johnson H, Paulsen JS, Long JD, Oguz I. Patch-Based Abnormality Maps for Improved Deep Learning-Based Classification of Huntington's Disease. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12267:636-645. [PMID: 34873594 PMCID: PMC8643359 DOI: 10.1007/978-3-030-59728-3_62] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Deep learning techniques have demonstrated state-of-the-art performances in many medical imaging applications. These methods can efficiently learn specific patterns. An alternative approach to deep learning is patch-based grading methods, which aim to detect local similarities and differences between groups of subjects. This latter approach usually requires less training data compared to deep learning techniques. In this work, we propose two major contributions: first, we combine patch-based and deep learning methods. Second, we propose to extend the patch-based grading method to a new patch-based abnormality metric. Our method enables us to detect localized structural abnormalities in a test image by comparison to a template library consisting of images from a variety of healthy controls. We evaluate our method by comparing classification performance using different sets of features and models. Our experiments show that our novel patch-based abnormality metric increases deep learning performance from 91.3% to 95.8% of accuracy compared to standard deep learning approaches based on the MRI intensity.
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Affiliation(s)
- Kilian Hett
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Rémi Giraud
- Bordeaux INP, University of Bordeaux, CNRS, IMS, UMR 5218, Talence, France
| | - Hans Johnson
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
| | - Jane S Paulsen
- Department of Neurology, University of Wisconsin, Madison, WI, USA
| | - Jeffrey D Long
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
| | - Ipek Oguz
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
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