Contrastive learning for neural fingerprinting from limited neuroimaging data
IntroductionNeural fingerprinting is a technique used to identify individuals based on their unique brain activity patterns. While deep learning techniques have been demonstrated to outperform traditional correlation-based methods, they often require retraining to accommodate new subjects. Furthermo...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Nuclear Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnume.2024.1332747/full |
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| author | Nikolas Kampel Nikolas Kampel Nikolas Kampel Farah Abdellatif Farah Abdellatif N. Jon Shah N. Jon Shah N. Jon Shah N. Jon Shah Irene Neuner Irene Neuner Irene Neuner Irene Neuner Jürgen Dammers Jürgen Dammers Jürgen Dammers |
| author_facet | Nikolas Kampel Nikolas Kampel Nikolas Kampel Farah Abdellatif Farah Abdellatif N. Jon Shah N. Jon Shah N. Jon Shah N. Jon Shah Irene Neuner Irene Neuner Irene Neuner Irene Neuner Jürgen Dammers Jürgen Dammers Jürgen Dammers |
| author_sort | Nikolas Kampel |
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| description | IntroductionNeural fingerprinting is a technique used to identify individuals based on their unique brain activity patterns. While deep learning techniques have been demonstrated to outperform traditional correlation-based methods, they often require retraining to accommodate new subjects. Furthermore, the limited availability of samples in neuroscience research can impede the quick adoption of deep learning methods, presenting a challenge for their broader application in neural fingerprinting.MethodsThis study addresses these challenges by using contrastive learning to eliminate the need for retraining with new subjects and developing a data augmentation methodology to enhance model robustness in limited sample size conditions. We utilized the LEMON dataset, comprising 3 Tesla MRI and resting-state fMRI scans from 138 subjects, to compute functional connectivity as a baseline for fingerprinting performance based on correlation metrics. We adapted a recent deep learning model by incorporating data augmentation with short random temporal segments for training and reformulated the fingerprinting task as a contrastive problem, comparing the efficacy of contrastive triplet loss against conventional cross-entropy loss.ResultsThe results of this study confirm that deep learning methods can significantly improve fingerprinting performance over correlation-based methods, achieving an accuracy of about 98% in identifying a single subject out of 138 subjects utilizing 39 different functional connectivity profiles.DiscussionThe contrastive method showed added value in the “leave subject out” scenario, demonstrating flexibility comparable to correlation-based methods and robustness across different data sizes. These findings suggest that contrastive learning and data augmentation offer a scalable solution for neural fingerprinting, even with limited sample sizes. |
| format | Article |
| id | doaj-art-a51a340b90294f46a41e6baf42fc3bd3 |
| institution | Kabale University |
| issn | 2673-8880 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nuclear Medicine |
| spelling | doaj-art-a51a340b90294f46a41e6baf42fc3bd32024-11-13T05:15:30ZengFrontiers Media S.A.Frontiers in Nuclear Medicine2673-88802024-11-01410.3389/fnume.2024.13327471332747Contrastive learning for neural fingerprinting from limited neuroimaging dataNikolas Kampel0Nikolas Kampel1Nikolas Kampel2Farah Abdellatif3Farah Abdellatif4N. Jon Shah5N. Jon Shah6N. Jon Shah7N. Jon Shah8Irene Neuner9Irene Neuner10Irene Neuner11Irene Neuner12Jürgen Dammers13Jürgen Dammers14Jürgen Dammers15Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, GermanyFaculty of Medicine, RWTH Aachen University, Aachen, GermanyJülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, GermanyInstitute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, GermanyFaculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, GermanyInstitute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, GermanyJülich Aachen Research Alliance (JARA) – BRAIN – Translational Medicine, Aachen, GermanyInstitute of Neuroscience and Medicine (INM-11), Jülich Aachen Research Alliance (JARA), Forschungszentrum Jülich GmbH, Jülich, GermanyDepartment of Neurology, University Hospital RWTH Aachen, Aachen, GermanyInstitute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, GermanyJülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, GermanyJülich Aachen Research Alliance (JARA) – BRAIN – Translational Medicine, Aachen, GermanyDepartment of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, GermanyInstitute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, GermanyFaculty of Medicine, RWTH Aachen University, Aachen, GermanyJülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, GermanyIntroductionNeural fingerprinting is a technique used to identify individuals based on their unique brain activity patterns. While deep learning techniques have been demonstrated to outperform traditional correlation-based methods, they often require retraining to accommodate new subjects. Furthermore, the limited availability of samples in neuroscience research can impede the quick adoption of deep learning methods, presenting a challenge for their broader application in neural fingerprinting.MethodsThis study addresses these challenges by using contrastive learning to eliminate the need for retraining with new subjects and developing a data augmentation methodology to enhance model robustness in limited sample size conditions. We utilized the LEMON dataset, comprising 3 Tesla MRI and resting-state fMRI scans from 138 subjects, to compute functional connectivity as a baseline for fingerprinting performance based on correlation metrics. We adapted a recent deep learning model by incorporating data augmentation with short random temporal segments for training and reformulated the fingerprinting task as a contrastive problem, comparing the efficacy of contrastive triplet loss against conventional cross-entropy loss.ResultsThe results of this study confirm that deep learning methods can significantly improve fingerprinting performance over correlation-based methods, achieving an accuracy of about 98% in identifying a single subject out of 138 subjects utilizing 39 different functional connectivity profiles.DiscussionThe contrastive method showed added value in the “leave subject out” scenario, demonstrating flexibility comparable to correlation-based methods and robustness across different data sizes. These findings suggest that contrastive learning and data augmentation offer a scalable solution for neural fingerprinting, even with limited sample sizes.https://www.frontiersin.org/articles/10.3389/fnume.2024.1332747/fulldeep learningcontrastive learningneural fingerprintingfMRIresting-statefunctional connectivity |
| spellingShingle | Nikolas Kampel Nikolas Kampel Nikolas Kampel Farah Abdellatif Farah Abdellatif N. Jon Shah N. Jon Shah N. Jon Shah N. Jon Shah Irene Neuner Irene Neuner Irene Neuner Irene Neuner Jürgen Dammers Jürgen Dammers Jürgen Dammers Contrastive learning for neural fingerprinting from limited neuroimaging data Frontiers in Nuclear Medicine deep learning contrastive learning neural fingerprinting fMRI resting-state functional connectivity |
| title | Contrastive learning for neural fingerprinting from limited neuroimaging data |
| title_full | Contrastive learning for neural fingerprinting from limited neuroimaging data |
| title_fullStr | Contrastive learning for neural fingerprinting from limited neuroimaging data |
| title_full_unstemmed | Contrastive learning for neural fingerprinting from limited neuroimaging data |
| title_short | Contrastive learning for neural fingerprinting from limited neuroimaging data |
| title_sort | contrastive learning for neural fingerprinting from limited neuroimaging data |
| topic | deep learning contrastive learning neural fingerprinting fMRI resting-state functional connectivity |
| url | https://www.frontiersin.org/articles/10.3389/fnume.2024.1332747/full |
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